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Alternative cleavage and polyadenylation ( APA ) results in mRNA isoforms containing different 3’ untranslated regions ( 3’UTRs ) and/or coding sequences . How core cleavage/polyadenylation ( C/P ) factors regulate APA is not well understood . Using siRNA knockdown coupled with deep sequencing , we found that several C/P factors can play significant roles in 3’UTR-APA . Whereas Pcf11 and Fip1 enhance usage of proximal poly ( A ) sites ( pAs ) , CFI-25/68 , PABPN1 and PABPC1 promote usage of distal pAs . Strong cis element biases were found for pAs regulated by CFI-25/68 or Fip1 , and the distance between pAs plays an important role in APA regulation . In addition , intronic pAs are substantially regulated by splicing factors , with U1 mostly inhibiting C/P events in introns near the 5’ end of gene and U2 suppressing those in introns with features for efficient splicing . Furthermore , PABPN1 inhibits expression of transcripts with pAs near the transcription start site ( TSS ) , a property possibly related to its role in RNA degradation . Finally , we found that groups of APA events regulated by C/P factors are also modulated in cell differentiation and development with distinct trends . Together , our results support an APA code where an APA event in a given cellular context is regulated by a number of parameters , including relative location to the TSS , splicing context , distance between competing pAs , surrounding cis elements and concentrations of core C/P factors .
Pre-mRNA cleavage and polyadenylation ( C/P ) is a 3’ end processing mechanism employed in eukaryotes for expression of almost all protein-coding transcripts and long non-coding RNAs by RNA polymerase II ( RNAPII ) [1 , 2] . The site for C/P , commonly known as the polyA site or pA , is defined by both upstream and downstream cis elements [3 , 4] . As with core RNAPII promoters [5] , core C/P signals are proving to be complex . In mammals , upstream elements include the polyadenylation signal ( PAS ) , such as AAUAAA , AUUAAA , or close variants , located within ~40 nucleotides ( nt ) from the pA; UGUA elements , typically located upstream of the PAS; and U-rich elements located around the PAS . Downstream elements include U- and GU-rich elements , located within ~100 nt downstream of the pA . Most mammalian genes express alternative cleavage and polyadenylation ( APA ) isoforms [6–9] . APA in the 3’ untranslated region ( 3’UTR ) , called 3’UTR-APA , leads to isoforms with different 3’UTR lengths . Because the 3’UTR plays an important role in aspects of mRNA metabolism , such as subcellular localization , stability , and translation , 3’UTR-APA can impact the post-transcriptional control of gene expression . In addition , about 40% of mammalian genes display APA in upstream introns and internal exons [10] , leading to changes of both coding sequences ( CDSs ) and 3’UTRs . This type of APA is called CDS-APA herein . Studies have shown that the APA pattern of genes is tissue-specific [7 , 9 , 11 , 12] , and is regulated under various conditions , such as cell proliferation , differentiation and development [8 , 13–16] and response to extracellular signals [17] . However , despite recent advances , the molecular mechanisms that regulate APA are still poorly understood ( see [4 , 18–20] for reviews ) . The C/P machinery in mammalian cells is composed of over 20 core factors [21] . Some form subcomplexes , including the Cleavage and Polyadenylation Specificity Factor ( CPSF ) , containing CPSF-160 , CPSF-100 , CPSF-73 , CPSF-30 , Fip1 , and WDR33; the Cleavage stimulation Factor ( CstF ) , containing CstF-77 , CstF-64 , and CstF-50; the Cleavage Factor I ( CFI ) , containing CFI-68 or CFI-59 and CFI-25; and the Cleavage Factor II ( CFII ) , containing Pcf11 and Clp1 . Single proteins involved in C/P include Symplekin , poly ( A ) polymerase ( PAP ) , nuclear poly ( A ) binding protein ( PABPN ) , RBBP6 , and RNAPII ( specifically the C-terminal domain of its largest subunit ) . In addition , protein phosphatase 1α ( PP1α ) and protein phosphatase 1β ( PP1β ) are present in the C/P complex and homologous to yeast C/P factors [22] , but their functions in 3’ end processing are yet to be established . Since the initial study indicating that CstF-64 can regulate APA of the IgM heavy chain gene pre-mRNA during B cell maturation [23] , a growing number of core C/P factors have been shown to impact pA choice , such as CFI factors [24–26] , PABPN1 [27 , 28] , CstF-64τ [29 , 30] , Fip1 [31] and RBBP6 [32] . Whether other C/P factors are involved in APA is not known , and to what extent different C/P factors differentially modulate APA has not been systematically examined . C/P can also be regulated by splicing ( reviewed in [33] ) , which has long been thought to help define the 3’ terminal exon [34] . U1 snRNP ( or U1 ) has been shown to suppress cryptic pA usage near the transcription start site ( TSS ) [35] , which may be attributable to its inhibitory activity on poly ( A ) polymerase α ( PAPα ) [36] . The interplay between U1 and C/P has recently been implicated in controlling expression of sense vs . antisense transcripts from bidirectional promoters [37 , 38] . Interestingly , mild attenuation of U1 that does not inhibit splicing was found to regulate 3’UTR length via APA [39] . This mechanism , dubbed telescripting , has been implicated in pre-mRNA shortening during rapid and transient transcriptional upregulation in neuronal activation when there is shortage of U1 . By contrast , no global regulation of APA has been associated with U2 snRNP ( or U2 ) , despite various interactions between U2 factors and the C/P machinery [40 , 41] . Here using C2C12 cells and siRNA-based knockdown ( KD ) of individual core C/P factors , we examine the role of C/P factors in global APA regulation . We also reduce the expression and/or activity of several core splicing factors in order to understand the role of splicing in APA . In addition , we investigate expression of sense and antisense transcripts using pAs near the TSS when C/P or splicing factors are inhibited . Lastly , we examine how APA events regulated by C/P factor KDs are related to those taking place in cell differentiation . Altogether , our results reveal multiple regulatory rules of APA and show that modulation of core RNA processing factor levels provides a powerful mechanism to control global APA and that different factors have distinct impacts on pA usage .
To examine the role of core C/P factors in APA regulation , we set out to knock down by siRNA the expression of each of the known factors ( Fig 1 and S1 Table , see Introduction ) , including CPSF-160 , CPSF-100 , CPSF-73 , CPSF-30 , Fip1 , WDR33 , RBBP6 , CstF-77 , CstF-64 , CstF-64τ , CstF-50 , CFI-25 , CFI-59 , CFI-68 , Pcf11 , Clp1 , PAPα , PAPγ , PABPN1 , and Symplekin . In addition , we designed siRNAs for several C/P complex-associated factors [22] , including PP1α , PP1β , and poly ( A ) binding protein C1 ( PABPC1 ) . Since a large fraction of pAs are located in introns and are regulated in development and differentiation [10] , we also included siRNAs for several core splicing factors that have been previously implicated in regulation of C/P through various mechanisms [33] , including U1 factor U1-70K , U2 factor SF3b155 and U2-associated factor U2AF65 , Moreover , because functional inhibition of U1 was previously shown to cause global regulation of APA in both introns [35] and 3’UTRs [39] , we included in our experiments an oligonucleotide ( oligo ) that can base pair with the 5’ end region of U1 snRNA involved in 5’ splice site ( 5’SS ) recognition ( Fig 1B ) . This oligo , named U1 domain oligo ( U1D ) , is composed of locked nucleic acids and 2’-O-methylated nucleotides , and has been shown to base pair efficiently with U1 snRNA , thereby functionally knocking down U1 [42] . An oligo that had the same chemical composition but contained two mutations at critical positions for base pairing with U1 snRNA , named mutant U1D oligo ( mU1D , Fig 1B ) , was used as a control . We chose C2C12 myoblast cells for this study because we previously found that during differentiation of C2C12 cells into myotubes , APA isoforms using proximal pAs ( relative to the 5’ end of gene ) , including those in 3’UTRs and introns , are relatively downregulated , while distal pAs are relatively upregulated [10 , 14] . Therefore , by using C2C12 cells , we could compare APA events regulated by C/P or splicing factors with those taking place naturally during cell differentiation . For knockdown ( KD ) experiments , we selected siRNAs that could reduce their target mRNA expression by >30% in C2C12 cells , as determined by reverse transcription-quantitative PCR ( RT-qPCR ) ( Fig 1C; primers listed in S3 Table ) . For the factors for which we had antibodies , Western Blot analysis was also carried out to confirm success of KD ( Fig 1C and S1 Fig; antibody information listed in S2 Table ) . We extracted total RNA 48 hr after siRNA transfection , or 8 or 24 hr after transfection with U1D or mU1D . To obtain a global view of APA , we applied 3’ Region Extraction And Deep Sequencing ( 3’READS ) , a method recently developed by our lab to sequence the 3’ end region of poly ( A ) + RNAs genome-wide [10] . The statistics of sequencing reads aligned to pAs are shown in S4 Table . We first examined APA changes in 3’UTRs , which results in differential expression of isoforms with different 3’UTR lengths ( illustrated in Fig 2A ) . To simplify analysis , we focused on the relative expression changes of the two most abundant 3’UTR isoforms of each gene . Using relative expression difference ( RED ) between proximal and distal pA isoforms in KD vs . control samples ( RED = difference in log2 ( ratio ) of read numbers of two pA isoforms between two samples; illustrated in Fig 2A ) , we found that several KD samples showed significant upregulation of proximal pAs ( negative RED median of all genes ) , including those of siCFI-68 , siCFI-25 , siPABPN1 , and siPABPC1 , and several others had the opposite trend ( positive RED median ) , including the siFip1 and siPcf11 samples ( Fig 2B ) . Differentiation of C2C12 also had a positive RED median , consistent with our previous finding of 3’UTR lengthening in this process [10 , 14] . To statistically examine APA regulation , we developed a method named Significance Analysis of Alternative Polyadenylation ( SAAP ) , which evaluated the significance of a RED score using the read distribution of APA isoforms in two comparing samples . A q-value was calculated based on data randomization ( illustrated in S2 Fig and see Materials and Methods for detail ) , which indicated the false discovery rate ( FDR ) . An example gene Timp2 ( tissue inhibitor of metalloproteinase 2 ) is shown in Fig 2C , whose APA was strongly affected by KDs of several factors: siCFI-25 , siCFI-68 , siPABPN1 , and siPABPC1 resulted in upregulation of the proximal pA isoform ( 3’UTR size = 110 nt ) , whereas siPcf11 and siFip1 led to upregulation of the distal pA isoform ( 3’UTR size = 2 . 6 kb ) . SAAP analysis of the two isoforms showed that APA regulations in these samples , as well as in C2C12 differentiation , were all significant ( q-value = 0 , Fig 2C , middle ) . This result was confirmed by reverse transcription-quantitative PCR ( RT-qPCR ) using amplicons before and after the proximal pA ( Fig 2C , right ) . Notably , APA of human Timp2 has also been reported in several studies [25 , 26] , suggesting the importance of its regulation across species . We next wanted to compare the extent of APA regulation across samples . We carried out KD experiments in several batches , each with a negative control . However , because samples from different batches were processed and sequenced at different times , systematic biases ( “batch effect” ) could be introduced into the data . In addition , samples with different levels of sequencing depth could lead to variable sensitivity for APA detection , making them not directly comparable . To address these issues , we developed a computational pipeline , named Global Analysis of Alternative Polyadenylation ( GAAP ) , in which we randomly sampled reads to control for sequencing depth and permutated treatment and control data to obtain expected data ( S2C Fig ) . Both expected and observed data were subject to SAAP for identification of significantly regulated APA events . The number of regulated APA events from the observed data was subtracted by that from the expected data to obtain normalized number of APA events . As such , each sample was internally controlled and cross-batch comparison was more feasible . Using GAAP , we found that almost all KD samples , including inhibition of U1 , resulted in considerable changes of 3’UTR-APA ( Fig 2D ) . The top ten treatments with respect to normalized number of genes with altered 3’UTR-APA were , in the order of number , siCFI-68 , siCFI-25 , siCPSF-160 , siFip1 , siSF3b155 , siPABPC1 , siPABPN1 , siPcf11 , U1D ( 24 hr ) and siRBBP6 . To gauge the overall trend of APA direction , i . e . , 3’UTR lengthening or shortening , we calculated the ratio of the number of genes with lengthened 3’UTRs ( Le ) to the number of genes with shortened 3’UTRs ( Sh ) , or log2 ( Le/Sh ) ( Fig 2D ) . We found that siCFI-25 and siCFI-68 led to the most substantial 3’UTR shortening , with log2 ( Le/Sh ) values being negative infinite after normalization to expected values ( Fig 2D ) . This result is consistent with previous reports showing significant 3’UTR shortening after CFI-25 or CFI-68 KD [24 , 43] . KDs of the two PABPs also led to considerable 3’UTR shortening , with log2 ( Le/Sh ) = -2 . 8 and -2 . 3 , respectively , for siPABPN1 and siPABPC1 . While the siPABPN1 result is in line with previous reports [27 , 28] , this is the first time PABPC1 is found to play a global role in APA and the extent of its modulation of 3’UTR-APA appeared to be similar to that of PABPN1 . We found that siFip1 and siPcf11 led to the most substantial 3’UTR lengthening , with log2 ( Le/Sh ) = 1 . 5 and 1 . 4 , respectively . In comparison , the log2 ( Le/Sh ) value was 1 . 8 for C2C12 differentiation ( Fig 2D ) . Consistent with previous reports [39] , we also found that reduction of U1 activity by U1D ( 24 hr ) or siU1-70K led to 3’UTR shortening . However , the 3’UTR-APA changes in these samples appeared modest compared to the more significant C/P factor KD samples described above . It is worth noting that a similar analysis using the two most significantly regulated pA isoforms of each gene gave essentially identical results ( S3A Fig ) , confirming the robustness of our findings . As expected , the GAAP result ( Fig 2D ) , which indicates the number of genes with significant APA regulation , correlated with the RED result ( Fig 2B ) , which indicates the extent of APA regulation . We further found that the GAAP result also correlated with the number of significantly regulated pA isoforms per gene ( Fig 2E and S3 Fig ) , i . e . , samples with more genes having 3’UTR-APA regulation tended to have a greater number of regulated isoforms per gene . For example , in siCFI-25 and siCFI-68 samples , ~30% of genes had more than two pA isoforms significantly regulated ( S3 Fig ) . This result indicates that multiple pAs are interrelated in regulation of their usage . The region between two regulated pAs is called alternative UTR ( aUTR; illustrated in Fig 2A ) . We found that when there was global 3’UTR lengthening , the lengthened aUTRs were generally longer than the shortened ones , and vice versa ( S3 Fig ) . Consistently , there was a good correlation between log2 ( Le/Sh ) and median aUTR size difference between lengthened and shortened 3’UTRs ( R2 = 0 . 57 , excluding siPABPN1 , Fig 2F; note that siCFI-68 and siCFI-25 were not used for this analysis because of their negative infinite log2 ( Le/Sh ) values ) . The 3’UTR regulation in C2C12 differentiation also followed this trend ( Fig 2F ) . However , the siPABPN1 sample was a notable exception , in which shortened aUTRs were generally shorter than lengthened ones despite a global trend of 3’UTR shortening ( Fig 2F and S3 Fig ) . This result indicates that change of 3’UTR size is generally coupled with the extent of 3’UTR-APA regulation , and the APA regulatory mechanism by PABPN1 is distinct from that of other factors . We next examined CDS-APA events , as defined by their pA location in introns or exons upstream of the 3’-most exon ( illustrated in Fig 3A ) . Using GAAP , we found that KDs of several splicing factors , such as by siU1-70K and siSF3b155 , as well as inhibition of U1 by U1D ( 8hr and 24 hr ) , led to substantial changes of the CDS-APA pattern ( Fig 3B ) . By contrast , siU2AF65 resulted in much fewer regulated events . All splicing factor KD samples showed overall upregulation of CDS-APA isoforms , as indicated by their log2 ( UP/DN ) values ( UP , number of genes with upregulated CDS-APA isoforms; DN , number of genes with downregulated CDS-APA isoforms ) ( Fig 3B ) . Notably , the upregulated CDS-APA isoforms were generally expressed at low levels in control cells ( relative abundance <10% , S4 Fig ) . Taken together , these results indicate that C/P in regions upstream of the 3’-most exon is generally inhibited by splicing and this mechanism is in effect under normal conditions . KDs of several C/P factors also led to regulation of many CDS-APA events , including siCFI-25 , siCFI-68 , siPABPN1 , siPABPC1 , siFip1 and siPcf11 ( Fig 3B ) . While siCFI-25 , siCFI-68 , siPABPN1 , and siPABPC1 resulted in overall upregulation of CDS-APA isoforms , siFip1 and siPcf11 samples showed the opposite trend . This difference between these two sets of KD samples mirrors that for 3’UTR-APA regulation ( Fig 2 ) , implying that regulations of CDS-APA and 3’UTR-APA events by these factors are mechanistically related . Most pAs of CDS-APA isoforms were located in introns ( >90% , S4 Fig ) . We next asked whether introns containing regulated APA events had special features . Focusing on KD samples with substantial regulations , we analyzed expression changes of the isoforms using pAs in the first ( +1 ) , second ( +2 ) , second to last ( -2 ) , last ( -1 ) , or middle ( M , not the first two or last two ) introns . We found that , in U1D ( 8 hr ) , U1D ( 24 hr ) , siU1-70K and siPABPN1 samples , APA isoforms using pAs in 5’ end introns tended to be relatively upregulated compared to those in 3’ end introns ( Fig 3C ) . By contrast , isoforms using pAs in the last intron tended to be upregulated in siCFI-68 and siCFI-25 samples ( Fig 3C ) . On the other hand , no obvious location bias could be discerned for upregulated intronic APA isoforms in the siSF3b155 , siPABPC1 , or siU2AF65 samples ( Fig 3C ) , nor for downregulated intronic APA isoforms in siFip1 or siPcf11 samples ( S5 Fig ) . We found that the middle introns ( not the first two or last two introns ) that contained pAs of upregulated isoforms by U1 inhibition or siSF3b155 tended to be much smaller than other middle introns ( P = 1x10-8-10-18 , Wilcoxon test , Fig 3D ) . In addition , the middle and last introns containing pAs upregulated by siSF3b155 tended to have stronger 5’SS ( P = 1x10-4 and 1x10-5 , respectively ) than their respective control introns ( Fig 3D ) . Because intron size and 5’SS strength are relevant to splicing kinetics , these results indicate that splicing activity has a substantial influence on C/P in introns . By contrast , 3’SS in general did not appear to play a major role in intronic C/P in the samples analyzed in this study , except for some modest significance for the last introns containing upregulated pAs by siSF3b155 and siU2AF65 ( Fig 3D ) . No significant intron features could be identified for intronic APA isoforms downregulated by siFip1 or siPcf11 ( S5 Fig ) , suggesting that the primary mechanism for their regulation is through modulation of C/P activity . The enrichment of pAs in the 5’ end introns for upregulated isoforms in siPABPN1 and U1D samples prompted us to examine C/P events near the promoter . Since a large fraction of RNAPII promoters in mammalian cells are bidirectional , leading to both sense and antisense transcripts ( reviewed in [44 , 45] and illustrated in Fig 4A ) , we examined C/P events in both sense and antisense orientations around the TSS . Consistent with previous reports [37 , 38] , we found that in our control samples , the pAs of upstream antisense transcripts , termed uaRNAs or PROMPTs , were distributed within 2 kb from the TSS , peaking around -700 nt ( Fig 4B ) . A similar peak was found in the sense direction with a similar mode of distribution ( Fig 4B ) . For simplicity , we called transcripts using pAs within 2 kb from the TSS in the sense direction ( excluding those in 3’-most exons or in single exon genes ) sense proximal RNAs ( spRNAs ) . Nucleotide frequency analysis of the regions around pAs of uaRNAs and spRNAs indicated that they each had distinct nucleotide profiles compared to those of 3’-most pAs ( Fig 4C ) . While similar U-rich and A-rich upstream peaks and a U-rich downstream peak were found for all types of pAs , the upstream regions of both uaRNA and spRNA pAs had a higher GC content , in line with their location close to promoters with CpG islands [46] . In addition , compared to uaRNA and 3’-most exon pAs , spRNA pAs had a lower A-content in surrounding regions ( Fig 4C ) . We next examined our KD samples with the goal of finding factors involved in expression of uaRNAs or spRNAs . Using isoforms whose pAs were beyond 2 kb from the TSS as a reference , we examined uaRNA and spRNA regulations in the KD samples by GAAP ( Fig 4D and 4E ) . Significant upregulations of both uaRNA and spRNA were observed with the siPABPN1 sample , with upregulation of uaRNAs being greater than that of spRNAs ( Fig 4D and 4E ) . By contrast , consistent with the findings of Almada et al . [37] , regulation by U1 inhibition , such as U1D ( 8 hr ) , U1D ( 24 hr ) or siU1-70K , showed the opposite trend , with spRNAs being more significantly upregulated than uaRNAs ( Fig 4D and 4E ) . The siSF3b155 sample also showed significant regulation of uaRNAs , but the number of upregulated events was similar to that of downregulated ones ( Fig 4D ) . In contrast , spRNAs were generally upregulated by siSF3b155 ( Fig 4E ) . The difference between siPABPN1 and U1 inhibition in regulating spRNAs and uaRNAs can also be seen with the isoform expression profiles around the TSS ( Fig 4F and 4G ) . While upregulation of spRNAs by U1 inhibition may be related to the role of U1 in suppressing C/P near the TSS [37] , the regulation of spRNAs and uaRNAs by PABPN1 is completely not clear . Studies have shown that the nuclear exosome is involved in degrading uaRNAs [38] and PABPN1 is involved in stability of nuclear RNAs [47 , 48] . We thus asked whether the regulation of uaRNA and spRNA expression by PABPN1 was related to exosome-mediated RNA decay . To this end , we knocked down Rrp44 and Rrp6 together by siRNAs , two nucleases associated with the exosome , followed by 3’READS . As expected , their KD led to higher abundance of uaRNAs and spRNAs , presumably through stabilization of these transcripts ( Fig 4H ) . Importantly , the uaRNA and spRNA profiles in the siRrp44 + siRrp6 sample resembled those of siPABPN1 , suggesting a functional connection between PABPN1 and the exosome in regulating uaRNAs and spRNAs . Since 3’UTR-APA had much greater regulations than CDS-APA or expression of uaRNAs or spRNAs , as indicated by the number of genes with significant APA changes in KD samples ( GAAP analysis results in Figs 2 , 3 and 4 ) , we wanted to further examine how different factors regulate 3’UTR-APA . While our GAAP analysis based on random sampling of genes enabled comparison of global APA patterns across samples , this approach is not suitable for comparing individual pA usage across samples due to the batch effect . We therefore repeated KDs of several key C/P factors in one batch , which showed substantial APA regulations in our initial study , including CFI-68 , PABPN1 , PABPC1 , Fip1 and Pcf11 . We did not include CFI-25 because the APA profile of its KD was highly similar to that of siCFI-68 ( S6 Fig ) . To mitigate indirect effects and the influence of mRNA decay in cytoplasm , we harvested cells 32 hr after siRNA transfection and extracted both total and nuclear RNAs for 3’READS analysis ( Fig 5A ) . Western Blot analysis indicated that the KD efficiencies in these samples were >60% at the time of cell harvest ( Fig 5B ) . We found that using total or nuclear RNAs from 32 hr KD samples gave rise to similar results to using total RNAs from 48 hr KD samples ( Fig 5C ) , with siCFI-68 , siPABPN1 , and siPABPC1 leading to 3’UTR shortening and siPcf11 and siFip1 causing 3’UTR lengthening . This result indicates that our initial observations were not obstructed , at least not substantially , by indirect effects ( which could be introduced over time ) , or by mRNA decay in cytoplasm ( which could lead to different APA profiles between nuclear and total RNAs ) . This notion was further confirmed by detailed comparisons of total RNAs from 32 hr vs . 48 hr KD samples ( S7 Fig ) and total vs . nuclear RNAs from 32 hr KD samples ( S8 Fig ) , which showed significant correlations of APA changes of genes . Moreover , gene expression changes were largely uncoupled from 3’UTR changes using both nuclear or total RNAs ( S9 Fig ) , indicating that APA isoforms are not likely to differ in mRNA stability in general . However , a mild difference in gene expression could be discerned between genes with shortened and lengthened 3’UTRs in the siPcf11 sample ( S9 Fig ) , suggesting a potential role of Pcf11 in post-transcriptional regulation of gene expression . This will be explored in the future . Because the five factor KD samples were processed in the same batch , we directly compared their APA profiles . Using RED scores based on the top two most abundant 3’UTR isoforms of each gene , we performed cluster analysis ( Fig 5D ) . We found that total and nuclear RNA data were clustered together for all KD samples , consistent with the notion that cytoplasmic mRNA decay did not substantially alter APA profiles in our samples . In addition , samples involving global 3’UTR shortening , i . e . , siCFI-68 , siPABPN1 and siPABPC1 samples , were separated from those involving global lengthening , i . e . , siPcf11 and siFip1 samples , suggesting that some common sets of pAs were regulated by different factors . On the other hand , Venn diagram analysis of APA events regulated by the five factors also indicated that a fraction of pAs were distinctly regulated by these five factors ( Fig 5E ) : Each factor regulated a set of unique APA events and the number of APA events regulated by all five factors was quite small ( 27 using total RNA data , and 52 using nuclear RNA data , Fig 5E ) , indicating distinct regulatory mechanisms among the factors . Because of the relevance of aUTR length to APA regulation ( Fig 2F ) , we next examined the extent of APA regulation for genes with different aUTR lengths ( Fig 5F ) . Genes were first divided into five equally sized bins based on their aUTR length ( Fig 5F ) . The extent of APA regulation was represented by the RED score . We found that genes that had longer aUTRs tended to have greater APA regulation than genes with shorter ones in all samples except siPABPN1 . This is supported by 1 ) the trend of RED score across gene bins and 2 ) the p-values indicating the difference in RED scores between the first and fifth gene bins ( with the shortest and longest aUTRs , respectively ) ( Fig 5F ) . Intriguingly , this analysis also revealed that in the siFip1 sample , genes with the shortest aUTRs ( bin 1 ) showed 3’UTR shortening in general whereas genes in other bins showed the opposite trend ( Fig 5F ) . This dichotomous APA pattern with respect to aUTR size was not seen with other samples , indicating a unique aUTR size-dependent mechanism of Fip1 in 3’UTR-APA regulation ( see below for more analyses ) . We next asked whether cis elements around the pA contributed to differential APA regulation in different KD samples . Since proximal and distal pAs tend to be surrounded by different cis elements [49] , we analyzed these two pA groups separately ( illustrated in Fig 6A ) . For each group , we compared pAs that were regulated in one of the five KD samples with pAs regulated in other samples . As such , the identified cis elements should be specific for the factor under investigation , and should not be caused by pA relative locations . We examined three sub-regions around the pA , namely , -100 to -41 nt , -40 to -1 nt and +1 to +100 nt ( the pA was set to position 0 ) , for significantly enriched and depleted K-mers ( 4-mer or 6-mer , P < 0 . 001 , Fisher’s exact test ) . Fig 6B shows the number of significant 4-mers for each region , reflecting the extent to which cis elements in the region were involved in pA regulation . We found much greater 4-mer biases around pAs regulated by siCFI-68 or siFip1 than those by siPcf11 , siPABPC1 or siPABPN1 ( Fig 6B and S5 Table ) . In the siCFI-68 sample , major biases were found in regions surrounding both proximal and distal pAs of genes with shortened 3’UTRs ( Fig 6B and 6C ) , including depletion of TGTA in the -100 to -41 nt region of proximal pAs ( significance score ( SS ) = -11 . 1; SS = -log10 ( p-value ) *S , where p-value was based on the Fisher’s exact test and S was 1 for enrichment or -1 for depletion ) and enrichment of this motif in the same region of distal pAs ( SS = 21 . 0 ) , suggesting that the presence or absence of TGTA in the -100 to -41 nt region is a major reason for APA regulation by CFI-68 . This result is consistent with the binding site for CFI complex [24 , 43 , 50] . Other motifs were also found to be significantly biased in these regions , as well as in the +1 to +100 nt region of proximal pAs , and the -40 to -1 nt and +1 to +100 nt regions of distal pAs , albeit with less significance ( Fig 6C ) . In the siFip1 sample , both pAs of shortened 3’UTRs and of lengthened 3’UTRs displayed motif biases ( Fig 6D ) . For lengthened 3’UTRs , there was an enrichment of TTTT in the -100 to -41 nt region of proximal pAs , and an depletion of the motif in the same region of distal pAs , suggesting that U-rich elements play a role in APA regulation mediated by Fip1 . This result is consistent with the reported U-rich sequence binding for Fip1 [51] and is in good agreement with the binding locations reported by Lackford et al . [31] . Several 4-mers displayed a strong bias in the +1 to +100 nt region of proximal pAs of shortened 3’UTRs , including TAAA , AATA and ATAA . Hexamer analysis indicated that these motifs were derived from AATAAA ( S6 Table ) . This result suggests that Fip1 inhibits usage of pAs with downstream AATAAA . To test this hypothesis further , we specifically examined pAs with or without downstream AATAAA and analyzed the influence of distance between pAs ( Fig 6E ) . We found that , in the siFip1 sample , when the distance between proximal and distal pAs was < 120 nt , proximal pAs with downstream AATAAA within 100 nt ( group 1 , Fig 6E ) tended to be much more used than those without the motif ( group 2 ) , as indicated by their RED scores ( -0 . 44 vs . -0 . 03 , P < 1x10-6 , Kolmogorov–Smirnov , or K-S , test , ) . This trend could also been seen when the distance was > = 120 nt ( 0 . 11 vs . 0 . 30 , group 3 vs . group 4 , P = 0 . 14 ) . Thus , Fip1 plays a role in selection of adjacent pAs , favoring AATAAA-associated downstream pAs . This result offers an explanation as to why genes with short aUTRs tended to have upregulated proximal pAs in the siFip1 sample ( Fig 5F ) . Some cis element biases were also found in siPcf11 and siPABPN1 samples , with lower statistical significance ( Fig 6B , S5 Table , and S6 Table ) . For example , TATT and TTAT were enriched for the +1 to +100 nt region of proximal pAs downregulated by siPcf11 , and several TA-rich motifs were depleted from the -100 to -41 nt region of downregulated distal pAs but enriched for the same region of upregulated distal pAs in the siPABPN1 sample . Of all samples , siPABPC1 displayed the least cis element bias around regulated pAs , suggesting that its regulation of C/P does not involve specific cis elements . We next asked whether the regulated APA events by five C/P factors were related to those taking place during C2C12 differentiation [14] . We examined 3’UTR-APA in C2C12 differentiation for genes that showed shortened or lengthened 3’UTRs in different KD samples ( 32 hr KD , total RNA ) . Using RED scores for differentiated vs . proliferating C2C12 samples , we found that genes regulated by siCFI-68 and siPcf11 showed significant RED differences in C2C12 differentiation ( Fig 7A ) : genes with shortened 3’UTRs in the siCFI-68 sample and those with lengthened 3’UTRs in the siPcf11 sample were more likely to have lengthened 3’UTRs in C2C12 differentiation than genes with the opposite 3’UTR regulation in their respective samples ( P = 4x10-3 and 7x10-5 , respectively , K-S test ) . Based on APA regulations by siCFI-68 and siPcf11 , we next divided genes into 10 groups ( Fig 7B and S7 Table ) and asked how different sets of genes were regulated in C2C12 differentiation . Genes in group 3 , whose 3’UTRs were shortened by siCFI-68 and lengthened by siPcf11 showed greatest 3’UTR lengthening in C2C12 differentiation as indicated by their RED median ( Fig 7C ) . They were followed by group 2 genes , whose 3’UTRs were shortened by siCFI-68 but not regulated by siPcf11 , and group 5 genes , whose 3’UTRs were lengthened by siPcf11 but not by regulated by siCFI-68 ( Fig 7C ) . Interestingly , genes in groups 3 , 2 and 5 had longer aUTRs than genes in other groups , with median size of 1 , 618 , 1 , 058 and 866 nt , respectively ( Fig 7B ) , highlighting the importance of distance between pAs for APA regulation in C2C12 differentiation . Further analysis using our previous 3’READS data from 3T3-L1 pre-adipocyte differentiation and embryonic development ( 15 day vs . 11 day ) [10] indicated that group 3 genes also tended to have significantly greater 3’UTR lengthening than other genes in these processes ( P = 6x10-8 and 1x10-2 , K-S test comparing group 3 genes with all other genes , Fig 7D ) , similar to their regulation in C2C12 differentiation ( P = 4x10-7 , Fig 7D ) . Interestingly , group 3 genes were significantly associated with several Gene Ontology ( GO ) terms ( S10 Fig ) , such as “single-organism membrane organization” , “spermatogenesis” , “actomyosin structure organization” , “intracellular protein transport” , “cell body” and “cytoplasmic vesicle . ” Notably , several different GOs were also found to be associated with group 2 genes , such as “endosome to lysosome transport” , “transforming growth factor beta receptor signaling pathway” , “cellular response to endogenous stimulus” , “cell leading edge” , “coated vesicle membrane” , “cell junction” , and “endomembrane system . ” Intriguingly , group 1 genes , whose 3’UTRs were shortened by both siCFI-68 and siPcf11 , also showed strong association with several GO terms , such as “regulation of phosphorylation” , “cell death” , “intracellular signal transduction” , etc . Taken together , these data indicate that genes in different functional groups may be differentially regulated by APA in cell differentiation due to distinct pA and aUTR features .
Among the CPSF subunits , Fip1 levels were found to have the greatest impact on APA , suggesting that Fip1 recruitment is a key step for CPSF’s function in C/P , at least in the context of C2C12 cells . Given the essential role of CPSF in the C/P reaction , it is not surprising that inhibition of Fip1 leads to 3’UTR lengthening: downregulated C/P activity would favor distal pAs , which in general have stronger C/P cis elements than proximal ones [6] . Interestingly , a group of genes had 3’UTR shortening after Fip1 KD; their proximal pAs tend to have the AAUAAA motif in the downstream region and the distance between proximal and distal pAs is short . This result is largely in line with what was observed by the Shi group with Fip1 KD in embryonic stem cells [31] . As proposed by Lackford et al . , there exist two modes of APA regulation by Fip1 depending upon pA strength and distance between pAs [31] . We additionally found that while selection of distal pAs in the siFip1 sample involves U-rich elements , activation of proximal pAs does not , suggesting that U-rich element binding by Fip1 plays a role in pA selection only when competing pAs are far apart . Recent studies have revealed that CPSF30 and WDR300 directly bind PAS , [52 , 53] . Intriguingly , KD of any one of these factors had a modest effect on APA . Whether these factors can compensate each other in PAS interaction is to be examined in the future . Consistent with the findings by the Zavolan and Keller groups [24 , 43] , KD of CFI-25 or CFI-68 , but not CFI-59 , led to a substantial shift to proximal pA usage in 3’UTRs , the extent of which is more significant than other samples examined in this study . The CFI complex , composed of either CFI-25/CFI-68 or CFI-25/CFI-59 , has been shown to interact with UGUA elements [50] , which are typically located upstream of the PAS [49] . Consistently , distal pAs downregulated by siCFI-25/siCFI-68 were enriched with UGUA element ( s ) in the -100 to -41 nt region whereas upregulated proximal pAs were depleted of them in the same region . Some other cis elements were also identified around regulated pAs ( Fig 6C ) , such as UAUU , CCUC . Whether they simply piggyback with UGUA or are actively engaged in binding with some proteins that interact with CFI need to be further studied in the future . We also found that CFI-25 or CFI-68 KD led to general upregulation of isoforms using pAs in the last intron , a feature that has not been reported in previous studies [24 , 43] , suggesting that CFI-25/68 also play a role in 3’ terminal exon selection . One possibility is that removal of the last intron is slow , creating a time window in which pAs in the last intron compete with 3’-most exon pAs for C/P . It is also possible that CFI-25/CFI-68 may facilitate the removal of the last intron , e . g . , through interaction with splicing factors [33] , thereby inhibiting pA usage in the last intron . Our data indicate that Pcf11 has a substantial impact on APA , which has not been detected previously in metazoan cells . In S . cerevisiae , deletion of the mRNA export adaptor Yra1 , which inhibits C/P through competition with Clp1 for Pcf11 binding , leads to widespread APA events [54] . The same study also suggested that Clp1 and Pcf11 are not necessarily recruited as a complete unit in yeast . Our data supports this view , because siPcf11 and siClp1 had different effects on APA , with respect to extent and direction of regulation . This notion is also in line with the report by Shi et al . showing that Clp1 was not present in the C/P complex purified from mammalian cells [22] . Given the significant role of Pcf11 in APA , it is possible that its recruitment to the C/P complex is a rate-limiting step for the C/P reaction . Inhibition of PABPN1 has been shown to elicit global shortening of 3’UTRs via APA [27 , 28] . It was suggested that this regulation may be relevant to the etiology of human disease oculopharyngeal muscular dystrophy ( OPMD ) , which is caused by an expansion mutation in the polyalanine repeat in the N-terminus of PABPN1 . Our results are largely in line with these findings but also revealed that modulation of 3’UTR-APA by PABPN1 is quite different than other C/P factors . For example , there is no relationship between the extent of 3’UTR-APA regulation ( based on number of genes with APA changes ) and 3’UTR size changes . We also found that PABPN1 plays a very significant role in inhibition of uaRNA and spRNA expression ( with regulation of uaRNAs being more significant ) . This property may be related to PABPN1’s role in RNA stability [47 , 48] . Consistently , the uaRNA and spRNA profiles in the siPABPN1 sample are similar to those in the siRrp44 + siRrp6 sample . A key question thus is whether the 3’UTR-APA regulation by PABPN1 is related to its functions around the TSS . We did not find significant 3’UTR-APA in the siRrp44/siRrp6 sample ( S12 Fig ) and genes with 3’UTR-APA regulation by siPABPN1 did not appear to have significant expression changes overall ( S9 Fig ) , suggesting that 3’UTR-APA regulation by PABPN1 is not related to its function in uaRNA and spRNA regulation . How PABPN1 exerts distinct functions at the 5’ and 3’ ends of genes respectively awaits further experimentation . Interestingly , PABPC1 , which binds the poly ( A ) tail in the nucleus [55] and shuttles between nucleus and cytoplasm [56] , appears to be as potent as PABPN1 in regulation of 3’UTR-APA , raising the possibility that PABPs in general can regulate the C/P reaction . However , it is also notable that the APA events regulated by PABPC1 are quite different than those by PABPN1 , with respect to uaRNA and spRNA expression changes and the role of aUTR size in regulation . There are no obvious cis elements enriched for pAs regulated by PABPC1 but aUTR size appears to be related to its APA regulation , suggesting that PABPC1 may have a general impact on APA without sequence specificity . Future work will be needed to delineate the respective roles of these PABPs as well as other poly ( A ) -binding proteins [57] in APA . CstF subunits appear to have modest impacts on APA in this study . While siCstF-50 and siCstF-77 led to global 3’UTR lengthening , consistent with their roles in C/P , siCstF-64 did not result in a directional change of APA and siCstF-64τ in fact led to mild general 3’UTR shortening . These puzzling results may be due to the fact that CstF-64 and CstF-64τ have overlapping functions [29] and can regulate each other’s expression [58] . As such , KD of one factor to different levels may give rise to different overall activities , leading to complex results . Thus , the single factor KD-based approach used in this study would be unwieldy in elucidating the exact roles of CstF-64 and CstF-64τ in APA . This issue may also apply to other factors which showed modest APA regulation in this study . For example , there are several PAPs in the cell and KD of one PAP , such as PAPα or PAPγ , may be compensated functionally by other PAPs . Splicing is believed to be intimately involved in C/P ( reviewed in [33] ) . Our data define two general modes of APA regulation involving splicing . First , U1 plays a significant role in suppressing C/P in 5’ end introns , which is consistent with the findings made by the Dreyfuss group [35] and is in line with the inhibitory activity of U1 on C/P [36] . In agreement with the telescripting model proposed by the Dreyfuss group [39] , inhibition of intronic pA usage by U1 displays 5’ to 3’ polarity ( Fig 3C ) . But we only observed mild 3’UTR lengthening in U1 inhibition samples , suggesting a modest role of telescripting in 3’UTR-APA . However , we cannot rule out the possibility that difference in cell type and experimental conditions may lead to this discrepancy . Second , as supported by the siSF3b155 data , splicing activity in general plays a role in suppressing intronic C/P ( Fig 3B ) . It remains to be seen how other splicing factors , particularly those involved in alternative splicing , globally regulate intronic APA and impact selection of 3’ terminal exons . Our data suggest that an APA event in a given cellular condition is regulated by a number of parameters , including relative location to the TSS , splicing context , distance between competing pAs , surrounding cis elements , and concentrations of C/P factors . In the context of C2C12 differentiation , which involves global 3’UTR lengthening , almost all C/P factors showed downregulation , at least at the mRNA level ( S13 Fig ) . However , given the diverse consequences of different C/P factor KDs , it would be too simplistic to attribute the 3’UTR lengthening to downregulation of C/P factors as a whole . On the other hand , several lines of evidence support similarities in 3’UTR-APA regulation between C/P factor KD and C2C12 differentiation . First , aUTR size appears to be an important factor in APA regulation in both KD cells ( with the exception of siPABPN1 ) and cell differentiation . In general , a longer aUTR confers more regulability . Since longer aUTRs could contain more cis regulatory elements for mRNA metabolism , this result suggests that genes with highly regulatable APA tend to be more controlled post-transcriptionally as well . Second , groups of genes with significant regulation by siCFI-68 and siPcf11 are also substantially regulated in differentiation , implying similar mechanisms in these KD conditions and cell differentiation . Importantly , these genes also displayed significant 3’UTR-APA in differentiation of 3T3-L1 cells and embryonic development , suggesting a general APA code in cell proliferation/differentiation . Future studies need to test the APA code with more perturbations , such as overexpression of different factors , and to explore the input of different RNA-binding proteins in condition- and tissue-specific APA regulations [59] .
C2C12 cells were maintained in Dulbecco's Modified Eagles Medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . Differentiation of C2C12 cells was induced by switching cell media to DMEM+ 2% horse serum ( Sigma ) when cells were ~90–100% confluent . All media were also supplemented with 100 units/ml penicillin and 100 μg/ml streptomycin . Differentiated C2C12 cells in this study were harvested four days after differentiation . Transfection with siRNAs was carried out by Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s recommendations . Transfection was carried out for 48 hr or 32 hr . siRNA sequences are shown in S1 Table . The U1D oligo ( 5'-gCcAgGuAaGuau ) and mutant U1D ( mU1D ) oligo ( 5'-gCcAgGcAcGuau ) , where locked nucleic acid ( LNA ) residues are in uppercase and 2’-OMe RNA bases in lowercase , were previously described in [42] . These oligos were transfected into C2C12 cells at 35 nM using Lipofectamine 2000 when the confluency of cells was about 50% . Cells were harvested 8 hr or 24 hr after transfection . For nuclear RNA extraction , cells collected by a scrapper were suspended in cell lysis buffer ( 10 mM Tris pH 7 . 4 , 10 mM NaCl , 0 . 5% NP40 , 1 mM DTT ) , followed by vortexing for 10 sec and incubation on ice for 10 min . After centrifugation of the lysate at 500 x g for 5 min at 4°C , the pellet was re-suspended in the cell lysis buffer for nuclear RNA extraction . Both total and nuclear RNAs were extracted using Trizol ( Invitrogen ) according to manufacturer's protocol . RNA quality was examined in an Agilent Bioanalyzer using the RNA pico600 kit before processing for deep sequencing . For RT-qPCR , mRNA was reverse-transcribed using an oligo ( dT ) primer , and qPCR was carried out with Syber-Green I as dye . Primer sequences are listed in S3 Table . Antibodies used for Western Blot analysis are listed in S2 Table . The 3’READS method used in this study was previously described [60] . Briefly , 25 μg of input RNA was used for each sample , and poly ( A ) + RNA was selected using oligo d ( T ) 25 magnetic beads ( NEB ) , followed by on-bead fragmentation using RNase III ( NEB ) . Poly ( A ) + RNA fragments were then selected using the chimeric U5 and T45 ( CU5T45 ) oligo conjugated on streptavidin beads , followed by RNase H ( NEB ) digestion . Eluted RNA fragments were ligated with 5’ and 3’ adapters , followed by RT and PCR ( 15x ) to obtain cDNA libraries for sequencing on the Illumina platform . All data can be obtained from the NCBI GEO database ( GSE62001 ) . Processing of 3’READS data was carried out as previously described ( Hoque et al . 2013 ) . Briefly , reads were mapped to the mouse genome using bowtie 2 ( Langmead and Salzberg 2012 ) . Reads with ≥2 unaligned Ts at the 5’ end are called poly ( A ) site-supporting ( PASS ) reads , which were used to identify pAs . pAs located within 24 nt from each other were clustered together . The number of PASS reads generated in each sample is listed in S4 Table . We developed a randomization-based method to statistically assess the significance of difference between two samples for each APA event , called Significance Analysis of Alternative Polyadenylation , or SAAP . The method is illustrated in S2 Fig . Briefly , for two pAs ( or two pA sets ) from two comparing samples , a Relative Expression Difference ( RED ) score is first calculated ( S2 Fig ) . The PASS reads are then sampled based on the assumption that the relative abundance of each pA isoform is the same in two samples . Sampling is preformed m times ( 20 in this study ) to obtain mean and standard deviation of RED . The observed and expected RED values are standardized to Z scores ( minus mean and divided by standard deviation ) . False Discovery Rate ( FDR ) and q-value are calculated by comparing observed Z ( Zo ) and expected Z ( Ze ) for a given Z cutoff value ( Zc ) . For 3’UTR-APA , we either selected the two most abundant pA isoforms for analysis , or used all pA isoforms for analysis ( one vs . others ) and then selected the top two most regulated isoforms . For CDS-APA , we combined all isoforms using pAs in upstream regions of the 3’-most exon and compared their expression change with that of isoforms using pAs in the 3’-most exon . Individual intronic pAs were also analyzed by comparing to all other pA isoforms of the gene . For uaRNA analysis , we combined all antisense transcripts using pAs within 2 kb upstream of the transcription start site ( TSS ) , excluding those mapped to other mRNA genes , and compared them to all sense strand transcripts , excluding pAs located within 2 kb downstream of the TSS . For spRNA analysis , we grouped pAs located within 2 kb downstream of the TSS , excluding those located in 3’-most exons or in single exon genes , and compared them with other sense strand isoforms . We used q-value < 0 . 05 ( SAAP ) to select significantly regulated APA events . We developed an approach named Global Analysis of Alternative Polyadenylation ( GAAP ) to normalize sequencing depth and to obtain expected values for a given data set . The method is illustrated in S2 Fig . Let A and B be two 3’READS data sets for two samples that are processed at the same time . For example , A is a KD sample and B is a control sample . Let a and b be two data sets sampled by bootstrapping from A and B , respectively . Bootstrapping is carried out n times with p number of PASS reads sampled each time . In this study , n = 20 , and p = 1 . 5 M . Let A’ and B’ be randomly permutated data sets based on A and B . In permutation , PASS reads from A and B are first combined and then sampled without replacement to obtain A’ and B’ , with the total number of PASS reads of the permutated sets A’ and B’ being the same as those of the original sets A and B , respectively . Permutation is carried out m times . Two sets after each permutation ( a’ and b’ ) are sampled with q number of reads from each set by bootstrapping . In this study , m = n , and q = p . The permutated sets provide expected values ( Exp ) , whereas the original set provides observed values ( Obs ) . APA analyses of a vs . b and a’ vs . b’ were carried out by SAAP . To identify cis elements biased to or against pAs regulated in a KD sample , we compared k-mer frequencies around the pAs regulated in the sample with other pAs regulated in other samples . To mitigate the possibility that identified cis elements are related to location , we first grouped all proximal and distal pAs together into two separate sets ( illustrated in Fig 6A ) . For each KD sample , regulated proximal pAs were compared to other proximal pAs to identify overrepresented and underrepresented k-mers . The same approach was used for distal pAs . We examined three regions around the pA , i . e . , -100 to -41 nt , -40 to -1 nt and +1 to +100nt . For each region , the Fisher’s exact test was used to examine whether a k-mer ( 4-mer or 6-mer ) was enriched for or depleted from a set of pAs vs . other pAs . The intron location was based on the RefSeq database , with all RefSeq splicing isoforms combined . Distribution of regulated intronic pA isoforms was compared to that of background set , which was derived from all detected intronic pAs in all control samples in this study ( isoform relative abundance ≥5% in at least two samples and read count ≥2 in at least two samples ) . To calculate 5’SS or 3’SS strength , we used all 5’SS or 3’SS supported by mouse RefSeq sequences . The maximum entropy scores were calculated by MaxEntScan [61] . The 5’SS or 3’SS strength of introns containing regulated pAs was compared to that of background introns with the same relative location in the gene by the Wilcoxon rank sum test . Gene expression changes were based on PASS reads mapped to the 3’-most exon of a gene , represented by the reads per million total PASS reads ( RPM ) value . Venn diagrams were generated by VennDIS [62] . Gene Ontology ( GO ) analysis was carried out using the Fisher’s exact test . GO annotation of genes was obtained from the NCBI Gene database . | A gene can express multiple isoforms varying in the 3’ end , a phenomenon called alternative cleavage and polyadenylation , or APA . Previous studies have indicated that most eukaryotic genes display APA and the APA profile changes under different physiological and pathological conditions . However , how APA is regulated in the cell is unclear . Here using gene knockdown and high throughput sequencing we examine how APA is regulated by factors in the machinery responsible for cleavage and polyadenylation as well as factors that play essential roles in splicing . We identify several factors that play significant roles in APA in the last exon , including CFI-25/68 , PABPN1 , PABPC1 , Fip1 and Pcf11 . We also elucidate how cleavage and polyadenylation events are regulated in introns and near the transcription start site . We uncover a group of APA events that are highly regulated by core factors as well as in cell differentiation and development . We present an APA code where an APA event in a given cellular context is regulated by a number of parameters , including relative location to the transcription start site , splicing context , distance between competing pAs , surrounding cis elements and concentrations of core cleavage and polyadenylation factors . | [
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| 2015 | Systematic Profiling of Poly(A)+ Transcripts Modulated by Core 3’ End Processing and Splicing Factors Reveals Regulatory Rules of Alternative Cleavage and Polyadenylation |
The Roma population is the largest transnational ethnic minority in Europe , characterized by a linguistic , cultural and historical heterogeneity . Comparative linguistics and genetic studies have placed the origin of European Roma in the Northwest of India . After their migration across Persia , they entered into the Balkan Peninsula , from where they spread into Europe , arriving in the Iberian Peninsula in the 15th century . Their particular demographic history has genetic implications linked to rare and common diseases . However , the South Asian source of the proto-Roma remains still untargeted and the West Eurasian Roma component has not been yet deeply characterized . Here , in order to describe both the South Asian and West Eurasian ancestries , we analyze previously published genome-wide data of 152 European Roma and 34 new Iberian Roma samples at a fine-scale and haplotype-based level , with special focus on the Iberian Roma genetic substructure . Our results suggest that the putative origin of the proto-Roma involves a Punjabi group with low levels of West Eurasian ancestry . In addition , we have identified a complex West Eurasian component ( around 65% ) in the Roma , as a result of the admixture events occurred with non-proto-Roma populations between 1270–1580 . Particularly , we have detected the Balkan genetic footprint in all European Roma , and the Baltic and Iberian components in the Northern and Western Roma groups , respectively . Finally , our results show genetic substructure within the Iberian Roma , with different levels of West Eurasian admixture , as a result of the complex historical events occurred in the Peninsula .
The diaspora of the Roma people , also known with the misnomer of Gypsies , is a not-well documented human movement , which is characterized by recent dispersals and multiple founder events . The Roma population is recognized as the largest transnational ethnic minority in Europe , with an estimated population of up to 10 million , although their exact number is difficult to estimate due to the lack of reliable census surveys . They consist of a heterogeneous and substructured mosaic of populations that differ linguistically , culturally , historically , and in their relation to nearby populations [1] . Their demographic history together with their endogamous social practices [1] have contributed to a particularly different spectrum of Mendelian disorders when compared with other neighboring European populations [2 , 3] . Historical records confirm the persecution and social marginalization that this population has suffered since their arrival to Europe [1] . Comparative linguistics has placed the origin of European Roma in India , particularly in the northwestern region , as Romani is closely related to Punjabi and Kashmiri languages [4 , 5] . However , the social organization and cultural dynamics in Indian populations lead to substructure in closely-related linguistic groups , as is reflected in the different proportions of Ancestral North Indian ( ANI ) and Ancestral South Indian ( ASI ) genetic components [6] shown in groups even living in the same geographic region , which prevents them to be considered as genetically homogeneous groups [7] and challenges the retrieval of the origins of Roma based solely on linguistic data . The Indian genetic component of the Roma population was first proposed after the identification of shared disease-causing mutations with Indian and Pakistani patients [8 , 9] . In addition , analyses of uniparental markers permitted to assign an Indian origin for some maternal and paternal lineages found in the Roma [10–12] , namely those belonging to the M-haplogroups ( M5 , M18 , M25 , M35 ) in the mitochondrial DNA [13] , and to H-M69 in the Y-chromosome [14] . Furthermore , genome-wide studies indicate that the European Roma originate from a reduced number of founders ( proto-Roma ) whose ancestral homeland was the current Punjab state of India [10 , 15 , 16] . According to previous historical and anthropological evidence , a subsequent migration from Northwest India through Persia and Armenia preceded the entrance in the Balkans , from where they spread across the entire Europe . During the 11th and 12th centuries , some Roma settled in the surroundings of the Ottoman Empire , in the Balkan Peninsula ( Balkan Roma ) , other groups spread across the Danubian Principalities ( present-day Romania , Moldova , and Hungary ) , where they were forced into slavery ( Vlax Roma ) , while the Romungro group started a dispersion across the Austro-Hungarian Empire [2] . Finally , other small groups moved into North , Central , and Western Europe ( Northwestern Roma ) , having arrived into Iberia in the early 15th century , as document a number of Iberian historical records mentioning the presence of Roma groups in Zaragoza and Barcelona in 1425 and 1447 , respectively [17] . The Roma diaspora through the Middle East , Caucasus , and Europe was a very complex process during which the emerging pattern of genetic substructure was highly influenced by differential gene flow from different West Eurasian ( European , Middle Eastern and Caucasian ) non-Roma populations [15 , 18] and even within Roma groups [19] . Genome-wide data showed that the Roma genomes harbor around 80% of Western Eurasian ancestry , while the remaining ancestry is from South Asian sources [16] . However , this estimate of the West Eurasian component is not only derived from their recent ( post-exodus ) admixture with non-Roma Europeans , as prior to their arrival into Europe , Roma might already carried an Ancestral West Eurasian ( AWE ) component from South Asian sources [16] , due to admixture events that occurred in South Asia around 1 , 900–4 , 200 years ago ( ANI component ) [20] , thus before the proto-Roma people left South Asia . However , previous genetic studies of the European Roma , despite the wealth of insights provided into their demographic history , show multiple limitations . First , South Asian populations have been primarily studied using the linguistic affiliation as criteria to classify individuals into groups , which often conflicts with genetic intra-group homogeneity . Second , the European Roma population has been approximated as a simple admixture between South Asian and European sources , without a detailed analysis of the West Eurasian component in Roma . In addition , most of the analyses relied in allele frequency-based methods , yet haplotype-based approaches provide a fine-scale characterization , and perform better than allele frequency analyses in populations that have been under strong genetic drift [21 , 22] . Finally , there are still few studies focused on the Iberian Roma population , which represents the westernmost expansion of the Roma diaspora in Eurasia . To overcome the mentioned limitations , the present study consists of a genome-wide analysis of the European Roma ( including new samples from the Iberian Roma ) , with the following aims: ( i ) to shed light on the South Asian origin of the proto-Roma population; ( ii ) to assess the level of admixture of the Roma with other European populations as well as with Middle Easterners and North Africans; and , ( iii ) to characterize the patterns of genetic substructure among the Iberian Roma . Our analysis unravels at fine-scale the genetic components of European Roma groups , dissecting the original South Asian , ancestral West Eurasian , and recent European components .
The European Roma population was first assessed in a worldwide context ( Dataset1 , S1 Table , see Materials and Methods ) . A Principal Component Analysis ( PCA ) was performed with samples from Europe , Africa , Middle East , Caucasus , Central and South Asia . Roma samples fall between non-Roma European and South Asian populations ( S1 Fig ) , in agreement with their demographic historical records [1] and previous genetic studies [15] . In addition , ADMIXTURE results further confirm PCA results , as at k = 3 , European Roma show a combination of two cluster components ( dark red and dark blue ) mainly found in South Asian and West Eurasian samples . At k = 6 ( lowest cross-validation error value ) , the Roma individuals displayed membership in a specific cluster and a yellow component mainly found in southwestern Eurasia , which reproduces previous results [15] ( S2 Fig ) . To further describe the Roma genetic substructure and to reveal fine-scale patterns , we used haplotype-based methods: ChromoPainter and fineSTRUCTURE . Most European Roma samples cluster together in a sister clade of MiddleEast-Caucasus and Europe super-group ( S3 Fig ) . These Roma samples belong to ten different clusters correlated with geography , grouping together individuals from the same European regions ( North , West , Central , and Balkans ) ( Fig 1A and 1B ) . As shown in the dendrogram ( Fig 1A ) and based on the Total Variance Distance ( TVD ) values , the most significantly differentiated Roma clusters are RomaIberia-2 and RomaMix-4 ( p < 0 . 001 ) ( S4 Fig , S2 Table ) . The non-Roma reference samples were classified in 51 genetic clusters from four different large super-groups ( Europe , MiddleEast-Caucasus , Central-SouthAsia , and MiddleEast-Africa ) ( S3 Fig ) . Admixture events that have shaped the genetic composition of the Roma population were inferred with GLOBETROTTER . For all European Roma clusters , “one-date” type of admixture event ( single admixture date between two sources ) was detected involving two sources: a West Eurasian-like major source and South Asian-like minor source , around 1270–1580 ( S3 Table , Fig 2 , Table 1 ) . This interval of admixture dates overlaps with the period when the first historical records report the presence of Roma groups in each European country , although these records represent the lower limits for the actual first Roma settlements . In general , Roma from the surroundings of the Balkan Peninsula and Central Europe ( RomaMix-1 , RomaMix-2 , RomaMix-3 , RomaUkr ) have earlier admixture dates ( Table 1 ) , which supports the dispersion into Europe via the Balkans [15] . Regarding the South Asian-like source , it contributes around 35% to the admixture and its most representative cluster is Punjabi-1 , from Northwestern India , ( Fig 2 , S3 Table ) . Although Punjabis have a linguistically uniform identity [23] , they are genetically heterogeneous . In fact , Punjabi samples do not cluster together , instead they are spread along PC2 ( S1 Fig ) , as well as in the fineSTRUCTURE dendrogram ( S3 Fig ) , with three different Punjabi clusters with increasing levels of ANI component ( S5 Fig , S4 and S5A Tables ) . Thus , most of the South Asian ancestry of the Roma is mainly shared with the group of individuals from Punjab with less West Eurasian component ( Punjabi-1 , S3 Table ) . The rest of South Asian surrogates identified in the minor source correspond to southeastern Dravidian-speaking populations ( E-India , Irula clusters ) ( Fig 2 , S3 Table ) , which also exhibit low levels of West Eurasian ancestry ( S5 Fig , S5A Table ) . Altogether , these findings suggest that the most likely proxy for the South Asian origin of the proto-Roma , is the ancestral source here described as a mixture of present-day South Asian groups with a low West Eurasian signature . The West Eurasian-like source contributes around 65% to the admixture event . This component captures the recent West Eurasian admixture between the proto-Roma and West Eurasians during their diaspora from India to Europe , in other words , it does not include the AWE component present in South Asian populations ( S1 Note , S6 Fig ) estimated to be around 15% ( S5B Table ) . This recent West Eurasian ancestry is lower in the Roma groups from the Balkan Peninsula and Central Europe ( RomaMix-1 and RomaMix-2 ) , around 60% , and it increases up to 80% ( RomaIberia-2 ) as the distance from the Balkans increases ( Fig 2 , S3 Table ) . The main contribution of this major source is from southeastern European clusters ( Balkan-1 and Balkan-2 ) , with this area being the historically reported gateway of the Roma groups into Europe [1] . The component from Middle East and Caucasian clusters was found to be moderate in the Roma groups . Besides these two components , additional distinct European ancestries are detected in the Northwestern Roma groups from the Baltic ( Estonia-Lithuania ) and Iberia ( Spain-Portugal ) . Specifically , while RomaBalt cluster shows a northeastern European component ( NE-Europe1 cluster ) , RomaIberia-1 and RomaIberia-2 contain a southwestern European component ( SW-Europe1 and SW-Europe2 ) each . This result indicates that , in the Roma groups that migrated to Northern and Southwestern Europe , the Balkan component left a footprint still clearly detectable today , though having been highly reconfigured by admixture in the Baltic region and the Iberian Peninsula , respectively ( Fig 2 , S3 Table ) . Regarding the Iberian Roma , the samples constitute two highly differentiated clusters ( RomaIberia-1 and RomaIberia-2 ) not found elsewhere , which suggests a deep genetic substructure within the Roma settled in Iberia ( Figs 1 and 2 , S3 Table ) . As mentioned above , the European Roma ancestry contains two main sources: the West Eurasian ( European and MiddleEast-Caucasus ) and the South Asian components . However , these ancestry proportions differ significantly when comparing the X chromosome to the autosomes: the South Asian ancestry is significantly higher in the X chromosome while the MiddleEast-Caucasus proportion is significantly higher in the autosomes ( S6 Table , S7 Fig ) . These results point to a sex-biased admixture during the Roma diaspora , likely characterized by a higher influx of non-Roma males than females from the Middle East and Caucasus . The proportions of European ancestry contained in the autosomes and the X chromosome are similar , although RomaBalt , RomaIberia-1 , RomaIberia-2 and RomaMix-4 show higher levels of European ancestry in the autosomes . These findings can also indicate different sex-biased gene flow processes in the European Roma groups , which might be the result of different social patterns among groups . Future studies with mtDNA and Y- chromosome data could add further insights into these results , as well as sex-specific fertility inheritance processes in the Roma population [24] . To investigate the effective population size ( Ne ) dynamics , we have estimated the Ne of each Roma group and the ancestry-specific Ne . On one hand , all Roma groups show a long uninterrupted Ne decrease followed by an increase of Ne ( without reaching the levels of the NorthItaly cluster , which we used as a European reference ) ( S8 Fig ) . The change of the Ne trend is slightly correlated with the start of the admixture in each Roma group ( S9 Fig ) , which might point to the gradual settlement of the Roma population in Europe . On the other hand , we inferred Ne through time for the three ancestral Roma source populations ( European , MiddleEast-Caucasus and SouthAsian ) , focusing on their Ne before the admixture: 34 generations ago , as the more ancient lowest confidence interval ( CI ) inferred from GLOBETROTTER is found in RomaMix-2 at 1164 CE ( S7A Table ) . The European Neg = 34 is 2 . 12 to 2 . 64 times higher than the South Asian Neg = 34 , which is 1 . 27–1 . 43 times higher than the MiddleEast-Caucasus Neg = 34 ( S7B Table ) . In contrast , the fold-change between the European and South Asian ancestry proportions is lower than 2 in all Roma groups ( except RomaIberia-2 and RomaMix-4 ) and between South Asian and MiddleEast-Caucasus ancestry proportions is higher than 1 . 5 fold in all Roma groups ( S7C Table ) . These differences between the ancestry proportions and the ancestry-specific Ne could be explained by the fact that a small South Asian proto-Roma group of founders had a continuous gene flow with different non-related groups from the MiddleEast and Caucasus and different non-Roma European populations , during their West Eurasian diaspora ( see S4 Note ) . Runs of homozygosity ( ROH ) were computed to assess the levels of inbreeding and the degree of genetic isolation in the Roma groups . In general , the mean ROH length of the Roma groups is significantly higher than the mean of the non-Roma reference Balkan-2 and Punjabi-1 clusters . For all ROH length categories , Roma groups present similar values than those of Kalash ( S10 Fig , S8A Table ) , which is known to be a highly inbred population [25] , possibly due to genetic isolation , although their isolation degree is in debate [26 , 27] . The average ROH lengths of the Roma maintain high values after a first significantly decrease between the first and the second categories ( 1–2 and 2–3 Mb , respectively ) ( S8B Table ) , which suggest that the inbreeding signals of Roma are the result of a continuous , although decreasing , level of isolation , from historical to recent times . Furthermore , the Roma groups with more West Eurasian ancestry ( IberianRoma-2 and RomaMix-4 ) are the clusters with the lowest mean ROH length values across all categories ( S10 Fig ) . Thus , these results additionally evidence a degree of heterogeneity within Roma from the Iberian Peninsula that need to be further investigated .
The demographic history of the Roma population is characterized by a series of bottlenecks and admixture events that have occurred since the proto-Roma left India , after their arrival to the Balkans and spread throughout Europe , and in the case of Iberian Roma , after their settlement in the Iberian Peninsula . The study of their genetic profile in a worldwide context places them between South Asians and Europeans , which confirms previous findings of admixture [10 , 15 , 16] . A fine-scale approach has allowed us to distinguish the recent West Eurasian component , which is the result of the admixture with non-Roma West Eurasian populations . Our estimates of this recent West Eurasian component , around 65% , are lower than the previously reported 80% [16] , as it only includes the “post-exodus from India” admixture and not the “pre-exodus from India” AWE component ( around 15% based on the f4 ratio estimates ) . This recent West Eurasian component was acquired between 1270–1580 . Although GLOBETROTTER infers this admixture as a single pulse event ( “one-date” ) , it would require large datasets to distinguish continuous from single pulse admixture [31] . Regarding the origin of the proto-Roma population , Northwestern India has been previously proposed as the putative source of their South Asian ancestry [4 , 5] . Although it is a geographically well-defined area , their populations are socially , linguistically , and genetically heterogeneous , with high levels of stratification and substructure: their lands comprise from tribe clans to upper-caste groups , and from Dravidian to Indo-European speaking groups [32] . Our analyses show that they are dispersed along the PC with different admixture proportions ( S1–S3 and S5 Figs ) . Within the boundaries of Northwestern India , the Punjab region has been further placed as the ancestral homeland of the proto-Roma , through different approaches: identity by descend ( IBD ) sharing analyses [16] , Approximate Bayesian Computation models [15] , and mitochondrial M lineages [10] and tau haplotype [33] comparisons between Roma and South Asians . However , the linguistic identity that characterizes the Punjabi population is independent of their historical origin and social designation [23] . Punjab is a strategic region that has suffered repeated invasions from different sources [32] , explaining why nowadays encompasses heterogeneous population with differential admixture and ancestral components . We have shown that the Punjabi samples are genetically heterogeneous , which mainly differ in the proportion of West Eurasian ancestry , further confirming previous results [7] . Our results add in the indication that the original genetic composition of the proto-Roma seems nearest to that of the Punjabi cluster from the less West Eurasian admixed group . Assuming that the individuals from this Punjabi cluster were already in Punjab when the rest of Punjabi clusters admixed with West Eurasians , socio-historical factors might have determined their differential admixture . In other words , this Punjabi cluster might derive from Punjabis who belonged to a lower caste group , since in agreement with previous studies , Indian lower caste groups are characterized by less West Eurasian admixture [6 , 7] . In addition , we have reported that Dravidian-speaking populations with high ASI ancestry ( i . e . E-India and Irula clusters ) are also involved in the South Asian source of the Roma individuals . These two sources of South Asian ancestry could solve the contradiction regarding the identification of uniparental Roma lineages with a Northwestern Indian origin [11] and the high Y-STR haplotype sharing among Roma and South Indian populations [34] , as these findings could be explained by two overlapping scenarios . The first one , first mentioned by Turner [4] , consists in considering a previous migration of nomadic groups into Northwestern India from Central India around 250 BCE and , after several centuries in Punjab with few external admixture , a single group of proto-Roma individuals left India . The second scenario refers to the fact that the genomes of present-day North Indians have more West Eurasian ancestry due to subsequent gene flow from West Eurasians after the proto-Roma left India [20] , which explains the combination of populations with low West Eurasian ancestry identified in the South Asian Roma component . These two scenarios fit the idea that the Roma people descend from a single initial founder population [15] . After the exodus from India and during the diaspora through West Eurasia , the Roma population admixed with multiple non-Roma European , Middle Eastern and Caucasian groups . First , the European Roma ancestors arrived to Armenia through Persia [1] . Our results agree with a moderate Middle East and Caucasus gene flow during a rapid migration across this territory [15] , specifically , we detect a higher rate of male gene flow , which could be related to the incorporation of Persian nomadic groups with the Roma [1] . Then , historical records suggest that , in Armenia , they followed the same route as the displaced Armenians towards Anatolia , due to the Mongol and Seljuq invasions ( a Persian Muslim dynasty ) , from where they were pushed to the west until their entrance into Europe through the Thrace region in the Balkan Peninsula [35] . They settled in the Balkans for almost 200 years [35] , where the Greek impact on the Romani language was much more extensive than the Persian [1] . Accordingly , we have identified the Balkan admixture footprint in the European Roma genomes with an ancestry gradient correlated with the distance to the Balkans: from 45% in Bulgarian , Greek , and Serbian Roma; to 25% in Lithuanian , Estonian , and Iberian Roma , which is further evidence that the dispersion into Europe took place via the Balkans [15] . After subsequent migrations and dispersions across Europe , Roma groups reached Northeastern Europe ( e . g . Lithuania and Estonia ) and Southwest Europe ( e . g . Iberian Peninsula ) , at the beginning of the 16th and 15th centuries , respectively [1] . Particularly in these groups , we have identified the Baltic and Iberian components besides the common Balkan component . In relation to the demographic dynamics , we have shown that the Ne reduction of the Roma groups ceased after the start of the admixture event , which points to the settlement of Roma in Europe and the beginning of more intense assimilation politics during the seventeenth century [1] . The Ne estimates ( as discussed in S3A Note ) might reflect Ne changes in the Roma groups due to a population expansion or the non-Roma West Eurasian admixture . In addition , the levels of inbreeding in the Roma population are higher than in non-Roma Europeans and similar to those of South Asian groups , which could be the result of endogamy practices and/or multiple founder events . In the Iberian Peninsula , Roma groups were well-accepted at their arrival , but at the end of the fifteenth century , with the unification of Castile and Aragon crowns , the nomad Roma groups were forced to become sedentary and suffered continuous persecutions [1] . As we remark , the present-day Iberian Roma exhibit high levels of non-Roma European ancestry , with an admixture event estimated around 1250–1600 . Although GLOBETROTTER did not infer two independent admixture events as might be expected in the Iberian Roma , two different European footprints are identified: the Balkan and the non-Roma Iberian components . The detection of a single signal of admixture could be explained by a rapid expansion from the Balkans to the Iberian Peninsula , with a short time gap between the two events , or due to continuous gene flow between non-Roma Europeans and Roma groups during their migration within Europe . In fact , if the time ranges between two events are close , the ability of GLOBETROTTER to distinguish between two admixture pulses from a single pulse decreases [31] . Besides between-country heterogeneity , the present study further identifies within-country Roma substructure in the Iberian Peninsula , partially correlated with geography: two clusters are restricted to the northwestern and central part of the peninsula ( IberianRoma-1 and IberianRoma-2 ) , another cluster mainly represents Roma samples from the south ( IberianRoma-3 ) and the last one contains all the northeastern individuals ( IberianRoma-4 ) . These groups differ both in ancestry proportions and inbreeding levels , which can be the result of different demographic patterns , as the different laws concerning the Roma people in the Iberian Peninsula were neither homogeneous nor permanent [1] . As mentioned above , IberianRoma-4 is the most differentiated cluster . It exhibits more non-Roma Iberian ancestry , the inferred date of the admixture event is the most recent one ( 1532–1730 ) , and it presents the lowest inbreeding levels . Altogether this can be explained by the extensive admixture with the non-Roma Iberian population . In fact , historical records confirm that both nomadic and sedentary Roma groups in the Principality of Catalonia were highly linked and interrelated with the non-Roma society [36] . In addition , their European ancestral source contains groups from North Italy and Northwestern Europe that are absent in the rest of Iberian Roma samples , which might point to either a posterior arrival to the Iberian Peninsula after admixing with these European populations or due to the constant movement of Roma groups between Southeastern France and Northeastern Spain [36] . The Iberian group representing the most southern location , IberianRoma-3 , has a genetic particularity: it has around 1% of Northwest African ancestry , which probably corresponds to the North African admixture found in the southern and western parts of the Iberian Peninsula , during the Arab expansion ( 711–1248 ) [28 , 29] . The fact that the North African component is only found in IberianRoma-3 samples , who also show Balkan ancestry , contributes to reject the hypothesis of a Roma migration route to Iberia from North Africa [30] . IberianRoma-1 has more non-Roma Iberian component than IberianRoma-2 , although these two clusters contain samples from the same region . These results highlight that , even within Roma groups who live in the same geographic region , distinct social dynamics ( ie . itinerant vs sedentary lifestyles ) caused the application of different laws that might have shaped their current genetic landscape . On the contrary , some geographical patterns have probably been diluted due to the continuous movement and admixture among Roma groups , especially after 1749 with the general imprisonment of Spanish Romani , who were captured and relocated , although the effects of this event were not uniform throughout the Roma community , enabling the identification of present-day geographical patterns within Iberia Roma [37] . The present study attempts to characterize the European Roma and describe their South Asian and West Eurasian components using fine-scale methods . On the one hand , we have targeted the putative South Asian ancestry of the Roma in a specific group of Punjabi and Southeastern Indian individuals , representing a small group of proto-Roma founders with low levels of the West Eurasian ancestry . Besides , our results show that the recent West Eurasian component ( around 65% of the Roma genomes ) was acquired between 1270–1580 , during the Roma diaspora . Specifically , we have detected and characterized the Balkan genetic footprint in all European Roma groups and the Baltic and Iberian components in the Northern and Western Roma groups , respectively , likely due to a continuous non-Roma gene flow during their dispersal through Europe . On the other hand , we have found genetic substructure within the Iberian Roma , with different groups and different levels of non-Roma admixture , as a result of the complex historical events occurred in the Peninsula . Further studies are needed to fully understand the genetic substructure of the Roma population as well as to provide new insights into the migration routes undertaken by the European Roma shaping their current genetic landscape . The use of migration group data ( Balkan , Romungro and Vlax group assignation ) would add an additional layer of information in both genome-wide and complete uniparental markers analyses , as it has been suggested that Roma genetic diversity might be primarily structured by migration route [11 , 12] .
Written informed consent was obtained from all the volunteers and the present project has the corresponding IRB approval ( CEIC-Parc de Salut Mar 2016/6723/I ) . A linkage disequilibrium pruning was performed for the analyses that require it using PLINK 1 . 9 [42] with standard parameters ( window size of 50 SNPs , 5 SNPs shift at each step , and an r2 threshold of 0 . 5 ) in both Dataset1 and Dataset2 , leaving 192 , 815 and 186 , 374 SNPs , respectively . In order to examine the Roma population structure in a worldwide context , a PCA was performed with SmartPCA program implemented in EIGENSOFT 4 . 2 package [44] , and 20 runs of ADMIXTURE [45] with different random seed tests were computed for different ancestral components ( k = 2 to 8 ) . We used pong [46] to identify and visualize modal ADMIXTURE results for each value of K . Both analyses were performed in Dataset1 and Dataset2 independently . The phasing of the Dataset1 and Dataset2 autosomal data was performed , independently , with SHAPEIT [47] , using the population-averaged genetic map from the HapMap phase II [48] and the 1000G dataset as a reference panel [38] . ChromoPainter [21] , based on a Hidden Markov Model ( HMM ) algorithm , aims to reconstruct the chromosome of each target individual ( “recipient” ) as a mosaic of haplotypes from the reference individuals ( “donors” ) . This procedure is known as chromosome painting and their results can be summarized in a coancestry matrix , which shows for each recipient the total counts and length in cM of haplotypes that share a most recent common ancestor with each donor [21] . Intuitively , this matrix shows the haplotypes shared between each recipient and each donor individual . First , in order to infer the switch rate and global mutation probability ( n and m parameters ) , ChromoPainter v2 was run in chromosomes 1 , 7 , 14 , and 20 , for 10 iterations of the expectation-maximization ( EM ) algorithm , painting each recipient ( all individuals in the dataset ) using all the donors ( the rest of individuals in the dataset ) . For Dataset1 , the inferred n and m parameter values were 251 . 11459 and 0 . 00023 , respectively . Then , ChromoPainter v2 was run again in all chromosomes fixing these parameters . The total counts and lengths coancestry matrices were obtained by adding the matrices of all chromosomes . FineSTRUCTURE [21] is an algorithm that infers the clustering of the samples considering the information in the ChromoPainter coancestry matrix . Using this clustering , it is possible to group the samples into genetically homogeneous clusters . First , fineSTRUCTURE was run for 2 million Markov Chain Monte Carlo ( MCMC ) iterations , sampling values every 10 , 000 iterations after 1 million “burn-in” iterations [49] . Then , fineSTRUCTURE was run again to perform 100 , 000 additional hill-climbing moves from the MCMC sample with the highest posterior probability to get the final cluster membership in a dendrogram format . This procedure was repeated three times and after comparing the consistency of the three dendrograms , we classified the 952 individuals from Dataset1 into 63 clusters , where the European Roma branch contains ten Roma clusters . The rest of Roma samples outside this clade ( e . g . Welsh Roma ) cluster with other European non-Roma samples , due to high levels of non-Roma European ancestry as described previously [15] , thus they were removed for further analyses . In order to estimate the copying profiles ( i . e . average proportion of ancestry attributed to each donor group ) , ChromoPainter v2 was run in a different mode than described above: haplotype sharing was inferred between groups rather than independent individuals [49] . For this analysis all the individuals were grouped in the genetic clusters established according to fineSTRUCTURE where the ten European Romani clusters were settled as recipients and the rest of clusters as donors . In addition , we calculated the TVD metric as described in [49] , which measures the differences between a pair of clusters ( A , B ) with copying vectors a and b and it can be calculated as: TVD ( A , B ) =0 . 5×∑i=1n ( ai‐bi ) ( 1 ) where n is the total number of donor groups . As suggested by Leslie S . et al [49] , for each pair of clusters , individuals were randomly reassigned in one of the two clusters , and the new copying vectors a’ and b’ , and the TVD values were recalculated for 1 , 000 permutations . P-values correspond to the proportion of permutations where TVD ( A’ , B’ ) > TVD ( A , B ) and reflect the strength of differences between the inferred pair of clusters . Corrected p-values were obtained after Bonferroni multiple test correction . For Dataset2 , the above procedures ( ChromoPainter , fineSTRUCTURE , and TVD metric calculations ) were also performed using the same approach , and the ChromoPainter switch rate and global mutation probability inferred using Dataset2 were 259 . 85269 and 0 . 00016 , respectively . The fineSTRUCTURE dendrogram of Dataset2 was used to classify the 1 , 332 individuals into 88 clusters , where four of them belonged to Iberian Roma clusters . One Iberian Romani sample from Madrid ( G32 ) was excluded , as it clustered with Iberian non-Roma samples , suggesting an extensive non-Roma ancestry . We checked whether the ChromoPainter algorithm is able to correctly distinguish between the two sources of West Eurasian ancestry in the Roma population , in order to avoid misleading results when inferring the admixture sources: the AWE component ( pre-exodus from India ) as South Asian ancestry , and the recent West Eurasian admixture ( post-exodus from India ) as West Eurasian ( see S1 Note , S5 and S6 Figs , S4 Table ) . GLOBETROTTER [31] is a method designed to characterize and date admixture events between source populations ( which are a composite of surrogate populations ) that have shaped the genetic history of a target population . The dating estimation is based on the principle that the size of the haplotypes decreases over successive generations due to recombination . GLOBETROTTER algorithm uses the haplotype sharing results from ChromoPainter considering donor and recipients as groups of individuals . GLOBETROTTER was run for each of the ten Roma clusters in the European Roma branch from Dataset1 using ten painting samples per individual from ChromoPainter and the coancestry matrix of the genome-wide length of haplotype sharing . In order to identify the admixture events between source populations that have shaped the genetic history of European Roma , the surrogate populations included were all the European , Middle Eastern , Caucasian , and Asian clusters . The sample size of these clusters was normalized to a maximum of 21 , which corresponds to the third quartile of all clusters sample sizes . First , in order to estimate p-values for evidence of admixture , GLOBETROTTER was run using the NULL procedure ( standardize the coancestry curves by a “NULL” individual ) , with 100 bootstrap resamples . Then , GLOBETROTTER was run using the non-NULL inference to characterize the admixture events . These two GLOBETROTTER runs were checked for consistency . To estimate admixture date CIs , 100 bootstrap iterations were performed and a generation time of 25 years was considered . The same procedure was used to infer admixture events that have shaped the genetic history of the Iberian Roma from Dataset2 . Thus , the target populations were the four Iberian Roma clusters , and the surrogate populations were all the European , North African , Middle Eastern , Caucasian , and Asian clusters . Spatial distributions of the major source proportions in each Iberian Roma cluster were computed in R using the kriging model in the package fields [50] . When describing the admixture sources that have shaped the Roma today , we use the term “non-Roma populations” to facilitate the understanding , although the admixture events occurred with “non-proto-Roma” groups . To further characterize the South Asian component of the Roma , we have estimated the proportion of WE ancestry in the South Asian clusters ( ANI component ) using f4 ratio estimation implemented in ADMIXTOOLS [51] as: α=f4 ( YRI , Basque;India , Onge ) f4 ( YRI , Basque;Georgians , Onge ) [20] , computing standard error with a Block Jackknife with a block size of 5cM . For this analysis , we have included Onge samples from [52] . We have calculated the ANI proportion in the Roma groups from the relative contribution ( inferred by GLOBETROTTER ) of each South Asian cluster . The X chromosome from Dataset1 was phased using the same parameters as the autosomes , as described previously [39]; and ChromoPainter v2 [21] was run with all European Roma samples as recipients and the non-Roma European , Middle East , Caucasus , and South Asian clusters as donors using only the X chromosome . Then , the ancestry profiles of the X chromosome were estimated for each individual in each Roma cluster by applying SOURCEFIND , a new Bayesian model-based approach [53] , with 200 , 000 MCMC samples , sampling every 1 , 000 iterations . Once we obtained the estimated proportions of each donor cluster in the X chromosome of the Roma from the MCM sample with the highest posterior probability , we summed them to get the European , MiddleEast-Caucasus , and South Asian proportions that contribute to the Roma ancestry . The same procedure was applied to the autosomes . To test for sex-biased gene flow in the Roma samples , we obtained the ancestry differences per individual by subtracting the European , MiddleEast-Caucasus , and South Asian proportions between the autosomes and the X chromosome grouping all Roma individuals together . A Wilcoxon signed-rank test across individuals between the autosomes and the X chromosome was applied to obtain a p-value of the differences , with Bonferroni correction . In addition , we tested the European ancestry differences for each Roma cluster . To avoid possible biases due to different number of SNPs , we not only compared the whole set of autosomes against the X chromosome , but also each autosome separately against the X chromosome ( see S2 Note , S7 Fig , S6 Table ) . ROH analyses were performed to assess the inbreeding levels among the Roma groups . ROH segments were identified using PLINK 1 . 9 [42] , considering ROH with at least 50 SNPs of length 500 kb and a maximum gap between a pair of consecutive SNPs of 100 kb , as these parameters account for locally low SNP density in SNP arrays [54] . For comparative purposes , Dataset1 analysis included two clusters with putative higher levels of inbreeding , from Europe ( Basque ) and from South Asia ( Kalash ) ; and two with low levels , from Europe ( Balkan-2 ) and from South Asia ( Punjabi-1 ) . For Dataset2 , we included Basque and Kalash clusters , and SW-Europe2 and Punjabi-1 . Changes in Ne through generations were estimated for the Roma groups from IBD segments . The Roma samples belong to an admixed population , and thus , in order to detect IBD segments , we applied RefinedIBD [55] , a haplotype-based method , with default parameters; and merged the segments with gaps to avoid the underestimation of segment lengths [56] . Then , using these IBD segments and the HapMap GRCh37 genetic map [48] , IBDNe [57] was run with default parameters to infer Ne estimates with 95% CIs at each generation , assuming 25 years per generation . Although these methods were first designed to deal with sequence data , this approach applied to genome-wide array data has a high confidence in recent periods ( from present to around 50 generations ago ) [57] . For Dataset1 , the analysis was performed on the ten European Roma clusters and the reference cluster NorthItaly . For Dataset2 , it was performed on the four Iberian Roma clusters and SW-Europe2 as reference . In addition , we checked whether the Ne estimations correlate with the admixture event detected with GLOBETROTTER in each Roma group , regarding both the proportion of West Eurasian source and the admixture dates ( see S3A Note , S10 Fig ) . Finally , we estimated the Ne of the ancestral Roma populations , following the same procedure as in Browning et al . [56] , to compare the ancestry-specific Ne of the European , MiddleEast-Caucasian and South Asian sources prior to the admixture , grouping all Roma samples together ( as we assume that the Roma groups split after the arrival to Europe ) . First , we performed a local ancestry inference ( LAI ) with RFMix v1 . 5 . 4 [58] , using as sources the donor populations identified in the GLOBETROTTER analysis , grouped in three categories: Europe , MiddleEast-Caucasus and South Asia . Although Europe and MiddleEast-Caucasus ancestries are similar , Xue et al . [59] showed that RFMix is able to accurately infer local ancestry segments , using balanced reference panels with key features comparable to our study ( e . g . SNP array data and admixture sources ) . After checking the correlation between the ancestry proportions of RFMix and GLOBETROTTER ( see S3B Note , S19 Fig ) , we followed Browning et al . [56] pipeline: rephasing of the RFMix output , filtering of the IBD segments by ancestry and calculation of the ancestry-adjustment number of pairs of sampled haplotypes . Then , IBDNe [57] was run with default parameters to infer ancestry-specific Ne estimates with 95% CIs at each generation , assuming 25 years per generation . Finally , we calculated the fold-change of the Ne CIs between the three ancestral populations , one generation before the start of the admixture ( i . e . lowest CI inferred from GLOBETROTTER ) and compared it with the fold-change between the current ancestry proportions inferred with GLOBETROTTER . | Human demographic processes and admixture events leave traceable footprints in the genomes of the populations and they can modulate the genetic architecture of complex diseases . Here , we aim to study the Roma people , an admixed population with a particular demographic history recognized as the largest ethnic minority in Europe . Previous studies suggest that they originated in South Asia 1 , 500 years ago and followed a diaspora towards Europe with extensive admixture with non-Roma West Eurasian groups . However , the genetic components of the Roma have not been deeply characterized . Our study reveals a common South Asian origin of all European Roma , closely related to a Punjabi group from Northwestern India . Through fine-scale haplotype-based methods , we describe a complex West Eurasian genetic component in the Roma groups , identifying a common Balkan ancestry and country-specific admixture footprints consistent with the dispersion through Europe . Our findings provide new insights into the demographic history and recent admixture events that have shaped the genetic composition of European Roma groups and could enable a better genetic characterization of complex disease in this population . | [
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]
| 2019 | European Roma groups show complex West Eurasian admixture footprints and a common South Asian genetic origin |
MicroRNAs ( miRNAs ) post-transcriptionally regulate the expression of thousands of distinct mRNAs . While some regulatory interactions help to maintain basal cellular functions , others are likely relevant in more specific settings , such as response to stress . Here we describe such a role for the mir-290-295 cluster , the dominant miRNA cluster in mouse embryonic stem cells ( mESCs ) . Examination of a target list generated from bioinformatic prediction , as well as expression data following miRNA loss , revealed strong enrichment for apoptotic regulators , two of which we validated directly: Caspase 2 , the most highly conserved mammalian caspase , and Ei24 , a p53 transcriptional target . Consistent with these predictions , mESCs lacking miRNAs were more likely to initiate apoptosis following genotoxic exposure to gamma irradiation or doxorubicin . Knockdown of either candidate partially rescued this pro-apoptotic phenotype , as did transfection of members of the mir-290-295 cluster . These findings were recapitulated in a specific mir-290-295 deletion line , confirming that they reflect miRNA functions at physiological levels . In contrast to the basal regulatory roles previously identified , the pro-survival phenotype shown here may be most relevant to stressful gestations , where pro-oxidant metabolic states induce DNA damage . Similarly , this cluster may mediate chemotherapeutic resistance in a neoplastic context , making it a useful clinical target .
MicroRNAs ( miRNAs ) are endogenous ∼22 nt RNAs that regulate gene expression post-transcriptionally . In animals , the ability of miRNAs to accomplish this regulation depends on complementarity between mature miRNA sequences and their mRNA targets . Most commonly , partial binding of miRNAs leads to destabilization of mRNA transcripts and/or inhibition of productive translation , and in rare cases perfect complementarity instead causes target cleavage . Both in vitro experiments and bioinformatics have shown that matches to positions 2–7 of the miRNA , referred to as the miRNA “seed , ” are generally required for effective miRNA-directed mRNA downregulation [1] , [2] . The roles of miRNAs in mouse embryonic stem cells ( mESCs ) have been of particular interest , as this knowledge may shed light on key aspects of mammalian development and generate useful insights into reprogramming and cancer , both of which recapitulate aspects of an ESC expression state [3] , [4] . In addition , the survival of mESCs in the absence of Dicer ( Dcr ) , the key RNase III enzyme that generates mature miRNAs , makes them a unique model system for dissecting miRNA function [5] , [6] . Several large-scale sequencing datasets [7] , [8] , [9] have revealed that the mir-290-295 cluster constitutes the dominant miRNA population in mESCs , giving rise to about 50% of all reads in these cells ( Table S1 ) . Many of the miRNAs in this cluster share the hexamer seed ‘AAGUGC , ’ which is also expressed at much lower levels by the mir-302 and mir-467 clusters , contributing less than 5% of total reads ( Table S2 ) . A similar percent contribution to total miRNA levels comes from the miR-17-92 family , which contains the shifted seed ‘AAAGUG , ’ and therefore may share some common targets ( Table S2 ) [7] , [8] , [9] . Given the abundance of the mir-290-295 cluster and these related sequences , much of mESC miRNA physiology is likely to be a function of this dominant seed sequence . Within the mir-290-295 cluster , the ‘AAGUGC’ seed is found in miR-290-3p , miR-291a-3p , miR-291b-3p , miR-292-3p , miR-294 , and miR-295 . Consistent with their high expression , these miRNAs ( which we shall refer to as the mir-295 cluster ) have been linked to a number of functions in ES cells including maintenance of pluripotency and proliferation . For instance , miR-290-295 miRNAs have been shown to target Rbl2 , which controls the expression of Dnmt3a and Dnmt3b [10] , [11] , suggesting a role for this miRNA cluster in regulating de novo DNA methylation . In addition , miR-290-295 miRNAs have been found to accelerate cell proliferation by promoting the G1 to S phase transition through targets such as p21 and Lats2 [12] . However , additional roles for this cluster remain to be elucidated . Using a combination of target prediction data with microarrays of mESCs before ( Dcr WT ) and after ( Dcr KO ) miRNA loss , as well as before ( 295 WT ) and after ( 295 KO ) specific deletion of the mir-295 cluster ( Medeiros et al . , manuscript in preparation ) , we have identified novel targets of the mir-295 cluster in ES cells . Initial analysis suggested strong enrichment of targets involved in apoptosis , a function that to date has not been linked to ES-cell specific miRNAs . Through gain and loss of function studies , we show that miR-290-295 miRNAs indeed serve a protective function in preventing mESC apoptosis during exposure to genotoxic stress . This protective effect appears to be mediated in part by direct repression of two novel targets , Caspase 2 and Ei24 . As activation of these targets is dependent on DNA damage , we propose that their regulation may be particularly relevant during physiological stress in embryonic development . In addition , given prior indications that these two genes act as tumor suppressors , misexpression of this cluster in the context of cancer may promote resistance to standard genotoxic therapeutics .
In order to identify additional endogenous targets of mESC miRNAs , we performed expression profiling of mESCs following Cre recombinase-mediated Dcr deletion using a previously characterized floxed Dcr mESC line [13] , [14] , [15] . As Dcr deletion leads to slower proliferation [6] , [12] , acute loss was examined in a polyclonal population , averaging over potential clonal variants and enriching for initial miRNA-mediated derepression rather than subsequent compensatory changes . Indeed , expression profiling from 3 biological replicates taken 5 days following deletion , a time point by which cells were predominantly Dcr null and a majority of miRNAs were lost ( Figure 1A and 1B ) , showed better clustering than 3 chronic deletion cell lines , as indicated by higher Pearson correlation coefficients ( Figure S1 ) . To confirm that targets of the mir-295 cluster show a transcriptome-wide signature in this dataset , we calculated a cumulative density function ( cdf ) plot comparing expression differences for the set of all mir-295 cluster targets as determined by Targetscan 5 . 1 [16] . Relative to a control set of genes ( control ) matched for 3' UTR length , dinucleotide composition , and expression level , the mir-295 cluster target set ( targets ) was more derepressed upon Dcr loss ( Figure 1C ) . An even larger derepression was seen for conserved mir-295 cluster targets ( conserved targets ) , suggesting further enrichment of genuine targets in this set ( Figure 1C ) . This observation supports the utility of these expression data for target discovery . To better understand the global effects of miRNA loss in ESCs , we next performed Gene Ontology ( GO ) analysis on an initial candidate set . Enrichment in specific GO categories was tested for all genes that increased on Dcr loss ( defined as ≥1 . 2 fold up-regulation ) . The top statistically significant categories included “Regulators of Apoptosis” and “Cell Cycle” ( p = 2 . 1e−8 and p = 5 . 6e−5 , respectively ) . We further refined our candidate list using available array data from the 295 KO line , which also showed cdf plot signature changes for mir-295 cluster targets ( Figure 1C ) ( Medeiros et . al . , manuscript in preparation ) . In all , 807 candidates were identified as Targetscan-predicted targets of the cluster that showed at least a 1 . 2 fold up-regulation in knockout populations from both datasets ( Figure 2A , Table S3 ) . Over 40% of upregulated transcripts were shared between the Dcr KO and 295 KO lines , consistent with the finding that the mir-295 cluster contributes around half of all miRNAs in ES cells . The fact that this overlap is not even greater may be due to direct and indirect effects of non-AAGUGC seeds , as there is significantly more overlap – in fact , closer to 70% – between the two data sets when considering only those genes that are Targetscan-predicted AAGUGC targets ( p<0 . 001 , Fischer’s exact test ) . Several candidate target genes were selected for further examination based on their degree of upregulation in Dcr KO and 295 KO cells , as well as their functional annotations . The tested targets span a range of biological functions and processes , from cell cycle regulators Lats2 and p21 to immunological signal transduction components Irf9 and Irak3 . Their 3' UTRs were cloned into luciferase constructs , and expression levels between Dcr WT and Dcr KO cells were evaluated ( Figure 2B ) . All candidates tested displayed at least mild repression relative to a control construct lacking miRNA target sites , ranging from strong ( ∼5-fold ) to modest ( ∼30% ) down-regulation . The magnitude of repression for the previously identified miR-295 targets Lats2 and p21 was comparable to that observed previously [12] . Additional transfection studies confirmed that repression could be conferred specifically by miR-295 in a Dcr KO background ( Figure S2 ) . These in vitro results support the enrichment of our candidate list for true miR-295 targets . We chose to more closely examine one of the most strongly down-regulated reporter targets , Caspase 2 ( Casp2 ) , along with Ei24 , as these targets could provide a novel link between ESC-specific microRNAs and cell survival . Casp2 , an initiator of apoptosis in response to genotoxic stress [17] , has four AAGUGC binding sites in its 3' UTR . Quantitative RT-PCR analysis demonstrated an approximately 5-fold increase in Casp2 transcript levels in Dcr KO cells , consistent with the degree of derepression observed with the luciferase reporter assay ( Figure 3A ) . This observation indicates that the majority of miRNA repression likely occurs at the level of transcript stability . In support of the reporter assay , Dcr KO cells showed a comparable increase in Casp2 at the protein level , which could be partially rescued by transfection of either miR-295 , miR-467a ( which shares the same hexamer seed ) , or a Casp2 siRNA , but not by siRNAs against other unrelated targets ( Figure 3B ) . Transfection of miR-295 also strongly repressed an intact Casp2 reporter in these cells , but not a reporter in which the four target sites were mutated ( Figure 3C ) . Combinatorial mutagenesis revealed that repression was not conferred equally by these four sites , as much of the repression was lost by mutation of the first two sites alone ( Figure 3D ) . Taken together , these data suggest that direct miRNA-mediated repression of Casp2 leads to approximately 5-fold repression , making it one of the most potently repressed mir-295 cluster targets identified to date . We additionally characterized the novel target Ei24 , which has also been implicated in apoptosis . Originally identified as a direct p53 transcriptional target that binds Bcl2 [18] , [19] , the Ei24 3' UTR contains one 7mer miR-295 site . The 3' UTR of Ei24 fused to a luciferase reporter conferred approximately 2-fold repression in Dcr WT cells relative to Dcr KO cells , an effect that could be restored following transfection of miR-295 ( Figure 3C ) . Notably , repression was lost upon mutation of the seed site , confirming that Ei24 is a direct target . Based on these repression data as well as the earlier informatic predictions , we tested whether mir-295 cluster miRNAs could modulate apoptosis in mESCs . The basal apoptosis rates of Dcr WT and KO ES cells in a 24 h period were compared by staining them with antibodies against cleaved Caspase 3 ( Casp3 ) and then analyzing cells by flow cytometry . Under these conditions , only modest basal apoptotic rates were observed , with Dcr KO ES cells showing a slightly higher apoptosis rate than Dcr WT cells ( Figure 4A , Figure S3A ) . Given that ESCs are highly sensitive to DNA damage [20] and both validated targets have been implicated in the DNA damage response , we hypothesized that the mir-295 cluster may be specifically protective in the context of genotoxic stress . To test this , we first examined the effect of exposing WT and Dcr KO cells to gamma irradiation or doxorubicin . Gamma irradiation induces DNA damage and activates ATM and p53 , as does doxorubicin , a topoisomerase II inhibitor [21] . These signals lead to activation of the intrinsic apoptosis pathway and result in the cleavage of Casp3 [22] . We were able to confirm this cleavage product by Western blot in our cell culture system , as well as cleavage of Nanog , a previously reported Casp3 target [23] ( Figure S5A ) . We also observed a decrease in Casp2 levels and the appearance of the previously described 35kD cleavage product [24] , confirming its activation in our system ( Figure S5B ) . Because this band was specific to DNA damage induction , the upregulation of Casp2 in Dcr KO cells appears to be insufficient to generate autocleavage . Both Dcr WT and KO ES cells showed minimal Casp3 activation immediately after 5-Gy gamma-irradiation , in line with previous descriptions of a 1–2 h lag phase in its activation ( Figure 4A ) [25] . However , there was a notable difference in their responses 24 h after the treatment ( and to a lesser extent 10 h after treatment , Figure S4A ) ; while 10% of WT cells became apoptotic , more than 30% of the Dcr KO cells exhibited Casp3 activity ( Figure 4A ) . Similar results were seen using Annexin V staining , a complementary assay for detecting early apoptosis ( Figure S3C ) . Importantly , mature miRNA levels from the mir-295 cluster were unchanged by these stressors ( Figure S3D ) . Therefore , it appears that loss of mature miRNAs leads to an enhancement of apoptosis in the presence of DNA damage . To examine whether the miR-295 targets modulated apoptosis , we transfected a series of siRNAs into Dcr KO cells and evaluated cell death following irradiation . The difference in Casp3 activation between 0 and 24 h timepoints was calculated in order to account for differences in transfection-specific toxicity ( Figure S6A and S6B ) . Relative to control siRNAs , transfection of miR-290-3p or miR-295 drastically decreased the apoptosis response of Dcr KO cells to gamma irradiation ( Figure 4B , Figure S6A and S6B ) . The reduction in apoptosis is specific to the AAGUGC seed , as seed mutants failed to rescue Dcr KO ES cells from apoptosis . When we applied siRNAs specific to each validated target , or to Bim , a well-characterized proapoptotic factor , cells exhibited a decrease of 5–10% in Casp3 activation 24 h after irradiation , a level similar to mir-295 cluster miRNA overexpression ( Figure 4B , Figure S6A and S6B ) . Similar findings were obtained when cells were treated with 100 nM –300 nM doxorubicin , suggesting that the identified pathways are relevant to DNA damage in general ( Figure 4C and 4D , Figure S4B , Figure S6C and S6D ) . Because deletion of Dcr involves global miRNA loss , and three additional clusters containing the same or similar hexamer seed , mir-302 , mir-467 , and mir-17-92 , are expressed in ESCs ( Table S2 ) , we examined the 295 KO line to determine the specific contribution of the mir-295 cluster to cell survival . Genetic deletion offers the best insight into physiological function as it avoids overexpression artifacts of exogenous miRNAs or toxicity effects of miRNA inhibitors . This system also avoids confounding by alternative miRNA-independent roles for Dcr itself in cell survival , as have been recently reported in C . elegans [26] . We first re-examined the reporter constructs for Casp2 and Ei24 in the 295 KO ESC line relative to its wild-type counterpart . In this context , the Casp2 reporter was derepressed approximately half as strongly as it was in Dcr KO cells , suggesting that the miR-302 and miR-467a families of miRNAs incompletely compensate for loss of the mir-295 cluster ( Figure 5A ) . This partial derepression in the mir-295 cluster deletion probably reflects the quantitative change in the total level of AAGUGC seed miRNAs , as exogenous miR-295 could further repress Casp2 protein levels ( Figure 5B ) . We next exploited 295 KO ES cells to determine whether these cells had an increase in apoptosis upon exposure to DNA damaging agents . 295 WT and KO ES cells were irradiated and the level of cleaved Casp3 activity was measured 0 and 24 h after treatment ( Figure 5C , Figure S3B ) . As expected , 295 KO cells were much more sensitive to irradiation than their WT counterparts ( Figure 5C ) . Again , overexpression of two miRNAs in the cluster , miR-290-3p and miR-295 , reduced the rate of apoptosis ( Figure 5D , Figure S7A and S7B ) . In addition , knockdown of the validated targets Casp2 or Ei24 , or the pathway component Bim , partially rescued cells from apoptosis caused by irradiation ( Figure 5D , Figure S7A and S7B ) . We repeated these experiments with 100 nM doxorubicin as before , obtaining similar results ( Figure 5E and 5F , Figure S7C and S7D ) . Therefore , deletion and restoration of mir-295 cluster miRNAs recapitulate the modulation of apoptosis rates seen in a Dcr null context .
Here , we provide the first demonstration that the mir-295 cluster can suppress apoptosis in mESCs following exposure to the genotoxic stressors ionizing irradiation and doxorubicin . Initially suggested by an informatic comparison of global expression data following Dicer loss , the link between ESC miRNAs and cell death may act in part through the novel targets Casp2 and Ei24 . In the case of Casp2 , this appears to occur through multiple seed match sites in the 3' UTR leading to a roughly 5 fold reduction in expression , while for Ei24 , targeting is achieved through just a single complementary site conferring approximately a 2 fold repression . Although the exact functions of these two mediators are still emerging , multiple lines of evidence suggest that they are important in cell survival . Initial studies of Casp2 knockout mice showed increased numbers of oocytes suggesting resistance to cell death , which was confirmed by their decreased sensitivity to doxorubicin [27] . Subsequent studies have extended this pro-survival phenotype of Casp2 loss to include a number of tissues and DNA damaging agents [28] . Ei24 , which was originally identified in a screen for etoposide-induced transcripts , has been shown to promote cell death by binding and sequestering Bcl2 [19] . Interestingly , these genes as well as several previously identified miR-295 family targets are known to be directly or indirectly associated with p53 . Indeed , Pathway Analysis of well-characterized miR-295 targets brought up a single significant network ( p = 10−14 ) , “Cell Death , Cell Cycle , Cellular Function and Maintenance , ” which prominently featured p53 ( Figure 6 ) . Activation of Casp2 can occur through a protein complex in which it associates with the p53 target PIDD [28] . Ei24 itself is a p53 transcriptional target , identified as one of 14 genes induced by adenoviral transfection of p53 into a p53-null colon cancer cell line [19] . We additionally confirmed two previously identified miR-295 targets , p21 ( also known as Cdkn1a ) and Lats2 [29] . In the case of p21 , direct activation by p53 promotes cell cycle arrest at the G1/S phase [30] . Lats2 , while also induced by p53 , exists in a positive feedback loop with p53 in which it binds and inhibits Mdm2 , thereby activating p53 [31] . Thus , miR-295 family miRNAs target a number of p53 associated genes and in all cases antagonizing p53 activation , consistent with the protective effect we have identified here . Like p53 , the mir-295 cluster affects both arms of cellular proliferation , namely cell death and cell cycle progression [12] , [29] . Unlike cell cycle progression , however , the anti-apoptotic role is likely to have the greatest developmental consequences following DNA damage , induced physiologically by oxidative stress or metabolites . Interestingly , even simply ex vivo cell passage may be sufficient to induce a low level of stress , as evidenced by the slightly higher apoptotic rate of Dicer null cells under basal culture . In their pro-survival capacity , these miRNAs may confer robustness during embryonic development , as has been demonstrated in Drosophila . For instance , miR-7 has been shown to participate in a complex network of feedback loops to ensure proper photoreceptor cell development despite temperature fluctuations in development [32] . Similarly , miR-263a/b appear to prevent patterning defects in bristle formation , again consistent with the notion that they promote the fidelity of developmental trajectories [32] , [33] . Early phenotypic data from mir-290-295 KO mice suggests an incompletely penetrant gestational phenotype ( Medeiros et . al . , manuscript in preparation ) , supporting the model that loss of this cluster is tolerated in certain developmental scenarios , perhaps including those with limited stressors during gestation . Beyond regulating development , the miRNAs described here may also have important consequences for cancer , as both Casp2 and Ei24 are considered tumor suppressors . In the case of Casp2 , this has been best demonstrated in the Eu-myc lymphoma model , where loss of even a single copy of Casp2 can accelerate malignant transformation [34] . Similarly , Ei24 is found in a region that shows frequent loss-of-heterozygosity in solid tumors , and its loss has been associated with increased breast cancer invasiveness [19] . In addition , knockdown of Ei24 in mouse fibroblasts or human breast cancer cell lines leads to increased resistance against etoposide-induced apoptosis [19] , [35] . Consistent with these findings , the mir-295 cluster itself has been speculated to be an “oncomir” cluster , as overexpression of its human homolog , the mir-371-373 cluster , has been found in various human tumors [36] , [37] , [38] and may promote malignant transformation [39] . Given our findings , we hypothesize that these miRNAs may have a survival promoting function with dual effects , helping cells navigate physiological stresses during development , and helping cancer cells maintain viability in the face of genotoxic chemotherapeutic agents . In conclusion , these data expand our understanding of ESC miRNA function , linking the ES cell specific miR-295 family to key players in cell death . Further , this analysis reveals a complex relationship between embryonic stem cell miRNA regulation and p53 activation .
Feeder-free Dicer1flox/flox and Dicer1−/− mouse embryonic stem cells ( mESCs ) were generated and maintained on gelatin as described previously [40] . mESCs cells containing a floxed and excised mir-295 cluster were generated in a similar manner and will be described in an upcoming publication ( Medeiros et . al . , manuscript in preparation ) . See Table S4 . MicroRNA mediated repression of each candidate gene was tested by cloning PCR amplified products corresponding to the entire 3' UTR downstream of a pRL-CMV Renilla luciferase reporter as described previously [41] . Nucleotides 5–7 of Casp2 , Bim , and Ei24 binding sites were mutated by Quickchange site-directed mutagenesis . Digests were performed using either XhoI or SalI to give the 5' site and ApaI or NotI to give the 3' site . Firefly luciferase ( pGL3 ) was used as a transfection control . Data shown are summaries of three or more independent trials . 24 hours before transfection 1e5 mESC cells were plated/well of gelatinized 24-well plate . Cells were transfected with 2 µl Lipofectamine 2000 ( Invitrogen ) , 0 . 1 µg of CMV-GFP plasmid ( Invitrogen ) , 0 . 7 µg of pWS ( carrier plasmid ) , and 50 nM siRNAs in 300 µl of Opti-MEM ( Invitrogen ) . 4 hours after transfection , transfection mix was removed from cells and replaced with ESC media . 24 hours after transfection , cells were lysed with 1X Passive Lysis Buffer ( Promega ) and Dual luciferase was measured using Dual Luciferase reporter assay system ( Promega ) according to manufacturer’s instructions . Total RNA was isolated from ES cells with or without acute Dicer deletion using Trizol ( Invitrogen ) , following the standard protocol . Approximately 50 µg of each RNA was loaded onto a 15% denaturing MOPS gel , according to the Northern Blot protocol outlined previously [42] . Membranes were probed for miR-292 and exposed to a phosphoimager before scanning . Prior to hybridizing with a different probe , membranes were stripped by incubating the membrane in boiling 0 . 1% SDS for 30 minutes and loss of signal was confirmed prior to rehybridization . 24 hours after transfection with short RNAs , Dicer1−/− , Dicerflox/flox , miR-290-295−/− , or miR-290-295flox/flox cells were lysed in RIPA buffer ( 1% NP40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , in pH 7 . 4 PBS ) containing protease inhibitors . 30–50 µg lysate was loaded onto 8–12% Bis-Tris gels ( Invitrogen ) and wet-transferred at 4°C to Westran PVDF membranes for 2 h at 70V . After 1 h blocking at room temperature in 5% milk-TBST , membranes were probed overnight at 4°C with 1∶2000 mouse anti-vinculin ( Santa Cruz Biotechnology ) or 1∶200 rat anti-Caspase 2 ( Millipore , 10C6 ) . After 2×10 min . TBST washes , membranes were probed for 1 h at room temperature with 1∶2000 corresponding hRP-conjugated secondary , washed an additional 2×10 min . in TBST , and visualized using Western Lightning Plus ECL ( PerkinElmer ) . Trizol ( Qiagen ) was used to extract RNA from Dicer1flox/flox and Dicer1−/− cells . A Superscript III kit ( Invitrogen ) was used to reverse transcribe 1 µg RNA following DNAse treatment with the Turbo-DNA free kit ( Ambion ) , and real time PCR was performed with the primer sequences listed , using beta actin for normalization . 24 hours before transfection 2e5 mESC cells were plated/well of gelatinized 12-well plates . Cells were transfected with 4 µl Lipofectamine 2000 ( Invitrogen ) , 0 . 2 µg pCAGGS-mCherry plasmid , 1 . 4 µg of pWS , and 50 nM of siRNA in 600 µl of Opti-MEM ( Invitrogen ) . 4 hours after transfection , transfection mix was removed from cells and replaced with ESC media . 24 hours after transfection , cells were exposed to 5-Gy gamma radiation or 100 nM doxorubicin . Immediately after exposure , one plate of cells were trypsinized and fixed with 1× BD Perm buffer . Cells were stained with Rabbit Anti-Casp3 antibody ( BD Biosciences ) at 1∶100 for 20 min . at room temperature . Following washing , cells were incubated with Alexa-488-conjugated secondary antibody ( diluted 1∶250 ) ( Invitrogen ) for 60 min . at room temperature , washed , and resuspended in BD FACS buffer containing 1∶5000 Hoechst stain . 24 hours after the treatment , another plate of cells was trypsinized and treated with the same protocol for FACS analysis . Casp3 assays were also performed on Dcr KO and WT mESCs without transfection . 24 hours before collecting cells for the 0 h time point for Casp3 assay , 2e5 mESC were plated/well of gelatinized 6-well plates . In the context of genotoxic stress , 4e5 mESCs were plated/well of gelatinized 6-well plates . 24 hours after plating , cells were treated with 5-Gy radiation or 100 nM doxorubicin . Casp3 assays were performed at 0 h and 24 h after the treatment following the same protocol described above . 4e5 mESCs were plated/well of gelatinized 6-well plates . 24 h after plating , cells were exposed to 100 nM doxorubicin . Cells were trypsinized 0 h and 24 h after the treatment for Annexin V detection , following Annexin V-FITC apoptosis detection kit ( BD Biosciences ) . Microarray analysis was performed 5 days following transfection of Dicerflox/flox wild-type cells with either GFP alone or GFP and Cre recombinase , and data were analyzed using biological triplicates . Microarrays for the mir-295 cluster deletion were performed on two deletion and two wild-type lines independently derived . Spot replicates were condensed using geometric means . The log fold change ( LFC ) value for Dcr WT/Dcr KO was defined as the difference between the mean log expression in Dcr WT cells and the mean log expression in Dcr KO cells . The conserved set of targets were downloaded from TargetScanMouse5 . 1 website ( http://www . targetscan . org/mmu_50/ ) . To identify targets predicted for the AAGUGC seed family , we looked at all miRNAs that contain AAGUGC in their seed region . More specifically , they include “miR-291b-3p/519a/519b-3p/519c-3p” , “miR-290-3p/292-3p/467a” , “miR-467cd” , “miR-106/302” , and “miR-467b” . We excluded all the targets of “miR-302ac/520f” , as well as T1A 7mer targets of “miR-467b” , as they do not contain the 6-mer match to AAGUGC . Targets with top 10% of branch length scores were considered “conserved” . Gene Set Analysis Toolkit ( http://bioinfo . vanderbilt . edu/webgestalt/ ) was used to perform GO analysis . Targets and controls were generated as described in the text . Network data were analyzed through the use of Ingenuity Pathways Analysis ( Ingenuity Systems , www . ingenuity . com ) . Ingenuity Pathway Analysis ( IPA ) was performed on the set of validated miR-295 targets to identify the most strongly associated canonical pathways . All test statistics were calculated using R ( http://www . r-project . org ) . The Wilcoxon rank sum test was used because it does not assume normality of the underlying distributions . T-tests and Kolmogorov–Smirnov ( KS ) test using these data gave generally similar results . | In this study , we were interested in learning more about the roles of microRNAs—small segments of RNA that help turn off genes—during early development . By studying mouse embryonic stem cells , a unique cell type that can give rise to all adult tissues , we found that a number of genes related to cell survival appeared to be affected by global microRNA loss . Interestingly , these changes in gene expression did not lead to large increases in cell death during normal cell growth , but rather became apparent when cells were treated with agents that cause DNA damage , like the chemotherapeutic doxorubicin and gamma irradiation . Our results suggest that these microRNAs may provide robustness for mammalian development , ensuring proper development despite variations in blood flow and oxygen tension , known to cause DNA damage . Given that certain cancers share features of the embryonic state , including rapid proliferation and lack of differentiation , our results also suggest that the re-expression of these microRNAs in tumors may confer resistance to chemotherapeutic drugs . | [
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| 2011 | A Latent Pro-Survival Function for the Mir-290-295 Cluster in Mouse Embryonic Stem Cells |
How does pattern formation occur accurately when confronted with tissue growth and stochastic fluctuations ( noise ) in gene expression ? Dorso-ventral ( D-V ) patterning of the mandibular arch specifies upper versus lower jaw skeletal elements through a combination of Bone morphogenetic protein ( Bmp ) , Endothelin-1 ( Edn1 ) , and Notch signaling , and this system is highly robust . We combine NanoString experiments of early D-V gene expression with live imaging of arch development in zebrafish to construct a computational model of the D-V mandibular patterning network . The model recapitulates published genetic perturbations in arch development . Patterning is most sensitive to changes in Bmp signaling , and the temporal order of gene expression modulates the response of the patterning network to noise . Thus , our integrated systems biology approach reveals non-intuitive features of the complex signaling system crucial for craniofacial development , including novel insights into roles of gene expression timing and stochasticity in signaling and gene regulation .
A fundamental question in developmental biology is pattern formation , i . e . the acquisition of positional identity in cells resulting in spatially organized domains of gene expression . Computational analyses have long sought to address how patterning occurs in growing tissues that change their size and shape by modeling morphogen gradients , signaling between cells , geometric transformations and other mathematically-amenable aspects of development [1–4] . More recently , computational modeling has revealed how signaling networks integrate with one another and the importance of feedback loops in precise regulation of early developmental patterning systems [5–11] . Models for more complex developing structures such as vertebrate limb buds [12 , 13] , hair follicles [14 , 15] , pigment cells in the skin [16] , the spinal cord [17 , 18] or the palate [19 , 20] require integrating multiple signals within rapidly expanding three-dimensional ( 3D ) tissues . Pharyngeal arches are bilateral , segmentally-repeated structures that form in the ventral head of vertebrate embryos and give rise to skeletal , muscle and connective tissues of the face and neck , including the upper and lower jaws . Arches are complex both in their 3D morphologies and in their embryonic cellular origins . Streams of cranial neural crest ( NC ) cells migrate into each arch segment and surround cores of myogenic/vasculogenic mesoderm . The surrounding ectodermal and endodermal epithelia produce signals that subsequently pattern the arch along its dorso-ventral ( D-V ) axis , resulting in at least three early domains: ventral ( V ) , intermediate ( I ) , and dorsal ( D ) [21–23] . D-V arch patterning involves a highly-conserved signaling network consisting of the Bone morphogenetic protein 2/4/7 ( Bmp ) and Endothelin-1 ( Edn1 ) signaling pathways , secreted by ventral arch epithelia [24–28] , and dorsal Jagged1 ( Jag1 ) /Notch signaling [29 , 30] . Errors in these signals can lead to craniofacial birth defects , such as auriculocondylar syndrome in humans , in which Edn1 signal transduction is disrupted leading to partial homeotic transformation of ventral skeletal elements to a dorsal fate [31 , 32] . Understanding how D-V domains arise in the midst of NC migration and arch growth is both an experimental and a computational challenge . Both Edn1 and Bmp are crucial for ventral and intermediate arch development , but with distinct effects on gene expression [24 , 27 , 30 , 33–35] . Bmp , which acts as a morphogen in many contexts [36–38] , primarily induces and maintains genes expressed ventrally such as Hand2 , a critical transcription factor for ventral mandibular identity . In contrast , while Edn1 also induces ventral genes initially in a concentration-dependent manner [26 , 39] it later becomes primarily required for expression of intermediate genes such as Dlx5/6 , which are required for ventral/intermediate mandibular ( lower jaw and jaw joint ) development , and less dependent on Bmp [24 , 29 , 30] . Craniofacial patterning defects in edn1-/- mutants can be largely rescued by injection of Edn1 protein throughout the arch [27] , and recent work suggests Edn1 plays a more permissive than instructive role [40] . Given their common targets , what are the advantages of having these two ventral morphogens acting in parallel during early D-V arch patterning ? In addition , how does patterning occur robustly in the face of continuous cell divisions , rearrangements , and noise in both the signaling molecules and their downstream gene regulatory networks ( GRNs ) ? To address these questions , we have developed the first computational model of arch D-V patterning that incorporates growth , migration , gene expression and different sources of noise . We represent the known components of the arch GRN with a system of ordinary differential equations ( ODEs ) that accurately reproduces published genetic perturbations of arch D-V patterning . We establish the model using measurements of spatiotemporal patterns of gene expression and 3D NC cell movements obtained from time-lapse movies of live zebrafish embryos . Quantitative temporal gene expression data reveal that intermediate domain genes are expressed before genes marking the ventral domain and dorsal genes are expressed last . The model confirms that this temporal order of intermediate-ventral-dorsal ( IVD ) patterning improves some aspects of robustness of D-V patterning ( precision , referring to consistency across simulations/embryos ) , while making other aspects ( accuracy , referring to closeness to the theoretical ideal pattern ) more sensitive . The model further suggests that Bmp signaling primarily establishes the sizes and positions of patterning domains , while Edn1 plays a permissive role , and that noise in the GRN and each of the signaling pathways affects patterning differently . Our model reveals novel features of the early spatiotemporal dynamics of gene expression that are critical for patterning the complex 3D structure of the craniofacial skeleton during embryogenesis .
Institutional Animal Care and Use Committee protocol #2000–2149 .
Neural crest ( NC ) -derived ectomesenchymal cells in pharyngeal arches 1 ( mandibular ) and 2 ( hyoid ) in zebrafish are patterned into three D-V domains between 14–36 hpf , which give rise to distinct skeletal elements in the adult ( Fig 2A–2C ) . Arch D-V length roughly doubles over this period ( from 30 to 60 μm ) . Previous in situ hybridization ( ISH ) studies have shown that dlx3/4/5/6 are expressed together in an early ventral-intermediate ( V-I ) domain that later separates into V and I [24 , 29 , 30 , 42] . hand2 , the homolog of which is induced by Dlx5/6 in mice [34 , 60 , 61] , marks the new V domain and represses Dlx genes ventrally , restricting their expression to the I domain [42 , 62] . However , the precise timing of gene expression between 14–20 hpf , when these domains first appear , remains unclear . To address this , we have measured transcript levels of seven D-V patterning genes in FAC-sorted arch cells using NanoString analysis ( dorsal: jag1b , hey1; intermediate: dlx3b , dlx4b , dlx5a , dlx6a; ventral: hand2 ) ( Fig 2D ) . Surprisingly , expression of dlx3b peaks early at 20 hpf , followed six hours later by three other intermediate genes ( dlx4b , dlx5a , dlx6a ) , the ventral gene ( hand2 ) , and the dorsal gene jag1b at 26 hpf . Similarly with hybridization chain reaction ( HCR ) in situs , to facilitate co-localization and quantitation of expression ( Fig 2E–2T ) we find that within the domain of dlx2a expression , which marks NC cells in the entire arch , strong dlx3b expression is detected at ~17 hpf , at least an hour before dlx5a expression appears faintly at ~18 hpf . Meanwhile , hand2 expression is not detected until ~20 hpf . Interestingly expression of hand2 arises abruptly , while other genes such as dlx5a appear more slowly , yet both peak at a similar time point in the NanoString analysis . Thus , intermediate genes are the first to be expressed in the D-V sequence of arch patterning followed by ventral and finally dorsal genes . The 1D model ( Fig 3A–3E ) recapitulates the relative timing and sizes of D-V domains in the mandibular arch . Initially intermediate gene expression extends from the ventral end to approximately halfway up the D-V axis . Subsequently , dorsal genes are expressed at the dorsal end of the arch , leaving a section of “unpatterned” cells ( white regions , in which gene expression is below the arbitrary 20% threshold ) between I and D domains , which gradually diminishes . Initiation of ventral gene expression at 28 hpf creates a narrow V domain , which moves the I domain dorsally . The 2D model ( Fig 3F–3J ) captures these spatiotemporal dynamics of D-V domain formation . Here , individual cells are also defined as patterned if their gene expression exceeds 20% , and they are gradually colored correspondingly , such that grey indicates cells not yet expressing genes above the cut-off and more deeply colored cells indicate higher and higher levels of gene expression . Initially none of the cells express any of the genes above the 20% cut-off ( grey cells ) . Between 22–35 hpf the arch elongates anteriorly and ventrally . During this tissue deformation the cells acquire D , I and V fates , ( yellow , blue and pink , respectively ) and form domains of the correct size and shape . The simulation results agree with live imaging of hand2:GFP:sox10:lyn-tdTomato double transgenics ( Fig 3K–3O ) or dlx5a:GFP;sox10:lyn-tdTomato double transgenics ( Fig 3P–3T ) . We note that the boundaries of gene expression as shown by transgene reporter intensity are sharp ( S2 Fig ) . To generate a 2D cell-center based model that reflects arch morphogenesis as accurately as possible we have measured mandibular arch deformation in images of 6 sox10:lyn-tdTomato transgenic zebrafish embryos , the average of which is used to generate an arch outline ( Fig 3K–3T ) . By further analyzing time-lapsed images of sox10:nEOS transgenics we find that: 1 ) cell number roughly doubles between 22–36 hpf , 2 ) ~90% of this increase in cell number is due to cell division and 3 ) only ~10% is due to cells migrating into the arch dorsally . These parameters are incorporated into the model to compute the time intervals of cell division and cell migration . The 2D model also reproduces previously reported phenotypes of genetic or pharmacological perturbations that disrupt D-V patterning ( Fig 4 ) . V/I domains do not form and the D domain expands in embryos lacking Bmp or Edn1 signaling ( reduced in the modeling simulations to 1% of wild-type expression [24 , 27] ( Fig 4A and 4D ) . The I domain does not form and D expands ventrally in embryos overexpressing the dorsal factor Jag1 ( 500% of wild-type production ) ( Fig 4C ) . Conversely , the I domain expands to replace D in a jag1-/- mutant ( 1% of wild-type expression ) or when Edn1 is overexpressed ( 500% of wild-type gradient maximum ) ( Fig 4B and 4F ) . Importantly , the model also recapitulates the normal D-V patterning observed experimentally with moderate , uniform Edn1 expression ( 50% of wild-type gradient maximum ) , achieved with Edn1 protein injections [24 , 27] ( Fig 4E ) . Thus , despite the minimal GRN on which it was based , the model captures many aspects of patterning observed experimentally in vivo . For any three sets of D-V patterning factors , there are six possible different temporal orders of gene expression ( VID , VDI , IVD , IDV , DIV , DVI ) . Our gene expression studies indicate that dlx3b and its associated I domain appear first , so we asked how this order fares in our model as compared with other possible orders . By varying the production and degradation parameters for each gene group we simulate all six temporal orders ( S1 and S2 Tables ) and examine if any one is more robust than another . Changing the temporal order does not affect the final pattern at 35 hpf ( S3 Fig ) . To compare sensitivity between the three temporal orders of gene expression we compute si ( t ) ( Eq 9 ) , evaluated at 10 equidistant time points between 22–35 hpf . We summarize the 10 resulting data points in box plots ( Fig 5 , S4–S6 Figs ) , where the line in the middle denotes the median , the bottom and top edges of the box the 25th and 75th percentile , respectively , and the whiskers extend to the most extreme data points . We normalize the data with respect to the highest value of si ( t ) for both parameters p1 and p5 at all time points ( for unprocessed , time-dependent data see S7 Fig ) . The distance between the median line and zero ( dashed black lines ) indicates how sensitive gene expression is to perturbations in the control parameters p1-p7 , and the size of the box and length of the whiskers indicate how much the sensitivity changes with time . In general , all model simulations are relatively robust to parameter variations , indicating that the results are not due to the specific choice of parameters . When cells are exposed to morphogen concentrations typical for the V and I domains ( high Bmp and Edn1 concentrations ) the GRN is most sensitive to perturbations in the BMP signaling parameters p1 ( activation of V genes ) and p5 ( activation of I genes ) ( Fig 5; S4 and S5 Figs ) , and less so to variations in Edn1 signaling parameters p2 and p7 . This is particularly true in cases where I genes are expressed last ( VDI and DVI ) , since median sensitivity levels of dorsal genes deviate further from zero ( dashed black lines; Fig 5 and S4 and S5 Figs ) . However , when cells are exposed to low Bmp and Edn1 concentrations ( S6 Fig ) , typical for the D domain , perturbations in the Bmp gradient only affect the ventral genes , while intermediate and dorsal genes are sensitive to perturbations in the Edn1 parameters p2 and p7 . This agrees with published evidence that Edn1 plays a primarily permissive role in ventral and intermediate gene expression [27 , 40] . A permissive role for Edn1 is further evident when we compute the sensitivity to parameter variations in the GRN if only one of the two morphogen gradients is present ( S8 Fig ) . The results are similar to those for two morphogens in all three V , I and D domains . In all cases , the most crucial parameter is the one controlling the effect of the morphogen on the I domain , p2 . To simulate the stochasticity that may occur in vivo , we have added Gaussian noise to the morphogen gradients and to the GRN ( Eqs 11–14 , Figs 6 and 7 , S9–S11 Figs and S6 and S7 Movies ) . To compute a large number of stochastic simulations for statistics , and for better visualization , we investigate the effects of noise first in the 1D model . We plot the mean ( thick lines ) and ±σ ( shaded regions ) over 100 simulations ( Fig 6B–6E , 6A for comparison without noise ) . For the 2D model , we show end-states for single simulations ( Fig 6B’–6E’ , 6A’ for comparison without noise ) . The simulations reveal that noise in Bmp signaling is transmitted differently than noise in Edn1 into the GRN . While intermediate gene expression is affected by both signals , ventral gene expression is not affected by noise in the Edn1 gradient , since the only input into the V domain is the Bmp gradient . Dorsal genes are most robust to morphogen fluctuations ( Fig 6C–6D’ ) , since they are mostly controlled indirectly through the ventral genes , but they are the most sensitive to gene regulation noise ( Fig 6B and 6B’ ) . Since Edn1 is required for intermediate gene expression , but increasing Edn1 signaling does not expand the I domain ( Fig 4E and 4F ) , fluctuations in Edn1 only act in one direction , meaning that lower Edn1 levels due to noise reduce the I domain but higher levels have no effect . As a result the I domain is reduced with fluctuations in Edn1 ( Fig 6D and 6D’ ) , when compared to the deterministic case ( Fig 6A and 6A’ , S9 Fig ) in both 1D and 2D models . This means that Edn1 acts “unidirectionally” as a permissive factor . Gene expression noise appears to be the strongest driver of fluctuations in patterning , since even relatively small fluctuations ( ν = 0 . 05 ) in the GRN alter gene expression profiles substantially ( Fig 6B and 6B’ ) . The effects of individual fluctuations are additive when all sources of noise are combined ( Fig 6E and 6E’ , S11 Fig ) , i . e . both increased fluctuations in dorsal gene expression due to GRN noise , and expansion of the V domain due to noise in the Edn1 gradient . Precision refers to the level of variation in boundary positions in stochastic simulations . Similar to our analysis of sensitivity to different parameters in the GRN , we examine if the sequence of D-V domain formation influences precision , as a measure of robustness in response to noise . When fluctuations are limited to the Bmp gradient the genes expressed first absorb most of the noise ( Fig 7 ) . The exception is when dorsal genes are expressed earliest , which are in general the most robust to Bmp noise , such that the DIV and DVI sequences are the least susceptible to Bmp fluctuations ( Fig 7E and 7F ) . This is in contrast to noise in the GRN ( v = 0 . 05 ) , where genes expressed earliest are the most robust and genes expressed later are more susceptible to noise ( S9 Fig ) . This is particularly true when genes expressed in the I domain are first in the D-V sequence . This indicates that patterning I first is beneficial since fluctuations in intermediate gene expression affect both the precision of the V-I and I-D boundaries . With fluctuations in the Edn1 gradient , the I domain is severely reduced when intermediate genes are expressed last , while there is still a distinct I domain in the case of either V or I being expressed first ( S10 Fig ) , further indicating that an early expression of intermediate genes is beneficial for boundary accuracy . Noise in the Edn1 gradient has the most similar effect across the 6 possible temporal orders . Early expression of intermediate genes , however , leads to slightly stronger fluctuations in their expression as a result of Edn1 noise , while still preserving a distinct I domain . Due to the different effects of the three sources of noise in the context of different temporal orders , combining all sources of noise indicates that a later onset of intermediate gene expression leads to the strongest fluctuations and higher sensitivity ( S11 Fig ) . When differences in the temporal order are enforced explicitly in the simulations ( S12 Fig ) , the responses to the different sources of noise are similar to the case of intrinsic regulation , only with a more severe loss of the I domain due to noise in Edn1 ( S13 Fig ) . However we do not see any distinction between the temporal orders in the extrinsic model with noise in the Bmp gradient ( S14 Fig ) in contrast to the intrinsic model with Bmp noise ( Fig 7 ) . Accuracy refers to boundary positions relative to the measured wild-type positions ( Fig 8 ) . We simulate 10 runs of the 2D model , compute statistics of the boundary error ( E ) in Eq 8 , normalize according to the highest value and plot the mean ( line ) and ±σ ( error bars ) . Accuracy depends strongly on which gene group is expressed last , especially for the I-D boundary . When either Bmp or Edn1 are noisy , the I-D boundary is positioned most accurately when I is last , slightly less accurate when D is last and inaccurate when V is last ( Fig 8B and 8D ) . In contrast , when Bmp is noisy the V-I boundary shows no clustering of temporal orders of patterning ( Fig 8A ) , but when Edn1 is noisy accuracy increases when I is last ( Fig 8C ) . The boundary accuracy depends less distinctly on the temporal order of patterning when the gene regulation is noisy ( Fig 8E and 8F ) . When we combine all sources of noise a late appearance of the I domain clearly improves positioning of the V-I boundary and slightly improves the I-D boundary ( Fig 8G and 8H ) . In general the domain boundaries are more sensitive to fluctuations in Bmp than in Edn1 , especially at the V-I boundary . Presumably the mutual inhibition between intermediate and dorsal genes makes the I-D boundary more robust to stochastic fluctuations . When the sequence of patterning is controlled by extrinsic factors not inherent in our minimal GRN ( S12 Fig ) , the accuracy of boundary positioning is more sensitive to individual sources of noise in all cases except for positioning the V-I boundary with fluctuations in only Edn1 ( S15 Fig ) . However , with the external control of gene expression timing the noise effects appear to be less additive and the extrinsic model positions the boundaries more accurately . From the above results , different temporal orders lead to different degrees of noise in gene expression profiles ( Figs 6 , 7 and S9–S11 ) , while there are observed differences in accuracy of domain boundary positioning ( Fig 8 ) . We now quantify these effects to investigate how accuracy and precision relate to each other at both domain boundaries , and with the different sources of noise ( Fig 9 ) . Precision is measured as the maximal standard deviation ( Figs 7 and S9–S11 ) , corresponding to the maximal width of the shaded regions . Accuracy is the mean value of the boundary error ( Fig 8 ) . Hence for both , a low value indicates higher accuracy or higher precision . When all sources of noise are included in the simulation , an anti-diagonal trend is observed , indicating a trade-off between precision and accuracy , where temporal patterns with more precise boundary positioning have less accuracy , and vice versa . The observed IVD pattern favors precision over accuracy at both boundaries , suggesting that the patterning system has evolved to maximize precision in gene expression domain boundaries .
We have analyzed spatiotemporal patterns of expression of D-V patterning genes during pharyngeal arch morphogenesis in zebrafish embryos and combined our experimental observations with published data to generate the first computational model of the developing mandibular arch . Previous efforts have compiled information from imaging and gene expression databases , such as the FaceBase consortium [63] , and analyzed cellular behavior during craniofacial morphogenesis [20] , but ours is the first to integrate spatiotemporal gene expression patterns with morphogen gradients , tissue measurements , and known mutant phenotypes . Our model captures many of the spatial and temporal features of arch development and recapitulates genetic perturbations . It also provides novel insights into developmental constraints on the system , including: 1 ) Bmp is responsible for providing positional information to the cells , while Edn1 is permissive , 2 ) the temporal order of patterning is important for the system’s capacity to account for noise , and 3 ) the temporal order favors precision over accuracy in boundary positioning . Many developmental processes that involve periodic patterning reflect underlying reaction-diffusion systems that deal efficiently with noise through their intrinsic feedback loops [12 , 16 , 19] . We find that the temporal order of gene expression provides a previously unappreciated factor in improving responses to noise . Surprisingly in our experiments it is the intermediate gene dlx3b that is the first detected at 16–17 hpf by HCR and slightly later ( 20 hpf ) in our NanoString analyses , which may reflect the fact that sorted cells used for NanoString are derived from multiple arches ( mandibular , hyoid , branchial ) while with HCR we image the first arch directly . dlx3b might serve as an early response factor , possibly integrating input from multiple signals and priming other patterning genes in the GRN . The early appearance of the I domain means that the arch is not patterned consecutively from one end to the other ( i . e . the VID or DIV orders ) , but rather in the peculiar sequence of establishing the middle first . Intuitively one can assume that patterning the central domain first already establishes both boundaries . However , that is not the case here either , since expression of the intermediate genes initially extends to the ventral end of the arch and is only later replaced by the ventral genes , such that both boundaries are established independently and non-simultaneously . We have investigated if this temporal order is beneficial for the response of the system to stochastic fluctuations . Our model simulations suggest that avoiding having intermediate genes expressed last improves the robustness to perturbations in Bmp signaling parameters and precision in the positioning of D-V domain boundaries . While dlx3b knockdown does not cause significant patterning defects , this could reflect compensation by dlx4b or dlx5a [42] . The unique function of dlx3b in the GRN is supported by its distinct spatial expression pattern from the dlx5/6 pair and also from its neighboring dlx4 cluster counterpart [42 , 64–66] . Our results suggest that altering the temporal order of D-V gene expression , ( e . g . using optogenetic approaches to induce expression of hand2 in its normal spatial domain but prior to onset of intermediate gene expression and thus creating a VID sequence ) will disrupt the accuracy and robustness of D-V domain boundaries . Future studies are also needed to determine if the temporal order of gene expression in this system is controlled by gene-intrinsic differences in sensitivity to signals ( as is the case in our minimal model ) , or if trans-acting factors play a greater role . We also note that the precision and accuracy of D-V domain boundary formation are distinctly susceptible to noise , with the boundary between V and I domains especially sensitive to noise in Bmp signaling or in the downstream GRN . Precision and accuracy in the boundaries of gene expression domains are both goals for patterning systems , but our model reveals divergent paths to reach each of those goals in the mandibular arch . We find that the most precise gene expression profiles in response to Bmp signaling are achieved when dorsal genes are expressed first ( Fig 7 ) , while the most accurate boundaries were obtained when I genes were expressed last ( Fig 8 ) . Instead , our data in zebrafish indicate that I genes are expressed first , reflecting a trade-off that seems to favor precision over accuracy ( Fig 9 ) . It is important to note here that the model does not achieve high boundary positioning accuracy for the I-D boundary with any of the temporal patterns even in the absence of noise ( S1A Fig ) . This might be a result of measurement inaccuracy and due to the projection of a 3D geometry into 2D , or that other factors not included in our minimal GRN are responsible for pushing the I-D boundary more towards the ventral end . Such trade-offs between precision and accuracy in other systems typically involve negative feedback [12 , 18 , 67] , while it is temporal gene expression control that navigates this tradeoff in our model . Future analysis will reveal if feedback loops also operate at arch domain boundaries . Our modeling results for precision and accuracy derive from combinations of 10–100 simulations , somewhat analogous to variability in patterning that can occur between individual embryos . We speculate that developmentally this suggests that patterning will favor precision ( i . e . show a narrow range of variability between individuals ) , even if that range differs from the species ideal . Evolutionarily , this might permit rapid variation in craniofacial morphology during radiation events while still maintaining intra-population characteristics . Changes in craniofacial structures are one of the most striking adaptations in rapidly evolving vertebrate populations such as African rift lake cichlids , and it would be interesting to examine the parameters of patterning in these species [68 , 69] . Bmp and Edn1 signaling have overlapping but distinct functions in D-V patterning of the mandibular arch , with Edn1 primarily maintaining intermediate gene expression and playing a permissive rather than instructive role [24 , 27 , 30 , 40] . Our modeling results are consistent with Bmp signaling acting as the principal instructive ventralizing signal , at least for inducing V and I domains . They also suggest that moderate perturbations or noise in Bmp signaling , at levels that do not completely eliminate expression of various factors or formation of later cartilage structures , will still have effects on D-V arch patterning . In several reports , changes in levels of patterning signals result in a moderate-to-severe spectrum of phenotypes . Reduction in Bmp signaling by heat-shock induction of a dominant-negative Bmp receptor can lead to varying degrees of loss of ventral structures , depending on the timing of heat-shock and the dose of the dominant-negative transgene [24 , 30 , 70] . Increasing Bmp levels moderately by overexpression of Bmp ligand or injection/bead insertion of Bmp proteins causes homeotic transformations , such as the more dorsal palatoquadrate cartilage acquiring characteristics of the ventral Meckel’s cartilage , while more severe changes in signaling levels leads to significant losses throughout the jaw cartilage [24 , 30 , 71] . Modulation of Edn1 signaling in either direction similarly results in varying degrees of patterning changes , although consistent with its permissive role even very high levels of Edn1 do not expand the I domain , while reducing the D domain [24 , 26 , 27 , 30 , 39] . These divergent phenotypes , along with our analyses of parameter sensitivity and the effects of noise , suggest a region of stability where the patterning network can compensate for changes in signals , with moderate phenotypes appearing at the edges of this region , and severe phenotypes occurring when signaling falls outside the stable region altogether . Our computational model suggests that further experiments up- and down-regulating signaling will reveal transition points where stability breaks down , and those data will in turn improve the quality of our model . We can also extend our framework in the future to incorporate other signals that have been implicated in D-V patterning and growth , including Wnts [72] and Fgfs [73] as well as other GRN components [40 , 42] . Boundaries between different D-V domains in the mandibular arch are sharp , which is especially prominent in our live imaging ( S2 Fig ) . How is this sharpness achieved ? Our model simulates sharp domain boundaries in the absence of noise . However , when stochastic fluctuations are present the domain boundaries are no longer sharp since all cells are responding independently to the noisy signals . Computationally , we can extend our model to investigate if cell-cell communication and cohesion between cells of the same identity will improve boundary sharpening . Experimentally , we can examine if changes in signaling too small to disrupt overall patterning can nevertheless degrade boundary sharpness , which would suggest that this sharpness comes from the patterning pathways themselves . Although our current analysis of live cell dynamics does not suggest a significant degree of NC cell rearrangements once they have reached the pharyngeal arches , cell sorting contributes to domain boundary sharpening in some contexts , such as the neural tube [74] , and automated tracking of large numbers of arch NC cells in the future might reveal subtle but important cell movements in arches as they are patterned . D-V arch patterning of the mandibular arch and its associated GRN are largely conserved among vertebrates [21 , 23 , 75] . However , differences in the resulting anatomy of the jaw and skull in the adult , as well as the size , shape , and growth of the mandibular arch primordium make it difficult in some cases to draw clear homologies across species e . g . zebrafish and mouse or human [21 , 23] . In addition , differences in the timing of gene expression and our ability to identify clear functional homologues across species have made comparisons of the genetic pathways involved challenging . Having made a computational model for the zebrafish jaw that incorporates these spatiotemporal features we can now extrapolate to other species to determine if similar constraints apply . In addition , we can begin to ask questions at a more integrated , systems biology level , about how such changes in size , shape and timing may have arisen and how the GRN has adjusted to compensate for changes in such features as signal propagation and noise . | Proper development of the body requires boundaries to form between regions in which cells will form different structures , and these boundaries need to be properly organized in space . This must occur accurately even in moving , dividing cells and in the presence of the noise that is inherent in all biochemical processes . We use development of the upper and lower jaw as a model to study boundary formation . In this work , we combine detailed experimental measurements with computational modeling to investigate the role the timing of gene expression plays in organizing spatial boundaries , and find that the different orders of gene expression navigate a tradeoff between precision and accuracy in boundary positioning . | [
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| 2018 | Modeling craniofacial development reveals spatiotemporal constraints on robust patterning of the mandibular arch |
Leprosy is a curable neglected disease of humans caused by Mycobacterium leprae that affects the skin and peripheral nerves and manifests clinically in various forms ranging from self-resolving , tuberculoid leprosy to lepromatous leprosy having significant pathology with ensuing disfiguration disability and social stigma . Despite the global success of multi-drug therapy ( MDT ) , incidences of clinical leprosy have been observed in individuals with no apparent exposure to other cases , suggestive of possible non-human sources of the bacteria . In this study we show that common free-living amoebae ( FLA ) can phagocytose M . leprae , and allow the bacillus to remain viable for up to 8 months within amoebic cysts . Viable bacilli were extracted from separate encysted cocultures comprising three common Acanthamoeba spp . : A . lenticulata , A . castellanii , and A . polyphaga and two strains of Hartmannella vermiformis . Trophozoites of these common FLA take up M . leprae by phagocytosis . M . leprae from infected trophozoites induced to encyst for long-term storage of the bacilli emerged viable by assessment of membrane integrity . The majority ( 80% ) of mice that were injected with bacilli extracted from 35 day cocultures of encysted/excysted A . castellanii and A . polyphaga showed lesion development that was similar to mice challenged with fresh M . leprae from passage mice albeit at a slower initial rate . Mice challenged with coculture-extracted bacilli showed evidence of acid-fast bacteria and positive PCR signal for M . leprae . These data support the conclusion that M . leprae can remain viable long-term in environmentally ubiquitous FLA and retain virulence as assessed in the nu/nu mouse model . Additionally , this work supports the idea that M . leprae might be sustained in the environment between hosts in FLA and such residence in FLA may provide a macrophage-like niche contributing to the higher-than-expected rate of leprosy transmission despite a significant decrease in human reservoirs due to MDT .
Human beings have been afflicted by leprosy for over a millennium . Leprosy is a chronic granulomatous infection of skin and peripheral nerves caused by the bacillus Mycobacterium leprae . The bacilli are slow growing obligate intracellular organisms trophic for macrophages , dendritic cells ( DC ) and Schwann cells in peripheral nerves . The scientific community has reached a generally accepted consensus that M . leprae is principally a parasite of humans and is spread primarily thereby [1] . In addition , there have been autochthonous cases of leprosy among native-born Americans in the southern region of the United States with no prior history of foreign exposure . In the same regions , wild armadillos are infected with M . leprae . A unique M . leprae genotype had been found in the majority of armadillos that was identical to U . S . patients who resided in areas where exposure to armadillo-born M . leprae was possible [2] . This is highly suggestive of the fact that armadillos are a significant natural reservoir for the bacilli and , leprosy might be a zoonosis in the these areas . There has also been a substantial history of studies , anecdotal evidence , rationalizations and opinions that argue in favor of additional non-human sources of the bacillus [3] . What is more intriguing is that , despite many years of using multidrug therapy ( MDT ) resulting in a significant reduction in disease prevalence , transmission remains stubbornly high implicating among other issues , ineffective detection of early infection , case reporting deficiencies or a lack of a thorough examination of potential environmental sources of the bacillus [3] , [4] . M . leprae is an extremely fastidious organism that , despite over 100 years of endeavor , has not been successfully cultured in artificial medium [5] . It is , thus , classified as an obligate intracellular organism with an evolutionarily minimized genome that is believed to have constrained its growth to the intracellular niche . With such a stringent requirement for survival , several questions remain as to how the bacillus remains viable and infectious between human hosts . Are there environmental elements that are capable of sustaining viable M . leprae for long periods or are these bacilli dependent on close-quartered conditions necessary for aerosol transmission from human to human ? is M . leprae harbored in soil and water niches ? is the bacillus sheltered and capable of surviving intracellularly in ubiquitous protozoa such as free-living amoebae ( FLA ) that provide similar micro-niches as human macrophages ? Evidence of an environmentally sustainable entity for M . leprae would certainly explain , in part , the apparent lack of reduction of the rate of transmission of leprosy in spite of successful MDT [6] , [7] . The nature of the relationship between most intracellular organisms and host FLA is currently not defined . The terms “endosymbionts” , or “symbionts” fail to adequately describe these complicated interactions . It is currently proposed to define intracellular microorganisms that associate with FLA without any known directional host/bacterial benefit as “endocytobionts” [7] [8] [9] . Over the past three decades , numerous studies have reported that microorganisms can survive as endocytobionts in FLA . It was reported in 1980 that Acanthamoeba harbored Legionella pneumophila and that the bacterium resisted phagosome-lysosome fusion and multiplies within the amoebae [8] , [9] . This latter work implicated infected amoebae as a source of Legionnaire's Disease . Additionally , there are numerous reports describing infection of Acanthamoeba FLA with both pathogenic and environmental mycobacteria such as M . avium subsp . paratuberculosis , M . avium-intracellulare , and M . bovis [10]–[13] . In a study involving hospital networks , FLA such as A . polyphaga , and Hartmannella vermiformis were associated with many species of mycobacteria in water specimens including M . gordonae , M . xenopi , M . avium and M . kansasii subtype 1 lending to much circumstantial speculation regarding the means to which mycobacteria have adapted to environmental persistence [14] , [15] . The evolutionary response to amoebal predation is the acquisition of traits that confer resistance to digestion in food vacuoles of amoebae [16] . Many Mycobacterium species survive and even thrive intracellularly in protozoa [16] , [17] . As has been known for many years , mycobacteria have a rich hydrophobic cell wall and , as such , lend themselves quite well to attachment to cellular surfaces and are efficiently phagocytized by macrophages [18] and protozoa [19] . Many elements of the mycobacterial cell wall contribute to efficiently enable an active entry of the bacterium into phagocytes [20] [21] [22] . Furthermore , protozoa possess the remarkable ability to transform into cysts protecting them from harmful and often times rapidly fluctuating environmental influences such as extremes in temperature , drought and a spectrum of biocides [23] . Mycobacteria , in turn , can use the nutrients of protozoa as a food source and their intracellular life offers protection against the potentially harmful extracellular milieu . This poses the interesting question as to whether amoebae provide an environmental niche simply for persistence or are a selective proving ground enhancing virulence . Additionally , the dual lifestyles of amoebae ( trophozoite vs . cyst ) likely provides a survival niche to fragile , fastidious microbes such as M . leprae when the bacillus is subjected to relatively harsh environments such as those between hosts . Few studies have investigated whether mycobacteria infect amoebae in their natural environment . Thus , an inherent resistance to predation by amoebae likely has important consequences since bacteria that infect and evade amoebal digestion might exploit these traits to enter and resist destruction within macrophages or dendritic cells ( DCs ) thus thwarting or altering innate immune responses [16] , [24] . FLA are environmentally ubiquitous and most are non-pathogenic to immune-competent humans . Delivery of pathogenic mycobacteria within non-pathogenic amoebae to cells of the innate immune system will likely elicit alternative host immune response in comparison to that generated against the Mycobacterium alone . This endocytobionic relationship between the somewhat weakly pathogenic bacteria and ubiquitous amoebae and the potential to aid transmission to susceptible host is of great concern to human , animal and ecosystem health . In the present study we show that M . leprae remains viable up to 8 months as determined by the accepted criteria of assessment of membrane integrity by viability staining in 3 species of Acanthamoeba ( A . lenticulata , A . castellanii and A . polyphaga ) and 2 strains of Hartmannella vermiformis . Additionally , M . leprae extracted from cocultures of A . castellanii and A . polyphaga that were induced to encyst with the phagocytosed bacilli for 35 days remained viable causing infections and M . leprae proliferation in Foxn1nu/Foxn1nu ( nu/nu ) mouse footpads ( FP ) . This works shows for the first time that cysts from amoebae representing species from both Acanthamoeba and Hartmannella genera are capable of supporting the viability of M . leprae , a bacillus so fastidious that it has never been successfully cultivated axenically . The implications of this work relate to the environmental sustainability of M . leprae in the context of persistent transmission despite a vastly reduced human reservoir of infection .
All mouse work was conducted according to relevant U . S . and international guidelines . The procedures for isoflurane anesthesia , infection of nu/nu mouse FPs with M . leprae and fine needle aspirate ( FNA ) biopsy are Institutional Animal Care and Use Committees ( IACUC ) -approved protocols ( protocol # 12-3613A and 11-3037A ) that are approved/renewed yearly by an institutional review board of certified veterinarians and selected faculty . The mice are monitored twice weekly by trained animal laboratory technicians employed by our Laboratory Animal Resources ( LAR ) center . Any maladies , whether directly , indirectly or unrelated to the protocol are reported immediately to both the attending veterinarian and the PI ( WHW ) holding the approved protocol . The committee is in compliance with the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals . Stocks of axenic Acanthamoeba lenticulata ATCC 30841 , Acanthamoeba castellanii ATCC 30232 , Acanthamoeba polyphaga CCAP 1501/18 , Hartmannella vermiformis ATCC 50237 and Hartmannella vermiformis CHUV 172 were obtained from the American Type Culture Collection ( Manassas , VA ) and STERIS SA R&D Fontenay-aux-Roses , France . Amoebae stocks were derived from several sources as diverse as ATCC and hospital and city water supplies and were cultivated to axenic stocks using standardized methods [14] , [25] . Acanthamoeba trophozoites were axenically maintained in culture in 1X PYG medium which consists of Page's amoebae saline ( PAS ) [60mg NaCl , 2mg MgSO4·7H2O , 68mg KH2PO4 , 71mg NaHPO4 and 2 mg CaCl2 in 500 ml dH2O ( pH = 6 . 9 ) ] to which 1/10 volume of 10XPYG solution [50 g Proteose Peptone ( Difco ) ; 5 g yeast extract ( Difco ) ; 2 . 45 g MgSO4·7H2O; 2 . 5 g Sodium citrate·2H2O; 0 . 05 g ammonium iron sulfate ( NH4 ) 2Fe ( SO4 ) 2·6H2O; 0 . 85 g KH2PO4; 0 . 89 g Na2HPO4·7H2O; 22 . 5 g α-D-glucose; 0 . 295 g CaCl2 in 250 ml dH2O] was added [26] [14] . Hartmannella trophozoites were cultured in modified PYNFH ( ATCC medium 1034 ) medium . Viable M . leprae was obtained from the National Hanson's Disease Programs , Baton Rouge , LA . Trophozoite monolayers of A . lenticulata , A . castellanii , and A . polyphaga , were maintained at 28°C and passaged in 1X PYG . H . vermiformis str . ATCC 50237 , and H vermiformis str . 172 were maintained at 28°C and passaged in PYNFH medium . Amoebae were infected with viable M . leprae at a bacilli:amoebae ratio of 5–10∶1 and incubated for 48 hr at 32°C . Extracellular bacilli were removed by centrifugation at 600xg and washing the amoebae pellet in HBSS ( Hank's Balanced Salt solution ) 3 times . For some smaller scale experiments ( e . g . , for phagocytosis assays ) , infections were carried out at M . O . I . of between 1 and 100 as well as some cocultures kept at 4°C and aliquots withdrawn every hour to determine adsorption of PHK26-labeled bacilli ( see below ) to FLA by flow cytometry . FLA- containing M . leprae were induced to encyst by pelleting the cultures and subsequently suspending in encystment buffer ( 0 . 1M KCl , 0 . 02M Tris-HCl pH 8 . 0 , 8 mM MgSO4 , 0 . 4 mM CaCl2 and 1mM NaHCO3 ) . Intracellular M . leprae was extracted from amoebae cysts maintained at 32°C at various times ( one week , two weeks , 35 days , 45 days , 3 months and ≥6 months ) . Prior to extraction of bacilli , long-term encysted cocultures were induced to transform back to trophozoites in complete growth media at each of the above time points . Bacilli extracted from excysted trophozoites by suspending the pellet in 100 µl of sterile PBS containing 0 . 5% SDS , vigorously vortexing and washing three times with PBS were then processed for viability using BacLight staining procedure ( Molecular Probes; Life Technologies , Grand Island , NY ) , fluorescence microscopy or injection into mouse FPs . Athymic FoxN1nu/FoxN1nu ( designated as “nu/nu” throughout this manuscript ) mice , five in each group , were challenged in the plantar surface of the left hind foot with M . leprae bacilli extracted from A . castellanii or A . polyphaga cysts as described [27] . Mice were injected a total of 3 times every other week for one month . All bacilli used in experimental FP injections were extracted from 35-day encysted A . castellanii or A . polyphaga cocultures . This three-time injection scheme was performed because the bacillary yield from the extraction process seemed rather low and would ensure a relatively timely appearance of FP induration . The inocula were estimated based on direct counting of bacilli . [28] . Mice were anesthetized by inhalation of 5% isoflurane . Once fully anesthetized , infected mouse FPs were aspirated using a 0 . 5 cm , 23-ga needle syringe inserted subcutaneously into the infected area of the FP . Portions of the samples were prepared for microscopy by acid-fast staining or for nucleic acid extraction for PCR analysis . This procedure was performed monthly for 6 months . The Thai-53 isolate of M . leprae was maintained in the footpads of athymic nu/nu mice infected for approximately 6 months , and then harvested as described previously [27] . Extracted bacilli were washed by repeated ( 2X ) suspension and centrifugation in RPMI-1640 ( Gibco ) containing 10% fetal bovine serum ( ( FBS ) Gibco ) . Bacilli were enumerated by direct counting according to Shepard's method [29] . M . leprae suspensions were purified by NaOH treatment as described [27] . Briefly , 1 X109 fresh M . leprae were suspended in 1 ml of 0 . 1N NaOH and incubated for 3 minutes at room temperature to remove animal tissue . The bacteria were subsequently washed 3X in Hanks Balanced Salt solution ( HBSS ) and suspended in a final volume of appropriate medium . Freshly harvested viable bacilli were consistently used in experiments within 24–32 hr of harvest . nu/nu mice , five in each group , were challenged in the plantar surface of the left hind foot with 107 M . leprae harvested from passage animal FP as described [27] . Concentrates of M . leprae are separated from infected livers or spleens of 9-banded armadillos ( Dasypus novemcinctus ) . The tissues were collected aseptically and kept frozen at −80°C . Briefly , the procedure for preparation of M . leprae has been described earlier [30] , [31] and is carried out at 0 to 2°C . The tissue is homogenized and separated by density gradient centrifugation in sucrose and KCl . The bacilli are disrupted by ultrasonic oscillation . Tissues were treated with trypsin , chymotrysin , collagenase and 0 . 1N NaOH to remove any host tissue . The bacilli are intact but presumed nonviable due to prolonged storage of the tissue at below freezing temperatures . For conventional and confocal microscopy and phagocytosis assays M . leprae freshly harvested from FPs were stained with the vital fluorescent red PKH26 dye ( Sigma-Aldrich ) following the manufacturer's protocol . Briefly , bacilli were stained for 3 minutes at RT in a 1∶250 dilution of dye in Diluent-C ( Sigma-Aldrich ) . The staining suspension was washed three times in PYG containing 5% bovine serum albumin . Bacilli were counted by fluorescence microscopy by averaging several fields counted using a hemocytometer . Healthy actively dividing amoebae trophozoite cultures were seeded in 6-well plates at 3×106/ml containing appropriate growth medium ( above ) . Amoebae were infected with viable M . leprae , or M . leprae isolated from armadillo tissues ( presumed non-viable ) that were first stained with the red fluorescent vital PKH26 membrane dye . Triplicate M . leprae-infected amoebae cultures were prepared at bacilli∶amoebae ratios of 1∶1 , 5∶1 , 10∶1 , 50∶1 and 100∶1 . Cultures were maintained either in a humidified incubator at 32°C or in a cold room at 4°C . Amoebae were harvested at 0 time ( at the time of M . leprae challenge ) , 2 hr , 3 hr , 4 hr , 5 hr and 6 hr post-infection , washed twice to remove extracellular M . leprae and suspended in 300 µl of FACS buffer ( PBS +1%BSA ) prior to analysis by flow cytometry using a Becton Dickinson FACS Cantos II instrument . Results were gathered from gates of uniform size as determined from uninfected samples . The resulting mean fluorescence intensities ( MFI ) were acquired as one-color histograms and increases in MFI were plotted against time using GraphPad Prism software . Results are shown as the average MFI of triplicate cultures . The viability of M . leprae was determined by assessing membrane integrity using the LIVE/DEAD BacLight bacterial viability kit ( Molecular Probes; Life Technologies , Grand Island , NY ) . Bacilli extracted from amoebae cyst cultures were washed in normal saline ( 0 . 90% NaCl w/v ) and incubated for 15 min at RT with a final concentration of 1 . 67 mM Syto9 and 18 . 3 mM propidium iodide ( PI ) . The bacilli were subsequently washed twice in normal saline ( NS ) and the pellet was suspended in 25 µl of NS and 5 µl was spotted on a glass slide and mounted on a #1 . 5 cover glass using BacLight mounting oil . The dead and live bacteria were assessed by direct observation of fluorescent red ( PI+ ) and green ( Syto9+ ) bacilli respectively under a fluorescence microscope using appropriate single bandpass filter sets [FITC filter ( 480 nm excitation/500 nm emission for Syto9 ) ; TRITC filter ( 488 excitation/653 excitation for PI ) ] . In cases of nuclear staining of amoebae or mouse tissues , a DAPI filter ( 358 nm excitation/461emission ) was utilized . Auramine/rhodamine was used to visualize acid-fast bacilli ( such as mycobacteria ) using fluorescence microscopy . Staining was performed as is routine in the laboratory . Briefly , aliquots of M . leprae-infected or uninfected cysts or trophozoites were transferred to microscope slides and heated to 78°C for 30 min . Slides were then stained for 30 min at RT with auramine/rhodamine ( Becton Dickenson , Franklin Lakes , NJ ) . Slides were rinsed with acidified-alcohol ( 5% HCl/70% isopropanol ) followed by staining with Hematoxylin QS ( Vector Laboratories , Burlingame , CA ) for 5 sec . Slides were rinsed with dH2O and stained with DAPI ( 200 µg/ml ) for 20 min . The slide were washed with dH2O and mounted to cover glasses with Prolong Gold ( Life Technologies , Grand Island , NY ) . Slides were visualized using a fluorescence microscope within 24 hr of preparation/staining . Both fluorescence and confocal microscopes were used to visualize extracted and internalized M . leprae by all amoebae spp . studied . Fluorescence microscopy was performed with an Olympus IX71 microscope ( Center Valley , PA ) using Retiga 2000R ( Qimaging , Surrey , BC , Canada ) and Qcolor3 ( Olympus ) cameras . Qimaging and Slidebook software ( Intelligent Imaging Innovations , Inc . , Denver , CO ) were used for image acquisition and analysis on a Macintosh G5 dual processor computer ( Apple Computer , Cupertino , CA ) . Confocal microscopy was performed on a Zeiss LSM 510 confocal microscope . To determine the spatial occupancy of M . leprae within amoebae , serial optical sections were imaged of infected amoebae and were taken at 0 . 2 nm intervals using a 514 nm excitation laser and 560±20 nm emission filters . Nucleic acid extraction from amoebae cocultures from fine needle aspirate tissue samples was performed using the Qiagen DNeasy kit and PCR amplification was performed on 50 ng extracted DNA using primers that amplify the M . leprae-specific repetitive element ( RLEP ) [32] . Amplified PCR samples positive for the presence of M . leprae produced a 129 bp product . To assess growth and counting efficiency of M . leprae in FPs real-time TaqMan PCR assays were performed . M . leprae genomic DNA was obtained from FP tissue homogenates as described elsewhere [33] . Briefly , 200 µl aliquots of tissue homogenates were subject to 3 freeze/thaw cycles , and proteinase K was added to 10 mg/ml and the sample were incubated at 56°C for 2 hrs . The genomic DNA was processed and purified using the DNeasy Kit ( Qiagen , Inc , Valencia , CA ) according to the manufacturer's directions . Molecular enumeration of M . leprae was determined using purified DNA fractions from each specimen via TacMan technology using primers and a probe for a common region of the RLEP family of dispersed repeats in M . leprae as previously described [33] [32] . The specific sequences of the primers and probe have been described elsewhere [34] . All reagents used in the TaqMan assay were recommended by the manufacturer ( PE Applied Biosystems ) , including AmpErase UNG enzyme and AmpliTaq Gold DNA polymerase . PCR cycling conditions were 40 cycles with 60°C annealing/extension temperature for 60 seconds and 95°C denaturing temperature for 15 seconds . PCR and data analyses were performed on a 7300 RealTime PCR System ( Applied Biosystems , Foster City , CA ) .
To show that amoebae are capable of phagocytosing M . leprae bacilli , we infected amoebae trophozoite cultures with M . leprae at an M . O . I . of 5 . To facilitate infection , the trophozoite cultures were shifted from optimized medium to 1/10 the optimum nutrient concentration allowing for a parallel shift from a primarily pinocytotic nutrient acquisition mode to a macro-phagocytic mode that effectively optimizes the trophozoites to take up the bacilli [35] . Fig . 1 shows light microscopy of acid-fast staining of M . leprae in cocultures established by infecting three species of Acanthamoeba with freshly harvested viable M . leprae . Greater than 95% of the amoebae were observed to be internally occupied ( Fig . 1; Panels A–C ) with at least one acid-fast bacillus residing in the amoebic trophozoites . At relatively low M . O . I . ( 1∶1 to 5∶1 ) , ingestion of live M . leprae did not exert any observable adverse effect on amoebae that divided normally over several days . At higher M . O . I . ( >5∶1 ) , however , the bacterial burden negatively affected the growth of trophozoites and the cocultures showed low-level lysis of amoebae and considerable detachment from plate wells . M . leprae bacilli were also readily taken up by two strains of H . vermiformis ( str . ATCC 50237 and str . 172 ) as well . Fluorescence microscopy of PKH26-labeled M . leprae in cocultures of amoebae that were stained by the DNA-specific dye , DAPI , showed that the bacilli were taken up into areas that were mostly exclusive to nuclear staining indicative of primarily cytoplasmic staining ( Fig . 1 D–F ) . In order to determine whether the M . leprae bacillus is phagocytosed by amoebae as opposed to being merely adsorbed to the protozoan surface , we prepared cocultures as above and examined the fluorescently labeled bacteria by confocal microscopy following 16 hr of culture ( Fig . 2 ) . Confocal microscopy was utilized to resolve the physical location of the M . leprae bacilli within infected amoebae at various focal planes . Layered focal resolution of M . leprae-infected A . polyphaga showed that the best optical and fluorescent resolution of the bacilli was well within the interior of the amoebae suggesting that the bacilli resided within the amoebae interior as opposed to their external surface . Similar resolution was obtained for A . castellanii , A . lenticulata as well as both strains of H . vermiformis . Amoebae were cultured with live PKH26-stained M . leprae in appropriate amoebae medium for 16 hr to allow for complete envelopment of the bacilli . Following uptake of M . leprae , the cultures were pulsed at 32°C for 2 hr with 100 mM Lysotracker Green-DND-26 ( Molecular Probes , Life Technologies , Grand Island , NY ) in order to fluorescently stain the acid-rich organelles such as lysosomes residing within the amoebic cytoplasm . Fig . 3 shows that the fluorescently labeled M . leprae infecting either A . castellanii or A . polyphaga resided primarily within acid-rich organelles ( i . e . lysosomal compartments ) of amoebae similar to what is observed in macrophages , DCs and Schwann cells . The bacilli were similarly located within the cytoplasmic regions of A . castellanii , A . lenticulata , H . vermiformis str . ATCC 50237 and H . vermiformis str . 172 . To determine whether uptake of M . leprae by amoebae requires metabolic viability of either amoebae and/or bacilli , phagocytosis assays were performed that measure the extent of uptake of PKH26-labeled M . leprae by A . lenticulata , A . castellanii or A . polyphaga and the two strains of H . vermiformis . Fluorescently-labeled M . leprae were introduced to actively growing amoebae trophozoites and the extent of acquisition of fluorescence over a 6 hr period was determined by flow cytometry as measured by gain of mean fluorescence intensity ( M . F . I . ) by the amoebae ( Fig . 4 ) . Trial assays showed that the maximal extent of M . F . I . was routinely achieved at 6 hr post-challenge for all amoebae tested . Fig . 4 indicates such for infected axenic cultures of A . castellanii and A . polyphaga ( and all amoebae tested ( S1 Fig . ) . Infections of a M . O . I . greater than 100 proved detrimental to the amoebae and demonstrated lower overall M . F . I . per unit time . The acquisition of red fluorescence by the amoebae as a function of time at 32°C is shown in Fig . 4 . In all cases , the best acquisition of red M . F . I . occurred if the temperature was 32°C and the infecting M . leprae were viable ( i . e . from passaged nu/nu mouse FP ) . M . leprae freshly isolated from mouse FP and deemed viable by both radiorespirometry and viability staining ( >90% viable ) proved to be optimally phagocytosed . M . leprae harvested from armadillo tissues , has very low or no viability , [30] , [31] . Armadillo-derived M . leprae was not capable of transferring to amoebae the level of red fluorescence achieved by their mouse FP extracted counterparts , achieving only approx . 10% of the maximal M . F . I . ( Fig . 4 ) . In addition , assays performed at the sub-physiological temperature of 4°C showed that the amoebae achieved only about 1% of the M . F . I . of viable M . leprae and 10% of M . leprae from armadillo tissue when compared to their respective counterparts at 32°C ( Fig . 4 ) . Furthermore , most surface adsorption of the fluorescent M . leprae to the amoebae at 4°C was removed by rigorous washing of the cells . Collectively , these data suggest that uptake and internalization of M . leprae by amoebae optimally requires active amoebae metabolism driving phagocytosis of viable bacilli . To determine whether the viable bacilli extracted from 35-day amoebae cocultures were capable of causing characteristic M . leprae-induced FP indurations in infected mice , bacillary extracts were injected directly into the left FP of athymic nu/nu mice , and FP pathology was monitored over a period of 8 months . All experimental bacilli that were injected into nu/nu FPs were extracted from cocultures from either A . castellanii or A . polyphaga that remained encysted with M . leprae bacilli at 32°C in amoebae encystment medium for 33 days followed by excystment for 2 days as described above . The appearance of FP lesions in the nu/nu mouse model for leprosy is typically very slow with measurable swelling appearing only after 4–5 months post challenge ( using an infecting dose of 2–5×107 bacilli/FP ) [37] . Since the number of bacilli that were extracted from excysted cultures were considerably lower than the amount extracted directly from FP , we chose to inject FPs with coculture-extracted bacilli every other week for a total of three times in order to decrease the time of emergence of FP symptoms . As positive controls , five mice were challenged in the FP with 107 M . leprae bacilli freshly extracted from infected nu/nu FPs in a manner that is routinely performed to passage M . leprae in the laboratory ( Fig . 9A , panel 1 ) . Mouse FPs were also injected with M . leprae kept in amoebae medium alone for identical periods of temperature and days ( Fig . 9A , panel 2 ) . FP swelling was assessed monthly using a Vernier digital caliper and plotted as illustrated in Fig . 9 ( Fig . 9A; panels 1–4 ) . During the 6 . 5 months post-challenge , measurable swelling of the left FP was consistently evident in the positive control animals . In contrast , measurable swelling of the FPs challenged with bacilli extracted from A . castellanii and A . polyphaga cocultures was not detectable until 7 . 5–8 months post-challenge but the rates of swelling were similar to early stages of the positive controls ( Fig . 9A; compare panels 1 with 3 and 4 ) . There was no detectable FP swelling in animals that were injected with M . leprae kept for 35 days at 32°C in amoebae medium alone ( Fig . 9A , panel 2; compare photo insets ) . Any increase in measurement of right FPs or FPs injected with M . leprae in medium alone was due to increasing size of the FP because of the overall growth and development of the animal over the duration of the experiment . These results thus indicate that bacilli extracted from long-term ( 35 days ) cocultures of A . castellanii and A . polyphaga are capable of growth in nu/nu mice FP ( albeit with a 2 month delay ) similar to M . leprae extracted conventionally from donor nu/nu mouse FPs . To show that the swelling measured above contained a considerably high burden of acid-fast bacilli , small samples of tissue were extracted by FNA biopsy from challenged FPs . Smears were stained for subsequent fluorescence microscopic analysis with DAPI for cell nuclei and auramine/rhodamine for acid-fast bacilli . Fluorescence micrographs show considerable ( red staining ) acid-fast bacilli in FP tissue obtained from all of the mice that were challenged with M . leprae derived from freshly harvested passage mice ( positive controls ) ( Fig . 9B; panel i ) and in 4 out of 5 of those from each category challenged with bacilli extracted from 35 day cocultures of A . castellanii or A . polyphaga ( Fig . 9B , panels iii and iv ) . There was no evidence of acid-fast bacilli in FNA-extracted tissue from mice FPs challenged with M . leprae from axenic cultures without amoebae ( Fig . 9B , panel ii ) . These results suggest further that FP lesions in these mice were the direct result of viable M . leprae extracted from long-term amoebic cocultures . To further confirm that the acid-fast bacilli observed in tissue was indeed M . leprae , nucleic acid was extracted and tested in PCR analysis for amplification of the M . leprae-specific RLEP sequence as in Fig . 8 above . Positive PCR signals were obtained from FNA tissue from all mice challenged with M . leprae directly from passage mouse FPs and 4 out of 5 of the mice challenged with bacilli extracted from cocultures of A . castellanii or A . polyphaga ( Fig . 10 ) . The one mouse FP in each group that was negative by PCR was also negative by auramine-rhodamine staining for acid-fast bacteria . By contrast , after 8 months post-challenge , there was no evidence of the M . leprae RLEP PCR signal in any of the FPs from mice challenged with M . leprae maintained in axenic cultures of amoebae medium . PCR analysis of FNA tissue for amplification of Acanthamoeba-specific 18S rRNA sequences [38] was negative as well , suggesting that either the extraction method for obtaining the bacilli effectively killed the amoebae or the mice successfully resolved any residual amoebae infection over the long-term experimental period . This confirms that the acid-fast bacilli observed in FP lesions produced by challenge with bacilli extracted from amoebae are most likely M . leprae and that the bacilli remain viable and capable of transmitting FP pathology for up to 35 days in cocultures of two different Acanthamoeba spp . The observation that there is a considerable increase in the number of acid-fast bacilli in FPs ( Fig . 9 ) coupled with the strong PCR signal obtained from primers for the M . leprae-specific RLEP ( Fig . 10 ) , strongly suggested the presence and growth of M . leprae in FPs challenged with amoebae-derived bacteria . However , to confirm that the 35 day-amoebae cocultured M . leprae are indeed capable of replication within the FP , we performed a TaqMan quantitative PCR analysis for the RLEP region of M . leprae to compared the number of M . leprae present in FPs challenged with bacilli derived from amoebic cocultures vs . the number from FPs challenged with M . leprae grown alone for 35 days in amoebae medium . Fig . 11 shows the results of the Taqman analysis . The amount of M . leprae extracted from FP challenged with coculture-derived bacilli exhibited a significant increase of the RLEP signal when compared to those challenged with M . leprae kept in medium for 35 days . The data represents a 3–3 . 5 log increase M . leprae in FPs challenged with M . leprae from amoebae cocultures compared to those challenged with M . leprae maintained in axenic cultures medium . These data confirm that M . leprae extracted from 35 day encysted amoebae cultures are capable of replication in the nu/nu mouse footpad .
The precise manner in which leprosy is transmitted is unknown . Until recently it was widely believed that the disease was transmitted by proximal contact between untreated or asymptomatic cases of leprosy and healthy people . Currently , the possibility of transmission by the respiratory aerosol route has gained considerable interest [39] . Other means such as transmission through insects [40] [41] has been considered but there has not been any substantial evidence supporting that claim . The possibility of discharge of M . leprae from the nasal mucosa begs the question of how the discharged organism remains viable in between hosts . Since M . leprae is a fastidiously obligate intracellular bacterium , it would be reasonable to assume that it could find safe refuge in the environment by interaction with ubiquitous free-living organisms with physiological semblance to human phagocytes . Recently , it was shown that M . leprae could be taken up by FLA , survive and remain viable intracellularly in these protozoa for a period of at least 72 hr [42] . In the current study , we demonstrate that M . leprae can survive and remain virulent for at least 35 days within amoebal cysts from both A . castellanii and A . polyphaga as determined by their ability to transfer infection to recipient nu/nu mouse FPs . Furthermore , we show that acid-fast bacilli extracted from M . leprae/amoebae cocultures with A . lenticulata , A . castellanii , A . polyphaga , H . vermiformis str . ATCC 50237 and H . vermiformis str . 172 remain viable for over 8 months in encysted amoebae as determined by viability staining of bacilli in situ within cysts or from the those extracted from the cysts . These data provide a proof of concept that M . leprae can be phagocytized and lysosomally occupy common environmental FLA trophozoites , survive encystment while remaining viable and are fully capable of infectivity under suboptimal conditions endured by the amoebic cyst . Although M . leprae has been shown to be approximately 30% viable in terms of membrane integrity by BacLight after two weeks in optimized medium [36] , survival in either amoebae medium described here is very detrimental to axenic M . leprae and necessitates refuge within amoebae . It can be reasonably argued that possible environmental reservoirs for this fastidious bacillus are common FLA . M . leprae cannot be cultured , and therefore there are limited means available to ascertain its viability in long-term amoebae culture . No single method of investigation can be utilized to confirm its viability in amoebic cysts . For example , the specificity of serological techniques and PCR can be impaired by antigenic cross-reactivity and PCR contamination , respectively . Therefore , we embarked on an exhaustive set of experiments in order to be absolutely certain that M . leprae can be engulfed and remain viable within the amoebae examined and to be confident that the acid-fast organisms detected in FPs from mice challenged thereof were indeed M . leprae from amoebae cocultures . We built upon this conclusion by first demonstrating that M . leprae is phagocytized by amoebae as determined by microscopic and flow cytometric analysis revealing that optimal uptake requires active temperature-dependent metabolism and viability of both organisms . Subsequently , we assessed bacillary viability by use of a two-color , Syto9/propidium iodide fluorescence staining that scores for membrane damage in individual bacilli and is a proven technique that correlates well with other viability measures for M . leprae such as radiorespirometry [36] . Virtually all of the bacilli extracted from long-term cocultures at 32°C were propidium iodide negative ( PI- ) and Syto9+ indicative of viable organisms . Control cultures containing M . leprae alone in amoebae medium showed considerable degradation of the bacilli that was virtually 100% propidium iodide positive after two weeks and were undetectable after 35 days at 32°C ( Fig . 5 ) . We also observed occupancy of acid-fast organisms within amoebal cysts for 8 months post culturing . The extracted bacilli emerged either as intact extracellular bacilli or residents of acid-rich organelles of recently excysted trophozoites ( Figs . 5 and 7 ) . Most emergent bacilli were deemed viable by Syto9+/propidium iodide negative staining . PCR analysis of nucleic acid from encysted cocultures amplified the M . leprae-specific RLEP element strongly supporting the fact that the acid-fast bodies observed in cysts were indeed M . leprae . The loss of RLEP PCR signal in 35 day axenic M . leprae cultures is curious since M . leprae DNA has been known to persist in tissues for very long times after host death . The loss of signal in these cultures is likely due to release of soluble nucleic acid from the dead bacilli in the liquid medium that is lost when washing . In addition , detection of RLEP in archeological samples is performed using the more sensitive TaqMan PCR methodology . Most importantly , M . leprae survival and retention of virulence in cocultures were confirmed by transference of extracted bacilli from 35-day cocultures of A . castellanii and A . polyphaga into nu/nu mouse FPs . 80% of the mice ( 4 out of 5 ) challenged with either coculture developed FP swelling with histological evidence of acid-fast bacilli . Collectively , these data confirm that M . leprae can indeed survive for extended periods of time in encysted FLA cultures and is capable of growth in nu/nu mice FP . The number of viable M . leprae extracted from these cocultures was significantly less than the initial number used to infect the trophozoites . 1 . 5×107 bacilli were used to infect 3×106 ( MOI = 5 ) amoebae and , based on microscopic field counts , the estimated number of M . leprae harvested from amoebic cysts and injected into FPs was between 105–106 per injection . This may be due to several reasons: i ) Only approximately 30% of the trophozotic amoebae were observed to be capable of encystment as the shift in the transcriptional program necessary for this transformation is considerably complicated and incompletely understood [35] . Since incomplete transformation to cysts might impose a restriction on the actual numbers of bacilli housed therein there would be a considerable culling and reduction in the numbers of protected and viable M . leprae . ii ) To facilitate processing , bacilli were extracted from cysts that were first induced to excyst . Studies have shown that bacteria residing in Acanthamoeba cysts are generally housed both within the cytoplasm as well as within the cyst walls between the endo- and ectocyst shells as is the case for Acanthamoeba spp . [35] . The extrusion process of emerging trophozoites from cyst wall pores known as ostioles has the potential of leaving a considerable number of bacilli in the cyst wall remnants that may be either unavailable for infection or are pelleted in the slow speed centrifugation steps used in the purification of the extracted bacilli [43] . iii ) The extraction process of recently emergent trophozoites in this study involved treatment with SDS . Due to their unique cell wall , mycobacteria can survive relatively long exposures to detergents [44] but the effect of SDS treatment on long-term viability of M . leprae has , to our knowledge , not been determined and may be a factor in reduced viability of extracted bacilli . However , there was no indication of membrane damage in extracted bacilli as assessed by viability staining . iv ) There may be a slow loss of viability over time in the amoebae cyst cocultures if the cultures are unable to optimally support the bacilli and the cysts are simply “buying time” for M . leprae . Also it is possible that not all the extracellular bacteria were capable of being endocytosed by the amoeba . It has been shown that the inevitable clumping that occurs in cell-free Mycobacteria suspensions contain aggregates that are not efficiently taken up by either amoebae or macrophages [11] , [16] , [45] . Regardless , the encystment of the bacilli prolongs viability empirically . Molecular enumeration , and analysis of viability transcripts by reverse transcriptase-based quantitative real-time PCR will likely provide some insight into the longevity of the bacteria in cysts . Recently , transcripts encoding the M . leprae-specific ESAT-6 , heat-shock protein 18 , superoxide dismutase A and the 16S rRNA subunit have been determined to be sensitive viability indicators for M . leprae [28] , [34] . Many of these approaches are planned in future endeavors . Prior observations that amoebae can house and transport L . pneumophila and can serve to increase the virulence of M . avium have raised concern that protozoa have the potential to be general environmental reservoirs or vectors of human pathogens [11] , [45] . It has been considered that adaptation of organisms to parasitism , commensalism or simply endocytobiontism of FLA might have molded environmental microorganisms to infect and persist in human phagocytes . That is to say , the process of the selection of environmental microorganisms for resistance to digestion by predatory FLA behaving as feral macrophages might be a driving force in the evolution of pathogenic environmental bacteria . Such a process may likely be the “missing link” between ecology and pathology [46]-[48] . FLA are present worldwide [49] and have been isolated from soil [50]–[53] , water [54]–[58] , air [59] , and the nasal mucosa of otherwise healthy human volunteers [60]–[62] . The fact that there are repeated observations of clinical leprosy in those that appear to have no history of exposure to known cases [63]–[66] and that leprosy tends to cluster in areas proximal to water sources [67] , [68] strongly suggest that M . leprae has extra-human environmental sources [69] , [70] and those environs are also compatible with the globally ubiquitous FLA . The natural environmental landscape for amoebae ( as is for most organisms ) is not static and , as such , various adaptive genetic programs have evolved to survive dynamic and potentially detrimental conditions . Exposure to suboptimal conditions such as starvation , extreme temperatures , excessive UV light , radiation , pH changes , as well as exposure to biocides induce amoebae trophozoites to undergo encystation [16] . Amoebae exist in the environment cyclically transforming from free-feeding trophozoites to highly dispersible and resilient cysts . M . leprae , by virtue of having a slow generation time can likely withstand the confines of the amoebal cyst allowing bacillary viability to persist longer in this manner . Moreover , the coculture of M . leprae with Acanthamoeba or Hartmannella spp . is particularly suited for both bacteria and amoebae since their temperature optima are compatible for both initial infection of trophozoite and long-term “storage” of cysts . It has been shown that M . leprae can remain viable if lyophilized in the presence of 10% skim milk-water [71] . In addition , viability was preserved up to 4 years at 4°C . This work also demonstrated clearly that the composition of the solution for suspending the bacilli was critical for the maintenance of M . leprae viability by lyophilization—with skim milk being 100-fold more effective that water or water with 10% fetal calf serum . With respect to viability in amoebic cysts , these results are intriguing . While desiccation may provide a means of survival/viability to the bacillus , it is unlikely that drying per se is a “natural” means of persistence since most if not all of the remaining endemic areas are those of high humidity and abundant water . The amoebic cyst ( in particular the acanthamoeba cyst ) is a very efficient desiccant that is essentially devoid of water . The natural "arid" environment inside of cysts allows long-term survival ( years ) of the amoebae in the face of drought etc . by virtue of its impermeable cellulose wall [35] . Could the same mechanisms ( dehydration etc . ) that provides viability to the amoebae be "hijacked" by the captured M . leprae to provide long-term viability to the bacillus ? Future experimentation will likely reveal answers to these rather intriguing questions . It would be intriguing to determine whether M . leprae , by virtue of residing in cysts , has evolved its own dormancy program in order to persist and maintain or enhance viability or virulence . Also , active prompting of protozoan encystment by bacteria has thus far only been demonstrated for L . monocytogenes suggesting that these bacteria have a selective advantage of exploiting the cysts' ability to serve as vehicles and to assume dormant stages that aid dispersal in the environment [48] , [72] . Whether this is the case for M . leprae as well awaits further investigation . Other outstanding questions include the determination of ( A ) whether M . leprae , by virtue of being transmitted via amoebae , can enter host macrophages via a Trojan horse mechanism thereby changing the overall pathogen-associated molecular pattern ( PAMP ) presented to innate immune cells and subsequently altering innate and adaptive responses to the benefit of the pathogen; ( B ) whether M . leprae is capable of multiplying within amoebae or is simply maintaining survival therein . The results described in this current work do not demonstrate that M . leprae is capable of multiplying inside amoebae but are suggestive of a role for FLA providing sustenance to maintain viability of the bacilli; ( C ) whether the leprosy bacillus requires periodic excystment as is likely the case in the natural world in order to re-infect emergent trophozoites or human host cells; and , finally ( D ) whether the virulence of M . leprae is affected either positively or negatively by its passage through amoebae . Future experimentation including testing for appearance of disease by transferring M . leprae from our other existing cocultures of A . lenticulata and H . vermiformis strains to nu/nu mouse FPs and determining whether mice challenged directly with M . leprae-infected amoebae ( cysts or trophozoites ) display any differences in progress to disease should resolve some of these issues . In summary , we show that M . leprae is capable of prolonged survival in three common and ubiquitous species of Acanthamoeba and two strains of Hartmannella . At this point we are unsure of whether this endocytobiotic relationship in nature serves to allow some FLA to function as transmission vehicles/vectors , a Trojan horse and/or biological reservoirs for M . leprae . It will be fascinating to determine whether FLA in general provide an environmental sanctuary possibly facilitating virulence and contributing to microbial survival in harsh conditions along with aiding transmission to susceptible hosts . Future experimentation will clearly unravel these issues . | Leprosy is a progressive disease of the skin and nervous system caused by the bacillus , Mycobacterium leprae . Implementation of multiple drug therapy ( MDT ) for leprosy has significantly reduced the global cases of leprosy . Currently , only a few endemic countries remain where relatively high number of cases persists . Despite global reduction of leprosy and the concomitant decrease in human reservoirs , leprosy transmission and incidence have not declined as expected , suggesting a possible extra-human or environmental source of the bacilli . In the current study , we demonstrate that M . leprae can survive long-term within cysts of common environmental free-living amoebae . M . leprae residing in amoebal cysts for over 30 days remain fully capable of transferring disease to mouse footpads and retain viability phenotypes after several months residence within amoebal cysts . It is hypothesized that these protozoa provide an intracellular refuge for M . leprae in environments for which they would otherwise seem ill suited . Traits allowing bacilli to survive in macrophages may likely be acquired via an evolutionary response against predation by amoebae . The results from this work suggest alternative non-human reservoirs for M . leprae exist fostering further study to determine the role of amoebae in the transmission of this Mycobacterium to humans . | [
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| 2014 | Long-term Survival and Virulence of Mycobacterium leprae in Amoebal Cysts |
Genetic association analyses of rare variants in next-generation sequencing ( NGS ) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies . Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level . Nonetheless , pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases . Due to the extreme rarity of mutations and high-dimensionality , significances of causal variants cannot easily stand out from those of noncausal ones . Consequently , standard false-positive control procedures , such as the Bonferroni and false discovery rate ( FDR ) , are often impractical to apply , as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants . To provide informative analysis of individual variants in large-scale sequencing studies , we propose the Adaptive False-Negative Control ( AFNC ) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal . The AFNC provides a general framework that can accommodate for a variety of models and significance tests . The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings . Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants , whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies . In the analyses of the CoLaus dataset , AFNC has identified individual variants most responsible for gene-level significances . Moreover , single-variant results using the AFNC have been successfully applied to infer related genes with annotation information .
Recent advances in next-generation sequencing ( NGS ) technologies have extended the focus of genetic studies of complex traits from that of common to rare variants . Having low minor allele frequencies ( MAFs ) , usually defined to be less than 1% to 5% , rare variants are often evolved from recent mutations that have not yet been subjected to the pruning mechanism of natural selection and can potentially retain a larger proportion of inheritable variability than common variants . [1–5] Recent studies have already implicated the relevance of rare variants on several complex traits . [6–13] Despite its potential to uncover genetic factors contributing to missing disease heritability , the analysis of rare variants association studies bears fundamental challenges . As only a small proportion of samples may carry variant alleles at each locus , associations of individual rare variants are often underpowered . [1 , 14 , 15] Moreover , the number of candidate variants can be extremely large in high-throughput sequencing studies , in which available multiple testing strategies may impose excessively severe corrections , preventing the selection of potentially causal variants . [16] Recent proposals for rare variants association analysis often resort to collapsing or pooling multiple variants in a gene or pathway . Examples include the combined multivariate collapsing ( CMC ) [17] , cohort allelic sum ( CAST ) [18] , C-alpha [19] , sum of squared scores [20–23] , sequence kernel association ( SKAT ) [24] , quality-weighted multivariate score association ( qMSAT ) [25] , and similarity-based regression ( simReg ) [26] tests . The strategy increases power by aggregating effects of low-frequency variants and decreasing data dimension in multiple testing . It has been successfully applied in several applications that identified functional regions that may contain potentially relevant rare variants . [17–20 , 23–26] Nonetheless , variants-pooling tests that aggregate over a gene or pathway do not provide information at the individual locus and are ill-equipped to tap the full potential of NGS data in identifying causative mutations at the single-nucleotide resolution . Pinpointing potentially causal variants is a critical goal of genomic studies because such information would faciliate precise delineations of molecular mechanisms and functions of genetic factors on diseases . [27] Moreover , studies have shown that pooling over multiple variants may result in reduced power , as the inclusion of many noncausal variants may dilute the effects of relevant variants on a trait . [28–30] Thus , pooling over multiple variants can sometimes be inadequate for the identification of functional genomic regions . On the other hand , analysis of individual rare variants can provide practical advantages . Information of single-variant association can be used to pinpoint a small number of potentially causal variants for follow-up studies to facilitate the precise characterization of functions via molecular modeling and genetic experimentation , which are often too expensive and time consuming to conduct for all variants in a gene . [27] Further , single-variant results can be utilized a posteriori to objectively infer disease-related genes or pathways by comparing with annotation and functional databases . [31–34] This is useful as gene-level results can oftentimes be uninformative when the significance of a few causal variants are diluted by a large number of noncausal ones in the same gene . In the Results section , we will illustrate both strategies for applying single-variant results using the CoLaus data set . Genome-wide association ( GWA ) studies , as the pre-eminent means for genetic discovery over the last decade , have largely relied on statistical genomic tools that can identify common variants at the individual single-nucleotide polymorphism ( SNP ) level . [35] Standard procedures for GWA studies evaluate each variant individually . [36 , 37] Potentially causal variants are identified by multiple-testing control on significances at each locus . The simplest strategy for multiple testing utilizes the Bonferroni correction that controls family-wise error rate , or the probability of having one or more false positives . [38] However , the Bonferroni correction can often be too conservative for GWA studies under the presence of thousands of SNPs . [39] To address this issue , the false discovery rate ( FDR ) is often utilized that provides a more liberal criterion by controlling the expected proportion instead of the presence of false positives . [40–42] Despite being extremely successful for common variants in GWA studies [43–46] , procedures based on false-positive control are often underpowered in NGS studies involving rare variants ( as illustrated in Fig 1 ) . New approaches are needed to provide a meaningful way for powerful variants selection in large-scale sequencing studies . Fig 1 compares the statistical landscape of rare variants analysis in NGS studies with that of common variants in GWA studies . In GWA studies , we observe three regions of statistical inference: the Signals ( “S” ) region where strongly associated variants can be readily identified by controlling false positives , the Noise ( “N” ) region where noncausal variants can be identified by controlling false negatives , and the indistinguishable ( “I” ) region where causal and noncausal variants are inextricably mixed . [47 , 48] We have recently developed theoretical characterizations for the three regions in high-dimensional data analysis . [49] In NGS studies with rare variants , the Signals region tends to be very narrow and can often degenerate due to extremely low MAF and high dimensionality . Consequently , few causal variants can be identified by evaluating false positives , and results can be very unstable due to random perturbations of noncausal variants . To address the challenge of rare variants association analysis at the single-locus level , we propose the Adaptive False-Negative Control ( AFNC ) procedure in order to allow a large proportion of causal variants to be retained with high probability . Specifically , the AFNC applies a novel metric called the signal missing rate ( Eq 2 ) , defined as the probability of having a nontrivial proportion of false negatives among all causal variants ( i . e . , FN/s in Table 1 ) , to achieve informative variant selection by controlling the signal missing rate to be small ( see Methods section ) . That is , AFNC seeks to determine those variants that can be confidently dispatched as noncausal and identifies variants from both the Signals and Indistinguishable regions . The results can provide informative inference in NGS studies where the Signals region is very small or degenerate ( Fig 1 ) . We note that this is quite different from classical methods that control false positives . For example , the Bonferroni controls for the presence of any false positives ( i . e . , FP ≥ 1 ) , whereas the FDR controls for the expectation of the proportion FP/R when R > 0 ( see Table 1 ) . Neither of these involve the number of causal variants s; thus , they cannot be used for controlling the proportion of causal variants selected . On the other hand , the AFNC , based on the proportion FN/s or 1 − TP/s , allows powerful variants selection by controlling the type II error or 1 − statistical power . Although there may exist a corresponding control level for the FDR ( albeit very large ) that can include the variants selected by the AFNC at a given false-negative control level ( see Results section ) , this corresponding FDR control level is not known a priori and is expected to vary haphazardly across different studies . An arbitrarily assigned FDR control level would be inefficient for controlling false negatives in NGS studies , that can over- or under-select uncontrollably depending on the size of the Noise region . A corresponding control level usually does not exist for the stringent Bonferroni selection in large-scale sequencing studies ( see Results section ) . The AFNC provides a general framework that can accommodate for a wide spectrum of models and test statistics , that may include biological prior knowledge and global genotype information ( see Methods section ) . Moreover , it readily adapts to the quality of statistical tests employed . With improved quality of statistical tests , the Indistinguishable region ( see Fig 1 ) narrows , and the AFNC can , in turn , select a smaller set of potentially causal variants . Extensive studies ( see Results section ) demonstrate that the AFNC can identify a modest number of potentially causal variants while avoiding a deluge of noncausal ones for follow-up analyses that focus on targeted variants . Our proposal employs recent developments in ultra high-dimensional statistical inference to derive a data-driven procedure that can readily adapt to the underlying sparsity and effect sizes of the data . [50–53] It readily controls type I error rates ( see Results section ) . In addition , it is computationally very efficient and can be applicable for whole-genome sequencing ( WGS ) and whole-exome sequencing ( WES ) studies .
We considered the Cohorte Latusannoise ( CoLaus ) sequence study [58–61] , where almost 6000 unrelated Caucasian residents of Lausanne , Switzerland were assessed for risk factors of cardiovascular diseases ( CVD ) . Targeted sequencing genotypes on 202 drug-targeted genes ( human genome build 36 ) were obtained for n = 1769 of these subjects . Cholesterol levels were collected for each subject to evaluate risk of CVD , along with 12 clinical factors—age , gender , and 10 ethnicity covariates using the first 10 principal components [62] . We considered d = 9665 autosomal rare variants from the sequencing study with 0 < M A F < 1 / 2 n = 0 . 0072 . For each variant , t-statistic was obtained by linear association with log cholesterol levels as the response while adjusting for the 12 clinical covariates . The AFNC , FDR , and Bonferroni were , then , applied on significances of t-statistics to identify potentially causal variants . At threshold levels of 0 . 05 , Bonferroni and FDR only identified 4 variants . At α = 0 . 05 and β = 0 . 1 , AFNC identified 56 candidate rare variants . The AFNC algorithm obtained cd = 0 . 0494 based on M = 10 , 000 randomly generated samples under the global null of no causal variants and π ^ = 0 . 001784 ( Eqs 4 and 5 ) . As CVD tends to be influenced by multiple factors [63 , 64] and the study focused on genes having clinical relevance , one expects a larger number of causal variants than those identified by the FDR and Bonferroni . Our estimated number of signals , s ^ = π ^ × 9665 = 17 . 244 , suggests that at least 18 variants need to be selected , and potentially much more due to signals dispersed in the Indistinguishable region , to encompass a high proportion of causal variants . That is , false-positive control procedures can be much too conservative in NGS studies , where the Signals region tends to be degenerate ( see Fig 1 ) . In the following , we illustrate potential applications of the AFNC for pinpointing individual variants in candidate genes and inferring disease-related genes with annotation information .
We have proposed a novel bioinformatic approach that allows the identification of individual rare variants in large-scale sequencing association studies . Extensive studies based on simulated data generated with COSI at realistic population parameters have been used to compare our method with the Bonferroni and FDR across various scenarios . [54] Results have suggested that the AFNC can provide informative variant selection by including a large proportion of causal variants while avoiding a deluge of noncausal ones . On the other hand , the Bonferroni and FDR are shown to be excessively over-conservative under extremely low MAFs and high dimensionality . Analyses of the CoLaus dataset for cardiovascular diseases using the AFNC have pinpointed individual variants most responsible for explaining significances of genes identified in gene-level aggregation tests . Moreover , single-variant results have been successfully applied to objectively infer potentially relevant genes when cross-referenced with annotation information . The R package ‘AFNC’ for performing the AFNC is publicly and freely available at https://github . com/zjdaye/AFNC or http://sites . google . com/site/zhongyindaye/software . The AFNC provides a unified framework to accommodate for a wide spectrum of models , test statistics , and data scenarios . To achieve a succinct presentation , we focused on quantitative traits using p-values obtained from linear association tests in this paper . The AFNC can be easily adopted for case-control studies [23–25 , 105] , family-structured data [106 , 107] , and many other scenarios . Moreover , empirical p-values , as from permutation or bootstrap , can be employed for improved significance ranking . [108] Clearly , performance results of the AFNC using p-values based on associations with quantitative traits , shown in this paper , can be extended to those obtained under a spectrum of models and data scenarios . Moreover , the analysis of large-scale genomic data is a dynamic and fast-evolving field . The AFNC , that readily adapts to the quality of statistical tests employed , will be able to provide increasingly efficient inclusion of causal variants as ever more accurate and computationally efficient means for assessing significances are developed . A few very recent works have sought to identify individual rare variants by incorporating prior-knowledge information in statistical inference . [109 , 110] These methods typically upweight individual variants predicted to be most likely to be causal based on prior GWA studies , functional annotation , sequence conservation , and other computational means . The AFNC can be readily utilized with models and test statistics that incorporate biological prior knowledge . In the Results section , we illustrated an alternative way to incorporate this bioinformatic knowledge . Specifically , we started with an agnostic interrogation of each variant and obtained a set of statistically promising variants using AFNC . We then compared the selected variants with prior-knowledge information to allow investigators to form educated hypothesis in designing follow-up studies . Statistically promising variants , that are selected objectively by AFNC , can also be explored in follow-up studies without comparing with annotation information , such as when prior knowledge is not available for novel variants or believed to be inaccurate . Due to extremely low MAFs , rare variants do not usually exhibit strong linkage disequilibrium . [1 , 111] Thus , we designed the AFNC for rare variants association studies , in which dependence among test statistics is assumed to be weak . The AFNC procedure is also applicable in the situation when causal variants are dependent , but noncausal variants are independent . [112] In other applications where noncausal genetic factors are expected to be strongly dependent , the AFNC procedure can be adapted to account for arbitrary dependence using several recent techniques for multiple testing . [113 , 114] One potential limitation of AFNC is that it may underperform when the signal intensity of the causal variants is too low . The signal intensity of a causal variant depends on the effect sizes and sample size . As shown in Figs 2 and 3 , the sensitivity of AFNC deteriorates as effect size or sample size becomes smaller . Indeed , low effect sizes and small sample size are fatal limitations to all methods . In single-variant analysis of rare variants , such challenges may arise from identifying the extremely rare causal variants ( e . g . , singletons in the data ) . Although effect size is believed to be high for rare causal variants , the overall signal intensity may still be low given the extremely low sample size . Under this scenario , gene-based tests coupled with functional annotation would have better potential to identify these causal variants . Therefore , gene-based tests , functional annotation and AFNC should be used in an integrated fashion in the detection of rare causal variants: as we have illustrated in our analysis of the CoLaus data , AFNC coupled with gene-based tests can help to pinpoint potential causal variants that lead to gene-level significance; AFNC coupled with functional annotation can help to identify causal genes that are insignificant at gene level due to a few causal variants mixed with a large number of noncausal variants; finally , gene-based tests coupled with functional annotation can facilitate the identification of extremely rare causal variants . Recent developments in the multiple testing literature have introduced the false nondiscovery rate ( Fndr ) . [115–117] We note that this is quite different from the AFNC control procedure . The Fndr controls for the expectation of the proportion FN/ ( d − R ) , which do not involve the number of causal variants s ( see Table 1 ) . Moreover , this is not a sensitive measure and will be very close to zero in large-scale NGS studies , as the number of variants that are not selected d − R will be very large . On the other hand , the AFNC , based on the proportion FN/s , allows robust variants selection in large-scale sequencing studies , as the number of causal variants s is expected to be small and the proportion FN/s is receptive to changes in the number of false negatives . In S7 Fig , we compared the AFNC with the Fndr at a threshold level of β = 0 . 1 . Results suggest that the AFNC dominates the Fndr in terms of overall performances of g-measure and the Fndr performs poorly in terms of specificity . Innovative technological advances have imposed new bioinformatic and statistical challenges by introducing genomic data at ever increasing resolution and dimensions . The proliferation of GWA studies in the last decade has largely led to the development and adaptation of the FDR as a conventional genomic tool . [42–46] In this paper , we introduced the AFNC to enable the identification of rare variants in large-scale sequencing studies . It is computationally efficient for applications in WGS and WES studies and can provide informative results for investigators charged with the task of analyzing large-scale sequencing studies .
The proposed procedure is general and can accommodate a spectrum of models and significance tests . Suppose that we have test statistics for each variant T1 ( G , Z ) , T2 ( G , Z ) , … , Td ( G , Z ) based on i = 1 , 2 , … , n subjects , such that G = {Gij} is a matrix of vectors of genotypes across all variants j = 1 , 2 , … , d and Z = {Zik} is a matrix of vectors of additional covariates across various clinical factors and prior biological knowledge k = 1 , … , K . Examples for Tj ( G , Z ) include the classical t-test statistic that depends only on genotypes of the jth variant and the local FDR statistic that utilizes genotypes across all variants in an empirical Bayes construction . [108] Further , prior knowledge from functional annotation can be incorporated , such as by using a generalized linear mixed-effects model . [110] We assume that the test statistic Tj ( G , Z ) for j = 1 , 2 , … , d is drawn from the mixture distribution ( 1 - π ) F 0 + π F 1 , ( 1 ) where π = s/d is the signal proportion , s is the number of causal variants , F0 is the null distribution of Tj ( G , Z ) when the jth variant is noncausal , and F1 is the alternative distribution when the jth variant is causal . [52 , 53 , 118] Let T ( 1 ) ( G , Z ) ≥ T ( 2 ) ( G , Z ) ≥ … ≥ T ( d ) ( G , Z ) be the ordered test statistics at decreasing significances . To evaluate false negatives in NGS studies , we introduce the signal missing rate ( SMR ) for selecting the top j ranked variants as S M R ϵ ( j ) = P F N ( j ) / s > ϵ , ( 2 ) where FN ( j ) is the number of causal variants missed by selecting the top j ranked variants and ϵ > 0 is a small constant . The SMR can be interpreted as the probability of neglecting at least a small proportion of causal variants among the top j ranked variants . By controlling the SMR , potentially causal variants can be included from both the Signals and Indistinguishable regions while dispatching with a very large number of irrelevant variants in the Noise region ( see Fig 1 ) . Compared to another possible measure of false negatives , P ( FN ( j ) >0 ) , SMR provides a more liberal control as it allows some , instead of zero , false negatives . SMR is also substantially different from the control of false nondiscovery rate ( Fndr ) , which is an analog of FDR in terms of false negatives . Fndr is defined as the expectation of the proportion of false negatives among the accepted null hypotheses . [115 , 119] In the analysis of data with extremely high dimensions and relatively small number of causal variants , Fndr is very close to zero and hence not an informative measure . To provide informative analysis of rare variants in NGS studies , we propose the false-negative control screening ( AFNC ) procedure as follows . | Next-generation sequencing technologies have allowed genetic association studies of complex traits at the single base-pair resolution , where most genetic variants have extremely low mutation frequencies . These rare variants have been the focus of modern statistical-computational genomics due to their potential to explain missing disease heritability . The identification of individual rare variants associated with diseases can provide new biological insights and enable the precise delineation of disease mechanisms . However , due to the extreme rarity of mutations and large numbers of variants , significances of causative variants tend to be mixed inseparably with a few noncausative ones , and standard multiple testing procedures controlling for false positives fail to provide a meaningful way to include a large proportion of the causative variants . To address the challenge of detecting weak biological signals , we propose a novel statistical procedure , based on false-negative control , to provide a practical approach for variant inclusion in large-scale sequencing studies . By determining those variants that can be confidently dispatched as noncausative , the proposed procedure offers an objective selection of a modest number of potentially causative variants at the single-locus level . Results can be further prioritized or used to infer disease-associated genes with annotation information . | [
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| 2016 | Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level |
Cytochrome c oxidases ( Ccoxs ) are the terminal enzymes of the respiratory chain in mitochondria and most bacteria . These enzymes couple dioxygen ( O2 ) reduction to the generation of a transmembrane electrochemical proton gradient . Despite decades of research and the availability of a large amount of structural and biochemical data available for the A-type Ccox family , little is known about the channel ( s ) used by O2 to travel from the solvent/membrane to the heme a3-CuB binuclear center ( BNC ) . Moreover , the identification of all possible O2 channels as well as the atomic details of O2 diffusion is essential for the understanding of the working mechanisms of the A-type Ccox . In this work , we determined the O2 distribution within Ccox from Rhodobacter sphaeroides , in the fully reduced state , in order to identify and characterize all the putative O2 channels leading towards the BNC . For that , we use an integrated strategy combining atomistic molecular dynamics ( MD ) simulations ( with and without explicit O2 molecules ) and implicit ligand sampling ( ILS ) calculations . Based on the 3D free energy map for O2 inside Ccox , three channels were identified , all starting in the membrane hydrophobic region and connecting the surface of the protein to the BNC . One of these channels corresponds to the pathway inferred from the X-ray data available , whereas the other two are alternative routes for O2 to reach the BNC . Both alternative O2 channels start in the membrane spanning region and terminate close to Y288I . These channels are a combination of multiple transiently interconnected hydrophobic cavities , whose opening and closure is regulated by the thermal fluctuations of the lining residues . Furthermore , our results show that , in this Ccox , the most likely ( energetically preferred ) routes for O2 to reach the BNC are the alternative channels , rather than the X-ray inferred pathway .
Cytochrome c oxidases ( Ccoxs ) are the terminal enzymes of the respiratory chain in eukaryotes and in aerobic prokaryotes ( reviewed in [1] ) . These integral membrane proteins belong to the heme-copper oxidases superfamily and couple dioxygen ( O2 ) reduction to the translocation of protons across the membrane . Ccox takes up four electrons from cytochrome c ( cyt c ) in the positively charged side of the membrane ( the inter-membrane space in mitochondria or the periplasm in bacteria ) and eight protons from the negatively charged side ( eq . 1 ) [2] , [3]: ( 1 ) where the subscripts P and N refer to the positive and negative sides of the membrane , respectively . Four of the eight protons reported in equation 1 are used to reduce one O2 molecule and form two water molecules [2] , [3] , whereas the remaining protons are pumped from the negative to the positive side of the membrane . This overall process contributes to the generation and maintenance of a transmembrane electrochemical proton gradient , which can be further utilized for several energy-requiring processes , such as ATP synthesis [4] . Based on structural and phylogenetic analysis , the heme-copper oxidases superfamily is currently divided into three major subfamilies [5]: A , B and C . The main differences between the three families are the pathways and mechanisms of proton transfer/pumping . The A-type Ccoxs , which are the subject of this work , are widespread through all kingdoms of life [5] and among them are the most thoroughly explored Ccoxs [3] , [6] , such as the bovine heart mitochondria , the Paracoccus ( P . ) denitrificans and the Rhodobacter ( R . ) sphaeroides enzymes . These Ccoxs contain , in the catalytic subunit ( subunit I ) , a low spin heme a and a heterodinuclear center named binuclear center , BNC ( Fig . 1A ) . The BNC is deeply buried in the core of the protein and it is formed by a high-spin heme a3 and a copper ion ( CuB ) . In subunit II , these Ccoxs contain only one redox center , a binuclear copper center named CuA , which accepts electrons from the soluble cyt c and then transfers them to the BNC via heme a . It is believed that protons ( both chemical and pumped ) are transported from the N-side of the membrane to the BNC via two special proton conducting pathways [3]: the D- and K-pathways ( Fig . 1A ) . A third putative proton-conducting pathway , the H-pathway , was proposed for the mammalian Ccox only [6] , [7] , and it was suggested to be exclusively used for the transfer of the pumped protons [8] . Several high-resolution crystallographic structures of the A-type family are nowadays available in the literature ( e . g . mammalian [7] , [9]–[11] and bacterial Ccoxs [12]–[15] ) and , based on these structures , it is known that all A-type members share a remarkable structural similarity of the core functional unit formed by subunits I and II ( Fig . 1A ) . Subunit I consists of twelve transmembrane α-helices and contains the BNC and the heme a center . Subunit II is formed by a solvent exposed globular β-sheet domain ( which functions as a docking surface for cyt c ) and two transmembrane α-helices . It contains only one redox center , the binuclear copper center ( CuA ) . Moreover , at the interface between subunits I and II , Ccox has one Mg+2 ion whose function is still not well understood , but it was suggested to be part of the exit pathway for the pumped protons and for water formed in the BNC [16] , [17] . Subunit III , although not considered to be part of the core functional unit , is also highly conserved among the A-type subfamily . Nevertheless , its absence significantly increases the probability of suicide inactivation [18] , [19] and thereby reduces the catalytic lifespan of Ccox ( in 600-fold or more ) [19] . Based in the X-ray data available ( eg . [7] , [12] , [13] ) , a putative O2 channel for the A-type family was proposed ( Fig . 1B ) . Iwata and co-workers , after pressurizing R . sphaeroides Ccox crystals with xenon , were able to identify a continuous hydrophobic channel that starts in the membrane region of subunit I [13] . This putative O2 channel has two possible entrances that merge together in a region close to the proton-gating residue , E286I ( the residues are numbered according to the R . sphaeroides Ccox sequence and the subscript indicates the subunit number ) . This pathway presents a constriction point which does not allow the access of O2 to the BNC , at least without the occurrence of some conformational change in the protein . Unfortunately , until now , none of the mutagenesis and biochemical studies performed in this channel [20]–[22] was able to clearly demonstrate that it serves as an O2 route into the BNC . All the tested mutations were located too close to the BNC [20] , [21] , which made the interpretation of the results difficult and did not allow to unambiguously distinguish between the structural obstruction of the O2 channel and the perturbation of the BNC binding kinetics . However , and contrary to the A-type family , in the B-type family the channel used by O2 to reach the BNC is nowadays considered to be well established . The crystallographic studies ( with xenon pressurization ) performed in the Thermus ( T . ) thermophilus ba3 enzyme [23]–[25] , lead to the identification of a “Y-shaped” hydrophobic channel that runs from the membrane region towards the BNC . This channel , although located roughly at the same position of the putative O2 channel in the A-type Ccox , does not possess a constriction point close to the BNC . In the A-type Ccoxs , the narrowing of the O2 channel is mainly caused by two conserved bulky residues ( W172I and F282I in R . sphaeroides [13] ) , whereas in the B-type Ccox , smaller residues occupy these positions ( Y133I and T231I in T . thermophilus [23] , [24] ) . The differences between the A- and B-type regarding the O2 channel are thought to reflect the different functional environments of each type of Ccox . Although the static crystal structures have been a valuable tool for providing insights into the O2 diffusion and for identifying potential O2 channels in Ccox , the elucidation of the molecular basis of O2 diffusion requires the knowledge of the Ccox conformational dynamics . Transiently formed cavities and openings inside the protein ( frequently regulated by side chain rotation or by water movements ) are not visible in the static X-ray structures , but have already been shown to be very relevant for ligand diffusion ( see for example [26] ) . In this context , molecular dynamics ( MD ) simulation techniques ( with sufficient simulation time and conformational sampling ) appear as an alternative for studying the dynamic behavior of proteins and to determine their ligand occupation probabilities inside the protein . In the last decade , computational methods have been widely used to study gas migration in a number of proteins and MD simulations have successfully allowed the identification of several alternative routes for ligand diffusion ( e . g . hydrogenase [26]–[28] , myoglobins [29] , [30] , oxidases [31] , [32] and laccases [33] ) . Moreover , the combination of MD simulations with Implicit Ligand Sampling ( ILS ) [29] calculations allows the calculation of the energy cost of transferring any small , apolar molecule ( like O2 or H2 ) from the solvent to the protein and consequently to compute a 3D free-energy landscape for that specific ligand molecule ( e . g [29] , [33] , [34] ) . Over the last decades , most of the Ccox research using computational methods focused on the mechanisms and energetics of reduction and/or proton pumping ( e . g [17] , [35]–[56] ) . In the A-type Ccox , little emphasis has been given to the identification of the routes used by O2 to move from the solvent towards the BNC , a question only addressed , to our knowledge , by Hofacker and Schulten [31] and by Farantos and co-workers [31] . In the first work , Hofacker and Schulten [31] used MD simulations to study O2 diffusion in the vicinity of the BNC in a bacterial Ccox from P . denitrificans and in the bovine CcOx enzyme . Their simulations revealed a unique , well-defined O2 diffusion channel starting in the membrane-spanning surface of subunit I , close to the interface with subunit III . More recently , Farantos and co-workers [32] have applied the ILS method in order to study the binding of several small gas molecules around the BNC region in the A-type Ccox from P . denitrificans and in the B-type Ccox enzyme from T thermophilus . From these calculations , the authors were able to identify several cavities around the heme a3 region that are conserved in both the A-type and B-type enzymes . This study is however limited to the BNC region , not including other parts of the protein and , consequently , not allowing the analysis of the whole O2 permeation process . The main objective of this work is to identify the O2 channels in the fully reduced Ccox from R . sphaeroides [15] using a combination of MD simulations ( with and without explicit O2 ) and ILS calculations . Our results revealed the existence of three putative O2 diffusion channels . One of channels correlates very well with the channel inferred from the X-ray data available , whereas the other two are alternative routes for O2 to reach the BNC , and were not observed in the X-ray structures pressurized with xenon . Both alternative channels start in the membrane phase and terminate close to Y288I .
Although A-type Ccoxs have been widely studied during the last four decades , the details of the O2 diffusion mechanism are still very incomplete . In particular , the existence and the characteristics of the channel ( s ) used by O2 to travel from the solvent/membrane to the BNC are still unclear . In this study , we have used an integrated strategy of all-atom MD simulations ( with and without explicit O2 molecules ) and ILS calculations , designed to examine and characterize the O2 delivery channels in fully reduced Ccox from R . sphaeroides . Altogether , our results suggest that O2 does not diffuse unspecifically inside this protein and instead , uses three well-defined channels running from the interior of the membrane ( where O2 solubility is higher than in the aqueous phase ) towards the Ccox core . The first pathway has two entrance points , located between helices 5 and 8 and helices 11 and 13 of subunit I , which converges into the constriction point just before the BNC . This channel correlates very well with the channel inferred from the available X-ray structures . The second pathway has only one entry located between the transmembrane helices 13 and 16 of subunit I and it terminates close to Y288I . The third identified pathway approaches the BNC from the subunit II side . This channel runs parallel to the heme a3 hydroxylethylfarnesyl tail and also terminates just below Y288I . According to our observations , the hydrophobic channel detected in the X-ray structures does not constitute the most likely ( energetically preferred ) entrance point for the O2 molecules in this Ccox . From the O2 affinity map , O2 accesses the BNC via the alternative dynamic channels formed by transient hydrophobic cavities , whose opening and closure is regulated by the thermal fluctuations of the protein . This may be the reason why these channels were not visible in the static X-ray structures . In summary , our results suggest that the original hypothesis ( based on static X-ray structures and mutational studies on A-type Ccox ) that proposed , that O2 permeation occurs via a unique , continuous and permanently open channel , is indeed a simplification . Our current work does not rule out the role of the X-ray inferred channel , but suggests other alternative routes to the BNC . Furthermore , it emphasizes the need to take into account the dynamic behavior of the protein in order to obtain a more complete description of the O2 putative channels and a more detailed picture of the mechanisms underlying O2 diffusion in these Ccoxs .
The 2 . 15 Å resolution crystal structure of the fully reduced Ccox from R . sphaeroides ( pdb code: 3FYE ) [15] was used as the starting point for this work . This X-ray structure only contains the minimum functional unit ( subunits I and II ) for Ccox . Only the water molecules with a relative accessibility to the solvent lower than 50% were kept . The relative accessibility of water was computed using the program ASC [66] , [67] , resulting in the selection of 240 water molecules . Since the GROMOS 54A7 force-field [68] lacks the proper parameterization for the Ccox redox centers , the atomic partial charges for reduced CuA , heme a and BNC centers were calculated using quantum mechanical calculations with the software Gaussian09 [69] and RESP fitting [70] , as described in detail in S1 Text in section 1 . The van der Waals parameters for the iron atom ( located in the two heme groups ) were taken from the universal force field [71] whereas the remaining bonded and van der Waals parameters for the metal centers were adapted from the GROMOS 54A7 force field [68] . The protonation state of each individual protonable group at pH 7 . 0 was determined using a combination of Poisson-Boltzmann calculations , performed with the package MEAD ( version 2 . 2 . 5 ) [72]–[74] , and Metropolis Monte Carlo simulations , using the program PETIT ( version 1 . 3 ) [75] . These calculations were performed using the methodologies described in [75] , [76] . For details related with the determination of the protonation state of the protonatable residues , see section 2 in S1 Text . Subunits I and II of Ccox were inserted in a pre-equilibrated dimysristoylphosphatidylcholine ( DMPC ) lipid membrane ( for details related with the membrane construction , equilibration and characterization see [77] ) . The optimal position of the protein relative to the membrane was determined using the location of the charged residues in the transmembrane helices as a reference . After Ccox insertion into the membrane , all the DMPC molecules located within a cut-off distance of 1 . 2 Å from the protein atoms were removed , as described in detail elsewhere [77] , [78] . Subsequently , the system ( protein , membrane and crystallographic waters ) was hydrated in a orthorhombic box using a pre-equilibrated box of SPC water molecules [79] . The water molecules misplaced in the center of the membrane ( formed by the highly hydrophobic lipid tails ) , were removed upon visual inspection . The final system contained the reduced Ccox embedded in a 175 DMPC lipid membrane surrounded by 19 , 645 water molecules , in a total of 75 , 178 atoms . All MD simulations were performed using the software package GROMACS 4 . 0 . 4 [80] together with the united atom GROMOS 54A7 force-field [68] for the protein and lipids and the previously described atomic partial charges and parameters for the redox centers . The simple point charge ( SPC ) water model was used [79] . Periodic boundary conditions were applied to all simulations . Non-bonded interactions were calculated using a twin range method [81] with short and long-range cut-offs of 8 and 14 Å , respectively . A reaction field correction [82] , [83] was applied for the truncated electrostatic interactions , considering a dielectric constant of 62 [84] . The SETTLE algorithm [85] was used to constraint the bond lengths and angle in water molecules , while the LINCS algorithm [86] was used to keep all remaining bonds constrained . The time step for integrating the equations of motion was 0 . 002 ps and the neighbor list was updated every 5 steps . The simulations were performed at the constant temperature of 310 K , which is above the phase transition temperature for the DMPC lipids ( Tm = 296–297 K ) in order to ensure that the membrane is in the liquid crystalline state [87] . A Berendsen heat bath [88] was used , with separate couplings for the protein , membrane and solvent , using a relaxation time constant of 0 . 1 ps . The pressure was coupled semi-isotropically ( coupling constant of 5 . 0 ps and isothermal compressibility of 4 . 6×10−5 bar−1 [84] ) , resulting in an independent coupling of the lateral ( Px+y ) and perpendicular ( Pz ) pressures . For all simulations , the x+y and z pressure components were kept at 1 atm and no surface tension was applied [84] . These simulation conditions were shown by Poger et al . [84] , [89] to correctly reproduce several experimental measurements for this type of membranes . The system was energy minimized with the steepest-descent method in order to remove excessive strain by performing 5000 steps of steepest-descent minimization with harmonic restraints applied to all non-hydrogen atoms ( protein and lipids ) , followed by further 5000 steps restraining the non-hydrogen atoms of the protein , ending with 5000 steps with restraints applied to the Cα atoms only . After the minimization procedure , and in order to allow proper repacking of the lipids around the protein , a 20 ns MD relaxation was executed in three steps . First , a 0 . 5 ns simulation was performed with position restraints to all non-hydrogen atoms of the protein and solvent , at constant temperature and pressure . Afterwards , an additional 0 . 5 ns simulation was performed , with position restraints applied to the non-hydrogen atoms of the protein only . Finally , only the Cα atoms were restrained for a period of 19 ns . A force constant of 1000 kJ mol−1nm−2 was used for all the steps that included harmonic position restraints . The unrestrained simulations started after these 20 ns of restrained simulation . In order to reduce the well known sampling problems in membrane-protein simulations , five MD simulations , 100 ns each , were performed , resulting in 0 . 5 µs of total simulation time . All replicates were initiated with different sets of random velocities . These simulations will be hereafter designated as O2-free simulations . After 20 ns of restrained simulations , we randomly added 84 molecules of dioxygen ( O2 ) in the solvent zone of each system . No O2 was placed inside the protein nor inside the hydrophobic core of the membrane ( see S4 Figure in S1 Text ) . This new set of simulations will be , hereafter , designated as O2 simulations . The water molecules within a 2 Å distance from the O2 molecules were deleted , similarly to the procedure described in [33] . In order to allow the solvent to adapt to the newly added O2 molecules , a 0 . 5 ns MD simulation with position restraints on all non-hydrogen atoms ( force constant of 1000 kJ mol−1nm−2 ) was performed . After this initialization procedure , unrestrained MD simulations were carried out and the simulation conditions and parameters were similar to the ones described previously for the MD simulations without O2 , except for the temperature coupling groups used . In this set of simulations , the O2 molecules were included in the same group as the protein . 5 MD simulations , 100 ns each , were performed . The parameters for the O2 molecules were taken from the previously published work of Victor et al [90] . The 84 O2 molecules added to the system corresponds to an O2 concentration of ∼0 . 235 M , which is higher than the experimental solubility of this gas in water . However , this high O2 concentration does not affect the structural properties of the protein as shown in S5 Figure in S1 Text . Moreover , the use of this high number of O2 molecules is necessary to obtain reliable statistics within a reasonable simulation time . Sites with high O2 affinity were determined using the ILS method , as previously described in [29] . In this method , the potential of mean force for placing an O2 molecule in any position inside the protein is calculated according to: ( 2 ) In equation 2 , the implicit ligand potential of mean force , , is an average over a finite number of protein and solvent configurations ( ) and over a number of different equally probable orientations of the ligand ( ) . Moreover , is the Boltzmann constant , is the absolute temperature , and is the interaction energy between the protein and solvent configuration ( ) with the ligand located at position with the orientation . In our case , the O2 free energy map was constructed using the last 50 ns ( for each replicate ) of the O2-free simulations . For the calculations , all 50005 conformations ( = 10001 conformations x 5 replicates in equation 2 ) were fitted to the X-ray structure using the Cα atoms . A grid of 51×55×87 dimensions was used with a grid spacing of 1 Å and 400 O2 insertions were performed per grid point ( = 400 in equation 2 ) . All calculations were carried out using a version of GROMACS 4 . 0 . 4 Widom TPI algorithm , modified almost in the same way as described in [33] . The only difference is that the ligand insertions here were made within the whole space of the grid cube ( the grid cube is centered at the insertion point and with edge length equal to the grid spacing ) , while in the previous work ( described in [33] ) the insertions were only possible within the inscribed sphere on the grid cube . The 3D free energy map obtained describes the Gibbs free energy of moving an O2 molecule from vacuum to a given position in the system , ΔGvac→prot ( O2 ) . This map was then converted into the ΔGwat→prot ( O2 ) map of interest using a ΔGwat→prot ( O2 ) calculated as described in [33] . The secondary structure assignment was performed with the program DSSP [91] . To determine the percentage of secondary structure loss relative to the X-ray structure , the secondary structure classes considered were: α-helix , 310-helix , 5-helix , β-sheet and β-bridge . For the energy landscape analysis , we used the method described in [33] . In short , this method classifies the energy landscape into energy basins through a steepest-descent tessellation and , afterwards , identifies the lowest-energy point within the boundaries between each pair of neighboring basins , i . e . the saddle point between those basins . After this procedure , a network of paths between all energy minima of the landscape can be constructed using the steepest-descent paths from the saddle points to the minima . A cutoff of 20 kJ·mol−1 was used for the network construction . The errors of the free energy profiles were calculated using two blocks: the first block corresponds to the frames ranging from 50 ns to 75 ns ( for all five replicates ) whereas the second block contains all the frames ranging from 75 ns to 100 ns . The errors were determined as half the difference of the energies observed between the two blocks for each minina and for each transition . The method used for error calculation assumes that similar minima and pathways can be identified in the two blocks . However , for the O2 channel 1 , the pathways connecting M6 to M8 and M10 to M11 were not visible in one of the blocks and by this reason their associated errors could not be calculated . | Cytochrome c oxidases ( Ccoxs ) , the terminal enzymes of the respiratory electron transport chain in eukaryotes and many prokaryotes , are key enzymes in aerobic respiration . These proteins couple the reduction of molecular dioxygen to water with the creation of a transmembrane electrochemical proton gradient . Over the last decades , most of the Ccoxs research focused on the mechanisms and energetics of reduction and/or proton pumping , and little emphasis has been given to the pathways used by dioxygen to reach the binuclear center , where dioxygen reduction takes place . In particular , the existence and the characteristics of the channel ( s ) used by O2 to travel from the solvent/membrane to the binuclear site are still unclear . In this work , we combine all-atom molecular dynamics simulations and implicit ligand sampling calculations in order to identify and characterize the O2 delivery channels in the Ccox from Rhodobacter sphaeroides . Altogether , our results suggest that , in this Ccox , O2 can diffuse via three well-defined channels that start in membrane region ( where O2 solubility is higher than in the water ) . One of these channels corresponds to the pathway inferred from the X-ray data available , whereas the other two are alternative routes for O2 to reach the binuclear center . | [
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| 2014 | Exploring O2 Diffusion in A-Type Cytochrome c Oxidases: Molecular Dynamics Simulations Uncover Two Alternative Channels towards the Binuclear Site |
Transcriptional regulatory networks play a central role in optimizing cell survival . How DNA binding domains and cis-regulatory DNA binding sequences have co-evolved to allow the expansion of transcriptional networks and how this contributes to cellular fitness remains unclear . Here we experimentally explore how the complex G1/S transcriptional network evolved in the budding yeast Saccharomyces cerevisiae by examining different chimeric transcription factor ( TF ) complexes . Over 200 G1/S genes are regulated by either one of the two TF complexes , SBF and MBF , which bind to specific DNA binding sequences , SCB and MCB , respectively . The difference in size and complexity of the G1/S transcriptional network across yeast species makes it well suited to investigate how TF paralogs ( SBF and MBF ) and DNA binding sequences ( SCB and MCB ) co-evolved after gene duplication to rewire and expand the network of G1/S target genes . Our data suggests that whilst SBF is the likely ancestral regulatory complex , the ancestral DNA binding element is more MCB-like . G1/S network expansion took place by both cis- and trans- co-evolutionary changes in closely related but distinct regulatory sequences . Replacement of the endogenous SBF DNA-binding domain ( DBD ) with that from more distantly related fungi leads to a contraction of the SBF-regulated G1/S network in budding yeast , which also correlates with increased defects in cell growth , cell size , and proliferation .
Eukaryotic cells have evolved complex transcriptional regulatory networks to ensure faithful cell division . One example is the G1/S cell cycle network that includes a large set of co-regulated genes whose expression peaks at the G1-to-S transition . Activation of G1/S transcription promotes entry into S phase and the initiation of a new cell division cycle . Previous work has established that the regulatory mechanisms involved in controlling G1/S transcription are conserved from yeast to man [1–4] . In animals , E2F/DP is a large family of winged helix-turn-helix transcription factors that regulate G1/S target genes . In budding yeast ( S . cerevisiae ) , the main G1/S transcription factor ( TF ) components , Swi4 , Swi6 and Mbp1 , form two heterodimer transcription factor complexes: a common Swi6 subunit plus one of the DNA binding proteins Swi4 or Mbp1 constitute SBF and MBF complexes , respectively ( Fig 1A ) . The related components in fission yeast ( S . pombe ) , the common Cdc10 subunits with Res1 and Res2 , forms a tetramer complex [1] . In budding yeast , SBF and MBF regulate distinct branches of the G1/S transcriptional network [7–9] . The MCB ( MluI Cell-cycle Box ) recognition sequence ACGCGT , bound by Mbp1 , can be found in the promoters of G1/S genes in different fungi , such as C . albicans [4] and S . pombe [6] . However , the SCB ( Swi4 Cell-cycle Box ) recognition sequence CRCGAAA , bound by Swi4 , is only found in budding yeast ( Fig 1A ) . It is generally presumed that ancestral Res ( the progenitor of Swi4 and Mbp1 in hemiascomycetes ) bound an MCB-like motif and that SCB is the more specialized DNA-binding motif that emerged sometime after Res duplication . This scenario represents a classic case of neofunctionalization after gene duplication , where one of the paralogs ( Swi4 ) evolves a new function and DNA-binding specificity ( SCB ) to regulate old and new G1/S target genes [10] . However , recent work suggests that SCB might be the ancestral motif . Yeast SBF and MBF are winged helix-turn-helix proteins that play a similar role to the E2F/DP transcription factors in animal G1/S regulation [3] . Comparative genomics shows that E2F/DP in the fungal ancestor was functionally replaced by horizontal transfer of an ancestral Res transcription factor , which has homology to a viral KilA-N domain [5] . The similar functional role and overlap in the known binding motifs of budding yeast Swi4 ( CRCGAAA ) and human E2F ( GCGSSAAA ) suggested that ancestral Res could bind the same cis-regulatory elements targeted by E2F/DP . In vivo experiments showed that Swi4 ( but not Mbp1 ) recognized and bound heterologous E2F sites in budding yeast [5] . This result and the presence of MCB-like elements in other fungi [11] raise the possibility that ancestral Res may have bound an extended SCB/MCB-like motif ( RCB , Res Cell cycle Box ) . The ancestral Res duplicated into Swi4 and Mbp1 in the hemiascomycetes , where each paralog may have split the broader RCB into more specialized motifs ( SCB and MCB ) to regulate old and new G1/S genes . This scenario represents sub-functionalization after gene duplication [10] . The number of target genes in the G1/S network ranges from ~200 in budding yeast to ~80 in the distantly related fission yeast [8 , 9 , 12 , 13] . The difference in size and complexity of the G1/S transcriptional network in yeast makes it well suited to investigate how DNA binding domains ( Swi4 and Mbp1 ) and DNA binding sequences ( SCB and MCB ) co-evolved to rewire and expand the network of G1/S target genes . To address these different evolutionary scenarios and to examine in vivo function , we generated 16 different chimeric TFs by systematic replacements of native S . cerevisiae DBD in Mbp1 and Swi4 with orthologs from different fungal species . We show that chimeric TFs containing the DBD of distant orthologs fused to S . cerevisiae Swi4 activation domain regulate the expression of a progressively limited subset of SBF-dependent target genes in budding yeast . The subset of SBF-targets regulated by the chimeric TFs contain motifs more closely related to SCB/MCB-like motifs ( RCB ) consistent with a Res-like ancestor , as found in S . pombe . This suggests that network expansion took place by expanding the ancestral SBF regulon , which contained RCB motifs , via inclusion of the modern SCB motif . We found that Swi4 exhibits much higher affinity for the SCB motif suggesting an optimized binding motif for Swi4 . Our case study can also be used to investigate if complex transcriptional regulatory networks are optimized by natural selection for cellular fitness or whether they are the result of evolutionary trajectories of ‘least resistance’ [14] . We show that network expansion is important for maintaining accurate cell division and normal growth rate of S . cerevisiae . More generally , our experimental analysis reveals that network expansion can depend on gradual co-evolution of DBD with diverse promoters to include genes containing new regulatory motifs for optimizing cellular fitness .
Our phylogenetic analysis of Mbp1 and Swi4 DBDs shows that both duplicates originated from the same duplication event from a Res ancestor ( Fig 1B ) . We next tested DBD functional conservation through Ascomycete evolution by systematic replacements of the native S . cerevisiae Mbp1/Swi4 DBD with those from different ascomycete fungi which share high sequence similarity [11 , 15 , 16] . Expression of ScMbp1 or ScSwi4 rescues the lethality of an mbp1Δswi4Δ double knockout in S . cerevisiae because a critical fraction of rate-limiting G1/S genes is expressed , e . g . CLN2 [17] . Thus , we expect that Mbp1 and/or Swi4 chimeric TFs could rescue the lethality of an mbp1Δswi4Δ double knockout if these DBD can bind to critical ScSCB or ScMCB motifs to sufficiently activate gene expression . This complementation analysis could reveal both the specificity ( MBF and/or SBF-dependent ) and extent ( level of rescue ) of functional conservation , and provide us with a perspective on how the G1/S cis-regulatory network might have changed during the evolution of Hemiascomycetes . We generated sixteen different chimeric TFs by replacing the ScMbp1 DBD or ScSwi4 DBD with homologues from representative fungal species from five Ascomycete clades , including K . lactis and K . waltii from clade 1 , D . hansenii and C . albicans from clade 2 , Y . lipolytica from clade 3 , N . crassa from clade 4 and S . pombe from clade 5 ( Fig 2A and 2C ) . We chose the recombination point between the DBDs and the S . cerevisiae AD at the end of the Sc DBD ( in the case of Mbp1 at aa 125 and in the case of Swi4 at aa 166 ) based on the conservation level between the DBDs and previous structure/function analysis of recombinant S . cerevisiae Mbp1 and Swi4 DBDs [11 , 15 , 16] . Our rationale was to generate chimeric proteins in which the C-terminal AD domains of ScMbp1 or ScSwi4 were preserved . To support our choice of recombination points , we performed IUPred analysis of Mbp1 and Swi4 [18 , 19] . We found that the functional domains of the proteins ( e . g . the DBD , Ankyrin repeat and the C-terminal domain [20] ) are predicted to be structured while the regions connecting these domains are predicted to be unstructured ( S1 Fig ) . This analysis supports a ball-and-string model where structural and functional regions are connected by flexible unstructured regions . The ball-and-string model is further supported by previous work that generated and tested chimeric proteins using similar recombination points between the DBD , Ankyrin repeat , and C-terminal domains [20] . These chimeric proteins were expressed from the native ScMbp1 or ScSwi4 promoter on a centromeric LEU2 plasmid in mbp1Δswi4Δ double knock-out strain , which is rescued by the expression of ScMbp1 or ScSwi4 from a centromeric URA3 plasmid ( S2 Fig ) . To generate yeast haploid strains expressing the chimeric protein as their sole source of G1/S transcription factor ( TF ) , we selected against the URA3 rescue plasmid using 5-fluoroorotic acid ( 5-FOA ) ( S2 Fig , details are provided in Materials and Methods ) . The ability of a chimeric TF in a mbp1Δswi4Δ strain to grow on 5-FOA media indicates that the chimera binds and activates a critical subset of target G1/S genes , which includes CLN2 [17] . We found that all strains expressing chimeric TFs with Swi4 Activation Domain ( Swi4AD ) fused to homologous DBD ( Swi4 , Mbp1 , Res ) from all fungi tested were viable , although to different degrees ( Fig 2D ) . In contrast , only those strains expressing chimeric Mbp1AD fused to orthologous Mbp1 DBD from clade 1 were viable whereas all other DBDs ( Clades 2–5 ) were non-viable ( Fig 2B ) . Since the chimeric proteins are largely expressed at a similar level as the native ScMbp1 or ScSwi4 protein ( S3 Fig ) , the observed trends in viability of chimeric TFs are not explained by differences in protein abundance . Rather , they are most likely a consequence of reduced DNA binding potential . The inability of the Mbp1AD chimeras outside of clade 1 DBDs to complement ScMbp1 function and rescue the mbp1Δswi4Δ lethality was surprising because all Swi4AD chimeras were viable . We therefore tested if the chimeric TFs with Mbp1AD can regulate the transcription of a set of G1/S targets including the MBF target CDC21 , the SBF-MBF switch gene TOS4 , and the SBF target SVS1 in a mbp1Δ SWI4 background [21] . We found that , as expected , the expression levels of CDC21 and TOS4 in strains containing clade I BD-Mbp1ADs are periodic , similar to the WT ScMbp1 , but strains containing clades II-V BD-Mbp1ADs show non-periodic gene expression , similar to the mbp1Δ strain ( S4 Fig ) . Surprisingly , when we analyzed the periodic expression of the Swi4 target gene , SVS1 , we observed that the Mbp1AD chimeric TFs from clades II-V lead to deregulation of the periodic expression of SVS1 when cells exit from G1 phase ( S4 Fig ) . These data indicate that the Mbp1AD chimeras might interfere with the endogenous Swi4 protein ability to properly regulate SBF-dependent transcription because SBF usually releases its target promoters at this stage [22] . We focused on chimeric TFs with Swi4AD because our data suggest that they are functional TFs that can bind a critical subset of G1/S targets . However , while all chimeric TFs with Swi4AD can rescue mbp1Δswi4Δ lethality , there are differences in the robustness of the rescue ( Fig 2 ) . To examine the in vivo complementation of the chimeric TFs with Swi4AD in more detail , we replaced the ScSwi4 DBD with those from different yeasts at the endogenous locus in a mbp1Δ background . We reasoned that any loss of SBF-dependent regulation by the chimeric TFs with Swi4AD is more likely to result in defects in growth rate and cell morphology in a mbp1Δ background . Examining the phenotype of mbp1Δ strains expressing chimeric TFs with Swi4AD revealed that the growth rate of strains containing DBD orthologue from clades 1–2 ( KlMbp1BD-Swi4AD , KlSwi4BD-Swi4AD or CaMbp1BD-Swi4AD ) are similar to that of the WT ( Fig 3 and S5 Fig for additional strain analysis ) . In contrast , the strains expressing the chimeric TFs with Swi4AD containing DBD orthologue from clade 3–5 ( YlResBD-Swi4AD , NcResBD-Swi4AD or SpRes2BD-Swi4AD ) exhibit impaired growth rate with up to a two-fold increase in doubling time ( Fig 3A ) . In agreement with these results , we also observed impaired growth of these strains at 37°C ( S5 Fig ) . Finally , examination of cell morphology , using light microscopy reveals that the different chimeric Swi4 TFs variants show a range of defects such as elongated cells and the formation of cell bundles due to possible defects in G2/M phase ( Fig 3B–3C ) . Importantly , the severity of the defect in growth , temperature sensitivity and cell morphology correlated with their evolutionary distance from Sc . Specifically , we found that strains containing the YlResBD-Swi4AD , NcResBD-Swi4AD or SpRes2BD-Swi4AD cells are elongated or created bundles of cells suggesting severe defects in cell division ( S1 and S2 Movies ) . Our analysis of phenotypes of cells expressing chimeric TFs with Swi4AD suggests a range of scenarios . For example , DBDs from more distantly related yeast could bind the same SBF-dependent genes as ScSwi4 , but activate them less robustly ( e . g . decreased activation potential ) . Alternatively , these DBDs could regulate a different and/or smaller set of genes ( e . g . changed binding specificity ) . To establish the extent that chimeric Swi4 TFs can restore SBF-dependent transcriptional control , we performed qPCR on candidate genes in cell cycle synchronized cells of selected strains . We focused on specific chimeras containing Mbp1/Res DBD that spans broad evolutionary distance and displays a range of cell phenotypes . We analyzed four strains containing chimeric TFs with Swi4AD from K . lactis from clade 1 , C . albicans from clade 2 , N . crassa from clade 4 and S . pombe from clade 5 , in addition to WT and swi4Δ strains as controls . All chimeras were examined in the background of MBP1 to maintain normal growth rate , cell morphology , and efficient synchronization in response to alpha-factor . Strains were synchronized with alpha-factor and gene expression was measured at five time points ( 0 , 15 , 30 , 45 , 60 minutes ) after release . Time-points correspond to low G1/S expression levels in arrested cells at time 0 , peak levels at 30 minutes and transcriptional inactivation at 60 minutes , as shown for RNR1 ( an MBF-regulated gene ) and CLN2 ( an SBF-regulated gene ) for chimeric strains and wild-type ( S6 Fig ) . Based on budding index , cell cycle progression is comparable in all alpha-factor arrested and released strains . SBF-dependent gene expression of CLN2 was strongly reduced in swi4Δ , although it still exhibited a weak pulse of activation after release presumably due to endogenous Mbp1 . Our results demonstrate that chimeric TFs created from DBDs of more distantly related species exhibit decreasing CLN2 expression , which is consistent with our observations regarding cell phenotypes ( Fig 3 and S5 Fig ) . This suggests that defects in the expression of SBF-dependent genes by chimeric TFs with Swi4AD underlie the decreased cell fitness . Based on these results , we used genome-wide expression analysis ( RNA-seq ) to first define the SBF-dependent regulon as those sets of genes that show significant changes in swi4Δ relative to wild type over time . All RNA-seq experiments were performed using three biological replicates . Strains were synchronized with alpha-factor and gene expression was measured at two time points ( 0 , 30 minutes ) after release . Potential SBF-regulated genes were identified by using likelihood ratio test ( LRT ) on WT versus swi4Δ gene expression over time , in which we compare the full model ( ~strain + time + strain:time ) with a reduced model in which we removed strain specific differences over time ( i . e . , the interaction term strain:time ) . Genes that show differences in their pattern of expression at a 10% false discovery rate ( i . e . Benjamini-Hochberg adjusted pBH-value of 0 . 1 ) were considered likely SBF targets ( S7 Fig ) . Genes that change their pattern of expression in the same way in WT and swi4Δ were not considered significant . We ranked significance of SBF target genes by their strength of regulation based on log2 fold change in expression relative to WT . A total of 68 genes showed significant down-regulation in expression in swi4Δ relative to wild type , which we defined as the “SBF-activated” regulon ( Fig 4 and S1 Table ) . Thirty of these 68 SBF-activated genes ( TOS6 , SVS1 , CLN1 , MNN1 , CLB6 , PRY2 , YOX1 , CIS3 , HHF1 , MCD1 , CLN2 , BBP1 , EXG1 , NRM1 , SRL1 , HHF2 , YPS3 , YOL019W , SUR2 , HTA1 , HHT2 , NDD1 , CRH1 , SPC29 , STU2 , PSA1 , SUR1 , NUD1 , TOS4 , OCH1 ) are shared with the SBF targets suggested by Ferrezuelo et al . [12] and the identity of our genes suggests that our assay is indeed identifying potential SBF-activated targets . The gene that shows the strongest regulation in our SBF-activated regulon was TOS6 ( >19 fold ) , which was ranked second in the previous study [12] and seven SBF target genes from [12] are within our top 10 SBF target genes ( TOS6 , SVS1 , CLN1 , MNN1 , CLB6 , PRY2 , YOX1 ) . There were an additional 64 genes that showed up-regulation in expression in swi4Δ , which we defined as the "SBF-repressed" regulon ( S7 Fig ) . As expected , the highest enrichment in our SBF-activated gene set corresponds to the “cell cycle” GO term ( e . g . GO:0007049 pBH-value 1 . 01E-4 ) and other related terms ( S8 Fig ) . For example , the cyclin PCL1 , which is our second most strongly differentially expressed SBF-activated gene ( -3 . 7 log2 fold change ) , regulates polarized growth and morphogenesis during cell cycle and has been suggested previously to be regulated by Swi4 [23] . Some genes ( TOS6 , HTA1 , HHF2 , SVS1 & PRY2 ) are known to respond to alpha-factor , which was how we synchronized our cells via pheromone block-release . These genes still showed significant differences in expression between swi4Δ and WT . We then performed the same RNA-seq experiments with several chimeric TFs with Swi4AD ( KlMbp1BD-Swi4AD , CaMbp1BD-Swi4AD , NcResBD-Swi4AD , SpRes2BD-Swi4AD ) , which previously had restored viability in swi4Δmbp1Δ but exhibited different effects on growth rate and cell morphology . The ability of a chimeric TF to rescue the gene expression phenotype was determined using a LRT to evaluate whether gene expression was statistically indistinguishable from WT . As expected , chimeras with DBD from species more closely related to S . cerevisiae ( Clade 1–2 ) were better able to recapitulate wild type gene expression ( Fig 4 ) . K . lactis and C . albicans Mbp1BDs could recapitulate the expression of 26 and 27 ( 38 . 5% ) members of the SBF-activated regulon , respectively ( 19 shared between K . lactis and C . albicans of the original 68-gene regulon ) . On the other hand , N . crassa and S . pombe ResBDs recapitulated wild type expression of 10 and 8 genes from our SBF-activated regulon , respectively . Although most of the SBF-activated regulon was rescued by the most closely related DBD ( Fig 4 ) , 48 . 5% of the SBF-activated regulon ( 33 out of 68 genes ) was not rescued by any of the DBD swap experiments , including many of the most strongly regulated genes ( Fig 4; S . cerevisiae-specific SBF regulon ) . Only three genes from our SBF-activated regulon were rescued by all the DBDs used in our experiments ( DSE2 , CIT2 , TRF5 ) . Using real time PCR analysis we validated changes in expression of four target genes , SVS1 , CLN1 , CLN2 and PCL1 , in eight chimeras containing Swi4 AD and detected a gradual decrease in their periodic expression in correlation with their evolutionary distance , RNA-seq data and phenotypes when tested on the background of mbp1Δ ( S9 Fig ) . Current consensus and in vitro protein binding microarray data indicate that ScSwi4 binds the motif CRCGAAA while ScMbp1 binds ACGCGT [4] ( Fig 5A ) . To determine if there were differences in the promoter regions of members of the SBF-activated regulon that might explain why some genes were rescued by some chimeric TFs and not others , we performed motif enrichment analyses on the 1000 bp region upstream of the transcription start site for each of our chimeric TFs ( S . cerevisiae-specific regulon , K . lactis Mbp1-rescued , and C . albicans Mbp1-rescued , Fig 4 ) . We were unable to do this analysis for N . crassa and S . pombe because there were too few Res-rescued promoters . We found differences in the enriched motifs from each of the subsets of the SBF-activated regulon ( Fig 5B ) , with a tendency for MCB-like motifs in the C . albicans Mbp1-rescued regulon ( CGCGT[T/C]T[T/A] ) . As expected , the motif with the highest enrichment in the Scer-specific regulon corresponds to a Swi4 motif ( Fig 5; CGCGAA: p-value 3 . 1e-12 ) . Although we expected only an enrichment for a “pure” SCB sites in the S . cerevisiae-specific regulon , we also detected enrichment for a second motif more MCB-like than SCB-like ( p-value 7 . 83e-4 vs . 1 . 98e-3 , respectively ) , possibly due to the 5’ “A[A/T]” . K . lactis Mbp1-rescued regulon enriched motif ( [A/C]CGCGAA ) shows higher similarity of S . cerevisiae Swi4 ( p-value 2 . 53e-5 ) than to Mbp1 ( p-value 8 . 32e-3 ) , but this difference is subtle when compared with the PBM-based Mbp1 motif ( MA0329 . 1; p-value 3 . 76e-4 ) . Interestingly , the motifs enriched in C . albicans Mbp1-rescued regulon have a tendency towards more MCB-like motifs by replacement of the Swi4 “A” by a “T” ( i . e . CGCGA to CGCGT ) . This Mbp1-like motif would be consistent with a Res-like ancestor that binds expanded SCB/MCB-like motif ( RCB ) , as suggested by current motifs found in S . pombe . Our results suggest that some SBF target genes , which can be regulated by closely related DBDs , are enriched for MCB-like motifs in their promoter . To test the ability of Swi4 and chimeric TFs with Swi4AD variants to bind SBF target promoters with different motifs we carried out anti-Swi4 Chromatin ImmunoPrecipitation ( ChIP ) . Specific polyclonal antisera to the C-terminal domain of Swi4 [26] , recognizing the common Swi4AD in WT and chimeras , was used for all ChIP experiments . We analyzed a set of SCB/MCB-like targets including the MCB targets NRM1 , MCD1 , and ELO1 and SCB targets CLN2 , PCL1 and PRY2 , representing SBF target genes . Since Swi4 binds SBF target promoters only during G1 , we measured binding affinity of Swi4 for these promoters in alpha factor arrested cells using swi4Δ cells as a negative control . Our data shows that , as expected , Swi4 ChIP specifically enriches SBF target promoter sequences ( Fig 6A ) . Unexpectedly , we find that Swi4 binds SBF target promoters with SCB motifs with much higher affinity than those with the MCB-like motifs . To further establish that the high affinity to promoters with SCB motifs is specific , we carried out Swi4 ChIP on cell cycle synchronized cells ( Fig 6A bottom panel ) . These data show that binding is lost once cells enter S phase at 60 minutes after alpha factor release , confirming Swi4’s binding specificity for these promoters . Whilst it has been known for some time that SBF binds target promoters with different affinity , the basis of this has remained unclear . Our data indicate that Swi4’s affinity for the MCB-like motifs , which likely resemble the ancestral motifs , is sub-optimal compared to the clade 1 specific SCB motif . To test if SCB motifs are required for Swi4’s ability to bind with high affinity and regulate transcription we carried out ChIP and expression analysis on two PRY2 SCB motif mutants ( Fig 6 ) . We mutated the SCBs at the PRY2 endogenous promoter to resemble MCB-like motifs ( ATCGCGA to AACGCGT: wt MCB ) and disrupted the core binding motif ( CGCG to CAAG: wt AA PRY2 ) to validate that expression is controlled by the SCB motifs . Anti-Swi4 ChIP analysis was carried out to establish binding affinity to the PRY2 promoter in the PRY2 promoter mutants ( wt MCB PRY2 and wt AA PRY2 ) and wt and swi4Δ cells as positive and negative controls , respectively while CLN2 was used as a positive control ( Fig 6B ) . Whereas Swi4 binds the CLN2 promoter with similar affinity in the wt and PRY2 promoter mutant strains , it is unable to bind the PRY2 promoter when the core sequence is mutated and only binds with low affinity when the SCBs are mutated into MCB-like motifs ( Fig 6B ) . These data show that the SCB motifs are required for high affinity binding of Swi4 to the PRY2 promoter . To establish the importance of binding affinity on the ability of Swi4 to regulate PRY2 expression , we carried out expression analysis in alpha factor arrested and released synchronized cells at 0 , 30 and 60 minutes in these same strains ( Fig 6C ) . As expected , mutating the core motif ( AA ) resulted in loss of cell cycle periodicity with expression levels comparable to swi4Δ . This demonstrates that the SCB motifs are required for SBF-dependent regulation . Importantly , whilst not to the same extent , mutating the SCBs into MCB-like motifs severely reduce PRY2 expression levels indicating that the SCB motifs in the PRY2 promoter are required for Swi4-dependent regulation . Our data indicates that the clade 1 specific SCB motif , not present in distantly related yeast , represents an optimized DNA binding motif solution that is required for proper transcriptional regulation by Swi4 . In addition , we find that chimeric TF variants with Swi4AD can regulate SBF target genes that are enriched for MCB-like motifs in their promoter , not SCBs . Based on this observation , we predicted that chimeric TFs can bind the promoters of genes with the MCB-like motifs , but not those with SCBs . To test this , we carried out anti-Swi4 ChIP on WT and the K . lactis , C . albicans , N . crassa and S . pombe chimeras ( Fig 6D ) . Again , we analyzed the set of SCB/MCB-like targets ( NRM1 , MCD1 , and ELO1 ) and SCB targets ( CLN2 , PCL1 and PRY2 ) . These data show that the chimeras bind the promoters of SBF target genes with MCB-like motifs to the same extent as WT Swi4 . However , the chimeras do not bind promoters with SCBs to the same extent as WT Swi4 . These results indicate that the chimeras’ capability to regulate only a subset of SBF target genes is likely based on their ability to bind the MCB-like motif , but not SCBs .
In budding yeast the MBF transcriptional complex recognizes the conserved MCB element ( ACGCGT ) whilst the SBF complex is thought to bind the SCB recognition motif ( CRCGAAA ) . Our findings show that a subset of SBF-dependent targets contain a MCB-like sequence demonstrating that Swi4 BDs can recognize both MCB and SCB regulatory sequences across species . These results suggest that Swi4/SBF likely represents the ancestral G1/S transcriptional regulator that activates the transcription of genes containing both the modern SCB motif and that the ancestral binding sequence was MCB-like . This is in line with previous functional analysis of Mbp1 and Swi4 in C . albicans that identified Swi4 as the functionally important TF recognizing MCBs ( [27] ) . Further support for the binding flexibility of MBF and SBF comes from studies performed with plasmids , containing repeated MCB or SCB sequences , that show no significant bias of MBF or SBF towards binding either sequence [15] . In accordance , the core CGCG recognition sequence is highly conserved and can be found in G1/S target promoters from yeast to human [3] . Thus , it seems that while the core-conserved sequence ( CGCG ) might be important for binding , the specificity is likely dictated by much larger stretches of DNA sequence gene and the TF binding site . In addition , biochemical and structural analysis of Mbp1/Swi4 DBD revealed that residues that are essential for DNA binding are highly conserved suggesting a conserved mode of protein-DNA interaction [11 , 15 , 16] . Thus , tuning of specificity in these DBDs during evolution is probably achieved through a series of subtle non-conserved substitutions enabling binding to a wider range of promoters to enable network expansion . Our work also suggests a possible important role for the AD in dictating the specificity of gene regulation . Although the dynamic expression patterns of SBF and MBF-dependent targets appear similar in an unperturbed division cycle , the mechanisms of regulation are different . Remarkably , while SBF is a transcriptional activator required to activate G1/S transcription during G1 , MBF is a transcriptional repressor that inhibits transcription outside of G1 [9] . Thus , inactivation of SBF inhibits the expression of its targets , while inactivation of MBF leads to constitutively high levels of MBF-dependent transcription . Our work identifies the SBF activator complex as the likely ancestral G1/S transcriptional regulator . This is in line with work carried out in S . pombe and C . albicans that shows that activation , rather than repression , of G1/S transcription is essential for cell viability [28 , 29] . Overall , our results suggest that the G1/S network expansion in Sc could have co-occurred through the divergence of the ancestral RCB into a Res-like MCB and a more divergent SCB in the regulatory regions of the core G1/S regulon and in new genes , thus expanding the existing/ancestral SBF regulon .
Homologs from the SBF/MBF family within Dikarya were retrieved as previously described [5] . The phylogenetic analysis was performed on an alignment build using MAFFT-L-INS-i ( -maxiterate 1000 ) [30 , 31] We then used probabilistic alignment masking using ZORRO to create different datasets with varying score thresholds , where the top 20% scoring columns was chosen as the best alignment ( S1 Dataset ) . Next , we used ProtTest 3 to determine the empirical amino-acid evolutionary model that best fit each of our protein datasets using several criteria: Akaike Information Criterion , corrected Akaike Information Criterion , Bayesian Information Criterion and Decision Theory [32] . Last , for each dataset and its best-fitting model , we ran different phylogenetic programs that use maximum-likelihood methods with different algorithmic approximations ( RAxML and PhyML ) to reconstruct the phylogenetic relationships between proteins . For RAxML analyses , the best likelihood tree was obtained from five independent maximum likelihood runs started from randomized parsimony trees using the empirical evolutionary model provided by ProtTest . We assessed branch support via rapid bootstrapping ( RBS ) with 100 pseudo-replicates . PhyML 3 . 0 phylogenetic trees were obtained from five independent randomized starting neighbor-joining trees ( RAND ) using the best topology from both NNI and SPR moves . Non-parametric Shimodaira-Hasegawa-like approximate likelihood ratio tests ( SH-aLRTs ) and parametric à la Bayes aLRTs ( aBayes ) were calculated to determine branch support from two independent PhyML 3 . 0 runs . In order to generate chimeric TFs expressed from its native promotor , we first constructed a set of plasmids . For the generation of plasmids containing Mbp1 and Swi4 genes each gene was amplified by PCR from S . cerevisiae in two parts: the first one is the promoter and the second one is the ORF gene and its terminator . The PCR amplification products of the whole gene of Mbp1 or Swi4 were introduced into pRS315 centromeric plasmid that was cut using BamHI and SalI or NotI and SalI sites , respectively by homologous recombination into yeast ( W303 strain ) . The resulting pRS315-Mbp1-terminator and pRS315-Swi4-term that were cut using SacI and NotI and the promoter PCR amplification product was added . The result of this homologous recombination in yeast ( W303 ) led to the creation of pRS315-pro-Mbp1-term and pRS315-pro-Swi4-term . A similar process was performed in order to create the pRS316 plasmid where the whole gene with its promoter and terminator were amplified by PCR and introduced to the digested plasmid by SalI and SacI by homologous recombination in yeast ( W303 ) . The chimeric Mbp1 TFs were constructed by amplification of the DBD of the TF from: K . lactis , K . waltii , C . albicans , D . hansenii , Y . lipolytica , N . crassa and from S . pombe ( RES1 DBD and RES2 DBD ) . The DBDs PCR products were transformed with the Mbp1 activation domain ( AD ) into yeast ( W303 ) to generate the pRS315-pro-chimericMbp1-term plasmid . The same process was done with Swi4 AD to generate chimeric Swi4 TFs shown in Fig 2 . For a complete strain list please see S2 Table . In order to check the effect of the chimeric TFs , we generated swi4Δmbp1Δ strain complemented with pRS316 plasmids expressing Mbp1 or Swi4 . The swi4Δmbp1Δ was generated on the background of 15Daub containing the swi4Δ ( genotype: ade1 , leu2-3 , 112 his2 trp1-1 ura3 swi4: KanMX Δbar1 ) . First the 15Daub containing the swi4Δ was transformed with the pRS316-pro-Swi4-term and plated on SC-ura . Then , mbp1Δ was generated by transformation of CloNAT cassette PCR amplification product followed by platting on SC-ura+G418+nat . The swi4Δmbp1Δ strain was then transformed with different pRS315 plasmids containing the different chimeric TFs and plated onto SC-leu-ura . To examine the effect of the chimeric TFs as a sole source of Mbp1/Swi4 in the yeast , the colonies were replicated to SC-leu+5FOA plates and incubated two days in 30°C ( S2 Fig ) . To integrate the different chimeric Swi4 TF genes into the genome , we used the previously used strain ( 15Daub swi4Δ ) containing the KanMX cassette . To replace the KanMX with the chimeric TFs we used the SB221 plasmid as previously described [33] . Briefly , the whole chimeric Swi4 TFs were amplified with a 500bp flanking of the promoter and transformed into 15Daub swi4Δ together with a PCR product amplified from the SB221 to include the URA3 gene flanked by KanMX recognition site . This allowed for homologous recombination into the strain at the endogenous SWI4 locus . After the integration of the TF chimeras MBP1 deletion to CloNAT by homologous recombination in those strains were performed by the PCR amplification product of the CloNAT targeted to the promoter and terminator of MBP1 . In order to generate yeast strains containing mutations within their promoters we deleted 1000bp upstream to the gene . Specifically , the yeast strains were transformed with PCR amplification of the CloNAT gene that by homologous recombination replaced the endogenous promoter in the genome , and the cell were plated on YPD+CloNAT . The mutated promoters were generated using the CRISPR/Cas9 system with a specific guide to the CloNAT gene and donor DNA that contained the promoter with the specific mutations flanked by regions enabling genomic integration following CAS9 cleavage . Single colonies of the strains containing the WT or the chimeric TFs were grown over-night in 5 ml YPD at 30°C , then the cultures were diluted to OD600 ≈ 0 . 1 in 15 ml YPD and grew at 30°C to OD600 ≈ 0 . 4 ( logarithmic stage ) . Next , 100ng/ml of alpha factor was added and the cultures were grown at 30°C for another two hours [34] . Synchronization was checked by microscope ( unbudded shmoo shape ) . Synchronized cells were washed two times in fresh YPD to get rid of the alpha factor and continued to grow in fresh YPD media . Cells were grown in 7 ml of SC over-night at 30°C . Next morning , cells were diluted to OD600 ≈ 0 . 1 in fresh SC medium and grew for 24 hours in 96-well plate ( Nunc ) . The plates were constantly shaken in Varioscan Flash plate reader ( Thermo Scientific ) at 30°C and OD measurements ( OD600 ) were taken every 30 minutes . The resulting curves were used to calculate the generation times ( τ ) of the different strains using the ODt = OD0∙2t/τ equation . Logarithmic transformation allowed obtaining the linear equation Log2ODt = Log2OD0+t/τ , where the slope value of the linear fit is 1/τ . The curves are averages of at least three independent repeats . The curves include the exponential ( logarithmic ) growth phase allowing the calculation of the generation times ( τ ) of the different strains as described above . For temperature sensitivity the genomic integrated strains with the chimeric Swi4 TF and mbp1Δ were grown over-night at 30°C in 7ml of liquid SC medium , washed twice and diluted to an initial OD600 ≈ 0 . 6 . A series of serial dilutions was conducted and the cells were spotted on SC plates . The plates were incubated in 30°C and 37°C [35] . Cells were grown in YPD liquid medium at 30°C to OD600 ≈ 0 . 6 and then photographed using Zeiss Axioplan 2 microscope and QImaging QIClick CCD camera . In order to explore morphological differences between the strains , we grew each of the genomic integrated strain with the mbp1Δ and divided them to a 96 well plate ( Nunc ) suitable for microscopy analysis and used the Operetta High Content Imaging System ( Perkin Elmer ) . Bundles of cells were defined as two or more cells connected to form a large cell size and elongated cells were defined to have a bud size that is higher than 3 microns . Exponentially growing cells were diluted to the same OD600 in 50 ml , centrifuged and cell lysates from the WT and chimeric TFs were prepared for Western blotting . We used alkaline extraction by 5-minute room temperature incubation of the pellet in 100 mM NaOH solution prior to resuspension and boiling for 3 minutes in SDS sample buffer . Approximately 10 μg/μl of total proteins were loaded onto 12% SDS-PAGE and analyzed by Western blot analysis using primary antibodies anti-Mbp1 or anti-Swi4 , ( 1:2000 ) followed by α-rabbit HRP-conjugated secondary antibodies ( 1:3000 ) [26] . For each immuno-precipitation , exponentially growing cells were diluted to the same OD600 in 50 ml . In order to cross-link DNA and protein we used 1 . 25 ml formaldehyde ( 37% ) and incubate it for 20 minutes at room temperature . Then 2 . 3 ml of 2 . 5 M glycine was added to stop the reaction and incubated for another 5 minutes . Next , the pellet was washed twice in TBS and frozen in liquid nitrogen . Frozen pellets were mechanically disrupted by 30 minutes vortexing with glass beads ( BioSpec ) in lysis buffer ( 50 mM Tris-HCl pH 7 . 5 , 1% Triton X-100 , 250 mM NaCl ) containing protease inhibitors ( Complete Mini , Roche ) and phosphatase inhibitors ( Phosphatase Inhibitor Cocktail 1 , Sigma-Aldrich ) at 4°C . Lysates were spun for 5 minutes at top speed and supernatant sonicated ( QSonica Q800R sonicator; amplitude 100% , process time 5 min with pulse-on 30 sec and pulse-off 2 min ) and then immunoprecipitated with anti-Swi4 polyclonal sera by incubating lysates overnight at 4°C then 35 μl of protein A Sepharose beads were added for additional 3 hours at 4°C . Beads were washed ( straight after 3 hours of incubation ) , spun down and DNA fragments were purified with 10% Chelex Resin solution ( Bio-Rad ) and boiling for 10 min [36] . Quantitative PCR ( qPCR ) was performed on specific target genes using the iQ SYBR Green supermix ( Bio-Rad ) kit . Samples were run on a Chromo-4 Real-Time PCR Detector ( Bio-Rad ) . Data was analysed using MJ Opticon Analysis Software 3 . 0 [37] . Cells were synchronized by alpha factor arrest and release ( see above ) and 1 . 5 ml cells were collected at different time points ( 0 , 30 minutes ) and frozen in liquid nitrogen . Next , total RNA extraction was performed using Epicentere kit . Each RNA sample was used as a template for synthesis of single strand cDNA for detection of different target gene of the TF by reverse transcriptase ( Thermo ) using oligo-dT as primer . Relative transcript levels were then determined by qPCR analysis using a 7300 Applied Biosystem machine . The reaction mix contained 10 μl of SYBR green mix ( Applied Biosystem ) , 10 μM of primers , 2 ng/μl cDNA and DDW to total reaction volume of 20 μl . ACT1 was used as the internal standard . The resulting curves represent the averages of at least three independent repeats . Total RNA was extracted from alpha factor synchronized WT and swi4Δ strains , as described in qPCR , and checked for quality using agarose gel or Bioanalyzer . The libraries were constructed using the Tru-seq RNA library kit ( Illumina ) and checked by Bioanalyzer and Quabit for quality assessment . The samples were run on the Illumina HiSeq 2500 instrument at the Technion Genome center ( Technion , Israel ) . Original complete dataset can be found in Dryad Digital Repository ( doi: 10 . 5061/dryad . 2rf10 ) . The quality of the raw sequence data was assessed using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . Reference genome and annotations were downloaded from Ensembl ftp , fasta and GTF annotation file ( ftp://ftp . ensembl . org/pub/release-75/gtf/saccharomyces_cerevisiae/Saccharomyces_cerevisiae . R64-1-1 . 75 . gtf . gz ) . Reads were aligned to reference genome using STAR [38] and Htseq-count was used for counting number of mapped reads per gene ( http://www-huber . embl . de/users/anders/HTSeq/doc/overview . html ) . All differential expression analyses were performed on S2 Dataset using DESeq2 [39] . We defined the SBF regulon as the set of genes that show significant changes in swi4Δ relative to wild type over time ( 0 , 30 min ) . These genes were detected using likelihood ratio test ( LRT ) , in which we compared the full model ( ~strain + time + strain:time ) with a reduced model in which we removed strain specific differences over time ( the interaction term strain:time ) . Genes that show differences in their pattern of expression at a 10% false discovery rate ( i . e . Benjamini-Hochberg adjusted p-value of 0 . 1 ) were considered significant . Genes that change their pattern of expression in the same way in the wild type and swi4Δ strain are not considered significant . We then ranked significant genes by their strength of regulation ( overexpressed ( TF-repressed ) or negatively regulated ( TF-activated ) ) based on log2 fold change in expression relative to WT . Heatmaps were generated with the “pheatmap” R package from rlog-transformed data , while the differential expression analyses were performed on raw data . The regularized-logarithm transformation ( rlog ) addresses the problem of RNA-seq data in which the variance increases with its mean . With the rlog , genes with low counts are shrunken towards gene’s averages across samples . The rlog-transform is not different from a log2 transform for genes with high counts . Gene ontology analyses were performed for the “Biological Process” ontology using the clusterProfiler R package [40] using as universe all genes represented in our RNA-Seq dataset . Significance was assessed using Benjamini-Hochberg corrected p-value threshold of 0 . 01 and q-value cutoff of 0 . 05 . Enriched motifs in each of the TF-activated rescued subsets were estimated with MEME-ChIP program through the MEME Suite v4 . 11 . 2 [25] . Enrichment ( p-value < 0 . 05 ) was calculated for the promoter regions ( comprising 1000 nucleotides upstream of the transcription start site ) of the target genes relative to the promoter regions of genes ( 5565 genes ) that showed no significant differential expression between swi4Δ and wild type through the likelihood ratio test ( discriminative mode ) . Retrieved SCB and MCB motifs ( DREME ) were then compared to the JASPAR CORE ( 2016 ) database for Fungal transcription factors ( Tomtom ) to determine how similar they were to the motifs of budding yeast Swi4 and Mbp1 ( p-values in Fig 5 ) . To reconstruct the MCB1 and MCB2 motifs from S . pombe in Fig 1 , the promoter regions ( 1000bp upstream ) for the set of genes previously suggested to contain MCB1 and/or MCB2 motifs [6] were analyzed with MEME to identify enriched motifs . We recovered an MCB2 motif with an E-value 5 . 4e-27 , but were unable to recover the MCB1 motif . The published MCB1 motif was reconstructed by scanning for enrichment ( p-value < 0 . 001 ) of the corresponding seed motif ( MCB1: AACGCGTT from[6] ) in the promoter regions ( 1000bp upstream ) for the set of genes previously suggested to contain MCB1 and/or MCB2 motifs [6] using the FIMO package from the MEME suite Suite v4 . 11 . 2[25] . Similarly , we reconstructed the published MCB motif for C . albicans by scanning for enrichment ( p-value < 0 . 001 ) of the corresponding seed motif ( MCB: ACGCGW ) in the promoter regions ( 1000bp upstream ) from genes from the G1/S regulon [4] using the FIMO package . | Cell cycle progression is essential for the division of living cells . The G1/S transition through the cell cycle is promoted by the periodic expression of a large set of genes regulated by a G1/S transcriptional gene network . Despite extensive comparative studies in different eukaryotic organisms , little is known regarding the evolution of G1/S transcriptional network expansion and how such an expansion was important for cell division and growth rate . Here , we explored the evolution of G1/S transcriptional network in the budding yeast S . cerevisiae by examining 16 different chimeric transcription factor complexes containing DNA binding domain from different fungal species . Analysis of the 16 chimeric SBF and MBF complexes in S . cerevisiae that bind specific SCB and MCB DNA sequences , respectively , suggests that SBF is more closely related to the ancestral regulatory complex . We found that some of the chimeric SBF complexes can induce the expression of a subset of genes in S . cerevisiae that are enriched with an MCB-like element . | [
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| 2017 | Gene duplication and co-evolution of G1/S transcription factor specificity in fungi are essential for optimizing cell fitness |
Thousands of human deaths from rabies occur annually despite the availability of effective vaccines following exposure , and for disease control in the animal reservoir . Our aim was to assess risk factors associated with exposure and to determine why human deaths from endemic canine rabies still occur . Contact tracing was used to gather data on rabies exposures , post-exposure prophylaxis ( PEP ) delivered and deaths in two rural districts in northwestern Tanzania from 2002 to 2006 . Data on risk factors and the propensity to seek and complete courses of PEP was collected using questionnaires . Exposures varied from 6–141/100 , 000 per year . Risk of exposure to rabies was greater in an area with agropastoralist communities ( and larger domestic dog populations ) than an area with pastoralist communities . Children were at greater risk than adults of being exposed to rabies and of developing clinical signs . PEP dramatically reduced the risk of developing rabies ( odds ratio [OR] 17 . 33 , 95% confidence interval [CI] 6 . 39–60 . 83 ) and when PEP was not delivered the risks were higher in the pastoralist than the agro-pastoralist area ( OR 6 . 12 , 95% CI 2 . 60–14 . 58 ) . Low socioeconomic class and distance to medical facilities lengthened delays before PEP delivery . Over 20% of rabies-exposed individuals did not seek medical treatment and were not documented in official records and <65% received PEP . Animal bite injury records were an accurate indicator of rabies exposure incidence . Insufficient knowledge about rabies dangers and prevention , particularly prompt PEP , but also wound management , was the main cause of rabies deaths . Education , particularly in poor and marginalized communities , but also for medical and veterinary workers , would prevent future deaths .
Rabies is an acute viral infection which causes horrifying neurological symptoms that inevitably result in death . Although human rabies encephalitis remains untreatable [1] , the infection is entirely preventable , both by post-exposure prophylaxis ( PEP ) of bite victims , and by population-level vaccination of the zoonotic reservoir , which across most of Africa and Asia is the domestic dog [2] . Modern cell culture vaccines used in combination with rabies immunoglobulins are virtually 100% effective in preventing human deaths if administered promptly to rabies-exposed patients following appropriate wound management [3] and mass vaccination of domestic dogs has successfully eliminated or controlled domestic dog rabies in many parts of the world [4] , [5] . It is therefore inexcusable that an estimated 55 , 000 human deaths from rabies occur annually [6] , of which over 99% are in developing countries where the disease is endemic in domestic dog populations [7] . Recent estimates of human rabies mortality are based upon a probability decision-tree model [6] , because current surveillance systems have been shown to substantially underreport the number of deaths from rabies . For example , in Tanzania more than 100 human rabies deaths are estimated to occur for each officially reported case [6] . Hospital studies further suggest that clinical diagnosis of human rabies may be hindered by confusion with common neurological syndromes , such as cerebral malaria [8] . These and other studies on rabies incidence and exposure risk rely on bite victims reporting to hospital , yet not all rabies-exposed individuals seek medical attention . To investigate the validity of methods being used to estimate the burden of rabies we established a contact-tracing study . Data collected using these methods provides a more comprehensive picture of the reality facing communities in regions where canine rabies is endemic . Using these data we quantify the risk of disease and exposure and attempt to understand why human deaths from canine rabies still occur and thus how this number can be reduced .
Data was collected from two rural districts in northwest Tanzania: Serengeti , which is inhabited by multi-ethnic , agro-pastoralist communities and high-density dog populations , and Ngorongoro , which is inhabited by low-density pastoralist communities and lower density dog populations . Contact tracing of potential rabies-exposures was initiated using data from hospitals and medical dispensaries on patients with animal-bite injuries , and case reports from livestock offices and community-based surveillance activities . Visits were made to investigate incidents that occurred between January 2002 and December 2006 involving potentially suspect rabid animals . Interviews were conducted to assess the case history and identify the source of exposure and other contacts if known . The same procedure was followed for all resulting exposures and preceding cases where identified , and UTM coordinates were recorded at each household and at the location of the exposure event ( where possible ) . Interviews were conducted by veterinary or livestock field-officers , often with a community leader in attendance . This created an active local reporting network . Animal cases were diagnosed on epidemiological and clinical criteria adapting the ‘six-step’ method through retrospective interviews with witnesses [9] . Wherever possible brain samples from animals that caused bite injuries were collected and tested for case confirmation [10] . A structured open-ended questionnaire was administered to bite victims at 3 designated district hospitals ( in Magu , Misungwi and Tarime , n = 166 ) to obtain information on intervals between exposure and reporting to hospital for PEP , and ways used to raise funds to pay for PEP . Information was collected on household socioeconomic status , using indicators sensitive to local determinants of wealth , previously identified through Rapid Rural Appraisal approaches [11] . Specifically numbers of cattle and housing quality were chosen as independent wealth indicators because individuals may own many cattle and hence be considered to be wealthy but they may not necessarily own “modern” houses . Individuals with houses constructed from cement/baked bricks , which have cement floors and corrugated roofs were categorized as belonging to high socioeconomic status and those owning houses constructed from other materials were classified as low socioeconomic status . Regardless of housing quality , individuals owning >50 heads of cattle were categorized as high socioeconomic status; those with <50 heads were classified as low socioeconomic status . UTM coordinates were collected for each district hospital and household visited . The study was approved by the Tanzania Commission for Science and Technology with ethical review from the National Institute for Medical Research ( NIMR ) . In Tanzania , NIMR ethical guidelines stipulate that written consent is required for participants in clinical trials . However , as this was a retrospective study involving collection of interview data only , without clinical intervention or sampling , we considered that informed verbal consent was appropriate and this was approved by NIMR . Permission to conduct interviews was obtained from district officials , village and sub-village leaders in all study locations . At each household visited , the head of the household was informed about the purpose of the study and interviews were only subsequently conducted following verbal consent from both the head of the household and the bite victim . Bite-injury records were compiled for hospitals in Serengeti and Ngorongoro districts and neighboring districts of Tarime , Musoma and Bunda . Records were extracted for patients originating from Serengeti and Ngorongoro and correlations with rabies exposures and observations of rabid animals were examined by regression . Fisher's Exact Test was used to determine whether any factors were associated with delays in PEP delivery and to assess differences in the source of funds used to pay for PEP by different socioeconomic classes . Binomial confidence intervals were reported for proportions . Chi-square tests were used to examine differences in exposure incidence across age-classes , and to different parts of the body . The odds of developing rabies following exposure and associated risk factors were calculated by logistic regression . All statistical analyses were implemented with the statistical programming language R .
1080 people were traced and interviewed who had been bitten by animals between 2002 and 2006 in Serengeti ( 776 ) and Ngorongoro ( 304 ) districts . On the basis of descriptive case histories >97% of animals that caused bite injuries were classified as suspected rabid ( 648 ) or normal ( 406 ) . The status of animals that bit the remaining 2 . 5 percent ( 26 ) of cases visited was unclear . Approximately 75% of samples from suspected rabid animals tested positive , indicating that recognition of rabies is accurate and that classification using the case history description is valid [12] . Over twenty-five percent of visited cases bitten by suspected rabid animals ( 180 ) were identified through contact tracing alone because the victim did not seek medical attention . Of 1322 bite injury records from medical facilities over the same period , 57% ( 760 ) were successfully traced , 9% ( 118 ) were not visited because the record indicated the animal was healthy and the remaining 444 cases were either impossible to trace , not present to interview , or have yet to be visited ( 139 were from 2006 ) . At least 50 of these exposures were by suspected rabid animals . Conservative estimates suggest around 63/100 , 000 people in Serengeti and 17/100 , 000 in Ngorongoro are bitten by suspected rabid animals annually . Including animals of undetermined status raises those figures to 100 and 30 exposures/100 , 000 respectively . The risk of being bitten by a suspected rabid animal varied through time ( approaching 150/100 , 000 during the epidemic peak ) , but was consistently higher in Serengeti , the more populated district ( Table 1 ) . Most suspected rabies exposures were due to domestic animals ( 89% ) , particularly dogs ( Table 2 ) . A higher proportion of bites by suspected rabid animals were from wild animals in Ngorongoro district compared to Serengeti district ( ∼20% versus <10% ) , but annual incidence of bites by wild animals was still lower in Ngorongoro than Serengeti ( 0 . 5 versus 0 . 7/100 , 000 ) . The seventy-one exposures by suspected rabid wild animals were predominantly due to jackals ( 23 ) , hyenas ( 20 ) and honey badgers ( 17 ) , with additional exposures from white-tailed mongooses ( 5 ) , bat-eared foxes ( 2 ) , genets ( 2 ) , wildcats ( 2 ) and a leopard ( 2 ) . 75% of victims bitten by suspected rabid hyenas required prolonged hospital stays due to the severity of their injuries . Children were most at risk of exposure to rabies: 65% of exposures were children ( <16 yrs , median 12 , range 1–79 ) ; children from 5–15 years old had an elevated risk of exposure compared to the rest of the population ( Fig . 1 , p<0 . 001 ) ; and a higher probability of being bitten on the head , face , or neck ( Table 3 , p = 0 . 008 ) . The ratio of male to female exposures was 0 . 52∶0 . 48 . Animal-bite injury records were correlated with suspected rabies exposures in both districts ( Fig . 2 , p<0 . 0001 , excluding 2006 data because of incomplete contact tracing ) , although less variation was explained in Ngorongoro ( r2 = 50% ) than Serengeti district ( r2 = 74% ) . Some rabies-exposed patients were recorded in hospitals of neighboring districts , not their district hospital , particularly during periods of vaccine shortage . Bite injury records were also correlated with monthly numbers of reported rabid animals ( p<0 . 001 ) , although the relationship was weaker ( r2 = 58% in Serengeti and 48% in Ngorongoro ) due largely to variation in biting behavior of individual rabid animals . Between 15 and 24% of suspected rabies exposures ( 169 people , Table 4 ) did not seek medical attention and so did not receive prompt PEP , though some may have subsequently attended a hospital as a result of the study ( advice on rabies dangers and prevention was given at every household visited , including accessible sources of PEP and although we did not provide PEP we occasionally transported exposed bite victims to medical facilities ) . More than 10% of suspected rabies exposures that attended a medical facility did not receive PEP because none was available ( nor was sought or found elsewhere ) , because the patient was unable to pay , or because of inappropriate medical advice . Overall , only 65% of identified rabies exposures received PEP . The cost of PEP and the regimen delivered varied depending upon the health facility and the date of presentation , varying from >100 , 000 Tsh ( ∼US$85 ) to free ( for limited periods ) , although courses were typically 75 , 000 Tsh in Ngorongoro district ( five doses ) and 30 , 000 in Serengeti ( 3 doses ) , in comparison to monthly per capita expenditure and per household expenditure of 8 , 538 Tsh and 52 , 649 Tsh respectively in 2001 [11] ( although in 2008 prices are now approaching ∼30 , 000 Tsh per dose ) . However , the probability of receiving PEP following exposure was very similar in the two districts ( 0 . 70 in Serengeti versus 0 . 68 in Ngorongoro ) . Rabies immunoglobulins were not offered to any bite victims . Most people who attended a medical facility did so shortly after exposure , but there was considerable variance in delays before receiving the first dose of PEP ( Fig . 3 ) ; at least 25% of courses were started more than one week later . Distance from the nearest medical facility and socioeconomic status were both significant predictors of delays in PEP delivery ( p<0 . 0001 in both cases , Fig . 3 ) . Of victims that attended hospital for PEP , those located near district hospitals ( <10 km ) reported earlier than those located further away , with 85 . 7% ( 95% CI 77–92% ) of victims near district hospitals reporting within 7 days of exposure compared to only 66 . 2% ( 54–76% ) of victims located farther away . Bite victims of high socioeconomic status reported significantly earlier to hospital than those of low status ( p<0 . 0001 ) . All bite victims with high socioeconomic status that reported to a medical facility did so within three days of being bitten compared with only 24% ( 95% CI 17–33% ) of victims with low socioeconomic status . None of the victims with low socioeconomic status reported on day 0 compared with 30 . 9% ( 19–45 ) of bite victims with high status . Four major means of raising funds for PEP were reported: i ) family savings; ii ) borrowing money; iii ) selling household properties and iv ) payment by the owner of the rabid animal . Socioeconomic status had a significant impact on the source from which households obtained funds ( p<0 . 0001 ) . Households with higher socioeconomic status were more likely to use savings , whereas households with low socioeconomic status either obtained loans from relatives , friends and neighbors or depended on the owner of dogs which inflicted the bites to pay ( Fig . 4 ) . Not all patients completed the PEP course , or adhered to the PEP schedule . Reasons given for not starting , completing or adhering to PEP regimes in the most commonly cited order were: i ) unable to afford treatment; ii ) no vaccine at the hospital; iii ) the wound was small; iv ) the dog owner would not pay; v ) they were not aware the animal was rabid; vi ) they were not aware of the danger of rabies; vii ) medical staff did not advise PEP and viii ) they thought they had received treatment but contact tracing revealed vaccination only against tetanus . Bite victims often quoted several reasons . Twenty-eight deaths from suspected rabies were recorded during the five-year period in the two districts ( Table 5 ) , an average of 1 . 5/100 , 000 per year in Serengeti and 2 . 3 in Ngorongoro ( Table 1 ) . The odds of developing rabies following exposure were dramatically higher for those who did not receive PEP ( odds ratio [OR] 17 . 33 , 95% CI 6 . 39–60 . 83 , p<0 . 0001 ) . Accounting for the variation due to whether PEP was delivered or not , the odds of developing rabies were three-fold higher for children ( <15 yrs ) versus adults ( OR 3 . 08 , CI 1 . 10–11 . 04 , p = 0 . 0498 ) and more than five-fold greater in Ngorongoro than Serengeti district ( OR 6 . 12 , CI 2 . 60–14 . 58 , p<0 . 0001 ) . A less powerful analysis that included only cases where PEP was not delivered showed the same patterns but only the effect of district was significant . Three people who died from rabies received some PEP: two children in Serengeti district started PEP promptly ( PEP was sought on the day of exposure , but delivered the following day because the medical facility was closed on weekends ) and one teenager in Ngorongoro received the first dose of PEP several days after exposure and completed four doses before symptoms began . The vaccine in Ngorongoro district was tested and found to be viable . Vaccine was not available for testing in Serengeti but no other exposed patients died after receiving vaccine from the same batch . Moreover , the two children had severe injuries to the head , neck and spine , neither received immunoglobulins and the post-exposure regimen used was not WHO standard . One child developed symptoms shortly after receiving the second PEP dose and the second child died after completing the third dose . The remaining 25 cases did not receive any PEP , although at least 6 attended a medical facility promptly . Most rabies victims did not seek medical attention until after symptoms had begun , then in some instances the patient was taken to multiple medical centers in the hope of receiving a more positive prognosis . At least 5 cases ( >17% ) were initially diagnosed with cerebral malaria , but as symptoms progressed and when the history of a bite was discovered , the diagnosis was changed to rabies . Exposed individuals who developed rabies generally lived further from medical facilities than those who did not , although this was not statistically significant ( p = 0 . 08 ) . Risks of ( and trauma from ) human-to-human transmission are also not inconsequential; three rabies-infected individuals ( >10% ) bit a family member and a fourth hit her mother , apparently due to disease-induced changes including aggression . Additionally a twenty-year old woman died of tetanus following a suspected rabid dog bite . She developed symptoms of tetanus before completing her third dose of PEP . Because she was pregnant it was assumed that she must have been previously vaccinated against tetanus . | Thousands of human deaths from rabies occur annually despite availability of effective vaccines for humans following exposure , and for disease control in domestic dog populations . We established a 5-year contact-tracing study in northwest Tanzania to investigate risk factors associated with rabies exposure and to determine why human deaths from canine rabies still occur . We found that children were at greater risk of being bitten and of developing rabies than adults and that incidence of bites by suspected rabid animals was higher in an area with larger domestic dog populations . A large proportion ( >20% ) of those bitten by rabid animals are not recorded in official records because they do not seek post-exposure prophylaxis ( PEP ) , which is crucial for preventing the onset of rabies . Of those that seek medical attention , a significant proportion do not receive PEP because of the expense or because of hospital shortages; and victims who are poorer , and who live further from medical facilities , typically experience greater delays before obtaining PEP . Our work highlights the need to raise awareness about rabies dangers and prevention , particularly prompt PEP , but also wound management . We outline practical recommendations to prevent future deaths , stressing the importance of education , particularly in poor and marginalized communities , as well as for medical and veterinary workers . | [
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| 2008 | Rabies Exposures, Post-Exposure Prophylaxis and Deaths in a Region of Endemic Canine Rabies |
Chikungunya virus ( CHIKV ) is a re-emerging pathogen responsible for causing outbreaks of febrile disease accompanied with debilitating joint pain . Symptoms typically persist for two weeks , but more severe and chronic chikungunya illnesses have been reported , especially in the elderly . Currently , there are no licensed vaccines or antivirals against CHIKV available . In this study , we combined a CHIK virus-like particle ( VLP ) vaccine with different adjuvants to enhance immunogenicity and protection in both , adult and aged mice . CHIK VLP-based vaccines were tested in 6-8-week-old ( adult ) and 18-24-month-old ( aged ) female C57BL/6J mice . Formulations contained CHIK VLP alone or adjuvants: QuilA , R848 , or Imject Alum . Mice were vaccinated three times via intramuscular injections . CHIKV-specific antibody responses were characterized by IgG subclass using ELISA , and by microneutralization assays . In addition , CHIKV infections were characterized in vaccinated and non-vaccinated adult mice and compared to aged mice . In adult mice , CHIKV infection of the right hind foot induced significant swelling , which peaked by day 7 post-infection at approximately 170% of initial size . Viral titers peaked at 2 . 53 × 1010 CCID50/ml on day 2 post-infection . Mice vaccinated with CHIK VLP-based vaccines developed robust anti-CHIKV-specific IgG antibody responses that were capable of neutralizing CHIKV in vitro . CHIK VLP alone or CHIK plus QuilA administered by IM injections protected 100% of mice against CHIKV . In contrast , the antibody responses elicited by the VLP-based vaccines were attenuated in aged mice , with negligible neutralizing antibody titers detected . Unvaccinated , aged mice were resistant to CHIKV infection , while vaccination with CHIKV VLPs exacerbated disease . Unadjuvanted CHIK VLP vaccination elicits immune responses that protect 100% of adult mice against CHIKV infection . However , an improved vaccine/adjuvant combination is still necessary to enhance the protective immunity against CHIKV in the aged .
Chikungunya virus ( CHIKV ) is a re-emerging pathogen responsible for causing outbreaks of febrile disease accompanied with debilitating joint pain . CHIKV was first discovered in Tanzania in 1952 , but outbreaks became more widespread , encompassing countries in Africa , Asia , Europe , and islands of the Pacific and Indian Oceans before emerging in the Americas in 2013 [1 , 2] . More recently , in 2016–2017 , there has been a resurgence of autochthonous CHIKV transmission in India [3] , Pakistan [4] , and Italy [5] . The virus is transmitted to humans through the bite of Aedes aegypti and Aedes albopictus mosquitoes . Chikungunya infection results in illness , in which fever and joint polyalrthralgia , are typically reported symptoms [6] . Acute symptoms persist for two weeks , but more chronic arthralgias may persist for months to years in a subset of subjects . More severe and/or chronic chikungunya illnesses were first widely reported in retrospective studies of the chikungunya epidemics of Reunion Island [7 , 8] and India [9] . Following infection , patients experience renal , respiratory , hepatic , and cardiovascular system failures . In addition , diseases of the central nervous disease and encephalitis are major areas of concerns [7–9] . People over 60 years of age are at particular risk for severe chikungunya-associated illnesses , with case fatalities reported [8–10] . However , the incidence of CHIKV infection in this population is not remarkable in comparison to other age groups [11–13] . The specific mechanisms that lead to increased severity of CHIKV illness in the elderly are not known , but increased understanding could lead to better treatments and vaccines for this at-risk population . Vaccinating elderly individuals presents a special challenge since they are more prone to severe illness and vaccine efficacy drops in this population [14] . The age-associated changes in the immune system are collectively termed immunosenescence and include fewer circulating antigen presenting cells and tissue-associated dendritic cells , decreased phagocytosis , decreased toll-like receptor signaling , reduced naïve B and T cells , and chronic basal level of inflammation [15] . Elements of the immune system that remain intact include tissue macrophages and CD8+ T cell-mediated responses [14 , 15] . Different vaccine approaches to counter immunosenescence in the aging include the use of higher vaccine doses , booster vaccinations , adjuvants , and vector-based vaccines [15] . Many vaccine delivery platforms are in development for a chikungunya vaccine , including formalin-inactivated viral vaccines , live-attenuated viruses , chimeric alphaviruses , DNA-based vaccines , recombinant subunit vaccines , and virus-like particle [VLP]-based vaccines [16] . The most promising candidates , including a non-adjuvanted CHIK VLP vaccine , are being tested in Phase I and II clinical trials in adults between the ages of 18–60 years of age [17] . Thus , we will continue to have a gap in knowledge regarding 1 ) CHIKV vaccine efficacy in the elderly and 2 ) understanding the vaccine characteristics needed to elicit a protective immune response in this population . In this study , CHIKV virus-like particles were adjuvanated and used to vaccinate adult and aged mice . Adjuvants were chosen for their abilities to not only enhance , but skew immune responses . The goal was to identify a CHIK VLP vaccine formulation that would protect both adult and aged mice populations .
The complete sequence encoding structural proteins ( C-E3-E2-6K-E1 ) of the Chikungunya virus S27 strain [accession #AF369024] was codon-optimized for expression in Spodoptera frugiperda and synthesized by Genewiz [South Plainfield , NJ , USA] . The Bac-to-Bac baculovirus expression system [Thermo Fisher Scientific , Waltham , MA , USA] was subsequently used to generate recombinant baculoviruses expressing CHIKV structural proteins . Briefly , the structural gene sequence was inserted into the pFastBac1 vector , under the control of the Autographa californica multiple nuclear polyhedrosis virus ( AcMNPV ) polyhedrin for high-level expression in insect cells . The CHIK C-E VLP/pFastBac1 construct was then transformed into DH10Bac E . coli , where C-E genes flanked between mini-Tn7 sites on the pFastBac1 plasmid and the LacZ gene flanked between mini-attTn7 target sites on a AcMNPV bacmid are transposed to generate recombinant bacmid . The presence of C-E genes was verified by polymerase chain reaction ( PCR ) analysis using primers that hybridize to sites flanking the mini-attTn7 site: pUC/M13 forward 5’-CCCAGTCACGACGTTGTAAAACG-3’ and pUC/M13 reverse 5’-AGCGGATAACAATTTCACACAGG-3’ . Baculovirus was generated and passaged in Sf9 S . frugiperda insect cells , maintained in serum-free , SF900 II SFM medium [Thermo Fisher Scientific] . To generate the initial recombinant viruses , 8×105 Sf9 cells per well were seeded onto a 6-well plate and allowed to adhere for 15 min . The cells were then transfected with 1–2 μg bacmids using Cellfectin transfection reagent [Thermo Fisher Scientific] . The cells were observed for cytopathic effect and supernatants were harvested and clarified after 72h post-infection . The P1 virus was then passaged in a 30ml , spinner-flask culture of Sf9 cells at a cell density of 2×106 c/ml , and harvested 72h post-infection to generate P2 virus . For expression , Sf9 cells were cultured in spinner flasks to a density of 2×106 c/ml in a total volume of 250 ml and infected with recombinant baculovirus at a MOI of 1 . Cultures were harvested once cell viability was reduced to roughly 80% or 72-96h after infection , and the cells were pelleted at 500×g for 5 min at 4°C . Supernatants were collected and filtered through a 0 . 22μm pore membrane before sedimentation via ultracentrifugation . CHIK virus-like particles ( VLP ) were sedimented through a 20% glycerol cushion at 100 , 000×g for 4h . The sedimented VLP pellets were resuspended in sterile phosphate buffered saline ( PBS ) . Similarly , E1 and E2 genes , designed as transmembrane-truncated versions , were synthesized and cloned into the pFastBac HT vector . The pFastBac HT vector adds an N-terminal 6×His tag and and tobacco etch virus ( TEV ) proteolytic site to each gene . Recombinant bacmids and baculoviruses were generated as described above and soluble E1 and E2 proteins were expressed in Sf9 spinner flask cultures . The cultures containing soluble E1 and E2 proteins were harvested and cells were sedimented at 500×g for 5 min at 4°C . Supernatants were collected and filtered through a 0 . 22μm pore membrane and the proteins were purified by affinity chromatography using Ni-NTA resin [Thermo Fisher Scientific] . Briefly , the clarified cultures were incubated with Ni-NTA resin with shaking for 2 . 5-3h at room temperature before they were added to the columns . The medium was allowed to flow through and the Ni-NTA resin was washed three times with PBS containing 10mM imidazole . His-tagged proteins were then eluted twice with PBS containing 250mM imidazole . Upon verification of eluted proteins by SDS-PAGE analysis , E1 and E2 proteins were dialyzed and concentrated using Amicon Ultra-15 centrifugal filters [Millipore , Burlington , MA] with a 10 KDa molecular weight cut-off and sterile 10% glycerol in PBS as the exchange buffer . Total protein concentrations for E proteins and VLPs were measured using the Micro BCA protein kit as per manufacturer’s protocol [Pierce , Rockford , IL , USA] . Samples from each step of the purification process were prepared by combining 30μl of samples with 6μl 6×Laemmli buffer with beta-mercaptoethanol [βME] and heating to 100°C for 5 min . Proteins were separated on a Bolt 10% Bis-Tris Plus gel [Thermo Fisher Scientific] at 200V for 30 min and protein bands were stained with PageBlue protein staining solution [Thermo Fisher Scientific] and destained with distilled water . Samples were prepared by mixing 10μg of total protein in Laemmli buffer with βME , unless otherwise noted . These samples were boiled at 100°C for 5 min and proteins were separated on Bolt 10% Bis-Tris Plus gel as before . Next , the proteins were transferred from the gels onto PVDF membranes using the Trans Blot Turbo apparatus [Bio-Rad , Hercules , CA , USA] . The membranes were blocked for 5–10 min in iBind solution [Novex] . Polyclonal mouse anti-E1 and anti-E2 sera were recovered from mice vaccinated with E1 or E2 proteins in the lab and used to probe for these proteins . Mouse monoclonal antibody against E2 [Clone CHK-48 , BEI Resources , Manassas , VA , USA] was also used to probe for E2 . Goat anti-mouse conjugated with horseradish peroxidase [Southern Biotech , Birmingham , AL] was used as the secondary antibody . The membrane , antibody , and iBind solutions were loaded into the iBind Western System [Life Technologies , Carlsbad , CA] from which point all steps in the membrane blotting process proceed automatically by sequential lateral flow . Blotting using the iBind system was complete after 2 . 5 h . Following washing of the membrane twice more with PBS with 0 . 05% Tween-20 [PBS-T] , the membrane was exposed with Clarity Western ECL Substrate [Bio-Rad] . Images were captured using my ECL Imager [Thermo Fisher Scientific] . Vero cells [ATCC , Manassas , VA , USA] were cultured in Dulbecco’s Modification of Eagle’s Medium [DMEM , Mediatech , Manassas , VA , USA] supplemented with 10% fetal bovine serum [FBS] , 2mM L-glutamine , 100 U/ml penicillin , and 100μg/ml streptomycin [10%FBS-DMEM] and maintained at 37°C and 5% CO2 . C6/36 mosquito cells [ATCC] were cultured at 28°C and 5% CO2 in Eagle’s mimimum essential medium [EMEM , Mediatech] supplemented with 10%FBS , 2mM L-glutamine , 100 U/ml penicillin , and 100μg/ml streptomycin [10%FBS-MEM] . CHIKV LR2006-OPY1 virus was obtained from the World Reference Center for Emerging Viruses and Arboviruses ( WRCEVA ) . Upon receipt , this virus was passaged twice in C6/36 mosquito cells . Virus concentration was determined in Vero cells and reported as the 50% cell culture infectious dose ( CCID50 ) per volume [ml] . Female C57BL/6J mice were obtained from the Jackson Laboratory [Bar Harbor , ME , USA] at 6–8 weeks of age for studies in adult mice . Female C57BL/6J mice were also obtained at 12 months and allowed to age to at least 18 months for studies in aging mice . All procedures in the document were approved by the UGA Institutional Animal Care and Use Committee , # A2015 06-004-Y3-A12 . Mice were immunized on days 0 , 21 , and 42 and blood samples were taken on days 0 , 14 , and 35 via the submandibular method using 5mm lancets . [18] Vaccines were formulated to contain 30μg [~0 . 3–0 . 4μg E2 content] chikungunya VLPs adjuvanted with 20μg QuilA [InvivoGen , San Diego , CA , USA] , 10μg R848/Resiquimod [InvivoGen] , 1:1 by volume Imject Alum [Thermo Fisher Scientific] , or in PBS alone ( no adjuvant ) . Vaccines were delivered via intramuscular injection to the hindlimb quadriceps in a total volume of 50μl or subcutaneous injection to the scruff of the neck in a total volume of 100μl . Nunc Maxisorp 96-well plates [Thermo Fisher Scientific] were coated overnight at 4°C with 10μg/ml E1 , E2 , or VLP in PBS . The plates were then washed three times with PBS with 0 . 05% Tween-20 ( PBS-T ) and blocked with 200μl 1% bovine serum albumin in PBS ( blocking buffer ) for 1 hr at room temperature . Serum samples from individual mice were diluted to 1:100 in blocking buffer and added at 100μl/well in duplicate wells . The sera were allowed to react for 2 hr at room temperature . The plates were washed three times with PBS-T and bound sera were reacted with goat anti-mouse IgG-Fc [1:50 , 000] , IgG1 [1:10 , 000] , IgG2c [1:10 , 000] , or IgG3 [1:10 , 000] antibody conjugated with alkaline phosphatase [Bethyl Laboratories , Montgomery , TX , USA] for 1 hr at room temperature . The plates were washed three more times and allowed to develop for 20 min following the addition of 100 μl para-nitrophenylphosphate substrate [SeraCare , Milford , MA , USA] . The plates were read at a wavelength of 405nm using a BioTek PowerWave XS plate reader with Gen5 version 2 . 07 software [BioTek , Winooski , VT , USA] . Mouse sera from immunized or naïve mice were heat-inactivated at 56°C for 30 min . Two-fold serial dilutions of the sera were prepared in 10% FBS-DMEM and added to 96-well , cell-culture plates . CHIKV LR2006-OPY1 strain was then added at 200 CCID50 per well , and virus-antibody solutions were incubated together for 1 h at 37°C and 5% CO2 . The final serum dilutions ranged from 1:20 to 1:2560 . Each plate had two sets of assay controls: one column of wells contained virus only and a second column contained medium only . Vero cells were added at 104 cells/well and plates were incubated for 5 days at 37°C and 5% CO2 . The cells were fixed for 20 min with 10% formalin in PBS and stained with crystal violet for 5 min at room temperature . Neutralizing titers were measured and expressed as the reciprocal of the highest serum dilution that inhibited cytopathic effect . Adult C57BL/6J mice [6-8-week old] were challenged with CHIKV Reunion Island isolate LR2006-OPYI , which is of East Central South Africa lineage [ECSA] as previously described . [19] Mice were observed for 14 days following challenge . Prior to infection , the mice were anesthetized with a 100μl cocktail of 10 mg/kg xylazine plus 100 mg/kg ketamine in via intraperitoneal injection , and initial weight and hind foot measurements were recorded . Foot size was defined as width × breadth ( mm2 ) and measured using a digital micrometer with 0 . 001mm resolution . The virus was subcutaneously injected into the right hind footpad at 50μl/mouse , while the mice were still under anesthesia . Pilot viral dose challenge experiments were conducted in naïve adult and aging mice to determine optimal challenge conditions . Mice were observed twice daily and weight and foot measurements were recorded once a day . Blood samples were collected on between days 1–5 , and at day 14 post-infection . Based on these initial studies , a 105 CCID50 challenge dose of LR2006-OPY1 CHIKV was used to test vaccine efficacy , and blood collections were reduced to 2 , 4 , 6 , and 14 days post-infection . Approximately 40–60 μl of blood was collected from mice on sampling days , except on day 14 when the mice were anesthetized and terminally bled . A two-step assay was used to measure viral loads in serum samples of mice challenged with CHIKV . C6/36 cells were grown to 100% confluence in T75 culture flasks , detached by scraping , and divided equally into four 96-well plates per T75 flask . The next day , ten-fold serial dilutions ( 10−1–10−8 ) of the mouse sera were prepared in 10%FBS-EMEM and used to inoculate 96-well plates of confluent C6/36 at 100μl /well . The cells were allowed to incubate for 3 days at 28°C and 5% CO2 . Vero cells were seeded at 2×104 cells/well in a total volume of 100μl 10% FBS-DMEM per well and 25μl of C6/36 culture supernatants were transferred into triplicate wells containing Vero cells . Vero cells were incubated for 4 days at 37°C and 5% CO2 . The cells were then fixed with 10% formalin for 20 minutes and stained with 1% crystal violet solution for 5 minutes at RT . Cells with 95% or more cytopathic effect were counted for each dilution and viral loads [CCID50/ml] were calculated using the Spearman-Karber equation . [20] Mouse TNF-α , IL-6 and IL-1β ELISA MAX kits from BioLegend [San Diego , CA , USA] were used to detect inflammatory cytokines in sera of adult and aged mice , as per manufacturer’s protocol . Briefly , Nunc Maxisorp plates were coated coated overnight at 4°C wit 100 μl/well of anti-mouse TNF-α , IL-6 , or IL-1β diluted to 1:200 in carbonate buffer , pH 9 . 5 . After washing once with PBS-T , all subsequent steps were performed at room temperature , with shaking . Plates were blocked with blocking buffer ( 1% BSA in PBS ) . TNF-α and IL-6 standards were diluted and used at final concentrations 3 . 9–500 pg/ml , while IL-1β was used at 15 . 6–2000 pg/ml in blocking buffer . Pooled sera from adult or aged naïve mice were prepared by mixing 10 individual serum samples together . Pooled sera and standards were added to plates [100 μl/well] and incubated for 2 hr . Plates were then washed four times with PBS-T and incubated with biotinylated detection antibody at 1:200 dilution in blocking buffer for 1 hr . The plates washed 4 times with washing buffer and 1:1000 diluted avidin-HRP was added and incubated for 30 mins . After 5 washes , TMB substrate solution was added ( 100μl/well ) and plates were incubated in the dark for 15 mins . The reaction was stopped with 100μl/well stop solution ( 2N H2SO4 ) and plates were read using the PowerWave XS microplate spectrophotometer at a wavelength of 450 nm . Recombinant mouse TNF-α [Life Technologies] was diluted in 10% FBS-DMEM and mixed with 200 CCID50 CHIKV LR2006-OPY1 virus per well , and virus-antibody solutions were incubated together for 1 h at 37°C and 5% CO2 . The final TNF-α dilutions ranged from 5–80 ρg/ml . Each plate had two sets of assay controls: one column of wells contained virus only and a second column contained medium only . Vero cells were added at 104 cells/well and plates were incubated for 5 days at 37°C and 5% CO2 . The cells were fixed for 20 min with 10% formalin in PBS and stained with crystal violet for 5 min at room temperature . Wells with 95% or more cytopathic effect were counted for each TNF-α dilution and reported as the percentage of wells with CPE . GraphPad Prism 7 for Mac OS X software was used to perform statistical analyses . One-way , two-tailed ANOVA , followed by Tukey post-hoc tests were performed for data derived from one time-point . Two-way , two-tailed ANOVA followed by post-hoc tests were performed for data collected over multiple time-points . A p-value of less than 0 . 05 was considered significant . All mouse-related experiments were conducted in compliance with the guidelines of the University of Georgia Institutional Animal Care and Use Committee [A2015 06-004-Y3-A12] , and in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals , The Animal Welfare Act , and the CDC/NIH’s Biosafety in Microbiological and Biomedical Laboratories guide . Management of animal experiments , care , and was conducted by the University of Georgia’s Animal Resources Department that is accredited by the AAALAC .
CHIK VLPs , as well as CHIKV E1 and E2 proteins were produced and purified , as vaccines or reagents to analyze the immune responses elicited against CHIKV antigens . Multiple AcMNPV bacmids were generated to encode CHIKV C-E genes for expression and subsequent VLP assembly and for 6×His-E1ΔTM and 6×His-E2ΔTM . CHIKV gene insertions were verified by PCR analysis [Fig 1A] . Three bacmid clones ( c1-c3 ) were chosen for each construct . The C-E [3765 bp] insert plus flanking sequences ( 2300 bp ) resulted in a 6065 bp band and all three CHIK C-E VLP bacmid clones contained the correct insert as determined by electrophoresis through 1% agarose in tris-acetate-EDTA ( TAE ) [Fig 1B , top] . The bacmid clones containing E1ΔTM ( 1266 bp ) plus flanking sequences , including upstream HIS-tag region ( 2430 bp ) resulted in a PCR product of 3696 bp and these constructs were also verified by gel electrophoresis [Fig 1B , middle] . The bacmid clones containing E2ΔTM ( 1122 bp ) plus flanking sequences , including upstream HIS-tag region ( 2430 bp ) resulted in a PCR product of 3552 bp [Fig 1B , bottom] . These bacmids were independently used to transfect SF9 cells to successfully produce recombinant baculoviruses Ac-C-E VLP , Ac-6×His-E1ΔTM , and Ac-6×His-E2ΔTM capable of expressing CHIKV VLP and E proteins [Fig 1C–1E] . VLPs self-assembled from C-E proteins and were recovered from Ac-C-E VLP-infected SF9 cell culture medium . These particles were purified from non-particles by ultracentrifugation through a 20% glycerol cushion , as we previously reported [21] . The purified particles were probed using the Chk48 anti-E2 monoclonal antibody and a 50 kDa band representing the CHIKV E2 protein was detected by Western blot [Fig 1C] . Five of six batches of purified VLPs [4/12/17-4/17/17] were pooled and used for vaccination . These lots were compared to a batch produced two years earlier [3/25/15] that were stored at -80°C , demonstrating stability of VLP over time , when frozen . Zika subviral particles ( SVP ) were not detected using the Chk48 anti-E2 antibody . Like the VLP , HIS-tagged E1 and E2 proteins were secreted into the culture media of Sf9 cells infected with their respective baculoviruses: Ac-6×His-E1ΔTM and Ac-6×His-E2ΔTM . These E1 and E2 proteins were purified via affinity chromatography using NiNTA resin . For each set of purifications , unbound protein flow-through , three separate washes , and two separate elutions were collected and analyzed by SDS-PAGE , with PageBlue protein staining . Untagged proteins were removed by the second and third washes as shown by SDS-Page analysis [Fig 1D and 1E] . Recombinant E1 was successfully recovered in elutions 1 and 2 , as demonstrated by the presence of an intense band at approximately the expected size of 48 KDa [Fig 1D] . E1 protein was recovered in the elutions and then pooled , dialyzed , and concentrated . Anti-E1 mouse polyclonal sera reacted strongly with a~48 kDa band by Western blot analysis . Elutions 1 and 2 containing a 44 kDa recombinant E2 protein [Fig 1E] were pooled together , dialyzed , and concentrated . Anti-E2 polyclonal sera recognized the purified , recombinant E2 band . Adult C57BL/6J mice were vaccinated at weeks 0 , 3 , and 6 with VLP alone or coupled with one of three different adjuvants: QuilA , R848 , and Imject Alum . Preliminary studies identified CHIK VLP , VLP plus QuilA , and VLP plus Alum formulations as the top vaccine candidates in adult mice when delivered intramuscularly based on CHIKV antigen-specific IgG responses [S1A-S1C Fig] , neutralizing antibody responses [S1D Fig] , and protection against CHIKV-associated arthritis [S1E Fig] . Thus , these top candidates were also assessed in aged mice , following the same regimen . All adult and aged miced vaccinated with CHIK VLP formulations seroconverted after 3 doses as determined by ELISA for anti-VLP total IgG [Fig 2A] , and anti-VLP total IgG levels were statistically significant when compared to both , adult and aged PBS control sera . However , anti-VLP total IgG titers were significantly higher in adult mice vaccinated with VLP alone in comparison to aged mice vaccinated with VLP alone ( abs 0 . 9211 to 0 . 512 , p = 0 . 0439 ) . Morever , adult vaccinated with VLP plus Alum had higher anti-VLP total IgG titers then any of the aged mice vaccinated with VLPs ( all p < 0 . 01 ) . The aged CHIK VLP-vaccinated mice were able to mount anti-VLP IgG1 responses that were comparable to adult CHIK VLP-vaccinated mice [Fig 2B] , but significantly lagged in their anti-VLP IgG3 responses [Fig 2D] . Anti-VLP IgG2c responses were insignificant in CHIK VLP-vaccinated adult and aged mice [Fig 2C] . The IgG responses against CHIK E1 protein were robust in all adult mice vaccinated with CHIK VLPs , but were insignificant in aged mice vaccinated with CHIK VLPs [Fig 2E–2H] . For adult mice vaccinated with VLP alone , the anti-E1 IgG subclass responses was dominated by IgG2c [abs 0 . 805] and IgG3 ( abs 0 . 792 ) , followed by IgG1 ( abs 0 . 621 ) antibodies . For adult mice vaccinated with VLP plus QuilA , the anti-E1 IgG subclass responses consisted of IgG1 ( abs 0 . 943 ) , followed by IgG3 ( abs 0 . 732 ) , and finally IgG2c ( abs 0 . 547 ) . In contrast , adult mice vaccinated with VLP plus Alum largely consisted of IgG1 antibodies [abs 0 . 733] , less IgG2c [0 . 354] , ad no IgG3 . The strongest anti-E2 total IgG responses were elicited in adult mice vaccinated with VLP plus QuilA or VLP plus Alum [Fig 2I] . Vaccination with VLP plus QuilA in adult mice resulted in a strong anti-E2 IgG3 response [abs 0 . 751 , Fig 2L] , followed by anti-E2 IgG1 [abs 0 . 527 , Fig 2F] , but no significant anti-E2 IgG2c [Fig 2G] . In contrast , the VLP plus Alum vaccination only elicited significant anti-E2 IgG1 ( abs 0 . 324 ) antibodies . Adult mice vaccinated with VLP alone elicited significant anti-E2 IgG3 ( abs 0 . 861 ) and IgG2c antibodies ( abs 0 . 313 ) , but no significant anti-E2 IgG1 . CHIK VLP vaccinations in aged mice did not elicit any significant anti-E2 IgG responses . Vaccinations with CHIK VLP-based formulations induced antibodies in adult mice that were able to neutralize CHIKV LR2006-OPY1 [Fig 3 , mean titer range 1:691–1:1136] . In adult mice , CHIK VLP only and CHIK VLP plus QuilA formulations elicted significant neutralization titers as compared to control sera . However , in aged mice , the VLP formulations failed to elicit CHIKV-neutralizing antibodies . Mice were infected with 105CCID50 CHIKV LR2006-OPY1 via subcutaneous injection of the right hind footpad . The infected mice were then monitored for 14 days following CHIKV infection . Blood samples were collected on days 2 , 4 , 6 , and 14 . While there was some gradual weight loss in the control adult mice , there was little weight loss observed in the adult mice vaccinated with CHIK VLPs only or CHIK VLPs plus QuilA [Fig 4A and 4D] . Unvaccinated aged mice did not lose any weight . However , aged mice vaccinated with CHIK VLPs or VLPs plus QuilA experienced some gradual weight loss , similar to what was observed in unvaccinated adult mice [Fig 4A and 4D] . We also measured the size of the infected right hindfeet and the uninfected left hindfeet were measured as additional controls . Any foot size deviation beyond 15% of the intitial foot size was considered a significant change . Adult mice vaccinated with PBS experienced significant swelling of the CHIKV-injected right hind foot . Swelling was visible by day 6 post-infection . Peak right foot swelling occurred on day 7 , reaching approximately 170% of the initial foot size , before gradually returning to normal size by day 12 post-infection [Fig 4B and 4E] . Immunizations with VLP alone [Fig 4B] or VLP plus QuilA [Fig 4E] in adult mice offered complete protection throughout the complete time-course of the experiment . No measurable inflammation of the left hind feet were observed in any of the adult mice [Fig 4C and 4F] . In contrast to adult mice , naïve aged mice were resistant to CHIKV-mediated arthritis of the injected right hind foot [Fig 4B and 4E] . On the other hand , CHIK VLP-vaccinated old mice were more susceptible to CHIKV infection than naïve old mice . Aged mice vaccinated with VLP had pronounced foot swelling with one peak at day 2 post-infection [125% of initial foot size , Fig 4B] , and then later on for a sustained period between 7–12 days post-infection ( approximately 130% of initial foot size ) . There was significant right hind foot swelling in the aged group vaccinated with VLP plus QuilA on days 1–4 post-infection and again on days 6–9 [Fig 4E] . At peak swelling , the right foot size was 156% of its original size . There was little or no change in the size of left hind feet of challenged old mice change size over time [Fig 4C and 4F] . Infectious virus was recovered from adult mice vaccinated with PBS and challenged with CHIK LR2006-OPY1 at day 2 [2 . 53 × 1010 CCID50/ml] , day 4 [2 . 77 × 105 CCID50/ . ml] , and day 6 ( 2 . 52 × 104 CCID50/ . ml ) [Fig 5] . Peak viral titers were observed on day 2 [Fig 5] and virus was completely cleared by day 14 post-infection . Virus was not recovered in adult mice vaccinated intramuscularly with VLP or VLP plus QuilA [Fig 5A and 5B , respectively] at 2 , 4 , or 6 days post-infection . Viral infection in unvaccinated old mice produced significantly lower viral titers on day 2 [5 . 31×103 CCID50/ml] , with infection peaking on day 4 [5 . 08×104 CCID50/ml] , before decreasing on day 6 [507 CCID50/ml] , and eventual clearance by day 14 [Fig 5] . In contrast , old mice vaccinated with VLP developed higher viremia [4 . 2×107 CCID50/ml , Fig 5A] than unvaccinated old mice as measured on day 2 post-infection . However , viral loads in old , VLP-vaccinated mice decreased to similar levels as unvaccinated mice on days 4 and 6 post-infection . Old mice vaccinated with VLP plus QuilA also had higher viremia on day 2 post-infection ( 3 . 15×109 CCID50/ml ) than unvaccinated mice . The viral loads in old mice vaccinated with VLP plus QuilA decreased on day 4 , but peaked again on day 6 post-infection [3 . 15×109 CCID50/ml , Fig 5B] . The resistance to CHIKV infection observed in naïve , aged mice may be associated with chronic low-grade inflammation that accompanies aging [22] . To test this theory , sera , collected from naïve adult mice and aged healthy mice , were assayed for the presence of TNF-α , IL-6 , and IL-1β [Fig 6] . TNF-α , IL-6 , and IL-1β were below levels of detection in healthy , naïve , adult mice . Pooled sera from groups of aged mice that appeared otherwise healthy had significantly elevated basal levels of TNF-α at 5 . 971 ± 3 . 82 pg/ml in comparison with adult mice [Fig 6A] . Aged mice also had significantly elevated basal levels of IL-6 at 15 . 17 ± 3 . 766 pg/ml in comparison to adult mice [Fig 6B] . Serum cytokine levels of IL-1β were not significantly altered in aged mice versus adult mice [Fig 6C] . However , two groups of pooled sera from healthy , aged mice had high IL-1β levels of 15 . 4 and 56 . 9 pg/ml . Overall , naïve aged mice have elevated basal levels of inflammatory cytokines in comparison to naive adult mice . Moreover , the presence of TNF-α inhibited infection of Vero cells infected with CHIKV LR2006-OPY1 as measured by reduced CPE in the monolayers with as little as 5 pg/ml [Fig 6D] , and an IC50 of 14 . 49 ± 2 . 99 as determined by nonlinear regression using GraphPad Prism software .
Two of the most promising vaccines against CHIKV infection , a CHIK VLP vaccine and a measles-vectored vaccine expressing CHIK VLPs , have cleared Phase I trials with positive outcomes . These vaccines 1] are overall safe and tolerable , and 2] elicit CHIKV neutralization titers in adults 18–50 years of age . These two candidates are now being tested in healthy adults of 18–60 years of age in five Caribbean island nations . Based on results from Phase I trials , at least two immunizations with the live-vectored or CHIK VLPs are needed for 100% seronconversion and induction of neutralizing antibodies in healthy adult subjects [23 , 24] . The goals of our study were to improve these vaccines by adding adjuvants , and also show that these formulations would be efficacious in an aged model of infection . R848/resiquimod was used as a TLR7/8 agonist to induce a Th1 biased response [25] . Imject Alum induces primarily a Th2 biased response [26] and QuilA was selected because it enhances T-dependent and T-independent immune responses [27] . In adult mice , CHIK VLP administered alone or with QuilA administered by IM elicited strong antibody responses that were neutralizing in vitro , and these vaccines protected 100% of mice against CHIKV challenge , as determined by lack of swelling of the injected foot , lack of weight loss , and lack of viral titers . Antibodies play a critical role in the clearance of CHIKV infections by both neutralizing virus infection and enhancing the clearance of virally infected cells [28 , 29] . Potent neutralizing antibodies , composed of multiple subclasses , are directed against the E2 protein [30] . Early induction of anti-CHIKV anti-E2 human IgG3 subclass antibodies successfully clear CHIKV infections and lead to faster recovery . In adult mice , the most effective vaccine candidates were CHIK VLPs with no adjuvant or VLP plus QuilA , both administered by IM injections . Both of these vaccine formulations elicited antibodies directed against both the E1 and E2 proteins . A combination of robust IgG1 , IgG2c , and IgG3 anti-E1 responses were detected [Fig 2F–2H] . In contrast , IgG3 antibodies were the predominant anti-E2 response elicited by these vaccines , with lower titers of pro-inflammatory IgG2c elicited by VLP alone or IgG1 by VLP vaccines formulated with QuilA [Fig 2J–2L] . Mouse IgG3 , which is not a homolog of human IgG3 , is induced independently of T-cell help and appears shortly after vaccination [31] . This anti-CHIKV IgG3 response may enhance clearance of CHIKV infected cells in adult mice , since CD4 knockout mice are still able to control and clear CHIK virus as effectively as wild-type mice [32] . In contrast , B cell deficient mice ( μMT ) remain persistently infected [33] . Mouse IgG3 binds FcγRI , but is thought to function primarily through activation of complement [34] . These anti-E1 and anti-E2 responses were absent in aged mice vaccinated with CHIK VLPs or VLP plus QuilA . The sera from aged mice vaccinated did react with whole VLP preparations by ELISA , with similar levels of anti-VLP IgG1 as the adult mice . Perhaps the presence of these antibodies that did not bind specifically to soluble E1 or E2 , contributed to CHIKV disease enhancement in these animals . A phenomenon coined antibody-dependendent enhancement has been described for dengue and other viruses whereby non-neutralizing antibodies facilitate entry of antibody-bound virions via FcγR [35] . Antibody-dependent enhancement has also been observed in vitro with another alphavirus , Ross River Virus [36] . A more recent study shows that convalescent sera from CHIKV-infected subjects mediates enhanced binding , but not enhanced replication of CHIKV in primary human monocytes and B cells in vitro via FcγRs [37] . In contrast , increased chikungunya viral replication is observed in Raw 264 . 7 mouse macrophages in the presence of mouse anti-CHIKV IgG in vitro . Mice infected with CHIKV and then treated with subneutralizing levels of anti-CHIKV IgG also develop higher levels of viremia and disease as measured by foot swelling [37] . Another unexpected feature of the aged mice was their resistance to chikungunya viral infection and disease [Fig 5] . We speculate that this resistance may be associated with elevated levels of inflammatory cytokines present in aged mice , but not in adult mice . Chronic , low-level inflammation associated with aging has been coined “inflammaging” . The elevation of some cytokines , such as IL-6 , are associated with longevity , while elevation of other pro-inflammatory cytokines , such as TNF-α , are associated with higher rates of mortality in humans [22] . The role of inflammaging is complicated because while enhanced inflammation leads to disease , mortality , and poor vaccine outcomes , inflammation can help the immune system resist virally-induced disease [22] . Healthy aged mice with detectable levels of inflammatory markers TNF-α and IL-6 [Fig 6A and 6B] were resistant to CHIKV-associated disease [Fig 4B & 4E , Fig 5] . Furthermore , addition of exogenous TNF-α to Vero cells inhibited CHIKVinfection in vitro , based on reduction in CPE . Future studies testing the effects of exogenous TNF-alpha on CHIK viral infections in vivo would help to corroborate these results . Basal inflammation in the naïve , aged mice may have been helped these mice resist CHIKV infection , but it may also have contributed to the poor immune responses to vaccination with CHIK VLPs . Reduction of vaccine efficacy due to inflammation has been observed in elderly people vaccinated with standard vaccines against influenza virus [38] and hepatitis B [39] . Furthermore , elevated plasma levels of TNF-α correlate with lower antibody titers generated in post-menopausal women following vaccination with influenza vaccine . Thus , it is possible that the basal levels of TNF-α observed in age mice , but not in young mice , contributed to the decrease antibody responses observed after vaccination with CHIK VLP-based vaccines . A recent publication suggests that this problem may be circumvented by pre-treament of elderly patients with anti-inflammatory drugs , such as Losmapimod , a small molecule p38 mitogen-activated protein kinase inhibitor [40] . In addition , given that elderly people are already in a pro-inflammatory state , using an adjuvant to enhance inflammatory responses to a vaccine may not be the most beneficial approach for CHIKV . In contrast to our observations in CHIKV-infected aged mice , a study by Uhrlaub et al . [41] found that CHIKV infections resulted in more severe infections in aged mice . There are a few key differences between our studies . The ages of the adult and aged mice were similar , but we used female mice , while Uhrlaub et al . used male mice . They also used a different CHIKV strain: SL15649 . This strain resulted in a different disease progression in adult male mice than what we observed in adult female mice: foot-swelling was biphasic with two peaks on days 3 and then on day 8 with SL15649 infection , while we observed one main peak on day 7 with LR2006-OPY1 . While the progression of CHIKV-associated foot swelling we observed is comparable to what has been previously published by Gardner et al [19] and Metz et al [42] using CHIKV LR2006 OPY1 strain , biphasic foot-swelling has also been in observed with this same strain at 106 CCID50 in female mice [43] and at 103 focus-forming units in female and male mice [44] . Thus , the use of different strains cannot be solely responsible for the difference in results . While not previously anticipated in our study , the stark difference in results may have to do with gender in aged mice . A longitudinal , retrospective study on prognostic factors of inhospital deaths in elderly patients by L . Godaert et al . [45] found that the male sex was an independent predictor of inhospital deaths due to CHIKV infection . Thus , perhaps in aged mice , the male sex may also predispose them to more severe disease . In addition study by Uhrlaub et al . also showed that CD4+ T cells and neutralizing antibody responses elicited by CHIKV infections were significantly decreased in aged , male mice compared to the adult , male mice . Thus overall , even in the male mice , the VLP vaccine formulations would likely also elicit poor protective responses as compared to those in adult mice . While our study suggests that naïve , aged , female mice are resistant to CHIKV infections , infections in aged people are much more complicated . Comorbidities may increase the risk of developing more severe CHIKV disease upon infection . CHIKV infection may be complicated by pre-exisiting comorbidities or may exacerbate chronic renal , respiratory , cardiac diseases [9 , 46] . A recent systematic meta-analysis of 11 different studies showed that hypertension , diabetes , cardiac disease , and asthma were the most frequent comorbidities associated with patients infected with CHIKV [47] . Furthermore , hypertension and diabetes had a 4-5-fold higher prevalence in patients over 50 years of age , and patients with diabetes at higher risk for severe CHIKV disease [47] . Thus , perhaps CHIKV infections in diabetic mouse or non-human primate models may provide a better understanding of CHIKV infection , spread , and virus-induced disease pathology observed in severe CHIKV-induced disease . In addition , it would be important to investigate these preconditions in both female and male animals . These models could then be used for preclinical testing of vaccines and antivirals against CHIKV . In summary , CHIK VLPs alone elicit strong neutralizing antibody titers that protect 100% of mice from CHIKV infection and disease . However , more research is needed to identify a vaccine that will protect the elderly and people with chronic conditions , such as hypertension and diabetes against CHIKV disease . If the current measles-vectored an CHIK VLP-based vaccines make it through all phases of clinical trials , licensing of these vaccines should be limited to the aged groups in which they were tested in , until further research can be conducted . It is possible that a completely different set of vaccine formulations or regimens may need to be developed for the elderly . These would need to be thoroughly vetted in preclinical studies to ensure that older people are not put at further risk for severe CHIKV infections . Finally , relevant models of severe CHIKV disease in adults and aged animals are needed to evaluate these vaccines . | Chikungunya virus is responsible for outbreaks of febrile illnesses accompanied with debilitating join pain in subtropical and tropical regions of the world . The disease caused by chikungunya virus typically resolves itself within weeks , but may be persistent and more severe in elderly individuals . Currently , there are no licensed vaccines , although a virus-like particle vaccine is currently being tested in Phase II clinical trials . In this study , we formulated chikungunya virus-like particles with adjuvants to skew and enhance the immune responses against chikungunya , and vaccinated adult and aged mice . Our aim was to identify a vaccine formulation that would protect adult and elderly populations . Results showed that the unadjuvanted vaccine was very effective in adult mice , eliciting strong virus-neutralizing antibody titers , and protecting mice against chikungunya infection and disease . In contrast , chikungunya disease was exacerbated in mice vaccinated with the virus-like particle vaccine alone or with QuilA adjuvant . This study highlights the need for an improved vaccine approach to safely and effectively vaccinate the elderly against chikungunya viral infections . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
]
| []
| 2019 | Vaccination with a chikungunya virus-like particle vaccine exacerbates disease in aged mice |
LIGHT ( TNFSF14 ) is a member of the TNF superfamily involved in inflammation and defence against infection . LIGHT signals via two cell-bound receptors; herpes virus entry mediator ( HVEM ) and lymphotoxin-beta receptor ( LTβR ) . We found that LIGHT is critical for control of hepatic parasite growth in mice with visceral leishmaniasis ( VL ) caused by infection with the protozoan parasite Leishmania donovani . LIGHT-HVEM signalling is essential for early dendritic cell IL-12/IL-23p40 production , and the generation of IFNγ- and TNF-producing T cells that control hepatic infection . However , we also discovered that LIGHT-LTβR interactions suppress anti-parasitic immunity in the liver in the first 7 days of infection by mechanisms that restrict both CD4+ T cell function and TNF-dependent microbicidal mechanisms . Thus , we have identified distinct roles for LIGHT in infection , and show that manipulation of interactions between LIGHT and its receptors may be used for therapeutic advantage .
Tumour necrosis factor ( TNF ) superfamily members are involved in many biological functions , including cell growth and differentiation , apoptosis and organogenesis [1] . This broad range of activities is achieved by TNF family members interacting with functional receptors associated with distinct cell signalling pathways [2] . TNF , lymphotoxin ( LT ) α , LTβ and LIGHT ( TNFSF14 ) comprise a closely related set of ligands in the TNF family [3] , [4] . TNF exists as a cell-bound or soluble homotrimer that binds TNF receptor ( TNFR ) 1 and TNFR2 [5] , [6] . LTα can form a soluble homotrimer ( LTα3 ) that binds TNFR1 , TNFR2 and HVEM [5] , [7] , but can also form a cell-bound heterotrimer with LTβ ( LTα1β2 ) that binds and signals through LTβR [8] . LIGHT exists in cell-bound and soluble forms that interact with both LTβR and herpes virus entry mediator ( HVEM ) [7] , [9] , [10] . HVEM also engages members of the immunoglobulin superfamily; B and T lymphocyte attenuator ( BTLA ) [11] and CD160 [12] , as well as the envelope glycoprotein D of Herpes Simplex virus [13] . HVEM activates BTLA inhibitory signalling via SHP phosphatases suppressing T cell activation [14] . LIGHT , LTα and the Ig superfamily ligands can also activate HVEM-dependent cell survival signalling via NF-κB [15] . LIGHT has emerged as a key mediator of inflammation and immune homeostasis [4] , [14] . There is broad expression of LIGHT and HVEM in the hematopoietic compartment [7] , [9] , [16] , [17] , [18] , while LTβR expression is largely restricted to stromal and myeloid cells [7] , [19] , [20] . LTβR and HVEM are implicated as key host defence mechanisms against persistent viral [21] and bacterial pathogens [22] . However , little is known about the role of these receptors in infection with parasites that establish persistent infections in their hosts . The protozoan parasite Leishmania donovani causes persistent infections in humans and experimental animals [23] , [24] . We and others have defined important roles for TNF and LTα in host resistance in a mouse model of visceral leishmaniasis ( VL ) caused by L . donovani [25] , [26] , [27] . This disease model is characterised by an acute , resolving infection in the liver involving the formation of pro-inflammatory granulomas around infected Kupffer cells , and the establishment of a chronic infection in the spleen ( reviewed in [24] , [28] , [29] , [30] ) . Mice deficient in TNF are highly susceptible to L . donovani infection , and die in the second month of infection with unchecked parasite growth [25] , [26] , [31] . However , TNF also induces disease pathology in the spleen , including the loss of marginal zone macrophages and down-regulation of chemokine receptor expression by dendritic cells ( DCs ) [31] , [32] . Mice lacking LTα display a less severe phenotype characterised by disrupted cellular trafficking into the liver and reduced control of hepatic parasite growth , although ultimately , infection is resolved in this organ [26] . Here we investigated the impact of L . donovani infection in LIGHT-deficient mice , as well as the roles of LIGHT binding each of its functional , cognate receptors during infection . We report a critical role for LIGHT in the resolution of hepatic infection , and more specifically , identify an important role for LIGHT-HVEM interactions in stimulating IL-12 production by DCs , and hence in the control of parasitic infections . Conversely , we also discovered that blockade of LIGHT-LTβR interactions dramatically enhanced early anti-parasitic immunity . Thus , we have identified distinct and opposing roles for LIGHT engagement of each of its receptors during infection .
Homeostatic levels of LIGHT mRNA in liver ( Figure 1A ) and spleen ( Figure 1B ) differed by an order of magnitude in naïve mice . Following L . donovani infection , LIGHT mRNA accumulation increased in the liver over the first 28 days , and remained elevated despite infection largely resolving ( Figure 1C ) . In contrast , the initially high splenic LIGHT mRNA levels decreased over the first 28 days of infection ( Figure 1B ) , and remained diminished as a persistent L . donovani infection became established ( Figure S1A ) . Thus , an organ-specific pattern of LIGHT mRNA expression emerged in response to L . donovani infection . To establish whether LIGHT was required to control infection , we infected LIGHT-deficient and control C57BL/6 mice with L . donovani and followed the course of infection in the spleen and liver for 90 days . Despite no difference in hepatic parasite burdens in the first 7 days of infection , parasite growth was significantly greater in the livers of LIGHT-deficient mice from day 14 p . i . onwards . Furthermore , these mice failed to fully resolve hepatic infection in the time period studied ( Figure 1C ) . TNF , IFNγ and nitric oxide ( measured as the surrogate marker inducible nitric oxide synthase; NOS2 ) are all critical for control of L . donovani in the liver [26] , [27] , [31] , [33] , [34] . Serum TNF and IFNγ levels were reduced , and the accumulation of hepatic NOS2 , IFNγ and TNF mRNA were all lower in LIGHT-deficient mice at 14 days , compared with control animals ( Figure S1B–E ) . In the spleen , there were no significant differences in parasite burdens between C57BL/6 and B6 . LIGHT−/− mice at any time point studied ( data not shown ) . The accumulation of NOS2 mRNA was much lower in the spleen of C57BL/6 and B6 . LIGHT−/− mice at day 14 p . i . , compared with the liver , and no difference in IFNγ , TNF and NOS2 mRNA accumulation in the spleen between mouse strains was observed at this time point ( Figure S1B–E ) . We therefore focused our attention on the liver . The formation of pro-inflammatory granulomas around infected Kupffer cells is a critical step in host control of parasite growth in the liver [24] , [28] , [29] , [30] . Liver immunohistochemistry revealed an increased number of inflammatory foci associated with increased parasite burden and impaired formation of inflammatory granulomas in B6 . LIGHT−/− mice at day 14 p . i . , relative to control mice , as indicated by a greater frequency of infected Kupffer cells with no surrounding leukocytes ( KC ) , and a lower frequency of immature ( IG ) and mature granulomas ( MG ) ( Figure 2A ) . To ensure that the failure to develop anti-parasitic immunity in the liver did not result from an as yet unidentified developmental defect in LIGHT-deficient mice , BM chimeras were made by engrafting LIGHT-deficient or control ( C57BL/6 ) BM cells into lethally irradiated C57BL/6 mice . These mice were infected and parasite burdens measured 14 days later . BM chimeric mice responded to hepatic infection in accordance with their source of BM ( Figure 2B ) . Hepatic parasite burdens were significantly increased in LIGHT-deficient BM chimeras compared to controls , indicating that LIGHT production by leukocytes was required for the efficient generation of anti-parasitic immunity in the liver at this early time point in infection . Additional experiments in T and B cell-deficient B6 . RAG1−/− mice receiving LIGHT-deficient or wild-type T cells showed that LIGHT production by T cells was not required for the development of anti-parasitic immunity in the liver during the first 14 days of infection ( Figure 2C ) . However , we cannot exclude a role for T cell-derived LIGHT in the generation of optimal early host immunity following L . donovani infection because we did find a small , but significant difference ( p<0 . 001 ) in parasite burden at day 14 p . i . between B6 . RAG1−/− mice that received LIGHT-deficient T cells and those that received wild-type T cells ( Figure 2C ) . We also found that T cells per se were not a major source of hepatic LIGHT mRNA , although their presence was required for LIGHT expression to increase in the liver during this early period of infection ( Figure 2D ) . Together , these data indicate that the generation of immune responses against L . donovani in the liver were impaired in the first 14 days of infection the absence of LIGHT . Treatment with anti-HVEM mAb ( LH1 ) that blocks LIGHT binding to HVEM , but not HVEM-BTLA interactions [35] , significantly increased hepatic parasite load at day 14 p . i . in mice , similar to the increase in parasite burden observed in LIGHT-deficient mice ( Figure 3A ) . Surprisingly , hepatic parasite burdens were significantly decreased by treatment of mice with anti-LTβR mAb ( LLBT2 ) that blocks LIGHT binding to LTβR , but not LTα1β2-LTβR interactions [35] ( Figure 3A ) . Antibody treatments had no significant effect on the low splenic parasite burden at this time point ( Figure S2 ) . The formation of granulomas was significantly impaired by anti-HVEM mAb , as indicated by a greater frequency of KC and a lower frequency of IG and MG ( p<0 . 05 , κ2 analysis; Figure 3B ) . In contrast , granuloma formation was significantly enhanced by anti-LTβR mAb , as indicated by a lower frequency of KC and a higher frequency of IG and MG ( p<0 . 05 , κ2 analysis; Figure 3B ) . Thus , these results indicate that HVEM and LTβR have distinct and opposing roles during the first 14 days of infection . To further investigate the role of LTβR in VL , we treated mice with the agonist anti-LTβR antibody ( 3C8 ) which blocks binding of both LTα1β2 and LIGHT to LTβR , yet functions as an agonist directly activating LTβR signalling pathways [36] , [37] . The anti-LTβR 3C8 enhanced parasite clearance in the liver during an established infection ( Figure 4A ) , but had no anti-parasitic effect in the first 14 days of infection ( data not shown ) , unlike the anti-LTβR mAb LLBT2 ( Figure 3A ) . Importantly , 3C8 also prevented parasite growth in the spleen between days 14 to 28 p . i . ( Figure 4B ) . In contrast , treatment with LLTB2 during established infection ( days 14–28 p . i . ) had no effect on parasite clearance in the liver or spleen ( data not shown ) . Thus , treatment of L . donovani-infected mice with two different anti-LTβR mAbs had distinct effects on the course of infection , reflecting different functional properties of these mAbs . We next sought to identify anti-parasitic mechanisms dependent upon LIGHT-HVEM signalling . We previously showed that early splenic IL-12/IL-23p40 production by DC is critical for the efficient generation of immunity in the liver [38] , [39] . Although no change in IL-12p35 mRNA accumulation was observed in any treatment group , the anti-HVEM mAb ( LH1 ) inhibited splenic DC IL-12/IL-23p40 mRNA accumulation in response to L . donovani infection ( Figure 5A ) . We next evaluated the importance of LIGHT-HVEM co-stimulatory signals for the development of L . donovani-specific CD4+ T cell priming and Th1 differentiation , the latter being a known IL-12-dependent process [39] , [40] , [41] . Mice were injected with CFSE-labelled OVA-specific CD4+ ( OT-II ) T cells , then infected with transgenic OVA-expressing L . donovani [42] , and antigen-specific CD4+ T cell proliferation was assessed 4 days later by CFSE dilution . No antigen-specific CD4+ T cell proliferation was observed when mice were infected with wild-type parasites ( Figure 5B ) , so no bystander activation had occurred , and OT-II cell proliferation occurred equally in control and anti-HVEM-treated mice ( Figure 5B ) , indicating that LIGHT-HVEM interactions were not required for early priming of CD4+ T cell proliferation . In support of this result , proliferation of polyclonal antigen-specific , CD4+ T cells was similar between cells isolated from the spleens of control-treated and anti-HVEM treated mice ( Figure 5C ) . However , production of IFNγ and TNF by these antigen-specific CD4+ T cells was inhibited by anti-HVEM mAb ( Figure 5C ) . Furthermore , direct ex vivo production of IFNγ by hepatic CD4+ T cells ( both total number and frequency ) was significantly reduced by anti-HVEM mAb ( Figure 5D ) , indicating that LIGHT-HVEM interactions play an important role in generating Th1 cell responses following L . donovani infection . The anti-LTβR mAb ( LLTB2 ) inhibited parasite growth in acute experimental VL ( Figure 3A ) , but had no effect on splenic DC IL-12/IL-23p40 or IL-12p35 mRNA accumulation at 5 hours p . i . ( Figure 6A ) , and no effect on the expansion of OVA-specific CD4+ T cells ( OT-II cells ) in mice infected with OVA-transgenic L . donovani ( Figure 6B ) . We also found no differences in antigen-specific recall responses in splenic CD4+ T cells isolated from infected mice on day 14 p . i . , yet the amount of TNF and IFNγ produced upon antigen-specific CD4+ T cell stimulation was greatly enhanced in these cells from mice treated with anti-LTβR mAb ( Figure 6C ) . In addition , the number and frequency of IFNγ-producing hepatic CD4+ T cells measured directly ex vivo on day 14 p . i . was significantly increased in these mice ( Figure 6D ) , suggesting LIGHT-LTβR binding suppresses the development of Th1 cell responses in VL . We next investigated timing requirements for treatment with the anti-LTβR mAb ( LLTB2 ) during acute infection with L . donovani . A single dose ( 100 µg ) of anti-LTβR mAb at the time of infection was sufficient to reduce hepatic parasite burden as early as day 7 p . i . ( Figure 7A ) . To test whether treatment with anti-LTβR mAb was simply shunting available LIGHT onto HVEM , we also co-treated mice with anti-LTβR ( LLTB2 ) and anti-HVEM ( LH1 ) mAbs ( Figure 7B ) , and found no additional effect of co-administration over anti-LTβR alone by day 7 p . i . , indicating that increased , early anti-parasitic immunity observed after anti-LTβR mAb ( LLTB2 ) treatment was not caused by enhanced HVEM-mediated co-stimulation . Of note , there was no effect of anti-HVEM mAb treatment alone at day 7 p . i . , indicating that the effect of this treatment on parasite burden only becomes apparent between days 7–14 p . i . , similar to what was observed in LIGHT-deficient mice ( Figure 1C ) . To test whether anti-LTβR mAb ( LLTB2 ) agonist activity might account for the above effect , we treated LIGHT-deficient mice with this antibody and measured liver parasite burdens at day 7 p . i . ( Figure 7C ) . Although a significant reduction in parasite burden was found in C57BL/6 mice treated with anti-LTβR mAb ( LLTB2 ) , no such effect was observed in LIGHT-deficient mice , indicating that the likely mechanism of action was via the blockade of LIGHT binding LTβR . We investigated the cellular requirements for the early anti-parasitic effects of anti-LTβR mAb ( LLTB2 ) . Treatment with anti-LTβR mAb had no impact on hepatic parasite burdens in B6 . RAG1−/− mice at day 7 p . i . ( Figure S3 ) , suggesting that B and/or T lymphocytes are required for the enhanced parasite clearance resulting from this treatment . We focused our attention on T cells because we have previously shown that B cells play a negative regulatory role in the liver during infection [43] . Depletion of CD4+ or CD8+ T cells alone during the first 7 days of infection had no effect on hepatic parasite burden ( Figure 8A ) , despite T cells being required for the control of parasite growth at later stages of infection [26] , [44] , [45] . However , depletion of CD4+ T cells , but not CD8+ T cells , prevented the anti-parasitic effect mediated by anti-LTβR at day 7 p . i . ( Figure 8A ) . Given that NKT cells comprise a significant proportion of hepatic CD4+ T cells , we also investigated whether this cell subset was required for the increased anti-parasitic activity . Treatment of NKT cell-deficient ( B6 . Jα18−/− ) mice with the anti-LTβR mAb ( Figure 8A ) had no impact on the decreased liver parasite burden , indicating that conventional CD4+ T cells , but not NKT cells , are required for the enhanced parasite clearance following anti-LTβR mAb treatment . We observed increased CD4+ T cell TNF and IFNγ production was associated with improved control of parasite growth resulting from anti-LTβR mAb treatment ( Figure 6 ) . We next assessed whether these cytokines were required for the enhanced parasite clearance in mice receiving anti-LTβR ( LLTB2 ) mAb . Hepatic parasite burdens were decreased similarly in anti-LTβR mAb treated control and IFNγ-deficient mice ( Figure 8B ) . However , anti-LTβR mAb treatment in TNF-deficient mice had no impact on hepatic parasite burden ( Figure 8B ) , indicating that TNF is critical for this enhanced parasite clearance . The failure of anti-LTβR mAb treatment in TNF-deficient animals was not caused by reduced expression of LTβR on the cells of these mice , as LTβR expression levels were no different to those on immune cells from C57BL/6 control mice ( data not shown ) . Furthermore , adoptive transfer of wild type and TNF-deficient CD4+ T cells into B6 . RAG1−/− mice and treatment with anti-LTβR mAb demonstrated that CD4+ T cells did not have to produce TNF ( Figure 8C ) . Thus , anti-LTβR mAb treatment increased early hepatic anti-parasitic immunity by mechanisms requiring conventional CD4+ T cells and TNF , the latter potentially coming from a non-T cell source .
We have identified distinct and opposing roles for LIGHT and its receptors during infection . LIGHT has important roles in T cell costimulation [3] , [14] . Blockade of LIGHT impairs allogeneic T cell responses and graft versus host disease [46] , [47] , while over-expression of LIGHT by T cells causes inflammatory disease of the gut and reproductive tissues [48] , [49] . Our results indicate that these effects could be mediated via the LIGHT-HVEM axis between T cells and DC . Early DC IL-12 production depends on the presence of T cells , and this IL-12 production is critical for generating anti-parasitic immune mechanisms that control L . donovani growth [24] , [39] , [40] , [50] . Our finding that anti- HVEM mAb blocks IL-12/IL-23p40 mRNA accumulation during infection is consistent with a previous study that reported BM-derived DCs from LIGHT-deficient animals were impaired in their ability to produce IL-12 following activation in vitro [51] . This study also showed that blockade of LIGHT with soluble receptors in mice infected with L . major , a cause of cutaneous leishmaniasis , resulted in reduced IL-12 generation , associated with diminished CD4+ T cell IFNγ production and increased parasite growth and disease . Our finding that T cells did not have to produce LIGHT in order to promote anti-parasitic immunity , together with data from L . major infection in mice [51] , support a model whereby DC-derived LIGHT interacts with T cell HVEM to promote DC IL-12 production . The defect in anti-parasitic immunity observed in the absence of LIGHT was restricted to the liver , and not the spleen . The reason for this is unclear , but could relate to the requirement for cellular recruitment and granuloma development for control of parasite growth in the liver . Although increased tissue weight and cellular expansion are features of L . donovani infection in the spleen , organised inflammatory granulomas are rarely observed in this tissue [24] , [28] , [29] , [30] . Importantly , parasite growth is contained in the spleen after 1–2 months of infection rather than efficiently controlled , as occurs in the liver . Therefore , it is possible that different anti-parasitic immune mechanisms operate in these two tissue sites during experimental VL with different requirements for LIGHT . The LIGHT-specific blocking mAbs we have employed ( LH1 and LLTB2 ) have previously been shown to selectively block interactions between LIGHT and its receptors ( HVEM and LTβR , respectively ) [35] . However , we cannot exclude the possibility that they may trigger some receptor activation following engagement , and that this may contribute to biological effects we have observed . In addition , because these mAbs cause the selective blockade of LIGHT binding to their respective receptors , we cannot rule out that they promote alternative receptor-ligand interactions ( Figure 9A ) . For example , blocking LIGHT interacting with HVEM may allow HVEM to more readily engage BTLA on cells to increase inhibitory signals ( Figure 9B ) , as well as increased CD160 signalling . Similarly , blockade of LIGHT binding LTβR may allow greater amounts of LIGHT to bind HVEM , thereby reducing negative signalling between HVEM and BTLA and potentially promoting LIGHT-HVEM-mediated T cell co-stimulation ( Figure 9C ) . However , this latter possibility seems unlikely in the current study given that co-administration of LH1 and LLTB2 resulted in improved control of parasite growth ( Figure 7B ) . Instead , LIGHT may send inhibitory signals via LTβR early during infection , although no such LTβR-mediated negative signalling pathway has yet been defined . The anti-parasitic effects of anti-LTβR mAb ( LLTB2 ) were observed when it was administered at the time of infection , but not in mice with an established L . donovani infection , suggesting that a mAb with similar functional characteristics would have limited therapeutic potential for treatment of VL . However , our finding that the defined agonist anti-LTβR mAb ( 3C8 ) improved the rate of parasite clearance in the liver and reduced parasite load in the spleen , not only demonstrated fundamentally different biological activities for LLTB2 and 3C8 mAbs , but also shows that LTβR activation can promote beneficial immune mechanisms during established infection . This agonist antibody has previously been shown to promote DC development and maturation in vivo [20] , [37] , and this may explain the anti-parasitic effects observed after administration to L . donovani-infected mice because we have previously shown that DC adoptive transfer can improve control of parasite growth in infected mice [32] . Hence , the activation of anti-parasitic immune mechanisms by stimulation of LTβR represents a potential therapeutic strategy against chronic infectious diseases like VL . However , a better understanding of the functional characteristics of the different anti-LTβR mAbs will be required in order to better harness their therapeutic potential , including identification of the specific epitopes they recognise and signalling pathways they activate . We previously reported increased monocyte recruitment into the spleen in an experimental model of cerebral malaria following treatment of mice with the anti-LTβR mAb ( LLTB2 ) , and that this treatment protected mice from disease [52] . Interestingly , no protection from experimental cerebral malaria was afforded by treatment with the anti-LTβR ( 3C8 ) mAb ( Randall and Engwerda , unpublished ) , again emphasising the functional differences between LLTB2 and 3C8 anti-LTβR mAbs . An intriguing finding from our current studies was an increase in hepatic and splenic monocyte recruitment following anti-LTβR mAb LLTB2 treatment ( CD11b+ Ly6Chi cells; Figure S4A and B ) . Flow cytometry analysis revealed that monocytes , along with DCs ( both cDC and pDC ) , and neutrophils expressed the highest levels of LTβR in the liver , as previously reported [19] , [20] , [37] , and furthermore , that expression of LTβR did not appear to change significantly on any of these cells during the first 5 days of infection with L . donovani ( Figure S5 ) . However , the increased monocyte recruitment was not necessary for improved early control of parasite growth in treated animals in the current study because mice lacking CCL2 that have an impairment in monocyte mobilisation [53] , also had improved control of parasite growth following anti-LTβR ( LLTB2 ) treatment at day 7 p . i . ( Figure S4C ) . Although the early anti-parasitic effect of anti-LTβR ( LLTB2 ) mAb appeared to involve blocking of LIGHT- LTβR interactions , as indicated by the failure of this antibody to improve parasite control in LIGHT-deficient mice ( Figure 7C ) , we cannot exclude the possibility that some effects of this antibody , such as increased monocyte mobilisation , might involve agonist activities . Regardless , given the important role for monocyte infiltration into sites of infection and tumour growth [54] , our results indicate that manipulation of the LIGHT-LTβR signalling axis offers a potential way to improve monocyte mobilisation for therapeutic applications . Furthermore , given the recent report that monocytes can migrate into secondary lymphoid tissues in response to interactions with gram negative bacteria and/or their products , and then develop into CD209a+ , CD206+ , CD14+ , CD11chi DC capable of activating CD4+ T cells and cross-priming CD8+ T cells [55] , our results suggest that manipulation of LIGHT signalling pathways may be one way to promote this process that may have applications in vaccination . In summary , our findings further delineate the complex interactions between LIGHT and its receptors and demonstrate the therapeutic potential of modulating these immune regulatory pathways to improve disease outcomes . Our results provide mechanistic insight into the roles of LIGHT-HVEM interactions on DC function and CD4+ T cell priming , as well as anti-parasitic immune responses activated by blockade of LIGHT-LTβR interactions . Finally , we have identified two different mAbs that target LTβR with distinct functional outcomes on anti-parasitic immunity at different stages of infection .
Inbred female C57BL/6 and B6 . SJL . Ptprca ( B6 . CD45 . 1 ) mice were purchased from the Australian Resource Centre ( Canning Vale , Western Australia ) , and maintained under conventional conditions . B6 . RAG1−/− [56] , B6 . LIGHT−/− [57] , B6 . TNF−/− [58] , B6 . SJL . Ptprca×OT-II [59] , B6 . SJL . Ptprca×OT-I [60] , B6 . IFNγ−/− [61] and B6 . Jα18−/− [62] were bred and maintained at the Queensland Institute of Medical Research . B6 . CCL2−/− mice [53] were bred at Monash University and maintained at the Queensland Institute of Medical Research . All mice used were age- and sex-matched ( 6–10 weeks ) , and were housed under specific-pathogen free conditions . Chimeric mice were prepared by irradiating B6 . SJL . Ptprca mice with 11Gy and then engrafting with 3×106 fresh bone marrow ( BM ) cells i . v . via the lateral tail vein . Mice were maintained on antibiotics for 2 weeks after engraftment and infected with L . donovani 8 weeks after receiving BM , as previously described [26] . Adoptive transfer of equal numbers ( 106 ) of purified CD4+ and CD8+ T cells ( 98% purity as determined by flow cytometry ) into B6 . RAG1−/− mice was performed as previously described [26] . All animal procedures were approved and monitored by the Queensland Institute of Medical Research Animal Ethics Committee . This work was conducted under QIMR animal ethics approval number A02-634M , in accordance with the “Australian code of practice for the care and use of animals for scientific purposes” ( Australian National Health & Medical Research Council ) . L . donovani ( LV9 ) and OVA-transgenic LV9 ( PINK LV9 ) [42] were maintained by passage in B6 . RAG1−/− mice and amastigotes were isolated from the spleens of chronically infected mice . Mice were infected by injecting 2×107 amastigotes i . v . via the lateral tail vein , killed at the times indicated in the text by CO2 asphyxiation and bled via cardiac puncture . In experiments examining DC IL-12/IL-23p40 production , mice were infected with 1×108 amastigotes intravenously , as previously described [63] . Spleens and perfused livers were removed at times indicated and parasite burdens were determined from Diff-Quik-stained impression smears ( Lab Aids , Narrabeen , Australia ) and expressed as Leishman-Donovan units ( LDU ) ( the number of amastigotes per 1 , 000 host nuclei multiplied by the organ weight in grams ) [64] . Liver and spleen tissue were also preserved in either RNAlater ( Sigma-Aldrich , Castle Hill , Australia ) or Tissue-Tek O . C . T . compound ( Sakura , Torrence , USA ) . Hepatic mononuclear cells and splenocytes were isolated as previously described [65] . All antibody-producing hybridomas were grown in 5% ( v/v ) foetal calf serum , RPMI containing 10 mM L-glutamine , 100 U/ml penicillin and 100 µg/ml streptomycin . Purified antibody was prepared as previously described [63] . Mice were administered 100 µg of anti-LTβR mAb ( LLTB2 ) or anti-HVEM mAb ( LH1 ) [35] i . v . on the day of infection and every 5 days thereafter for 14 day experiments , or as a single dose on the day of infection for 7 day experiments . . The anti-LTβR mAb 3C8 was administered at 200 µg i . v . [36] , [37] starting at the times indicated in the text and every 5 days thereafter . The anti-LTβR mAb ( LLTB2 ) or anti-HVEM mAb ( LH1 ) specifically block the binding of LIGHT to either LTβR or HVEM , respectively , but do not disrupt interactions between these receptors and other functional ligands ( i . e . , LTα1β2 for LTβR and BTLA for HVEM ) [35] . The anti-LTβR mAb ( 3C8 ) blocks binding of both LTα1β2 and LIGHT , but is an agonist directly activating LTβR [66] . Mice were depleted of CD4+ or CD8+ T cells with anti-CD4 ( YTS191 . 1 ) or anti-CD8β ( 53-5 . 8 ) mAbs , respectively , as previously described [64] . Depletion of T cell subsets was confirmed at completion of experiments by assessing T cell numbers in the spleen by flow cytometry . Greater than 95% of CD4+ and CD8+ T cells were depleted by antibody treatment . In all experiments , control mice received the same quantities of the appropriate control hamster IgG ( UC8-1B9; ATCC , Manassas , VA ) or control rat IgG ( Sigma-Aldrich ) . To assess antigen-specific T cell proliferation in vivo , mice were infected with OVA-transgenic PINK LV9 [42] . Splenic OVA-specific OT-II T cells were isolated and labelled with CFSE , as previously described [64] . CFSE-labelled OT-II cells ( 1×106 ) were adoptively transferred into mice 2 h prior to infection with LV9 or PINK LV9 . Expansion of CFSE+ cells in the spleen was monitored by FACS 4 days later . In all of these experiments , control animals were included that received the same number of CFSE-labelled OT-II cells , but were infected with wild-type parasites . No OT-II proliferation was ever observed in these animals . Re-stimulation assays for endogenous splenic CD4+ T cells were performed as previously described [63] . The maturation of granulomas was scored around infected Kupffer cells in acetone-fixed liver sections as previously described [64] , [65] . Allophycocyanin ( APC ) -conjugated anti-TCRβ chain ( H57-597 ) , B220 ( clone RA3-6B2 ) , CD11c ( clone N418 ) , phycoerythrin ( PE ) -Cy5-conjugated anti-CD4 ( GK1 . 5 ) , PE-conjugated IFNγ ( XMG1 . 2 ) , CD8β ( 53-5 . 8 ) , I-Ab ( clone AF6-120 . 1 ) , Ly6G ( clone 1A8 ) , CD45 . 1 ( clone A20 ) , CD45 . 2 ( clone 104 ) , rat IgG1 ( RTK2071 ) , fluorescein isothiocyanate ( FITC ) -conjugated CD19 ( clone 6D5 ) , BST2 ( clone 120G8 ) , Ly6C ( clone AL-21 ) and biotinylated anti-NK1 . 1 ( PK136 ) , CD11b ( clone M1/70 ) , LTβR ( 3C8 ) were purchased from Biolegend ( San Diego , CA ) or BD Biosciences ( Franklin Lakes , NJ ) . Biotinylated antibodies were detected with streptavidin conjugated alexa 488 , PE or PE/Cy5 ( Biolegend ) . Leukocyte populations were defined as follows; CD4+ T cells ( CD4+ , TCR+ ) , CD8+ T cells ( CD8+ , TCR+ ) , NKT cells ( NK1 . 1+ , TCR+ ) , NK cells ( NK1 . 1+ , TCR− ) , B cells ( B220+ , CD19+ ) , cDC ( CD11chi , MHCIIhi , TCR− , B220− ) , pDC ( CD11cint , MHCIIint , 120G8+ ) , monocytes ( CD11b+ , Ly6C+ ) and neutrophils ( CD11b+ , Ly6G+ ) . The staining of cell surface antigens and intracellular cytokine staining was carried out as described previously [63] . FACS was performed on a FACSCalibur or a FACS Canto II ( BD Biosciences ) , and data were analysed using FlowJo software ( TreeStar , Oregon , USA ) . Serum and/or tissue culture supernatants were assessed for the presence of soluble cytokines using flexset bead array kits and a FACSArray plate reader ( BD Biosciences ) according to the manufacturers' instructions . RNA extraction and real-time RT-PCR was performed as previously described [63] . The number of IFNγ , TNF , NOS-2 , LIGHT and hypoxanthine phosphoribosyltransferase ( HPRT ) cDNA molecules in each liver tissue sample were calculated using Platinum Sybr Green Master Mix ( Invitrogen Life Technologies ) [63] . Standard curves were generated with known amounts of cDNA for each gene , and the number of cytokine molecules per 1000 HPRT molecules in each sample was calculated . The number of IL-12/IL-23p40 and IL-12p35 cDNA molecules in each DC sample were calculated using Taqman Gene Expression Assays ( Applied Biosystems ) . Relative quantitation of gene expression was performed using the relative standard curve method as described by Applied Biosystems . Briefly , standard curves were prepared for all target and endogenous control genes using an uninfected control sample . HPRT was used as the endogenous control . The amount of target gene or endogenous control in each sample was calculated from the appropriate standard curves . The target amount was then divided by the endogenous control amount to give the normalized target value . The average normalized values for the four naïve samples were used as the calibrator . Statistical differences between groups was determined using the Mann-Whitney U test using GraphPad Prism version 4 . 03 for Windows ( GraphPad Software , San Diego , CA ) and p<0 . 05 was considered statistically significant . The distribution of hepatic histological responses was compared using X2 analysis with Microsoft Excel software . All data are presented as the mean values plus or minus standard error unless otherwise stated . | Visceral leishmaniasis ( VL ) is a potentially fatal human disease caused by the intracellular protozoan parasites Leishmania donovani and L . infantum ( chagasi ) . Parasites infect macrophages throughout the viscera , though the spleen and liver are the major sites of disease . VL is responsible for significant morbidity and mortality in the developing world , particularly in India , Sudan , Nepal , Bangladesh and Brazil . Because of the intrusive techniques required to analyse tissue in VL patients , our current understanding of the host immune response during VL largely derives from studies performed in genetically susceptible mice . We have discovered that mice which are unable to produce a cytokine called LIGHT have poor control of L . donovani infection in the liver , compared with wild-type control animals . In addition , we demonstrated that LIGHT has distinct roles during VL , depending on which of its two major cell-bound receptors it engages . Finally , we identified an antibody that stimulates the lymphotoxin β receptor ( one of the LIGHT receptors ) , that can stimulate anti-parasitic activity during an established infection , thereby identifying this receptor as a therapeutic target during disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
]
| [
"medicine",
"infectious",
"diseases",
"immunology",
"biology"
]
| 2011 | Critical Roles for LIGHT and Its Receptors in Generating T Cell-Mediated Immunity during Leishmania donovani Infection |
Cortical folding exhibits both reproducibility and variability in the geometry and topology of its patterns . These two properties are obviously the result of the brain development that goes through local cellular and molecular interactions which have important consequences on the global shape of the cortex . Hypotheses to explain the convoluted aspect of the brain are still intensively debated and do not focus necessarily on the variability of folds . Here we propose a phenomenological model based on reaction-diffusion mechanisms involving Turing morphogens that are responsible for the differential growth of two types of areas , sulci ( bottom of folds ) and gyri ( top of folds ) . We use a finite element approach of our model that is able to compute the evolution of morphogens on any kind of surface and to deform it through an iterative process . Our model mimics the progressive folding of the cortical surface along foetal development . Moreover it reveals patterns of reproducibility when we look at several realizations of the model from a noisy initial condition . However this reproducibility must be tempered by the fact that a same fold engendered by the model can have different topological properties , in one or several parts . These two results on the reproducibility and variability of the model echo the sulcal roots theory that postulates the existence of anatomical entities around which the folding organizes itself . These sulcal roots would correspond to initial conditions in our model . Last but not least , the parameters of our model are able to produce different kinds of patterns that can be linked to developmental pathologies such as polymicrogyria and lissencephaly . The main significance of our model is that it proposes a first approach to the issue of reproducibility and variability of the cortical folding .
The development of the human brain from the early gestational weeks to the buckling of the first folds at around 20 weeks follows a narrow pathway between determinism and pure randomness . On the one hand normal adult individuals offer quite similar - from a pure qualitative and descriptive point of view - folding structures: gyri and sulci . On the other hand we observe morphological variabilities between different brains [1] . This variability can reach extreme states in the case of rare abnormalities of the developing brain - such as lissencephaly , polymicrogyria or corpus callosum agenesis . The origin of variability remains an unclear and challenging issue [2] but it is , however , obvious that environmental factors have a deep impact on the sulcal and gyral pattern since even monozygotic twins exhibit important anatomical differences [3] . Folding or buckling are very general processes in nature and among living organisms . Especially one of the most studied step in the morphogenesis of metazoans is gastrulation which corresponds to a symmetry breaking of the spherical embryo and an invagination . The origin of this folding remains unknown even if mechanical factors are undoubtedly implied [4] . More disconcerting , it is shown in [4] that different mechanical actions ( constriction , contraction , traction , gel swelling ) can lead to similar shapes of the sea urchin primary gastrula . In these conditions it raises the issue of realistic modeling of far more complex buckling processes such as the gyrification of mammal brains . In this regard it is important to inspect carrefully previous models of gyrification . Le Gros Clark [5] raises first that the cortex grows by surface expansion rather than by increasing its thickness . He suggested that the expansion of the brain is constrained by the skull and basal ganglia and that compressive stresses cause sulcation . However experiments on sheeps whose large quantities of cortical and subcortical structures were ablated at the end of cellular migration revealed , at term , gyri and sulci of normal size and configuration [6] . This model and its refutation give us a way to categorize the hypotheses on the gyrification depending on whether they involve intra- or extra-cortical processes , in other terms intrinsic or extrinsic . In the same extra-cortical point of view a recent and very popular model considers that the folding of the brain takes its origin in the mechanical tensions produced by the white matter fibers [7] . This model has been recently tested in [8] with a finite element model of cortical folding . At the opposite there are numerous hypotheses arguing that the cortical folding has intrinsic origins . In [9] the differential growth of cortical layers causes sulcation and can explain anomalies of folding such as polymicrogyria and lissencephaly in terms of different mechanical properties of cortical tissues . Other models use mechanical hypotheses on the cortex such as elasticity or plasticity [10] , [11] . In particular in [11] the authors suggest that the cortical folding is only a consequence of its growth modulated by anisotropies in mechanical or geometric properties . Even if this last model reproduces several characteristics of a growing cortex , it remains however implemented in 2D and does not explain completely where the anisotropies come from . In the intrinsic origins of folding we encounter also purely morphogenetic hypotheses in which cortical convolutions are under genetic control [12] . In [13] it is proposed that the different cytoarchitectonic areas are provided by a protomap , that is a layer of predetermined neuronal units . In the next part we will see in detail another hypothesis for the cortical folding which is based on Turing instabilities [14] , [15] . We will show that these approaches of brain development can also be linked to the sulcal roots model proposed in [2] . These last authors offer indeed a descriptive model of the human sulcation based on the concept of sulcal roots that are elementary atoms around which the brain folding organizes itself . This concept has strong similarities with the sulcal pits one [16] , [17] . Sulcal pits correspond to the deepest points of the sulci whose reproducibility has been demonstrated rigourously in [17] . We will see that initial conditions of the reaction diffusion process can have an interpretation in terms of sulcal roots or sulcal pits . In this article we investigate the origin of anatomical variability from the early development and we propose a phenomenological model of the folding which is based on the putative existence of Turing morphogens . After recalling briefly some mathematical aspects of the model , we present the numerical schemes used for implementing the equations on a surface and for the deformation of the surface . We show some qualitative and quantitative results of the model . In particular we link sulcal pits maps to the average folding patterns across several realizations of a same noisy initial condition . And we study the variability of our model and demonstrate that it can lead to different modes of variability of one sulcus .
Following ideas of [21] and [22] , we suppose that the evolution of the studied surface is driven by the morphogens and . is the inhibitor and the activator . In mathematical terms we have that ( 3 ) where is the normal to the surface and a function of the two morphogens . The simplest case for that we have adopted in the following is a linear function of one morphogen:where is a parameter in . Since the surface on which evolve the morphogens is modified with time , we have to adapt the equations ( 1 ) and ( 2 ) to take into account the geometric changes . The problem of reaction-diffusion on growing domains has been well-studied in the past years . It leads generally to add convective and dilution terms to ( respectively ) that can be combined in where represents the flow velocity of the growing surface [27] . However this result does not directly apply to surfaces and we have to refer to [28] to see the influence on the curvature changes on the reaction-diffusion equations . The model proposed in [28] consists in adding a term reflecting the modification of the surface metric along time . If the surface is parameterized by then equations ( 1 ) and ( 2 ) read: ( 4 ) ( 5 ) where stands for the Laplace-Beltrami operator . is the determinant of the metric associated to the surface , that is:In the following we will use instead of and instead of for simplicity reasons but one has to remember that the surface on which the equations are defined is changing along time . Since we work on discrete meshes we have used a finite element method to discretize the linear terms in the equations 1 and 2 . First we derive a weak formulation of the system onwith non-linear terms included in using a test function :Then , integrating by part the Laplacian term , as in [29] , we get:where is the metric associated to the Riemannian manifold . Next we work on a discrete tessellation of the surface composed of vertices . We define functions which are continuous piecewise affine , with the property to be equal to 1 at node and 0 at all other triangle nodes . They are the basis functions for the approximation on the functional space of finite dimension . So any function continuous piecewise affine reads: . The weak formulation becomes: It is possible to treat the non-linear term with the following approximation as in [30]:then discretizing implicitly and explicitly in time between and and writing with matricial expressions:withand by definitionSo we can deduce:On each triangle we havewhere is the area of triangle and is the height of triangle from vertex . At last we need to compute . This can be performed on each triangle from the expression of the metric tensor [31]:
First we can model the growth of a normal brain with the value , , , , and . The initialization corresponds to a slight perturbation of the stable equilibrium in a position of a sphere composed of vertices . This perturbation consists of a broad line with and where is white noise of amplitude ( see first picture on Fig . 2 ) . Note that the evolution specified by the coupled reaction-diffusion equations and the surface deformation leads to a progressive folding of the initial sphere on Fig . 2 . It is possible to extract an order parameter at each time step which consists of the number of folds or sulci ( see Fig . 3 left ) . This index is defined from the curvature map of the surface . At each vertex of the mesh we compute the mean curvature following [33] . Once this curvature map has been obtained we compute automatically the number of sulci , that is , the number of connected components whose curvature is inferior to 0 . For this we use a region growing algorithm: we start from a vertex whose curvature is inferior to 0 and build a connected region of vertices whose curvature is inferior to 0 . We repeat this procedure until there are no more initial seeds . We can observe on Fig . 3 that the number of sulci is equal to 0 on the interval then increases quasi linearly on and reaches a sort of plateau on . Moreover we propose a simple way to characterize the spatial stability of the folds along time . In other terms we demonstrate that the position of folds formed at different time instants remains relatively stable . We extract a map of the curvature at each time instant . And we define a thresholded map by . This map depends on the mesh on which it is defined so we interpolate it on the final mesh ( which has the largest number of vertices ) . We note that we use a smoothed version of the meshes in order to avoid problems of interpolation . The smoothing of the folded meshes has been performed by using an iterative process that consists , at each iteration , to replace a node of the mesh by the mean of its neighbors . So the maps are defined on the same domain and we can compute an average mapIntuitively this quantity represents the proportion of the temporal interval during which a fold is present at each position . This yields the map shown on Fig . 4 . We can see on this figure that the average map is not uniform but has patterns . In other terms we can observe a certain stability of the folds along time . In particular there are parts of the initial sphere that never belong to a fold . We note that the maximum of is not since there are no folds during the temporal interval which represent of the full temporal interval on which the simulation has been performed . In this part we investigate the influence of noise in the spatial position of the folds . In particular we aim at demonstrating that the reaction diffusion mechanism is able to produce reproducible folds at certain specific locations but can also engender variability at other locations . For this we simulate realizations of the folding process from different noisy initial conditions and . We consider the curvature maps and the thresholded maps at time and interpolate them on the same smoothed mesh . Then we sum the binary thresholded maps in order to see areas of reproducibility:On Fig . 5 left , we can clearly observe that the sum of the binary maps representing the averaged pattern of folding have a spatial structure and do not organize randomly . In particular we notice a big longitudinal fold that comes across the surface and seems to be very reproducible among the 50 simulations . Moreover we note that other smaller reproducible folds are positioned along the main fold on both sides . This figure echoes the average cortical surface of 222 hemispheres that we have displayed on Fig . 5 right . This surface is the one described in [34] and has been visualized with anatomist [35] . The white arrow represents the cingulate sulcus that is comparable to our main fold in the 50 simulations while the three white stars show secondary folds that are parts of the para-cingulate sulcus and that we can link to the smaller reproducible folds of our model . On Fig . 6 we illustrate however the variability of the main fold through three different scenarios of buckling . On the first line , left figure , we can see a mode that follows the main distribution previously described on Fig . 5 that is , in which the main fold is in one part . On the right is shown a left hemisphere of a real brain displayed with anatomist [35] on which the superior temporal sulcus ( pink ) is in one part . On the two following lines we represent two other modes for the main fold , in two and three parts respectively , and their correspondence on real anatomies with a superior temporal sulcus ( pink ) in two and three parts respectively . More generally Fig . 7 shows the different modes of variability of the main fold among the 50 simulations . In of cases it is composed of one segment , in of two segments , in of three segments and in of four and five segments . The determination of connected components has been done by visual inspection rather than by automatic methods that tend to increase artificially the number of segments in the main fold . It is possible to represent directly the influence of one or several parameters of the model ( and in our case ) on the qualitative properties of the patterns . We vary the parameters and linearly over a spatial domain from to and to respectively with steps of which yields 28 couples . On Fig . 8 we display the surfaces obtained at time . The color represents the curvature of these surfaces ( red: positive curvature , blue: negative curvature ) . Note that the star-like patterns obtained with values , and , are just an artifact corresponding to the structure of the spherical mesh . As suggested by [14] it is possible to link the qualitative nature of the obtained patterns to different modes of brain development , i . e in particular to pathologies or anomalies ( see Fig . 9 ) . So we can see that for and no patterns emerge . This state appears similar to lissencephaly , a pathology in which the brain is smooth and offers no gyri or sulci . The values and might correspond to a normal brain with stripe-like patterns of gyrification . At last the range and show spot-like patterns which make one think of polymicrogyria .
Our model extends the initial proposal of Cartwright in [14] where no geometrical deformation of the cortical surface was proposed . We have demonstrated that it was possible to combine a reaction diffusion mechanism to a surface deformation in order to produce a model of the gyrification process . This approach is not new since it has been applied to model plant growth [21] but it seems to be the first to tackle the very old and controversial problem of brain folding in terms of reaction diffusion coupled to surface deformation . However the question about the origin of the morphogens used in our model remains open . In [14] the activation/inhibition process is supposed to model the mechanical tensions due to white matter fibers so the morphogenetic approach becomes indirect and extrinsic . On the contrary we prefer to view the folding process as the result of an intrinsic phenomenon , promoted by morphogens that decide the cytoarchitechtony . Different cytoarchitechtonic areas would correspond to different gyri and the limits between areas to sulci . This idea , suggested one century ago by Broadmann , has been recently pointed out in [36] . Moreover in [15] , the GIP model supposes that the morphogens responsible for the patterning of subventricular zone could be some specific genes such as Pax6 , Ngn2 , Id4 . Our model supports this hypothesis since mutations in the Pax6 gene for instance can be responsible for polymicrogyria [37] , so the parameters and of the model could reflect different gene expression of Pax6 . We can also mention an alternative scenario for pattern formation that has been recently exposed in [38] and does not necessarily require the interaction of a long range inhibitor ( ) and a short range activator ( ) as in our case . In [38] an activator-activator mechanism combined with domain growth can also lead to pattern formation . In our model we investigate also the variability of folding along the development of one individual and across several individuals - that is several realizations of the model . First we can see that for an unique development the position of the sulci remains stable along time . This result may seem trivial but is required for our model to produce definite patterns of gyrification that can be compared between different realizations of the model . Secondly the study of folding variability among 50 random realizations of the model reveals two important characteristics . The folding does not organize randomly even if we add noise to the initial condition of the reaction diffusion process . We have shown on one example that a main structure emerges that is strongly reproducible among several simulations . We can find a direct analogy between this main fold and the primary folds described in the literature [1] , [39] . Primary folds are indeed characterized by their early time of appearance and their reproducibility across subjects . If we follow the comparison we can link the smaller structures found on Fig . 5 to the secondary or tertiary folds that are more posterior and variable than the primary ones . Our average map on Fig . 5 left echoes the average cortical surface of 222 hemispheres displayed on Fig . 5 right . This average surface has been computed in [34] and used also to represent an average map of sulcal pits density in [17] . In particular the main fold found on our simulations can be compared to the cingulate sulcus while the smaller folds around evoke the small pits of the paracingulate sulcus . Moreover we have shown that in spite of its strong reproducibility the main fold could be broken in two separate parts by a gyrus . This result echoes previous studies [39] , [40] where it is shown that some primary sulci reveal variability in their topology . For instance in [40] the superior temporal sulcus is continuous in one third of the cases ( on the left and on the right ) , in two segments in on the right and on the left . The gyri that separate our main structure in two or three parts could also be interpreted in terms of ‘pli de passage’ , which is a fold that can divide a sulcus in two sulci or just be buried at the bottom of a sulcus [2] . On a more theoretical point of view , our results on the reproducibility of the folds seem to confirm the impact of growth domain on the robust selection of patterns as it has been previously shown in [41] [42] . In our study there remains however some points that will require some theoretical developments , in particular about the existence of Turing instabilities that occur in the simulations . Some results have been obtained recently for isotropic domain growth or specific growth function [43] [38] . In conclusion we have proposed an extended framework for modelling the cortical folding . It is based on a system of coupled reaction-diffusion equations defined on a surface that evolves through the action of morphogens . We show that for some parameters the model gives rise to geometric patterns that can be related to cortical sulci . We also demonstrate that under the effect of noise the system yields morphological variability in these cortical structures . Moreover changing slightly the values of the parameters of the model can have an important influence on the nature of the created patterns which suggest a link toward pathologies of the brain development such as lissencephaly or polymicrogyria . In future developments we plan to investigate the difficult issue of estimating good values of parameters with respect to a given sequence of cortical surfaces across development . | The anatomical variability of the human brain folds remains an unclear and challenging issue . However it is clear that this variability is the product of the brain development . Several hypotheses coexist for explaining the rapid development of cortical sulci and it is of the highest interest that understanding their variability would improve the comparison of anatomical and functional data across cohorts of subjects . In this article we propose to extend a model of cortical folding based on interactions between growth factors that shape the cortical surface . First the originality of our approach lies in the fact that the surface on which these mechanisms take place is deformed iteratively and engenders geometric patterns that can be linked to cortical sulci . Secondly we show that some statistical properties of our model can reflect the reproducibility and the variability of sulcal structures . At the end we compare different patterns produced by the model to different pathologies of brain development . | [
"Abstract",
"Introduction",
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"Discussion"
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| [
"mathematics",
"neuroscience/neurodevelopment",
"developmental",
"biology/pattern",
"formation"
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| 2010 | A Reaction-Diffusion Model of Human Brain Development |
Synthetic gene oscillators are small , engineered genetic circuits that produce periodic variations in target protein expression . Like other gene circuits , synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period . Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits . Here , we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells . We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations . We found that oscillation amplitude exhibited high cell-to-cell variability , while sister cells remained strongly correlated for many minutes after cell division . To understand how such variability arises , we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell . When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations , and accurately predicted outcomes under novel experimental conditions . Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits .
Random fluctuations in gene networks have a variety of origins: e . g . small molecule numbers within cells [1–6] , fluctuations in the environment [7 , 8] , spatial heterogeneity [9] , or the cell cycle [10] . A number of experimental and theoretical studies have examined the impact of noise on gene networks in equilibrium . Frequently , such studies focus on decomposing fluctuations into an intrinsic component which affects individual genes independently , and an extrinsic component which impacts all reactions within the cell or population [3 , 8 , 11–15] . However , different sources of extrinsic and intrinsic noise can have distinct impacts on network dynamics . For instance , metabolic and environmental fluctuations can affect genetic oscillations differently than variability induced by cell division , although all three can be characterized as extrinsic noise sources . An approach that identifies the effect and origin of different fluctuation sources would allow us to develop more accurate computational models of genetic circuits , and better understand the processes that affect their function . Here , we present a combined experimental and theoretical approach to identify the sources of noise in a synthetic dual-feedback oscillator [16] . We used microfluidic devices [17] to track the amplitude and period of oscillations in individual cells through multiple cell cycles . By following the entire lineage of progenitor cells , we were able to characterize fluctuations within and co-fluctuations between cells . We found that the period of oscillations varied little , while the amplitude varied considerably in time and across the population . Sister cells were highly correlated upon division , but these correlations decayed in time . To explain the mechanism behind these observations , we introduced a series of increasingly detailed computational models . We started with a deterministic model to estimate parameters and then used a modified discrete stochastic simulation algorithm that accounted for transcriptional delay to describe fluctuations due to small molecule numbers [18–22] . However , this model was unable to reproduce the amplitude variability and the correlation between sister cells simultaneously . Only by including various other extrinsic noise sources was the model able to explain both the amplitude variability and correlation observed experimentally . By increasing model complexity in steps we obtained a minimal model that explained the data . Importantly , our model predicted the level of amplitude fluctuations and correlations in a set of experiments we did not use in fitting it . Our analysis showed that different sources of noise must be taken into account in order to explain the observed temporal and cell-to-cell amplitude variability and correlation in our population of synthetic oscillators . This approach to building minimal predictive models is quite general . Gradually increasing complexity by adding physiologically plausible sources of noise leads to a simple model that can explain the data . Subsequent cross-validation on separate experiments ensures that the model was not overfit . We have thus elucidated sources and consequences of variability within genetic oscillators , and provided a framework for constructing predictive models of complex dynamical gene networks .
To measure the time-dependent GFP concentrations in individual cells , we used custom designed microfluidic devices that enable time-lapse fluorescence microscopy [26 , 27] . We acquired phase contrast and fluorescence images every three minutes for three hours . We next segmented the images and tracked each cell and its fluorescence across time , keeping track of all lineages as cells grew and divided . At cell division , we kept track of each sister cell separately . Starting from a single cell , we thus obtained a branched trajectory: After the first division the trajectory split into two branches , and each successive division increased the number of branches by one . The resulting “lineage fluorescence trajectories” thus contained information from all descendants of the cell or cells initially placed in the trap , and about the relation between all descendants . Fig 1B shows the lineage trajectory for a single initial cell , illustrating the branching of trajectories at each cell division . Oscillations were maintained throughout the cell lineages . Although all cells within a lineage are clonal copies of the initial cell , we observed large variability in oscillation amplitude ( as measured by peak height ) and smaller variability in oscillation period ( Fig 1C–1D ) . Variability in period resulted in the divergence in the phase of the traces obtained from sister cell trajectories across the lineage . The average period of the entire population was 41 min , and variability in oscillation period ( CV = 0 . 11 ) was small compared to the variability in amplitude ( CV = 0 . 47 ) . Computing the statistics for each lineage separately yielded similar results ( see Methods ) . To examine cell-to-cell co-variability in gene expression , we computed the Pearson correlation coefficient , ρ , of fluorescence between daughter cells t minutes after division using all pairs of daughter cells in a lineage ( on average 175 pairs per lineage ) . In the first frame after division , fluorescence of two daughter cells was nearly identical ( ρX1 , X2 ( 3 ) = 0 . 98 , Fig 2A ) , indicating that protein partitioning was highly symmetric . This is consistent with previous results demonstrating that protein numbers after division follow a binomial distribution [28] . As time progressed , the correlation decreased as the trajectories of the daughter cells diverged . For instance , 24 min after cell division the correlation in fluorescence between sister cells decreased to ρX1 , X2 ( 24 ) = 0 . 67 ( Fig 2B ) . Fig 2B also shows the correlation function of the 6 recorded lineages and their mean . To construct a mathematical model of the dual-feedback oscillator , we started with the following set of deterministic delay differential equations describing the dynamics of LacI ( r ) , AraC ( a ) , immature GFP ( g ) , and mature GFP ( G ) r ˙ = α r h r τ r , a τ r - γ r r R 0 + r + a + g + G - β r , ( 1 ) a ˙ = α a h r τ a , a τ a - γ a a R 0 + r + a + g + G - β a , ( 2 ) g ˙ = α g h r τ g , a τ g - γ g g R 0 + r + a + g + G - λ g - β g , ( 3 ) G ˙ = λ g - γ G G R 0 + r + a + g + G - β G , ( 4 ) where xτ = x ( t − τ ) for x in {r , a , g} , and h ( r , a ) = f - 1 + a C a ( 1 + a C a ) ( 1 + r C r ) 2 , ( 5 ) is the composite Hill function describing the activity of the hybrid promoters as a function of activator and repressor concentrations . Here αr , αa and αg are the maximal production rates; τr , τa , and τg are the transcriptional delay times; γr , γa , γg , γG , and R0 are the Michaelis-Menten parameters for ClpXP mediated proteolysis; Ca , and Cr are the concentrations needed for half-maximal induction and repression . Subscripts refer to repressor ( r ) , activator ( a ) , and immature GFP ( g ) . In addition , f is a unitless measure of the strength of the activation by a compared to basal production; λ is the maturation rate of GFP; and β is the dilution rate due to cell growth . We fit this deterministic model to experimental data to estimate the parameters . To do so we fit the shape of the solution as well as the period ( see Methods ) . In experiments the exact number of proteins within each cell is unknown . However , we can tune the protein number in the model by appropriately scaling the system in Eqs 1–4 without affecting the dynamics . More precisely , when we scaled each variable using the transformation x ( t ) →x ( t ) /Ω as well as appropriately changing the parameters ( see details in Methods ) , the protein numbers were changed , but the dynamics of the corresponding deterministic system in Eqs 1–4 had the same form . The parameter Ω can be interpreted as unitless volume when other parameters are assumed to be fixed , or as a scaling parameter when the volume is fixed . Here we used the second interpretation . To investigate the impact of noise due to finite size effects , we used a delayed stochastic simulation [18–21 , 29] with rates obtained from the deterministic fit as propensity functions in the reactions of the associated birth-death process ( see [22] and Methods ) . The parameter Ω directly controls the number of proteins in the system; for instance , doubling or tripling Ω , does the same to the number of proteins . Smaller values of the parameter Ω correspond to smaller numbers of proteins and hence larger intrinsic noise . For large values of Ω the model exhibits robust oscillations ( e . g . Ω = 4 in Fig 3A ) , but amplitude variability is smaller than observed in experimental data . As expected , decreasing Ω led to increased amplitude variability ( Fig 3B ) , matching the experimentally observed value when Ω = 0 . 9 ( See Fig 3C ) . However , at this value of Ω the coefficient of variation ( CV ) of the oscillation period was 5 times higher than in experiments . Thus , a model that only included fluctuations due to small protein number could not account for the experimentally observer variability in amplitude and period . We next asked if variability due to cell growth and division , and the associated random partitioning of proteins between daughter cells , could account for the discrepancies between our computational model and the experiments . To investigate this , we simulated the entire lineage of a progenitor cell ( see Methods for details ) . In the stochastic version of Eqs 1–4 we accounted for cell growth only by incorporating dilution at rate β . Here , we instead accounted for cell growth explicitly by including an equation for cell volume , V ˙ = β V , and scaling the production and proteolysis parameters to account for the doubling of molecular machinery such as plasmids and proteases ( Fig 4A ) . As before , we simulated the system using the delayed stochastic simulation algorithm . When the volume of a cell reached the size at which division occurred , we replaced it with two daughter cells with half the volume , and partitioned all proteins ( including those already created and the immature protein in the stack of delayed events ) according to a binomial distribution [28] . We repeated these steps for each sister cell , thus simulating an entire lineage ( Fig 4B ) . Furthermore , since we modeled cell growth explicitly , and cell growth and division exhibit variability [30] , we allowed β to vary from cell to cell as well as the size required for division , Vmax . We estimated the distributions of β and Vmax from experiments and used these estimates in simulations ( see Methods ) . Thus , in addition to intrinsic noise , our lineage simulations included noise due to: 1 ) variability in growth rates between cells; 2 ) variability in size at division; and 3 ) protein partitioning at cell division . Fig 5A and 5D show typical simulations of lineage trajectories for two different values of Ω . The model matched experimentally observed amplitude variability most closely when Ω = 0 . 5 ( Fig 5B ) . However , despite the close agreement in amplitude variability , lineage trajectories were qualitatively different from those observed experimentally ( Fig 1B ) . We therefore compared correlations in fluorescence intensity between sister cells after cell division between simulations and experimental data . Simulations with Ω = 0 . 5 resulted in correlations that decayed faster than observed experimentally ( Fig 5C ) . Decreasing intrinsic noise by setting Ω = 1 . 0 ( Fig 5F ) , produced a good match . However , while the model oscillated robustly , intrinsic noise was now too small to match the experimentally observed amplitude variability ( Fig 5E ) . We therefore concluded that intrinsic noise and fluctuations in cell growth and protein numbers after cell division could not explain the experimentally observed variability . The level of intrinsic noise required to match the experimentally observed amplitude variability resulted in phase diffusion that was too fast , and correlations between sister cells that decayed too quickly . If we tried to match correlation decay , noise in our simulations was too small to reproduce the experimentally observed variability in amplitude . Another source of variability are the random fluctuations in the cellular microenvironment and cellular resources necessary for gene expression [31–34] . This type of extrinsic noise can be viewed as variability in the parameters that describe protein creation . For instance , partitioning effects can result in fluctuations in protein numbers ( as described in the previous section ) , but also increase variability in the molecular machinery responsible for protein production . If plasmids or enzymes needed for protein production are unevenly distributed between daughter cells upon division , transcription rates within the two daughter cells will differ . Parameter variability within gene circuits has been modeled previously in several ways . In the absence of cell division , Mondragón-Palomino et al . randomly sampled parameters for each realization of an oscillator model [35] . If cell division is explicitly modeled , however , parameters might change significantly only when a cell divides [36] , or fluctuate continuously between divisions [14 , 15] . We modeled parameter variability by sampling the value of parameters at each cell division . For a parameter subject to variability , for example αr , we sampled its new value after cell division from a distribution centered at the value of the parameter of the mother cell . The coefficient of variation of this distribution , Γ = σαr/〈αr〉 , can then be used to tune parameter variability . Also , we incorporated a homeostatic mechanism in this distribution to ensure parameters did not diverge ( see Methods for details ) . We focused on the variability of αr , αa , and αg ( Eqs ( 1–3 ) ) . These parameters were chosen because the activity of molecular machinery such as plasmid copy number , ribosomes , and energy in the cell , are expected to fluctuate more than parameters representing binding affinities , Hill coefficient , and degradation rates . All other parameters were fixed . We also explored alternative models of parameter variability , such as sampling of parameters independently of the values of parameters of the mother cell . We found that lineage dependence was necessary to be able to estimate Ω and Γ from experiments ( see Methods ) . In simulations we varied noise due to finite protein numbers by changing Ω , and parameter variability by changing Γ . We computed the coefficient of variation of the amplitude for a range of Ω and Γ and compared it to the coefficient of variation seen in experiments . Fig 6A shows the differences in the coefficient of variation of the amplitude between simulations and experiments , |CVsim − CVexp| , as Ω and Γ were varied in the model . We also computed the correlation function from simulations , corrsim ( t ) ( t = time after cell division ) , and compared it to the experimentally obtained correlation functions , correxp ( t ) . Fig 6B shows the differences in correlation between simulations and experiments , |correxp − correxp| ( using the L2 norm ) , as Ω and Γ were varied in the model . As noted previously , in the absence of parameter variability , the experimentally observed amplitude variability can be matched in simulations with high intrinsic noise ( small Ω ) . However , this same amplitude variability is observed over a range of Γ . Indeed , a decrease in intrinsic noise ( increase in Ω ) can be compensated by increasing parameter variability ( Fig 6A , gray curve ) . Similarly , a good match for correlations in fluorescence can be obtained by appropriately tuning Ω and Γ ( Fig 6B , gray curve ) . Matching concurrently the experimentally observed amplitude variability and correlation functions allows us to determine both Ω and Γ . To explore further the dependence of amplitude variability and correlation on intrinsic and extrinsic noise , we used a simple oscillator model that captures the main features of the full model described above: d r = ρ ( r 0 - r ) d t + 1 Ω d ξ 1 d θ = 2 π T r 0 r d t + 1 r Ω d ξ 2 , ( 6 ) where r is the amplitude of a realization of the oscillation , r0 is the mean amplitude of the oscillations ( in the absence of intrinsic noise ) , ρ determines the stability of the oscillator , θ is the phase , T is the period , dξi’s are independent standard white noise processes with zero mean and unit variance , and Ω controls the level of intrinsic noise ( see Methods ) . Variability in r0 was modeled as in the full model using the coefficient of variation Γ as the parameter that controls extrinsic noise . We observed that intrinsic noise had a stronger effect on correlation than on amplitude variability; thus , large intrinsic noise can decorrelate cells faster as well as increase period variability . On the other hand , extrinsic noise has a stronger effect on amplitude variability . The same level of amplitude variability can be attained by decreasing intrinsic noise and increasing extrinsic noise ( see Methods for details ) . By varying intrinsic and extrinsic noise it is thus possible to achieve large amplitude variability and high , persistent correlations between sister cells at the same time . To validate our methodology experimentally , we compared the experimental lineage trajectories ( Fig 7A ) with simulated trajectories ( Fig 7B ) using the values of Ω and Γ estimated above . We observed that their qualitative behavior was similar; simulations and experiments exhibited robust oscillations with high amplitude variability . The distribution of the amplitudes and correlations in fluorescence show a close agreement between experiments and simulations ( Fig 7C and 7D ) . Additionally , the coefficient of variation of the period , which we did not use to fit our model , was 0 . 10 in simulations ( Fig 7E ) , close to the value observed in experiments ( 0 . 11 ) . To test the predictive power of our computational model we performed another experiment in which the concentration of IPTG was reduced from 2 mM to 0 mM . This reduction is known to decrease the period of the oscillator [16] , and hence should change the amplitude variability and correlation between sister cells . The period at 0 mM IPTG was approximately 29 min ( compared to ∼41 min in 2 mM IPTG ) , and amplitude variability and correlations were markedly different ( Fig 8 ) . To determine if our model could predict these changes , we decreased the parameter Cr in Eq ( 5 ) ( corresponding to decreasing IPTG ) until the model oscillated with the experimentally observed period . Importantly , only the parameter Cr , which represents the amount of LacI needed for half-maximal repression of the promoter , was changed , while all other parameters were fixed at the values determined from our previous fit , including Ω and Γ . This allowed us to cross-validate our model using the experimentally observed variability and correlations at 0 mM IPTG . In experiments , amplitude variability increased , but the oscillations were still robust ( Fig 8A ) ; this was also observed in simulations ( Fig 8B ) . The coefficient of variation of the amplitude was 0 . 55 in experiments , very close to the value predicted by simulations ( 0 . 56 ) . We also compared the probability distribution of the amplitude in experiments and simulations , as well as the correlation functions ( Fig 8C and 8D ) . The predicted distribution of amplitudes and the predicted correlation function matched the experimental data . Additionally , the coefficient of variation of the period was 0 . 19 in experiments ( Fig 8E ) , close to the value predicted by simulations ( 0 . 17 ) . These observations suggest that we did not overfit our model , and that our approach can be used to predict experimental outcomes . We also used the Kolmogorov-Smirnov test to compare the experimental and simulated amplitude distributions in Figs 7 and 8 . In both cases they passed the KS test ( within 95% confidence bound ) . The KS test also identified the two simulated distributions as different .
Understanding the sources and consequences of noise is key to designing robust synthetic genetic circuits . Oscillators provide an ideal platform for studying noise , as they fluctuate between large and small numbers of proteins . Hence , the relative impact of various noise sources changes with the oscillator’s phase . In experiments , we found that the amplitude exhibited high variability whereas the period did not , while GFP levels in sister cells were highly correlated for some time after cell division . Importantly , we determined that using just the amplitude variability to infer the level of intrinsic noise can result in model parameters inconsistent with other dynamical properties , such as correlation between sister cells after cell division . Instead , using a combination of intrinsic noise , cell cycle variability , and parameter variability we were able to obtain a model that better describes the stochastic dynamics of the oscillator . To estimate the impact of these sources of noise it was essential to track cell lineages across several generations . This allowed us to use long term correlations in addition to amplitude variability to distinguish between intrinsic noise and parameter variability . We used parameter variability to capture multiple possible sources of noise which are not well understood . Other sources of variability that were not investigated were the genetic instability of synthetic circuits and the health of cells ( filamentation , for example ) . However , these are very rare and difficult to examine due to the small number of occurrences , and unlikely to impact our overall conclusions due to the same reasons . Since intrinsic noise has a fast time scale [28] , it can quickly decorrelate the dynamics of sister cells . Other sources of noise can act on longer time scales [28] . This can result in large amplitude fluctuations from cycle to cycle , but a relatively slow decay of correlations between sister cells . Additionally , we found that it was necessary to implement long term correlations in parameter variability using a slow timescale . When our model did not include this feature , we could not find appropriate noise levels to explain amplitude variability and correlation at the same time ( see Methods ) . Thus , if a gene network shows high amplitude variability but sister cells are correlated for an extended time after cell division , then extrinsic noise sources are likely to be the cause of such variability . We found that modeling the perturbation of parameters at cell division can capture these effects . Importantly , this type of perturbation covers sources of noise that affect protein synthesis , including unequal partitioning of cellular resources upon division or differences in cellular energy due to fluctuations in metabolic enzyme concentrations and local carbon source availability . Our method thus points towards a general strategy to identify the sources of noise in gene networks with complex dynamics . We have examined the impact of different sources of noise in a system with relatively fast dynamics . These same sources could have a different impact in a slowly evolving system . However , our approach of using multiple statistical measures to characterize different aspects of the systems dynamics will work in such situations . For instance different sources of noise drive transitions between the two states of a genetic toggle switch [37 , 38] . Variability in these transitions likely reflect the internal and external sources of fluctuations that drive the alternation between the states . Different sources of noise likely have a different effect on the statistics of transitions between states [39 , 40] . Characterizing GFP variability , along with the first passage time distribution , and the decay of correlations in daughter cells could allow us to disentangle the different sources of fluctuations that contribute to this variability .
Devices were manufactured as in Hussain et al . ( 2014 ) . Briefly , a 4” silicon wafer ( Silicon Quest , San Jose , CA ) was cleaned with acetone and isopropyl alcohol , and then dried with compressed nitrogen . The wafer was coated with SU-8 series photoresist ( MicroChem , Newton , MA ) , then spun for 30 seconds in a spin coater ( Brewer Instruments , Roala , MO ) to distribute the resist . The wafer was baked at 95°C and let cool to room temperature . The wafer was mounted to the chuck of a mask aligner ( SUSS , Germany ) . The photomask ( CAD/Art Services , Bandon , OR ) was mounted to the mask aligner , and the wafer was aligned to the photomask . The resist was exposed to UV light for cross-linking . The wafer was baked at 95°C to finalize cross-linking . Uncross-linked resist was removed with SU-8 developer ( MicroChem , Newton , MA ) . The above steps were repeated until the device was completed . The wafer was hard-baked at 150°C to solidify the resist . To ensure PDMS liftoff , the wafer was coated with release agent ( ( ( tridecafluoro-1 , 1 , 2 , 2-tetrahydrooctyl ) -1-trichlorosilane ) , Pfaltz & Bauer , Waterbury , CT ) for 5 minutes under vacuum . Polydimethylsiloxane ( PDMS ) polymer base and curing agent ( Sylgaard 184 , Dow Corning , Midland , MI ) were mixed in a weigh boat at a 10:1 ratio until completely mixed ( ∼5 min ) . All bubbles were removed by degassing under vacuum . The mold for the selected device was wrapped in aluminum foil to contain the PDMS . Mixed PDMS was poured onto the wafer , and the degassing process was repeated . The wafer and PDMS were baked at 80°C for 2 hours . The cured PDMS monolith was removed from the wafer , and excess PDMS was trimmed from the monolith . Ports for fluidic connections were punched with a 0 . 5mm biopsy punch ( GE Healthcare , Pittsburgh , PA ) . Individual chips were cut from the monolith . Chips were sonicated in methanol for 8 minutes . Methanol was poured off , and fresh methanol was added . Another round of sonication was performed for 8 minutes . Chips were baked at 80°C for 30 minutes to remove methanol . Chips were cleaned with tape ( 3M , St . Paul , MN ) . #1 . 5 coverslips ( VWR , Radnor , PA ) were cleaned with isopropyl alcohol and dried with compressed nitrogen . Chips and coverslips were cleaned in a UV/ozone oven ( Jelight Co . , Irvine , CA ) for 3 minutes . Upon removal , chips were immediately inverted onto the coverslips to bind , and completed devices were baked at 80°C overnight to finalize binding . Microscope experiments were prepared and performed as described in [27] . The activator ( pJS167 ) and repressor ( pZA14 ) plasmids ( obtained from the Hasty lab , [16] ) were transformed into ΔaraCΔlacI E . coli cells ( JS006 , [16] ) , and plated onto LB/agar plates with 100μg/ml ampicillin and 50μg/ml kanamycin . Plates were incubated overnight at 37°C and stored at 4°C . 5–10ml overnight cultures were inoculated from transformed cells , and incubated overnight at 37°C with shaking . Prior to experiments , overnight cultures were passed into 50ml fresh LB//100μg/ml ampicillin//50μg/ml kanamycin at a 1:1 , 000 dilution ( i . e . 50μl of overnight culture in 50ml of fresh media ) . Passed cultures were grown to an OD600 of 0 . 1–0 . 2 , and 10ml was pelleted at 1 , 500×g for 5 minutes . Pelleted cells were resuspended in 10ml of fresh media with 100μg/ml ampicillin and 50μg/ml kanamycin prior to loading into the microfluidic device ( Fig 9 ) . Microscopy experiments were conducted on a Ti-E inverted fluorescence microscope ( Nikon Instruments Inc , Melville , NY ) with an acrylic incubation chamber ( In Vivo Scientific , St . Louis , MO ) . Microfluidic devices were prewarmed at 37°C for 30 minutes prior to flushing . Devices were flushed with 0 . 1% ( v/v ) Tween-20 ( Sigma-Aldrich , St . Louis , MO ) . After flushing , syringes containing media ( LB//100μg/ml ampicillin//50μg/ml kanamycin//2mM IPTG//0 . 7% ( w/v ) arabinose ) , resuspended cells , or sterilized water were connected to the microfluidic devices via 23 gauge luer stubs ( Becton-Dickson , Franklin Lakes , NJ ) , Tygon microbore tubing ( St . Gobain Performance Plastics , Paris , France ) and 23-gauge pins ( New England Small Tube Corp . , Litchfield , NH ) . Cells were forced into the trapping area by flicking the line containing the resuspended cells . After trapping cells , the flow to the trapping area was adjusted to flow fresh media across the cells by changing the height of the syringe containing the resuspended cells . Trapped cells were imaged with a 100× objective every 3 minutes for 4–6 hours . Segmentation of images was done manually . The tracking of cell lineage across images was done using our custom cell-tracking algorithm written in Matlab ( available at github . com/alanavc/rodtracker ) : For each cell , C , in an image we found its position and length , PC = ( x , y ) and LC , respectively . Then , we found all cells in the next image whose position Pnext was near P , that is |PC − Pnext| < dmove . The parameter dmove equals the maximal movement of a cell from one frame to the next . From the cells satisfying this criterion , we selected cells with length Lnext similar to LC , that is |LC − Lnext| < dgrowth . The parameter dgrowth equals the maximal expected growth between frames . We also found all pairs of cells whose length Lnext , 1 and Lnext , 2 approximately added up to LC , that is |LC − ( Lnext , 1 + Lnext , 2 ) | < dgrowth . With this criteria we created a “lineage graph” where each cell in an image had a set of possible transitions from one image to the next . Each transition corresponded to either movement between frames connecting the cell to a single descendant , or division and movement , connecting a cell to two descendants . This graph was then reduced by removing inconsistent transitions ( e . g . , a cell can only have one possible location in the next image or two locations if it divided ) . The reduced graph was further reduced by only selecting transitions that minimized ∑C ( |LC − Lnext| + |PC − Pnext| ) . The final graph consisted of transitions where each cell is associated with a unique cell ( if the cell moved ) or two cells ( if the cell moved and divided ) . The lineage trajectories were then computed using the lineage graph and the fluorescence data . One resulting lineage trajectory is shown in Fig 1B , and all trajectories are shown in Fig 10 . To simplify parameter estimation of the model given in Eq ( 4 ) we followed these steps: Since all genes were under the control of identical promoters , and the copy number of the plasmids was approximately 60 for the activator and reporter , and 25 for the repressor [23] , we considered αg = 60α ( monomer ) , αa = 60α/2 ( dimer ) , and αr = 25α/4 ( tetramer ) ; where α is a parameter that controls the maximal production rate of proteins per plasmid when fully induced . Since the maturation rate does not change the total amount of GFP , but only the smoothness of the oscillations , it was not necessary to estimate this parameter from data . The half-time of maturation of fluorescence proteins ranges from 5 to 40min [41] . We set the maturation rate of GFP to λ = ln ( 2 ) /10min−1 ( half-time equal to 10min ) , but as indicated before , our results do not depend on the precise value of λ . The promoter used in the dual-feedback oscillator is a strong promoter [23] , and thus had a large maximal transcription rate , for which the delay affects the period but has little effect on its variability [42] . Thus , it was not necessary to estimate the delay parameters from data . We considered τg = 5min as the transcriptional delay for GFP production . Since araC is a dimer and lacI is a tetramer , we considered larger delays equal to τa = 5 . 5min and τr = 6 . 0min , respectively . The growth rate was estimated from phase-contrast microscopy to be approximately β = 0 . 0295min−1 . We also assumed that the degradation of a mature GFP protein is identical to that of immature protein , and set γG = γg . Thus , the set of parameters to be estimated is P = ( α , γr , γa , γg , f , R0 , Ca , Cr ) . To estimate the remaining parameters , we first needed to normalize the data from Fig 10A–10F . This was necessary , as the actual protein levels could not be determined exactly from our fluorescence measurements . First , we rescaled the trajectories so that oscillations with different amplitude are comparable: For the fluorescence trace obtained for each oscillation , Gi ( t ) , we found the peak time , tpeaki , and consider the trajectory 33 minutes before and after the peak . That is , we considered the time series Gi ( t ) for tpeaki − 33 ≤ t ≤ tpeaki + 33 . We chose 33 minutes because that includes the previous and next trough in the trajectory completely . We then centered the trajectories at t = 0; that is , we redefined Gi ( t ) : = Gi ( t + tpeak ) for −33 ≤ t ≤ 33 ( here “≔” means reassignment , that is the quantity on the left side is assigned the value on the right side ) . We then normalized the time series using G i ( t ) ≔ G i ( t ) - m i M i - m i where mi = min{Gi ( t ) : −33 ≤ t ≤ 33} and Mi = max{Gi ( t ) : − 33 ≤ t ≤ 33} . This first normalization ensures that all peaks have height 1 and all troughs have height zero; thus , their magnitudes are now comparable . Finally , to obtain the “mean oscillation” , we took the average of all the shifted normalized oscillations: G ( t ) :=∑iGi ( t ) # of oscillationsand normalized a second time to obtain the final time series G ( t ) ≔ G ( t ) - m i n { G ( t ) : - 33 ≤ t ≤ 33 } m a x { G ( t ) : - 33 ≤ t ≤ 33 } - m i n { G ( t ) : - 33 ≤ t ≤ 33 } . For each set of parameters p , we computed the error between simulations and experimental data as E ( p ) = ∑ - 33 ≤ t ≤ 33 ( G ( t ) - G s i m ( t , p ) ) 2 G ( t ) 2 + ( μ p e r - s i m p e r ) 2 / μ p e r 2 , where Gsim ( t , p ) is the data obtained from simulations using parameters p ( centered at a peak ) , μper is the mean period of the experimental data , and simper is the period of the simulated data . The first term in E ( p ) measures the error in the shape of the oscillations and the second term measures the error in the period . We then used a gradient descend method to find the set of parameters , p , that minimized E ( p ) . The value of the estimated parameters are given in Table 1 . Fig 11 shows how the model fits experimental data . Based on the deterministic model in Eq ( 4 ) , the stochastic model is given by the reactions r→delayedτrαrh ( r , a ) r+1a→delayedτaαah ( r , a ) a+1g→delayedτgαgh ( r , a ) g+1r→r ( β+γrd ( r , a , g , G ) ) r−1a→a ( β+γad ( r , a , g , G ) ) a−1g→g ( β+γgd ( r , a , g , G ) ) g−1G→G ( β+γGd ( r , a , g , G ) ) G−1g→λgg−1 , G+1 ( 7 ) where h ( r , a ) = f - 1 + a C a ( 1 + a C a ) ( 1 + r C r ) 2 and d ( r , a , g , G ) = 1 R 0 + r + a + g + G . To simulate the system we used the delayed stochastic simulation algorithm ( delayed SSA ) . This algorithm is an extension of the standard SSA [18] , but for the delayed reactions the production of a protein is kept in a queue for the duration of the delay [19–21 , 29] . In order to control the level of intrinsic noise in Eq ( 7 ) , we first note that if we rescale all the variables in the deterministic model given in Eq ( 4 ) by the same factor , the scale of the system changes , but the dynamics do not . In other words , the levels of each protein can be rescaled so that the dynamics of the system is unchanged . In particular , using the rescaling x → x/Ω for each variable transforms the system of Eq ( 4 ) into r ˙ = Ω α r h Ω [ r ( t - τ r ) , a ( t - τ r ) ] - Ω γ r r Ω R 0 + r + a + g + G - β r , a ˙ = Ω α a h Ω [ r ( t - τ a ) , a ( t - τ a ) ] - Ω γ a a Ω R 0 + r + a + g + G - β a , g ˙ = Ω α g h Ω [ r ( t - τ g ) , a ( t - τ g ) ] - Ω γ g g Ω R 0 + r + a + g + G - β g - λ g , G ˙ = λ g - Ω γ G G Ω R 0 + r + a + g + G - β G , ( 8 ) where h Ω ( r , a ) = f - 1 + a Ω C a ( 1 + a Ω C a ) ( 1 + r Ω C r ) 2 . Thus , from the parameters in Table 1 , we obtain a family of parameters P Ω = ( Ω α , Ω γ r , Ω γ a , Ω γ g , f , Ω R 0 , Ω C a , Ω C r ) , for which the system in Eq ( 4 ) exhibits the same dynamics . Note that increasing Ω increases the number of each protein within the model . This is important , because we cannot infer the number of fluorescent proteins directly from the data . However , the amount of intrinsic noise within a biochemical system depends on the absolute number of the proteins within the circuit . Therefore , Ω controls the level of intrinsic noise in Eq ( 7 ) —increasing Ω decreases the level of intrinsic noise [43] . The parameter Ω can be interpreted as a unitless volume when parameters are fixed or as a scaling parameter when the volume is fixed . Here we used the second interpretation . We simulated 500 single-cell trajectories for each value of Ω in Fig 3B . To incorporate the cell cycle we account for cell growth explicitly and divide the volume of a cell by two when a cell divides . Thus , a variable for volume , V ( t ) ( satisfying V′ = βV ) , was introduced in the model and the cell divides when V ( t ) = Vmax , where Vmax is the volume required for division . At time t = 0 , we start with a volume equal to 1 , V0 ( 0 ) = 1 , and simulate the model until V0 ( t ) = Vmax . More precisely , if y is the state of the system and x is a protein of interest , then a reaction x ⟶ R ( y ) x + 1 becomes x ⟶ V R ( y / V ) x + 1 after incorporating cell size in the model [44] . The expression y/V is simply the concentration of proteins . Here V multiplies R because the molecular machinery in charge of production and degradation is being replicated proportionally to cell size ( e . g . plasmids ) . This can also be justified starting from the deterministic model , incorporating cell size explicitly , and then incorporating stochasticity as follows . Consider a variable x that evolves according to d d t [ x ] = R ( [ y ] ) - β [ x ] , where [⋅] denotes concentration and β is the parameter corresponding to dilution due to cell growth . Since [x] = x/V and [y] = y/V , we obtain d d t x V = R y V - β x V . Since d d t ( x V ) = ( x ′ V - V ′ x ) / V 2 and V′/V = β , we obtain x ˙ = V R y V , which describes how x evolves incorporating volume explicitly . Finally , the stochastic implementation takes the form x → V R ( y / V ) x + 1 . At cell division we divide the volume by half and partition all the existing proteins randomly into two groups corresponding to the two daughters using binomial partitioning . We similarly partition the stack of immature proteins in the production queue . We used a binomial distribution for partitioning ( each cell is equally likely to receive a protein ) [28] . Modeling cell division explicitly not only allows us to model variability caused by cell partitioning ( Fig 4A ) , but it also allows us to simulate lineages ( Fig 4B ) and compare them directly to lineage trajectories from experimental data . Modeling cell division explicitly also allows us to incorporate the variability in growth rate ( β ) and the size required for division ( Vmax ) . We estimated the variability from cell to cell of β and Vmax ( Fig 12 ) , and incorporated this information in the simulations . The statistics of Vmax and β were found from phase-contrast microscopy data . We used a normal distribution for β and a shifted-gamma distribution for Vmax: β = β0 ( 1 + Γβ η1 ) and Vmax = Vbase + η2 , where η 1 ∼ N ( 0 , 1 ) and η2 ∼ Gamma ( k , θ ) , where k and θ are the shape and scale of the gamma distribution ( Table 2 ) . We simulated 1000 lineages ( 8 generations each ) for each value of Ω in Fig 5 . We did not include variability in the size ratio of sister cells at division because it was not clear if the differences between sister cells sizes were due to real differences or due to errors in the segmentation of phase-contrast images . We estimated the variability of the size ratio ( size of daughter cell/size of mother cell ) to have a CV of about 0 . 09 . The true variability between sister cells in cell size would be smaller . We checked the impact of including this overestimated value of size ratio variability in simulations used to obtain Fig 5 , and the variability reported for Ω = 0 . 5 , 1 . 0 changed by only 3% . We implemented parameter variability in the model as described in Fig 13 . For a reaction x → R ( y , p ) x + 1 where y is the state of the system and p denotes a parameter of interest , the simulation proceeds as follows: When a cell is not dividing , the parameter has a constant value p = p0 and the reaction rate is x → R ( y , p 0 ) x + 1 . When the cell divides , the parameters for the sister cells may be different due to upstream fluctuations or partitioning effects . Thus , sister cell 1 is assigned the parameter p = p1 and cell 2 the parameter p = p2 . After cell division the reaction rates are therefore different between the cells . Changes in the parameter p may represent transient changes in plasmid copy number or the availability of enzymes needed for protein production , as well as enzymes needed for protein degradation . In our simulations for Figs 6 and 7 we modeled the evolution of a parameter of interest p by resampling it upon every division using the recursive relation p i + 1 ∼ N ( q p i + ( 1 - q ) ⟨ p ⟩ , σ p 2 ) . ( 9 ) Here σp was chosen so that the CV was Γ and 〈p〉 denotes the mean value of p . This is equivalent to pi+1 = ( qpi + ( 1 − q ) 〈p〉 ) ( 1 + Γη ) , where η ∼ N ( 0 , 1 ) . The parameter q represents the timescale of a homeostatic mechanism that ensures that the variance of p does not diverge . The initial parameters were themselves sampled from the stationary distribution of Eq ( 9 ) , and parameters of daughter cells were sampled from a distribution determined by the parameters of the mother cell , p daughter ∼ N ( q p mother + ( 1 - q ) 〈 p 〉 , σ p 2 ) . The parameters of two sister cells , pdaughter1 and pdaughter2 , were chosen so that their mean was equal to pmother + ( 1 − q ) pmean , that is ( pdaughter1 + pdaughter2 ) /2 = qpmother + ( 1 − q ) pmean . For example , to model the evolution of the parameter that controls the production rate of the repressor , we initialized the system with a random value of α r 0 , taken from the stationary distribution of the sequence α r i + 1 = ( q α r i + ( 1 - q ) 〈 α r 〉 ) ( 1 + Γ η ) , where η ∼ N ( 0 , 1 ) . Then , after one cell division , the new parameter was α r 1 = ( q α r 0 + ( 1 - q ) 〈 α r 〉 ) ( 1 + Γ η ) . At the next division , we used α r 2 = ( q α r 1 + ( 1 - q ) 〈 α r 〉 ) ( 1 + Γ η ) , and so on . To account for variability in the copy number of plasmids , and enzymes required for protein production , we considered variability in the parameters αx where x ∈ {r , a , g} and kept the others fixed . The mean value of the parameters were set to 〈αg〉 = 60α , 〈αa〉 = 60α/2 , and 〈αr〉 = 25α/4 , where α is given in Table 1 . The parameters changed independently at cell division according to the rule α x i + 1 = ( q α x i + ( 1 - q ) 〈 α x 〉 ) ( 1 + Γ η ) , for x ∈ {r , a , g} . To model the slow evolution of parameters we used q = e−ln ( 2 ) /5 , corresponding to a timescale of 5 cell generations . Fitting amplitude variability and cell-cell correlation yielded the values Ω = 2 . 1 and Γ = 0 . 12 ( Table 3 ) . We also considered simulations where the parameters were taken from the same distribution without changing the mean . That is , we considered p daughter ∼ N ( 〈 p 〉 , σ p 2 ) , regardless of pmother . Note that this corresponds to using an instantaneous time scale of parameters , q = 0 . With this choice , we could not find values of the parameters Ω and Γ that provided a match with experimental data ( Fig 14 ) . On the other hand , using p daughter ∼ N ( p mother , σ p 2 ) , still allowed us to fit Ω and Γ . Note that this corresponds to using an infinitely slow time scale of parameters , q = 1 . Thus , including the slow evolution of parameters was important in estimating the values of the noise sources . Here we explore the effects of intrinsic and extrinsic noise on amplitude variability and correlation using the toy oscillator in Eq 6 . We also illustrate that our method of estimating the levels of intrinsic and extrinsic noise can be expected to work in general . The stochastic model in Eq 6 can be derived by transforming a 2 dimensional oscillator x ˙ = f ( x , y ) + η 1 ( t ) , y ˙ = g ( x , y ) + η 2 ( t ) to polar coordinates , where η1 ( t ) and η2 ( t ) are independent noise terms [45] . Using x = rcos ( θ ) , y = rsin ( θ ) we obtain r ˙ = f cos ( θ ) + g sin ( θ ) + η 1 cos ( θ ) + η 2 sin ( θ ) , θ ˙ = g cos ( θ ) - f sin ( θ ) r + η 2 cos ( θ ) - η 1 sin ( θ ) r . Since in steady state the periodic solution of this equation has constant radius , we can use its rotational symmetry . Then , fcos ( θ ) + gsin ( θ ) = h1 ( r ) and gcos ( θ ) − fsin ( θ ) = h2 ( r ) . Also , we consider that h1 ( r ) = ρ ( r0 − r ) , where ρ again determines the stability of the limit cycle r = r0 . If the period of the limit cycle is T , we can consider h 2 ( r ) = 2 π r 0 T ( constant ) . We then obtain r ˙ = ρ ( r 0 - r ) + η 1 cos ( θ ) + η 2 sin ( θ ) , θ ˙ = 2 π T r 0 r + η 2 cos ( θ ) - η 1 sin ( θ ) r . Because the system has rotational symmetry , we can consider r ˙ = ρ ( r 0 - r ) + η 1 * , θ ˙ = 2 π T r 0 r + η 2 * r , where η 1 * and η 2 * are independent as in Eq 6 . Parameter variability can then be incorporated as fluctuations in r0 . We tested if our method can obtain the true values of Ω and Γ using only the amplitude variability and correlation . First , we chose a fixed level of intrinsic and extrinsic noise: Ω0 = 200 and Γ = 0 . 15 , and then generated simulations from which we computed the amplitude variability and the amplitude correlation: varexp and correxp ( in silico experimental data ) . Then , using the same analysis done for the full model , we computed the heatmaps of the difference between the amplitude variability from in silico experiments and from simulations ( |varsim − varexp| , Fig 15A ) as well as the difference in amplitude correlation ( |corrsim − correxp| , Fig 15B ) . Using these heatmaps , we found the predicted levels of noise to be ( Ω , Γ ) ≈ ( 200 , 0 . 15 ) , precisely the levels of noise used to generate the in silico experimental data ( Fig 15C . ) We also observe that intrinsic noise has a stronger effect on correlation than on amplitude variability , whereas extrinsic noise has a stronger effect on amplitude variability . | A goal of synthetic biology is to design genetic circuits using mathematical models that predict circuit function . However , various sources of noise impact gene regulation in different ways . This hinders the development of accurate mathematical models , especially when single-cell accuracy is required . Here , we first experimentally characterize the noisy dynamics of a synthetic gene oscillator at the single-cell level . Then , using measurements obtained from the experiments , we develop a minimal computational model that correctly predicts the statistical behavior of single cells within a growing colony . Our method can be used to construct simple computational models that not only capture the average dynamics of gene circuits , but also the statistical properties of single cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
]
| []
| 2015 | Sources of Variability in a Synthetic Gene Oscillator |
One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies . So far , few cis expression quantitative trait loci ( eQTLs ) have been reliably related to disease susceptibility . Trans-regulating mechanisms may play a more prominent role in disease susceptibility . We analyzed 12 , 808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1 , 490 European unrelated subjects . We applied a method of extraction of expression patterns—independent component analysis—to identify sets of co-regulated genes . These patterns were then related to 675 , 350 SNPs to identify major trans-acting regulators . We detected three genomic regions significantly associated with co-regulated gene modules . Association of these loci with multiple expression traits was replicated in Cardiogenics , an independent study in which expression profiles of monocytes were available in 758 subjects . The locus 12q13 ( lead SNP rs11171739 ) , previously identified as a type 1 diabetes locus , was associated with a pattern including two cis eQTLs , RPS26 and SUOX , and 5 trans eQTLs , one of which ( MADCAM1 ) is a potential candidate for mediating T1D susceptibility . The locus 12q24 ( lead SNP rs653178 ) , which has demonstrated extensive disease pleiotropy , including type 1 diabetes , hypertension , and celiac disease , was associated to a pattern strongly correlating to blood pressure level . The strongest trans eQTL in this pattern was CRIP1 , a known marker of cellular proliferation in cancer . The locus 12q15 ( lead SNP rs11177644 ) was associated with a pattern driven by two cis eQTLs , LYZ and YEATS4 , and including 34 trans eQTLs , several of them tumor-related genes . This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease .
Owing to the development of genome-wide association studies ( GWAS ) , the last two years have witnessed spectacular successes in the identification of new loci involved in the susceptibility to complex diseases [1] . However , most of these associations have yet to be translated into a full understanding of the genetic mechanisms that are mediating disease susceptibility . The possibility of assaying genome-wide expression ( GWE ) and genome-wide variability ( GWV ) simultaneously in large-scale studies opens new perspectives for unravelling these mechanisms [2] . Several studies on the genetics of expression have shown that a considerable number of genes are regulated by expression SNPs and that cis expression quantitative loci ( eQTLs ) largely outnumber trans eQTLs [3]–[8] . A reason for this imbalance might be that trans eQTLs are beneath the level of detection of most studies because , unlike cis eQTLs , they do not directly influence gene expression . Moreover , trans associations are more sensitive to confounding factors including technical experimental effects and stratification of the cell population [9] . Large-scale transcriptional modules , i . e . sets of genes highly co-regulated , which are thought to be involved in pathophysiological processes [10] , have been described in yeast [11] , [12] , Drosophila [13] , mice [14]–[16] and humans [6] . Identification of trans-acting SNPs affecting such transcriptional modules might enhance our understanding of the molecular mechanisms involved in pathophysiological processes . Since such trans-acting SNPs are expected to have pleiotropic effects on a large number of genes , each being modestly affected , their statistical mapping may be facilitated by prior recognition of subsets of co-regulated genes . In the present study , we have analyzed 12 , 808 genes expressed in circulating monocytes in relation to GWV in a population-based sample of 1 , 490 unrelated subjects participating in the Gutenberg Health Study ( GHS ) . We applied first a method of extraction of expression patterns – independent component analysis ( ICA ) [17]–[19] – in order to identify sets of co-regulated genes . These patterns were then related to 675 , 350 SNPs to identify major trans-acting regulators . We identified three genomic regions , centered on the ERBB3 , SH2B3 and LYZ-YEATS4 genes respectively , that were associated to expression patterns . Connecting these results with recent GWAS findings provided potential clues for better understanding the genetic basis of complex diseases .
The goal of ICA [18] , [19] is to find hidden variables , called “independent components” , which represent underlying processes that influence gene expression . The expression of each gene is written as a linear function of these components , where the influences of different components show minimal statistical dependencies . Each component defines groups of co-induced and/or co-repressed genes . These components may be viewed as reflecting distinct biological causes influencing gene expression , such as activation of signaling pathways , binding of transcription factors , posttranscriptional regulation… We consider an expression data matrix X whose rows correspond to genes and columns to individuals . The ICA model splits the matrix into a matrix product X∼SA ( see Figure 1 ) , subject to the condition that the statistical dependence between the K columns of S be minimized . The expression level of gene i in individual j iswhere sik is the contribution of component k on gene expression i and akj is the level of “activation” of that component in individual j . Note that the components can be interpreted in a dual view . First , each column of S is a vector of the linear contributions of the component on each gene expression which can be interpreted as the “signature” of the underlying biological process . To minimize the dependence between the columns of S , ICA identifies components that exhibit approximately sparse signatures , showing an increased proportion of contributions close to zero . Each component can then be characterized by a set of genes for which its contributions are “significantly” different from zero ( see the definition of modules below ) . Importantly , different components can be characterized by overlapping sets of genes . For this reason , ICA is likely to better reflect biological reality than methods that partition genes into distinct clusters . Alternatively , each component can be characterized by its pattern of expression in individuals ( rows of A ) which reflects the level of “activation” of the underlying biological process . Pattern levels are estimated by linear combinations of gene expression levels obtained by inverting the equation X∼SA . Patterns can be correlated with each other in the population . This is an advantage of ICA over classical methods of dimensionality reduction relying on orthogonality of factors like principal component analysis ( PCA ) [21] , [22] . In the following , we used the term of “signature” or that of “pattern” for a component according to whether it referred to columns of S ( genes ) or rows of A ( individuals ) . Figure 1 shows an illustration of ICA for K = 2 . Figure 2 shows the analysis workflow . After normalization of raw expression data , filtering of undetected probes and removal of outlier samples by multi-dimensional scaling ( MDS ) analysis ( Figures S1 , S2 , S3 , S4 ) , singular value decomposition ( SVD ) was used prior to ICA to reduce the dimensionality of data and determine the optimal number of components to extract by ICA [18] , [19] ( see Text S1 ) . As shown by the SVD screeplot ( Figure S5 ) , 30 orthogonal components were able to capture 50% of the global variability of the transcriptome . However , as we were interested in components potentially explaining small , but meaningful , variations of the transcriptome , we extended the number of components up to the limit beyond which variability appeared mostly attributable to random noise . According to the SVD screeplot , this limit was 112 ( Text S1 ) . The FastICA algorithm was then run with this fixed number of components . Twenty-one of the 112 components identified by ICA were characterized by a single individual who explained more than 10% of the variability of the pattern in the population . Actually , we found that most of these individuals , although not having been initially identified as outliers , were at the periphery of the main cluster of individuals obtained from the MDS analysis of expression data performed prior to ICA ( Text S1 , Figure S4 ) . These 21 “individual-specific” components were no longer considered , leaving 91 components for further analysis . The fundamental principle of ICA estimation is that the columns of S ( signatures ) must be as non-gaussian as possible , typically a peaked distribution with few genes at the tails to which the signature strongly contributes , and the majority of genes in the center being weakly or not influenced [18] , [19] . To determine the most non-gaussian signatures , hence the most informative components , we used the kurtosis which measures the peakedness of the distribution [18] and focused on signatures showing a kurtosis ≥3 . This criterion led to the selection of 64 signatures . As explained above , the 64 signatures correspond to 64 patterns of expression in the population . Some of these patterns exhibited strong pairwise correlations ( see the correlation matrix in the GHS_ICA_Modules database at http://genecanvas . ecgene . net/uploads/ForReview/ ) . For each of these 64 signatures , we defined the “module” as the subset of genes the most strongly influenced , i . e . genes at both extremes of the distribution . For this purpose , we used a method proposed for false discovery rate ( FDR ) estimation [23] . Genes associated with an FDR<10−3 were considered as belonging to the module characterizing the signature . The size of the modules varied from 14 to 670 genes ( median 179 ) . A Gene Ontology ( GO ) analysis was performed to identify modules that were associated with specific biological processes . For 42 of the 64 modules ( 66% ) , we found a significant enrichment of GO classes from genes of the module ( Table S1 ) . Over-represented biological categories included a large number of categories related to immune and inflammatory response ( reponse to virus , T-cell activation , response to bacteria/fungus , cytokine activity , acute inflammatory response , humoral immune response … ) and several low level biological process categories such as the nucleotide metabolic process , mRNA metabolic process , ribosome biogenesis , regulation of cell proliferation , nucleosome assembly ( histone genes ) , and cell-cycle . We next investigated whether the level of expression of patterns in the population was influenced by SNPs . GWV genotyping was carried out using Affymetrix SNP Array 6 . 0 . After quality control filters , 675 , 350 SNPs were available for testing association with the 64 patterns . Association between patterns and SNPs was tested in a 2-step approach ( Figure 2 ) . First , we applied a filtering to select SNP-pattern associations that were significant at P<10−7 ( suggestive associations ) . The significance threshold used in this first step was taken not too stringent in order to increase the sensitivity . The second step was aimed at discarding the SNP-pattern associations that were almost entirely explained by a single or very few genes of the module whose expression strongly correlated to the SNP . This would be the case , for example , for a SNP having a strong effect on a cis eQTL belonging to the module , but not associated with any other expression trait of the module . To exclude these cases of less interest for the present study , a SNP-pattern association was retained at step 2 if the corresponding module was significantly enriched in expression traits individually associated to the SNP by reference to the whole set of expression traits . Since the goal here was to detect associations not necessarily very strong but clustering within modules , a threshold of P<10−5 was taken for associations between SNP and individual expression traits ( the threshold adopted for results reported in the publicly available GHS_Express database of SNP-expression associations http://genecanvas . ecgene . net/uploads/ForReview/ ) . Enrichment of the module in significant associations was tested using a hypergeometric test with a threshold of significance of 1 . 15×10−9 ( Bonferroni-corrected for 64 modules×675 , 350 SNPs ) . This 2-step approach led to the detection of 11 patterns associated with one or several SNPs at the same locus . The proportion of variability of the pattern explained by the lead SNP at the locus varied from 1 . 9% to 24 . 8% ( Table 1 ) . Because the method of monocyte enrichment did not yield a 100% purity and even modest heterogeneity of cell content may induce artefactual correlations among expressions [24] , we checked whether contamination by non-monocyte cells might affect the associations observed . For this purpose , we generated surrogate variables of contamination corresponding to each blood cell type reported in the HaemAtlas [25] . We created 7 variables corresponding to the different cell types ( CD4+ , CD8+ , CD19+ , CD56+ , CD66b+ , erythroblasts and megakaryocytes ) by averaging in each individual his ( her ) levels of expression for the transcripts reported to be specific of that cell type . When re-testing the 11 SNP-pattern associations by multiple regression analysis simultaneously adjusting for the 7 contamination variables , 5 associations lost significance ( Table 1 ) . Worthy of note , the corresponding modules were enriched in GO categories relevant for the incriminated cell types ( Table S1 ) . Moreover , in several cases the best associated SNP was located in a gene highly relevant to the type of cell: the ARHGEF3 gene , which has been reported to influence mean platelet volume [26] , was involved in potential contamination by platelets; the CD8A gene , encoding the alpha chain of the CD8 antigen found on T cells , was involved in the level of likely contamination by T cells; the MAP3K7 gene , a gene involved in B-cell specific immune response [27] , was involved in the level of contamination by B cells . Following the same reasoning , we might anticipate a biological link between the MAGI2 gene and potential contamination by erythroblast-derived cells ( Table 1 ) . For associations that were not affected by potential contamination , we checked whether they replicated in the Cardiogenics Study in which monocyte GWE profiles and GWV genotypes were available in 758 subjects ( see Methods ) . Replication in Cardiogenics was assessed by examining the association between the lead SNP ( or a proxy when it was not available ) and each expression trait of the module . For three of the SNP-pattern associations ( rs1058348-pattern7 , rs2300573-pattern33 and rs2842892-pattern66 ) , replication was not achieved in Cardiogenics as none of the expression traits in the module was significantly associated to the SNP . Detailed results of these associations are available in the GHS_ICA_modules database ( http://genecanvas . ecgene . net/uploads/ForReview/ ) . Worthy of note , module of pattern 33 was strongly enriched in genes involved in the immune response and largely overlapped with the recently identified rat network centered on the transcription factor IRF7 , a master regulator of the type-1 interferon response [28] . For three modules , at least two expression traits were significantly associated to the SNP at a Bonferroni-corrected threshold , and a significant enrichment in genes associated with the SNP was observed at the suggestive threshold of 10−3 . The association between pattern 102 and rs11171739 on chromosome 12q13 ( P = 2 . 9×10−70 for association , P = 2 . 5×10−21 for enrichment ) is of particular interest as rs11171739 has been identified by GWAS as a marker for T1D susceptibility [29] , . The locus 12q13 encompasses two genes , ERBB3 coding for a receptor tyrosine kinase and RPS26 coding for a ribosomal protein . Cis regulation of RPS26 in diverse tissues , in particular the pancreas , has been used to argue that this gene was a more likely candidate than ERBB3 for T1D association although this is a matter of controversy [7] , [30] , [31] . Module 102 contained two cis eQTLs associated to rs11171739 , RPS26 and SUOX ( P<10−300 and 3 . 1×10−18 , respectively ) . The cis regulation of RPS26 in monocytes confirms that reported in other cell types [4] , [7] , [8] . Module 102 also contained several paralogs of RPS26 ( RPS26L , RPS26L1 and RPS26P10 ) whose association with rs11171739 was probably due to cross-hybridization artifacts ( Table S2 ) . Two other genes were significantly associated in trans , MADCAM1 on chromosome 19 and CCDC4 ( also known as BEND4 ) on chromosome 4 , a gene of unknown function whose expression was also found associated to the 12q13 locus in leukocytes [8] . All gene expressions individually replicated for association in Cardiogenics with a proxy of rs11171739 ( rs10876864 , LD r2 = 0 . 91 ) , except RPS26L1 ( P = 0 . 75 ) and RPS26P10 for which there was no probe in Cardiogenics ( Table S2 ) . Moreover , all associations were in the same direction in the two studies and the SNPs were associated with very similar R2 . Among all the genes of module 102 , MADCAM1 ( mucosal addressin cell adhesion molecule-1 ) appears as the strongest biological candidate for T1D [32]–[34] . However , caution is needed in the interpretation of the present results for several reasons . First , when performing the analysis at a probe level , the effect of rs11171739 was observed for only one of the two Illumina probes ( ILMN_1767973 ) . Second , this probe was detected ( i . e . expressed above background ) only in a small fraction of subjects ( ∼7% ) , the other probe being undetected . The SNP association for this probe , however , strongly replicated in Cardiogenics ( Table S2 ) , albeit that both probes were considered as “undetected” according to the detection criteria set for their analysis in Cardiogenics . Nevertheless , the consistency of the association with the SNP in both studies raises an important issue related to the difference between lack of detection and lack of expression , as recently indicated by a study showing that a large fraction of X-linked genes considered as non-expressed by microarray studies were actually detectable by RNA-sequencing quantification [35] . Even when its expression level is below the microarray detection threshold , a transcript may be of great interest if it proves to be related to a SNP or any other relevant factor . The increasing power of most contemporary transcriptomic studies should facilitate the detection of effects that were missed in earlier less-powered studies . Further validation experiments in monocytes using RT-PCR indicated hybridization problems around the MADCAM1 exon 4 region where the associated Illumina probe is located and did not replicate the association ( unpublished results ) . Since SNPs are present within the sequence used for replication ( www . ensembl . org ) , we cannot exclude the possibility that insufficient hybridization and/or cross-hybridization affects the present results . Nevertheless , using probes and primers located in other exonic regions of the gene , high expression of MADCAM1 in monocytes was detected . Further , unpublished expression data on exon level in peripheral blood mononuclear cells confirmed these observations of MADCAM1 expression and association with rs11171739 ( P<0 . 03 ) . The association between pattern 62 and rs653178 at locus 12q24 ( P = 2 . 4×10−9 for association , P = 5 . 5×10−10 for enrichment ) deserved attention for several reasons . First , the locus 12q24 has been reported in GWAS to be involved in pleiotropic phenotypes including celiac disease [36] , T1D [30] , asthma [37] , myocardial infarction and coronary artery disease [26] , [37] , blood pressure ( BP ) [38]–[40] , platelets counts [26] , eosinophil number [37] and hematocrit [39] . The locus encompasses two genes , SH2B3 and ATXN2 , SH2B3 being generally considered as the most likely candidate for disease susceptibility . Second , pattern 62 strongly correlated with systolic ( P = 2 . 7×10−20 ) and diastolic ( P = 5 . 7×10−15 ) BP in GHS subjects . Third , the most significant gene expression within module 62 was CRIP1 ( P = 2 . 8×10−7 ) which , in a previous analysis of GHS data , emerged as the strongest correlate of systolic BP [20] . The association of rs653178 with CRIP1 expression replicated in Cardiogenics ( P = 2 . 2×10−5 ) . The association was in the same direction in the two studies and the SNP was associated with comparable R2 ( 2 . 0% in GHS and 2 . 6% in Cardiogenics ) ( Table S3 , Figure 3 ) . SH2B3 , also known as LNK , is a member of the family of adaptor proteins mediating the interaction between the extracellular receptors and intracellular signaling pathways . It is expressed in hematopoietic precursor cells and endothelial cells and acts as a broad inhibitor of growth factor and cytokine signaling pathways [41] . The association of rs653178 with pattern 62 was not mediated by a cis effect on SH2B3 or by any other cis eQTL . In addition to CRIP1 , module 62 included four expression traits significantly associated in trans with rs653178 ( RAB11FIP1 , MYADM , TIPARP and TREM1 ) , among which RAB11FIP1 showed a borderline association in Cardiogenics ( P = 0 . 01 ) ( Table S3 ) . Rs653178 belongs to a long-range haplotype which also carries rs3184504 , a non-synonymous polymorphism ( R262W ) of the SH2B3 gene which is located in a pleckstrin homology domain involved in intracellular signaling . This haplotype has probably arisen from a selective sweep specific to Europeans since it is not observed in African and Asian populations [26] . The C allele of rs653178 , which is the allele associated with increased BP and higher risk of disease in GWAS , was associated with decreased expression of CRIP1 ( Figure 3 ) . However , in the GHS population , CRIP1 expression was positively related to SBP ( r = 0 . 28 ) and DBP ( r = 0 . 18 ) , suggesting a complex relationship between genetic variation , gene expression and disease . CRIP1 ( cysteine-rich intestinal protein ) belongs to a family of proteins with a LIM domain . LIM domains are protein interaction domains functioning in the regulation of gene expression , cell adhesion and signal transduction [42] . CRIP1 is highly expressed in immune cells and overexpression of CRIP1 in transgenic mice has been shown to alter the immune response [43] . CRIP1 has also been identified as a marker of cellular proliferation in several types of cancer [44] . Consistent with this role , module 62 included several genes involved in cellular growth and/or tumorigenicity ( MYADM , SGMS2 , EMP1 , ITGA5 , KLF6 , FOXO1 ) . The present results suggest that CRIP1 might play a central role in the pleiotropic effects of SH2B3 in several diseases . The strongest association was between rs11177644 at locus 12q15 and pattern 98 ( P = 1 . 1×10−92 for association , P = 1 . 2×10−86 for enrichment ) ( Table 1 ) . The block of association included 40 SNPs and the lead SNP explained 24 . 8% of the pattern variance . The module included two cis eQTLs , LYZ and YEATS4 ( 48 . 6% and 37 . 7% of expression variability explained by the lead SNP , respectively ) as well as 34 genes associated in trans , 17 of which with a P-value<10−12 . Almost all associations were confirmed in Cardiogenics ( Table S4 ) . Most expression traits of module 98 negatively correlated to LYZ and YEATS4 ( Figure S6 ) . When including expression levels of LYZ and YEATS4 as covariates in the linear regression model relating each trans eQTL to rs11177644 , all trans associations considerably decreased ( median R2 decreasing from 3 . 2% to 0 . 5% ) , suggesting that these trans associations were mediated by cis regulation at the locus . LYZ encodes human lyzozyme which is secreted by monocytes and has a bacteriolytic function . YEATS4 ( also known as GAS41 ) is a member of a large family of domain proteins which form complexes involved in chromatin modification and transcriptional regulation and has a strong link to cancer [45] . It was not possible from the present data to infer whether pattern 98 reflects a unique pathway involving LYZ and YEATS4 or whether it was a mixture of two independent pathways that showed coincidental correlation because of the physical proximity of LYZ and YEATS4 on chromosome 12 . To validate the approach used in this study , we compared the results obtained by ICA to those obtained by WGCNA , a method recently proposed to identify sets of co-expressed genes [46]–[48] . The WGCNA method is based on a clustering of genes into non overlapping classes , called “modules” , based on their profiles of co-expression . Each resulting module is then characterized by its first principal component referred to as the module eigengene ( ME ) . When applying the WGCNA method to our data with default parameters , the 12 , 808 gene expression traits were clustered into 26 modules ( Table S5 ) . We computed the correlations between the 26 MEs and the 64 patterns obtained by ICA ( Figure 4 ) . Twenty-three MEs ( 88% ) exhibited a correlation >0 . 8 with at least one ICA pattern . Conversely , only 20 ICA patterns ( 31% ) correlated to a ME with the same intensity , suggesting that ICA was able to identify patterns that were not represented by WGCNA modules , such as patterns 62 and 102 described above . Eleven of the 26 original WGCNA MEs ( 42% ) were found enriched in GO categories against 42 modules ( 66% ) for ICA using the same significance threshold . To get a more balanced comparison between the two methods , we increased the number of clusters extracted by WGCNA by tuning the parameters ( deepSplit and minModuleSize ) , leading to 71 WGCNA modules ( Table S6 ) . Although the advantage of ICA appeared weaker in that case , the tendency remained the same: the fraction of WGCNA MEs exhibiting a correlation >0 . 8 with at least one ICA pattern was 52% ( n = 37 ) , while 42% of ICA patterns ( n = 27 ) correlated to one of the 71 MEs ( Figure S7 ) . Only 14 ( 20% ) of these 71 MEs were found enriched in GO categories . The higher interpretability of ICA modules in known biological functions was however mostly attributable to the larger size of ICA modules ( median size: 178 . 5 genes ) compared to WGCNA modules ( median size: 46 genes ) . Indeed , when selecting the subset of the 200 genes the most correlated to each ME , or with the highest absolute contribution to the ICA pattern signature , respectively , the proportions of GO enriched subsets was similar between ICA ( 67% , n = 43 ) and WGCNA ( 63% , n = 45 ) . We next compared the power of the two methods for identifying SNPs associated with sets of co-expressed genes . Figure 5 compares the quantile-quantile plots of the Sidak-corrected P-values obtained when testing the 675 , 350 SNPs against the 26 ( or the 71 ) WGCNA MEs on one hand , and the 64 ICA patterns on the other hand . Much stronger associations were found with ICA patterns than with WGCNA MEs , regardless of the number of modules extracted by WGCNA . Worthy of note , the strongest associations ( P<10−16 ) detected with the 26 original WGCNA MEs involved SNPs of the ARHGEF3 locus , which turned out to be likely explained by contamination by platelet RNA ( see above ) .
Various methods have been proposed to detect sets of co-expressed genes , including nonnegative matrix factorization [49] , connectivity-based approaches such as WGCNA [6] , [46]–[48] , [50] or Bayesian networks [7] , [10] , [12] , [51] . The ICA method [18] , [19] used in the present study is based on the assumption that the co-expression of genes may be described by a small number of latent features exerting independent influences on expression . Ideally , these features may be related to distinct biological causes of variation , like regulators of gene expression , cellular functions or response to environment [19] . ICA has been applied to different types of microarray data , in particular to identify expression signatures in cancer [52]–[54] . Most of the components extracted by ICA could be characterized by a specific module of genes . A GO enrichment analysis indicated that two thirds of these modules were enriched in GO categories , thus highlighting the ability of ICA to recover biologically meaningful covariation . The proportion of enriched modules might be artificially inflated by the fact that ICA allows for modules of overlapping gene sets , leading to similarity between modules . However , the pairwise overlap between modules was generally limited ( ranging from 1 to 20% , median 6% ) . In addition , most of the GO categories that were shared between similar modules were relatively large categories ( e . g . immune response or intracellular components ) and it may be hypothesized that the corresponding modules reflected specific aspects of a more general process . As pointed out by others [13] , modules can help in functional annotation of genes of unknown function based on known annotations of other genes in the module , such as CCDC4 in the RPS26-associated module . Patterns of co-expression might be confounded by systematic variations introduced during sample processing or microarray measurements and by heterogeneity of the cell population [9] , [24] . In particular , patterns observed in unseparated peripheral blood mononuclear cells or whole tissues are more likely to reflect variations in the tissue composition rather than true cell-specific co-expression . In the present study , monocytes were isolated by negative selection . The choice of the method for separation of leukocytes is a matter of debate . Negative selection results in lower cell purity , while positive selection may induce cellular activation and altered transcription due to cross-linking cell surface antigens . A comparison of the two methods in 6 subjects suggested that positive selection did not induce important changes in gene expression [24] . However , the study had little power to detect modest variations such as those involved in trans associations . Thanks to the recent advances in the characterization of genes specific of the different blood cell lineages [25] , it is now possible to better control in silico for potential heterogeneity of the cell population under study . We used this information to test the robustness of the SNP-pattern associations after adjustment for surrogate variables of contamination ( S . Maouche et al . In preparation ) . It was not possible , however , to adjust expression data prior to ICA since we observed that such adjustment could induce other artifactual correlations , probably because genes supposed to be specific of a given cell type may also be expressed , although at lower levels , in the monocyte . Since none of the presently available methods yields an 100% purity , in silico adjustment appears as a solution for post hoc controlling the robustness of associations as recently proposed [55] , [56] . Using this robust approach , we investigated trans associations of pattern of co-expression in a large population-based study , the Gutenberg Health Study ( GHS ) . We replicated significant trans associations ( but not entire modules ) detected in the GHS in an independent study , Cardiogenics . Our results showed three genomic regions associated in trans with modules of co-expressed genes . For two of these regions , the trans effects appeared to be mediated by one ( or two ) cis eQTLs ( RPS26 and LYZ/YEATS4 ) while in the third case ( SH2B3 ) , the trans associations were likely to be explained by an alteration of the intracellular signaling . The biological hypotheses raised by these findings will have to be replicated in further experimental studies . In conclusion , the present study shows that a method exploiting the structure of co-expressions among genes such as ICA can help identify genomic regions involved in trans regulation of sets of genes and provide clues for understanding the mechanisms linking GWAS loci to disease . It also suggests that trans associations involving large sets of gene expressions may reflect stratification of the cell population that can be controlled for by in silico adjustment .
Study participants of both sexes aged 35–74 yr , were successively enrolled into the GHS , a community-based , prospective , observational single-center cohort study in the Rhein-Main region in western mid-Germany . The majority of participants were of European origin . A few non-European individuals detected by MDS analysis of genetic data ( see below ) were excluded prior to analysis , leaving 1 , 490 subjects for further analysis . All subjects gave written informed consent . Ethical approval was given by the local ethics committee and by the local and federal data safety commissioners . GWV genotyping was performed using the Affymetrix Genome-Wide Human SNP Array 6 . 0 and the Genome-Wide Human SNP NspI/StyI 5 . 0 Assay kit . Genotypes were called using the Affymetrix Birdseed-V2 calling algorithm and quality control was performed using GenABEL [57] ( http://mga . bionet . nsc . ru/nlru/GenABEL/ ) . Separation of monocytes was conducted within 60 min after blood collection . 8 mL blood was collected using the Vacutainer CPT Cell Preparation Tube System ( BD , Heidelberg , Germany ) and 400 µL RosetteSep Monocyte Enrichment Cocktail ( StemCell Technologies , Vancouver , Canada ) was added immediately after blood collection . This cocktail contains antibodies directed against cell surface antigens on human hematopoietic cells ( CD2 , CD3 , CD8 , CD19 , CD56 , CD66b ) and glycophorin A on red blood cells . Total RNA was extracted the same day using Trizol extraction and purification by silica-based columns . GWE assessment was performed using the Illumina HT-12 v3 BeadChip . Pre-processing of data and quantile normalization was performed using Beadstudio . Analysis was performed on the mean levels of probes of genes . To stabilize variance across gene expression levels , data were arcsinh-transformed . The Illumina HT-12 chip included 37 , 804 genes ( including probes not assigned to RefSeq transcripts ) . A gene was declared expressed when the fraction of samples with a detection P-value<0 . 05 for that gene was significantly higher than 5% ( Text S1 ) . After removing putative and/or non well characterized genes ( i . e . gene names starting by KIAA , FLJ , HS . , Cxorf , MGC , LOC , NT_ , ENSG ) , 12 , 808 genes remained for analysis . Multi-dimensional scaling ( MDS ) was performed on GWE and GWV datasets and outliers in either dataset were excluded from analyses ( Text S1 , Figures S1 , S2 , S3 , S4 ) . After normalization , the distribution of each expression trait across individuals was centred and standardized . The R function svd was used prior to ICA to reduce the dimensionality of data and determine the optimal number of patterns to be extracted by ICA ( Text S1 ) . ICA was performed with the R fastICA algorithm which uses negentropy to minimize the dependency between components . The algorithm was configured using parallel extraction method and logcosh approximation of negentropy with α = 1 . To avoid trapping in a local maximum , 10 runs of the algorithm were performed and the run with the maximal negentropy was kept . The fdrtool R package [23] was used to define the subset of genes characterizing each signature ( “module” ) . The statistics to which the method was applied was the entry sik of matrix S , considered as a normal score . The signature of each component was modeled as a mixture of two distributions ( null and alternative ) . The method fits a null ( Gaussian ) distribution around the median of the signature distribution . A gene i was considered as belonging to the module of the signature k if sik had a probability <10−3 of being drawn under the null ( FDR<10−3 ) . Functional annotations were made using the Gene Ontology database . Module enrichment was tested using a hypergeometric test . A threshold of 5 . 45×10−6 correcting for the number of categories tested was taken to declare that a category was significantly enriched in genes from a module . Pairwise overlap between modules was defined , for two modules A and B , as the ratio between the number of genes shared by A and B to the total number of genes belonging to A or B . Association of the 64 patterns with the 675 , 350 SNPs was first tested by ANOVA with 2 d . f . using the C variance program of the GNU library TAMU_ANOVA ( www . stat . tamu . edu/~aredd/tamuanova/ ) . In this first step , a P-value<10−7 was considered as suggestive . For suggestive SNP-pattern associations , we tested in a second step the enrichment of the module in expressions individually associated to the SNP by ANOVA at P<10−5 . For this second test , we used a hypergeometric test with a study-wise threshold of significance of 1 . 15×10−9 ( Bonferroni-corrected for 64 modules×675 , 350 SNPs ) . For each blood cell type ( CD4+ , CD8+ , CD19+ , CD56+ , CD66b+ , erythroblasts and megakaryocytes ) , we listed from the HaemAtlas [25] the genes reported as specific of that lineage ( Table S5 from [25] ) . Expression levels of the cell-specific genes were averaged in each subject and taken as a surrogate variable of the degree of contamination by each cell type ( S . Maouche et al . in preparation ) . Genes were considered specific from one lineage when they were over-expressed with a fold change higher than 2 in the considered lineage compared to all others [25] . In every GHS sample , the degree of contamination by a given cell type was assessed by averaging the expression levels of the subset of the cell-specific genes in that sample . This resulted in 7 surrogate variables for contamination . All significant SNP-pattern associations were re-tested by simultaneously including these 7 variables as covariates in the regression linear model . The population study included 363 patients with coronary artery disease recruited in Lübeck and Regensburg ( Germany ) , Leicester ( UK ) and Paris ( France ) and 395 healthy individuals recruited in Cambridge ( UK ) within the Cardiogenics Consortium ( http://www . cardiogenics . eu ) . All subjects were of European descent ( Text S1 ) . Genome-wide genotyping was carried out using the Illumina Sentrix Human Custom 1 . 2 M array and the Human 610 Quad Custom array . Monocytes were isolated from whole blood using CD14 micro beads ( Miltenyi ) . Gene expression profiling was performed using Human Ref-8 Sentrix Bead Chip arrays ( Illumina ) . Pre-processing of data and statistical analysis were performed in the R statistical environment . For the genes to be replicated , we did not apply any filtering on the level of detection since the detection power was lower in Cardiogenics than in GHS and some genes might be missed for that reason . Association of gene expression with genotype was tested by analysis of variance with adjustment on age , gender and center . Analysis was performed at the probe level and the probes showing the strongest association were selected . The association with a module was considered as replicated when the two following criteria were met: 1 ) at least two genes were significantly associated to the SNP at a threshold of 0 . 05 after Bonferroni correction for the number of genes present in the module; 2 ) the number of genes associated to the SNP at a 0 . 05 threshold was significantly higher than 5% , based on a binomial distribution . This implies that association with the module was considered replicated even when not all gene-specific associations were replicated . Full association results from the replication are available at the GHS_ICA_Modules database . For each replicated module , associations in Cardiogenics are reported in Tables S2 , S3 and S4 for all expression traits associated at P<10−6 in GHS . For each gene , the probe showing the strongest association is reported . WGCNA was performed on normalized expression data using the blockwiseModules function from the WGCNA R package ( v0 . 92 ) . The TOM matrix was computed from the whole set of 12 , 808 gene expressions ( maxblocksize was set to 12 , 808 ) and all other tuning parameters were set to their default value ( including dynamic tree cutting and automated merging of close modules ) . Module eigengenes ( MEs ) were computed by the blockwiseModules function as the first principal component of each module . In order to increase the number of clusters to get a more balanced comparison with ICA , a second run of the WGCNA algorithm was performed with parameters deepSplit = 4 and minModuleSize = 10 ( size of the smallest ICA module: n = 14 ) . Pairwise Pearson correlation coefficients were computed between the 64 patterns obtained by ICA and the 26 ( or 71 ) MEs obtained by WGCNA . To compare the power of the two methods to detect associations with SNPs , we tested the association of the 675 , 350 SNPs with the 26 ( or 71 ) WGCNA MEs by linear regression analysis assuming an additive allele effect . For each SNP , we retained the best P-value over the 26 ( or 71 ) MEs and applied a Sidak correction for the number of WGCNA MEs tested . The same analysis was performed with the 64 ICA patterns . QQ plots of the 675 , 350 corrected P-values were displayed for the three methods ( ICA , WGCNA with defaults parameters , WGCNA with tuned parameters ) . A downloadable SQL database compiling the results of the various associations between SNPs and expression traits is available online ( http://genecanvas . ecgene . net/uploads/ForReview/ ) . For using this database , see Methods S1 in [20] . More detailed results of the analyses performed in the present study are compiled in an HTML database that is available online ( http://genecanvas . ecgene . net/uploads/ForReview/ ) . These results include correlations between patterns , module composition and enrichment , associations between SNPs and individual expression traits within modules in GHS and Cardiogenics . | One major expectation from the transcriptome in humans is to help characterize the biological basis of associations identified by genome-wide association studies . Here , we take advantage of recent technical and methodological advances to examine the influence of natural genetic variability on >12 , 000 genes expressed in the monocyte , a blood cell playing a key role in immunity-related disorders and atherosclerosis . By examining 1 , 490 European population-based subjects , we identify three regions of the genome reproducibly associated with specific patterns of gene expression . Two of these regions overlap genetic variants previously known to be involved in the susceptibility to type 1 diabetes , celiac disease , and hypertension . Genes whose expression is modulated by these genetic variants may act as mediators in the causal relationship linking the variability of the genome to complex disease . These findings illustrate how integration of genetic and transcriptomic data at an epidemiological scale can help decipher the genetic basis of complex diseases . | [
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| 2011 | Integrating Genome-Wide Genetic Variations and Monocyte Expression Data Reveals Trans-Regulated Gene Modules in Humans |
Cyclic AMP ( cAMP ) -dependent protein kinase/protein kinase A ( PKA ) is the major transducer of cAMP signalling in eukaryotic cells . Here , using laser scanning confocal microscopy and ‘smart’ anti-phospho PKA antibodies that exclusively detect activated PKA , we provide a detailed in situ analysis of PKA signalling in intact adult Schistosoma mansoni , a causative agent of debilitating human intestinal schistosomiasis . In both adult male and female worms , activated PKA was consistently found associated with the tegument , oral and ventral suckers , oesophagus and somatic musculature . In addition , the seminal vesicle and gynaecophoric canal muscles of the male displayed activated PKA whereas in female worms activated PKA localized to the ootype wall , the ovary , and the uterus particularly around eggs during expulsion . Exposure of live worms to the PKA activator forskolin ( 50 µM ) resulted in striking PKA activation in the central and peripheral nervous system including at nerve endings at/near the tegument surface . Such neuronal PKA activation was also observed without forskolin treatment , but only in a single batch of worms . In addition , PKA activation within the central and peripheral nervous systems visibly increased within 15 min of worm-pair separation when compared to that observed in closely coupled worm pairs . Finally , exposure of adult worms to forskolin induced hyperkinesias in a time and dose dependent manner with 100 µM forskolin significantly increasing the frequency of gross worm movements to 5 . 3 times that of control worms ( P≤0 . 001 ) . Collectively these data are consistent with PKA playing a central part in motor activity and neuronal communication , and possibly interplay between these two systems in S . mansoni . This study , the first to localize a protein kinase when exclusively in an activated state in adult S . mansoni , provides valuable insight into the intricacies of functional protein kinase signalling in the context of whole schistosome physiology .
Schistosoma mansoni is an important parasitic blood fluke that causes human schistosomiasis , a neglected tropical disease that ranks second only to malaria when considering the number of people infected ( ∼200 million ) and at risk ( ∼779 million ) [1] . The life cycle of this parasite is complex involving snail-intermediate and human-definitive hosts . After the free-living cercariae infect the human host , they transform into parasitic schistosomules which mature in the vasculature via an adolescent stage to separate sex adults; sex organs develop approximately three weeks after infection and copulation between male and female worms begins after approximately four weeks [2] . The intimate association that exists between adult male and female worms in copula is vital to maintaining the full maturation of the female worm [3]–[5] , fertilization of eggs , and thus high levels of egg production to facilitate parasite transmission . Not all of the eggs produced by adult female schistosomes escape from the host . The immune response to those eggs that become trapped in tissues such as the gut wall , liver or spleen and the granulomatous reaction evoked by secretory egg antigens gives rise to chronic/advanced schistosomiasis , with an associated disease burden of ∼70 million disability adjusted life years [6] , [7] . Praziquantel is the current drug of choice for the treatment of schistosomiasis but after three decades of use in mono-therapy there remains a possibility that resistance to praziquantel will emerge . Recently the genomes of the three most medically-important schistosomes , S . mansoni [8] , Schistosoma japonicum [9] , and Schistosoma haematobium [10] were published , providing a valuable resource for integrative biological studies on schistosomes [2] and for identifying potential drug targets [11] . Cyclic AMP ( cAMP ) -dependent protein kinase/protein kinase A ( PKA ) is one of the best-characterized members of the protein kinase super-family [12] , [13] . In eukaryotes , PKA regulates diverse cellular processes including cell cycle progression [14] , proliferation/differentiation [15] , [16] , cytoskeletal dynamics [17] , and flagellar beat [18] . In an inactive state , the PKA holoenzyme comprises two identical catalytic ( C ) subunits bound non-covalently to two identical regulatory ( R ) subunits . Activation of PKA occurs in the presence of cAMP that is produced by G-protein coupled receptor ( GPCR ) -mediated activation of adenylyl cyclase . cAMP binds cooperatively to two sites on each R subunit driving a conformational change within the holoenzyme that results in the release of the catalytically active C subunits enabling them to phosphorylate serine/threonine residues in specific cytosolic and nuclear substrate proteins altering their biological functions [19] , [20] . Phosphorylation also plays an important part in the activation of PKA . In mammalian cells , the C subunit is phosphorylated at Thr197 in the activation loop by another C subunit or by phosphoinositide-dependent protein kinase 1 ( PDK1 ) [21]–[23]; in addition Ser338 is phosphorylated , which although not required for enzyme activation is important for processing and maturation of PKA [23] . The broad but selective substrate specificity of PKA is achieved by compartmentalization at different sub-cellular regions through interaction with A-kinase-anchoring proteins ( AKAPs ) [13] . Furthermore , endogenous protein kinase inhibitor ( PKI ) peptides inhibit the activity of the C subunit independently of cAMP and also serve to traffic free C subunits from the nucleus to the cytoplasm [24] . In 2009 , the first definitive evidence of PKA activity in adult worms was published with a full description of a gene encoding a S . mansoni PKA catalytic subunit ( Sm-PKA-C ) [25] . The putative Sm-PKA-C shared 70% similarity with PKA-C subunits from other organisms including the nematode Caenorhabditis elegans , the fruit fly Drosophila melanogaster , and Homo sapiens , and was most similar to PKA-C of the mollusc Aplysia californica . Furthermore , using both RNA interference ( RNAi ) and pharmacological approaches , PKA expression and activity were found to be essential for schistosome survival [25] , highlighting PKA as a possible anti-schistosome chemotherapeutic target . In the current paper we provide valuable insights into the precise locations and possible functions of phosphorylated ( activated ) PKA within intact adult S . mansoni . Our findings highlight particularly a neuromuscular role for PKA in schistosomes and the detailed analysis of PKA activation within worms provides an important physiological framework for future work on schistosome neurobiology and host-parasite interactions .
Laboratory animal use was within a designated facility regulated under the terms of the UK Animals ( Scientific Procedures ) Act , 1986 , complying with all requirements therein; regular independent Home Office inspections occurred . The experiments involving mice in this study were approved by the Natural History Museum Ethical Review Board and work was carried out under Home Office project licence 70/6834 . The Belo Horizonte strain of S . mansoni was used in all experiments . Adult schistosomes were recovered by hepatic portal perfusion of female mice ( BKW strain ) that were infected approximately 45 days earlier by paddling in water containing 200 cercariae . Worm pairs were collected carefully and were either placed immediately in Dulbecco's modified Eagle's medium ( DMEM; Invitrogen , Paisley , UK ) , or were fixed immediately in ice-cold absolute acetone and stored at 4°C for immunohistochemistry . Freshly collected adult worm pairs were placed individually in wells of a 12-well tissue culture plate ( Nunc , Thermo Fisher Scientific , Loughborough , UK ) each containing 1 ml DMEM and were incubated in forskolin ( 50 µM or 100 µM; Calbiochem , Merck , Nottingham , UK ) , KT5720 ( 25 µM or 50 µM; Calbiochem ) , dimethyl sulphoxide ( DMSO ) vehicle ( 0 . 02% ( v/v ) ) , or DMEM alone for 1 h at 38°C . Forskolin was used to activate adenylyl cyclase and produce cAMP to in turn activate PKA; KT5720 , a competitive antagonist of the ATP binding site on the PKA catalytic subunit , was employed as a PKA inhibitor . After treatment , each worm pair was homogenized on ice in 25 µl 1× RIPA buffer ( 20 mM Tris-HCl , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% ( v/v ) NP-40 ) containing 1 µl protease and phosphatase inhibitor cocktail ( Pierce; Thermo Fisher Scientific ) . The resulting homogenate was centrifuged at 13 , 000 rpm for 10 s at 4°C to remove insoluble material and protein estimations were carried out on the supernatant using the Bradford assay . An appropriate volume of 5× SDS-PAGE sample buffer was added and samples heated to 90°C for 5 min . Once cooled on ice , a further 1 µl of protease inhibitor and phosphatase inhibitor cocktail were added to the extracts and samples stored at −20°C for subsequent electrophoresis . SDS-PAGE was performed using 10% Precise pre-cast gels ( Pierce , Thermo Fisher Scientific ) and proteins were transferred to nitrocellulose membranes ( GE Healthcare , Amersham , UK ) using a semi-dry electrotransfer unit ( Bio-Rad , Hemel Hempstead , UK ) . After transfer , membranes were stained with Ponceau S ( Sigma , Poole , UK ) to confirm homogeneous transfer , and were blocked for 1 h in 5% ( w/v ) non-fat dried milk in tris-buffered saline containing 0 . 1% ( v/v ) Tween-20 ( TTBS ) , and briefly washed in TTBS prior to incubation overnight at 4°C in rabbit anti-phospho-PKA-C ( Thr197 ) polyclonal primary antibodies ( Cell Signalling Technology , New England Biolabs , Hitchen , UK; 1∶1000 dilution in 1% ( w/v ) BSA in TTBS ) . Next , blots were washed with TTBS and incubated for 2 h at room temperature with horse-radish peroxidase-conjugated secondary antibodies ( Cell Signalling Technology; 1∶5000 in 1% BSA ( w/v ) in TTBS ) and exposed to West Pico chemiluminescent substrate ( Pierce ) for 5 min . Immunoreactive bands were then visualized using a cooled CCD GeneGnome chemiluminescence imaging system ( Syngene , Cambridge , UK ) . Equal loading of proteins was checked by stripping blots for 3 h at room temperature with Restore western blot stripping buffer ( Pierce ) before briefly washing blots in TTBS and incubating blots with anti-actin antibodies ( Sigma , Poole , UK; 1∶3000 in TTBS ) followed by secondary antibodies and chemiluminescent imaging . Relative band intensities were quantified using Gene Tools software ( Syngene ) . In addition , to confirm that the anti-phospho-PKA-C ( Thr197 ) primary antibodies only detected the phosphorylated form of PKA-C , western blots were either incubated in primary antibody that had been pre-adsorbed for 30 min to the phosphorylated immunizing peptide or were pre-treated with lambda phosphatase ( New England Biolabs; 400 U/ml in TTBS containing 1% BSA and 2 mM MnCl2 ) for 4 h prior to incubation in primary antibodies; secondary antibody labeling and detection were then performed as described above . Worms processed for confocal laser scanning microscopy included samples fixed immediately after removal from the host and samples fixed after exposure to forskolin ( 50 µM ) as detailed above . Acetone fixed worms were washed twice with 1 ml phosphate buffer saline ( PBS ) and were further permeabilized with 0 . 3% ( v/v ) Triton-X100 in PBS for 1 h . After a brief wash in PBS , worms were blocked for 2 h with 10% ( v/v ) goat serum ( Invitrogen , Paisley , UK ) followed by incubation in anti-phospho-PKA-C ( Thr197 ) antibodies ( 1∶50 in PBS containing 5% ( w/v ) BSA ) for 72 h . Worms were then washed three times in 1 ml PBS for 20 min each and incubated in Alexa Fluor 488 secondary antibodies ( Invitrogen; 1∶500 in BSA ) and 200 ng/ml rhodamine phalloidin ( Sigma ) for 24 h in the dark . After further washing with PBS for 1 h , worms were placed on microscope slides and mounted in Vectashield anti-bleaching medium ( Vector Laboratories , Peterborough , UK ) . All washes and incubations were performed in screw-capped microfuge tubes on a microfuge tube rotator at room temperature . Worms were then visualized using a Leica TCS SP2 AOBS confocal laser-scanning microscope using 20× dry objectives or 40x/63x oil immersion objectives and images collected and analyzed with associated Leica software . Because adult S . mansoni autofluoresced at the same detection wavelength as the secondary antibody , the signal received from the negative controls ( i . e . those not incubated in primary antibody ) was negated from the positive samples by reducing the power level of the photomultiplier tube ( PMT ) and then maintaining constant PMT voltage throughout all observations . Worms were also incubated with anti-phospho-PKA-C ( Thr197 ) antibodies that had been pre-absorbed to the phosphorylated immunizing peptide to check for antibody specificity in immunocytochemistry . Freshly collected worm pairs were placed in DMEM at 28°C for 30 min to equilibrate . They were then observed until the first worm pairs uncoupled naturally , and the individual separated male and female worms ( five of each ) were immediately collected and fixed in ice-cold acetone . At the same times , five coupled worm pairs were removed from the medium and fixed . This provided for analysis worms that were paired and those that had just separated . In addition , immediately upon separation , individual worms were transferred separately to wells of a 24-well culture plate ( Nunc ) each containing 0 . 5 ml DMEM maintained at 28°C . These individual male and female worms were then fixed ( as described above ) at 15 min , 30 min and 60 min post separation ( five of each for each time point ) to allow for analysis of PKA signalling during pair separation . Similarly , paired worms ( five pairs for each time point ) were collected , incubated and fixed after these durations to provide paired-worm controls for each separation time point . Fixed worms were then kept at 4°C until they were processed for immunohistochemistry . Freshly collected adult worm pairs were placed in individual wells of a 12-well tissue culture plate ( Nunc ) each containing 1 ml of DMEM at 28°C . After 30 min , worms were treated with 50 µM or 100 µM forskolin , or DMSO ( 0 . 02% ( v/v ) ) vehicle . Exposing one sample at a time , adult worms were videoed over 30 min using an Olympus SZ4045 binocular dissecting microscope attached to a JVC TK-1481 composite colour video camera operating with Studio Launcher Plus for Windows software with 1 min long movies captured at 0 min , 5 min , 10 min , 15 min , 20 min , 25 min and 30 min post-treatment; videos were compressed using Panasonic dv codecs . A minimum of five worm pairs per treatment were analyzed . During analysis , cold light sources were employed and light intensity kept constant in order to stabilize light condition . The number of gross random muscular movements/min was then assessed visually for each sample at each time point . A gross random muscular movement was defined as a rapid observable change from the existing body position; an extreme example of such movement can be seen with the whip-like motion observed following forskolin treatment in the supplementary movie ( Video S1 ) . Next , movies taken for treatments displaying the maximum phenotypic effects were imported into the publicly-available software ImageJ for Windows ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997–2009 ) to further assess the nature of worm movement . Prior to import , videos were decompressed to avi format using Movavi Suite 10 SE for Windows and were converted to 8-bit grayscale; background subtraction was performed using ImageJ and the resulting movie was converted to binary format using the automatic Otsu thresholding algorithm . Binary objects representing the worms were then tracked for ‘thrashing’ ( body bend ) analysis using the open-source publicly-available custom ImageJ plugin , wrMTrck ( http://www . phage . dk/plugins ) with a bend threshold of 6° . Graphs depicting the angular movement ( degrees ) of individual worms ( shapes ) were generated against time ( frame number ) along with the total number of body bends above the threshold per treatment . Analysis of variance ( ANOVA ) and a post-hoc Fisher multiple comparison test were done to analyze the effects of individual treatments at specific time points using Minitab 15 .
Phospho-specific antibodies that bind to PKA-C only when phosphorylated at a site conserved with threonine 197 ( Thr197 ) of human PKA-C within the PKA activation loop were employed in an attempt to detect phosphorylated PKA in adult S . mansoni . Because phosphorylation at this residue is crucial for PKA maturation , optimal conformation and catalytic activity [26] , [27] , these antibodies are used to determine PKA activation [27]–[29] . Bioinformatic analysis of SmPKA-C [25] comprising 350 amino acids revealed that the amino acid sequence ( RVKGRTWTLCGTPEY ) surrounding and including Thr197 to which the anti-phospho PKA antibodies bind ( www . phosphosite . org ) is identical between S . mansoni and human PKA , with Thr195 being the crucial threonine phosphorylation site in S . mansoni PKA-C . Western blotting revealed that the anti-phospho PKA ( Thr197 ) antibodies detected two closely migrating bands with apparent molecular weights of approximately 40 KDa and 42 KDa in adult S . mansoni homogenates ( Figure 1A ) . Treatment of western blots with lambda phosphatase for 4 h prior to exposure to anti-phospho PKA ( Thr197 ) antibodies resulted in a total loss of immunoreactivity of both protein bands; in addition , incubation of these antibodies with the immunizing peptide before exposure to the nitrocellulose resulted in the same effect ( Figure 1A ) . When live adult worms were exposed to the PKA activator forskolin ( 50 µM or 100 µM ) for 1 h the immunoreactivity of both bands increased when compared to controls ( Figure 1B ) . In contrast , exposure to KT5720 , a competitive antagonist of the ATP binding site of PKA , decreased the phosphorylation of both bands with 50 µM KT5720 attenuating phosphorylation by approximately 90% ( determined by image analysis of two independent bots ) ( Figure 1B ) . These results are consistent with the expected effects of antigen competition and dephosphorylation on antibody immunoreactivity and of activation and inhibition on PKA phosphorylation status ( e . g . [28] , [29] ) , identifying the anti-phospho-PKA ( Thr197 ) antibodies as suitable for studying PKA activation in S . mansoni . The two bands of phosphorylated PKA-C therefore likely represent the 40 . 4 KDa SmPKA-C characterized by Swierczewski and Davies [25] and an additional PKA-C or splice variant thereof . The S . mansoni kinome [30] includes up to five predicted PKA-like proteins , and multiple sequence alignment of these proteins using ClustalW2 ( http://www . ebi . ac . uk/Tools/msa/clustalw2/ ) reveals that the residue corresponding to Thr197 of human PKA-C is conserved in all of these proteins ( data not shown ) . To visualize activated PKA in adult S . mansoni , anti-phospho-PKA ( Thr197 ) antibodies and confocal laser scanning microscopy were used; all images were obtained from whole mounts of intact worms . Schistosomes that were only incubated in secondary antibody ( negative control ) displayed almost no fluorescence when autofluorescence was negated by reducing the PMT voltage; only the F-actin staining was evident ( Figures 2 A–C ) . In addition , when adult worms were incubated with anti-phospho-PKA ( Thr197 ) antibodies that had been pre-adsorbed to the phosphorylated immunizing peptide no significant fluorescence was observed , with worms appearing similar to those shown in Fig . 2B . In contrast , incubation with anti-phospho-PKA ( Thr197 ) antibodies revealed activated PKA in various regions of the worms ( Figures 2 , 3 ) . Intense PKA activation was observed in the tegument of both sexes ( Figures 2D , 3A ) and this was particularly associated with the tubercles ( Figures 2G–2J ) . PKA activation in the tegument was also more prominent in males than females , and towards the central dorso-lateral region of the male where larger tubercles are present ( Figures 2D , 2F; cf . Figure 2M which shows the posterior of the worm ) . High magnification imaging revealed that regions of the tegument displaying PKA activation included the centres of the tubercles surrounded by spines and foci within the canyons between the tubercles ( Figures 2G–J ) ; these regions might represent putative sensory structures in the tegument surface . Furthermore , analysis of serial optical z-sections revealed activated PKA in the musculature immediately underlying the tegument ( data not shown ) . In adult males , deeper scanning revealed activated PKA in the muscular wall of the seminal vesicle ( Figures 2E , 2K , 2L ) , and gynaecophoric canal muscles ( Figure 2E ) with activation in the latter apparently associated with circular contractile rings ( Figure 2E ) ; the oesophagus and the highly muscular ventral sucker also possessed activated PKA as revealed by scanning the ventral side of the worm ( Figure 2F ) . Additionally , activated PKA was visible around an area that resembled the collecting duct of the excretory system at the posterior of the male worm ( Figure 2M ) and in uncharacterized tubular structures located dorso-laterally between the oral and ventral sucker ( Figures 2N–2P ) ; these tubular structures extended approximately to the region where the seminal vesicle is sited ( Figures 2K , 2L ) . In adult female schistosomes and additional to the tegument , activated PKA was associated with the oesophagus , ventral sucker and uterus ( Figure 3A ) , ootype ( Figure 3D ) , vitelline follicles and collecting ducts ( Figures 3G , 3H ) ; the common vitelline duct did not possess detectable activated PKA . Deeper scanning and cross-sectional analysis of the uterus and ootype regions revealed activated PKA to be associated with the muscular walls of these organs ( Figures 3C , 3E ) . In addition , in some female worms diffuse PKA activation was detected in the ovary ( Figure 3F ) . Finally , a striking ring-like distribution of activated PKA was detected in the female worm uterus surrounding the egg during egg expulsion along the uterus ( Figures 3I–3K ) ; such staining was only observed when an egg was present . Although the rhodamine phalloidin staining appeared diffuse in some cases , analysis of individual channels revealed these ring-like structures to be stained by rhodamine phalloidin thus defining their muscular nature . Normally , activated PKA was not significantly detected in the nervous system of adult male or female worms when paired; however , in about 10% of specimens used ( paired and separated ) which were all from a single batch of worms collected from a pool of mice from only one infection , striking activation was seen ( Figures 4 , 5 ) , the general distribution of which was similar between males and females . The basic neuro-anatomy of S . mansoni is similar in both sexes; however , the nervous tissue is particularly evident in males due to their larger size . Selective z-scanning to reveal the nervous system demonstrated that activated PKA was present in the anterior ganglia and their connecting commissures and in the dorsal and ventral nerve cords ( Figures 4A , 5A ) ; the typical orthogonal arrangement was clearly visible due to activated PKA within the nervous system . Furthermore , activated PKA was detected in the neuropile of the anterior ganglia and connecting commissure that comprised a widespread plexus of nerve fibres ( Figure 5B ) . Activated PKA also existed within the complex innervation of nerve fibres and plexus of the oral sucker and ventral sucker in both sexes ( Figures 4A , 4B , 5A , 5B ) that originated from the anterior ganglia ( determined by analyzing individual z-sections , data not shown ) . This plexus extended to nerve endings on the sucker tegument surface possessing distinct foci of PKA activation which we propose might have a sensory function ( Figures 4B , 4D , 5B ) ; activated PKA was associated with similar nerve endings and underlying plexus over much the worm body ( Figures 4A , 4C , 5B ) . High-resolution deep body scanning of the male worm peripheral nervous system revealed that activated PKA was also associated with large lateral peripheral ganglia that were positioned between the main nerve cord and more slender lateral nerve cord which both also displayed activated PKA ( Figures 4E , 4F ) ; analysis of optical z-sections revealed that some of these ganglia ( Figure 4F ) were close to the surface of the gut lining ( data not shown ) . Nerve fibres , cell bodies and the complex nerve plexus also stained positive for activated PKA ( Figures 4E , 4F ) . Analysis of z-sections revealed that this nerve plexus served the musculature of the gynaecophoric canal , sub-tegument and tegument surface where activated PKA was seen associated with the nerve endings amongst the spiny tubercles ( Figure 4E ) . Diffuse PKA activation was also seen in the testicular lobes with more evident activation in the nerve fibres around the testes ( Figure 4G ) . In the female , activated PKA was detected in the nerves associated with the ootype and Mehlis' gland complex ( Figure 5C ) and the ovary and seminal receptacle complex ( Figures 5D , 5E ) . Although extensive PKA activation in the nervous system was only observed in a small proportion of S . mansoni recovered , and was not commonly detected , we reasoned that it should be possible to activate neuronal PKA robustly in live S . mansoni with 50 µM forskolin . Incubation of worms in forskolin for 1 h resulted in extensive PKA activation in the nervous system ( Figure 6 ) , with control worms ( not shown ) appearing essentially as in Figures 2F and 3A . This increased PKA activation observed within intact worms in response to forskolin mirrors that observed by western blotting ( Figure 1B ) . Although the cause of the extensive activation of neuronal PKA observed in a proportion of worms from only one batch of mice was not known , given the distinct neuronal and muscular distribution of PKA we hypothesized that PKA might become activated in the nervous system during worm un-pairing . In agreement with our previous observations , confocal microscopy revealed that PKA was not activated extensively in the nervous system of paired adult worms immediately after perfusion ( data not shown ) . Increased PKA activation was however observed specifically within the nervous system , including the nerve cords 15 min after pair separation in both male and female worms when compared to their paired counterparts and was sustained for 30 min and 60 min post-separation ( Figure 7 ) . Partial PKA activation was also observed in the nerve cord of a female worm that remained in copula at 30 min but activation was seen only in areas where the worm had protruded considerably from the male's gynaecophoric canal ( Figure 7F ) ; analysis of worms from at least three independent experiments revealed that such neuronal PKA activation was consistently absent when the worms remained closely coupled but was present when they separated . Although it is theoretically possible that separated worms permeabilize more effectively than paired worms , enabling better antibody penetration and labeling , this was not the case in our hands . On occasions when paired worms became separated within rotating tubes during the primary antibody incubation stage no differences in neuronal phospho-PKA labeling could be seen between these paired or separated samples . In addition , PKA activation in the nervous system appeared similar in forskolin treated worms regardless of whether they were paired or separated ( data not shown ) . Because activated PKA localized to the musculature and nervous system of adult S . mansoni , an experiment was conducted to ascertain the effect of PKA activation on worm movement . Preliminary assays revealed that forskolin treatment induced a phenotype that displayed considerable random contractile movements . Movies of adult worms were therefore captured for visual semi-quantitative analysis . When worms were treated with 50 µM forskolin there was a significant increase in gross muscular movements with time ( P≤0 . 001; Figure 8A ) . After 15 min , the mean number of gross muscular movements in control worms was 7 . 2/min whereas the frequency in forskolin treated worms increased 2 . 6 times to 18 . 9/min ( P≤0 . 05 ) . This forskolin-mediated effect was even more pronounced after 20 min ( P≤0 . 001 ) and was sustained until the end of the experiment ( Figure 8A ) by which time movements had increased to 4 . 3 times that of control ( P≤0 . 001 ) . Forskolin-treated worms also displayed excessive ‘coiling’ when compared to their non-treated counterparts and this was particularly evident after 20 , 25 and 30 min exposure ( 30 min shown in Figure 8B ) . The effects of 100 µM forskolin on gross random muscular movements were more pronounced than with 50 µM forskolin , showing a significant increase after only 5 min ( P≤0 . 05 ) when compared to controls ( Figure 8A ) . After 10 , 15 , and 20 min , the frequency of movements was significantly greater in the presence of 100 µM forskolin than in 50 µM forskolin ( P≤0 . 01 ) , and after 20 min exposure worms displayed 41 . 5 movements/min compared to only 7 . 7 in the control group ( Figure 8A; P≤0 . 001 ) . Despite the increased motility observed in the presence of forskolin , there was no apparent difference in the number of worm pairs that separated during the course of the experiment . The movie ( Video S1 ) provides a visual inspection of the effects of forskolin treatment on worm movement at 20 min exposure . Movies of worms exposed to forskolin for 20 min were then subjected to quantitative thrashing ( body bend ) analysis using the ImageJ plugin wrMTrck . Both the number of body bends and the extent of angular movement increased considerably following exposure to either 50 µM or 100 µM forskolin when compared to DMSO controls ( Figure 8C ) , with irregular movements observed . Using a threshold of 6° change , the average number of body bends per worm following exposure to these concentrations of forskolin was 4 . 6 and 6 . 9 times that of controls , respectively , and was broadly similar to that determined for this time point by semi-quantitative analysis ( Figure 8A ) . Importantly , wrMTrck analysis also revealed the extent of change in angular movement of worms following treatment . Whereas thrashing in excess of ±25° was infrequent in controls , it was considerably more common with forskolin ( Figure 8C ) . Moreover , thrashing in excess of ±50° did not occur in controls , but did on eight occasions with 50 µM forskolin and 24 occasions with 100 µM forskolin . Collectively , this data highlights the nature of the hyperkinetic effect of forskolin and thus PKA activation on S . mansoni worm movement .
By using anti-phospho-specific antibodies and laser-scanning confocal microscopy we have mapped in detail activated PKA , the major transducer of cAMP signalling in eukaryotes [31] , to discrete tissues of intact male and female adult S . mansoni . In male worms , activated PKA was found particularly associated with the tegument , gynaecophoric canal muscles , oral and ventral suckers , oesophagus , seminal vesicle wall , other areas of somatic musculature , and anterior tubular structures of unknown function . In females , activated PKA was observed particularly in the tegument , suckers , oesophagus , uterus and ootype wall , and ovary . In addition , in a subset of worms obtained from a pool of mice all from the same infection and in forskolin-treated worms , striking PKA activation was observed throughout much of the central and peripheral nervous systems including at nerve endings at the worm surface . Activation of neuronal PKA appeared to increase during worm separation in vitro , and pharmacological activation of PKA by forskolin induced hyperkinesias in worms in a time and dose-responsive manner . Collectively , these data are consistent with PKA likely playing a vital part in S . mansoni muscular activity and neuronal communication , and interplay between these two systems . Schistosomes employ a variety of biogenic amines ( e . g . 5-hydroxytryptamine ( 5-HT/serotonin ) , dopamine and histamine ) and neuropeptides in their nervous system [32] , [33] . Biogenic amines signal through GPCRs and in some cases activate adenylyl cyclase , elevating intracellular cAMP levels which in turn activate PKA [34] , [35] . 5-HT increases the motility of intact schistosomes in vitro [e . g . 36]–[38] and has been localized to the male gynaecophoric canal and oral and ventral suckers [37] with a distribution similar to that seen with activated PKA in the current study . Other GPCRs such as SmGPR-3 [39] and SmD2 [40] which are activated by dopamine and are expressed in the central/peripheral nervous systems and body wall musculature , respectively , might influence S . mansoni movement in a complex fashion given that dopamine suppresses S . mansoni motor activity [39] , [41] but induces cAMP production via SmD2 [40] . Furthermore , l-glutamate induces muscle contraction in isolated S . mansoni muscle fibres [42] likely via l-glutamate receptors [43] and kainic acid , an agonist that mimics the effect of glutamate , causes hyperkinesias and coiling in adult worms [44] similar to that observed with forskolin in the present study . Motor activity in S . mansoni thus appears to be under complex regulatory control from neuromodulators and classical neurotransmitters some of which will likely signal to PKA . Our findings should thus help drive forward research aimed at elucidating some of the crucial downstream signalling mechanisms that govern muscular activity which is central to parasite survival and reproduction in the host . It is important to note , however , that the mechanisms by which PKA influences muscle contraction and relaxation in mammals are complex and are not fully understood ( see for example , [45] ) . Elucidating mechanistic control of motility in schistosomes will therefore require significant endeavor . Extensive PKA activation was evident in the muscular walls of the uterus and ootype . The ootype which comprises regularly arranged circular and longitudinal muscle fibres [46] is the site of egg formation where an egg is produced from a fertilized ovum , with secretions from the vitelline cells and Mehlis' gland . The uterus , which possesses mainly circular fibers [46] leads from the ootype to the genital pore and opens close to the ventral sucker of the female . Swierczewski and Davies [25] , [47] demonstrated that the PKA inhibitor H-89 , or PKI 14–22 amide , significantly impaired egg output by S . mansoni , and when used at 10 µM or higher , H-89 stopped egg production during the first day of observation . Our findings suggest that active PKA helps regulate S . mansoni muscular activity and thus coordinated activation of PKA in the ootype wall could be vital for successful egg formation . In addition , because activated PKA was observed around eggs within the uterus during egg expulsion and also in ring like structures that circumscribed the egg , PKA activity could be necessary for egg propulsion , a process presumably enabled through peristaltic movement . Thus , the effects of PKA inhibitors on egg output by female worms observed by Swierczewski and Davies [25] , [47] may have been , at least in part , due to blockade of egg formation in the ootype or dysregulated peristaltic movement along the uterus . Swierczewski and Davies [25] also reported that H-89 caused dissociation of worm pairs , although the duration required for separation was not reported . Here , in the presence of the PKA activator forskolin for 30 min , there was no difference in the number of worm pairs that separated when compared to controls despite the hyperkinetic effects of forskolin on worm motility . Striking PKA activation was observed in an extensive network of nerve endings at the surface of the tegument , including those associated with the oral and ventral suckers . These endings were linked to the underlying nerve plexus associated with the peripheral and central nervous systems , which in some worms also displayed extensive PKA activation . The presence of such an evolutionarily-conserved signalling pathway [31] at tegumental nerve endings is intriguing as it suggests that S . mansoni might use these structures to transduce signals from the host and perhaps from other individual worms via PKA signalling . In this context , it is interesting that after worm pair separation in vitro , PKA activation visibly increased specifically in the nervous tissue , including in the central nervous system; in closely coupled worms , active PKA in the nervous system was essentially absent . From the behavioral stand point this is an important finding as it suggests that worm pairing and maintenance of the in copula state may somewhat be governed by sensory neuronal mechanisms mediated by PKA . As recently highlighted [2] , integration of cell signalling into research on schistosome sensory biology will help drive forward this important area of research . The presence of activated PKA in the muscular uterus , oesophagus , suckers , and ring-like structures in the gynaecophoric canal and in the uterus during egg propulsion , the nerves innervating the musculature , and the effects of forskolin on gross worm movements ( hyperkinesia ) shown here , coupled with un-pairing in the presence of H-89 reported previously [25] support a role for activated PKA in the regulation of S . mansoni motor activity . It is worthy to note that PKA possesses wide ranging regulatory roles in animals . For example , in neurons PKA has also been implicated in processes such as protein degradation , protein trafficking and gene transcription [48]–[50] . Indeed , PKA signalling through the nervous system might well be one mechanism by which schistosomes transmit signals to maintain homeostasis . Certainly , the lethal effect of PKA-C knockdown by either RNAi or PKA inhibition [25] signifies the central importance of this enzyme to worm function . In S . mansoni PKA has also been implicated in regulating the ciliary motion of miracidia [51] likely through conserved axonemal mechanisms ( discussed by Ressurreicao et al . [52] ) . In addition , PKA inhibition by H-89 has been shown to speed up the rate of transformation of miracidia to mother sporocysts [53] , possibly through the effect of attenuated locomotion and thus early loss of epidermal ciliated plates . Expression levels of PKA-C were recently found to differ between different life stages of S . mansoni , and inhibition of PKA by H-89 or PKI 14–22 amide was found to be lethal to cercariae [47] as in adult worms . Our overall knowledge of the signalling mechanisms that regulate schistosome development and function is poor ( discussed in [2] ) and it is therefore important for future studies to encompass the importance of protein kinases , such as PKA , PKC [54] and MAPKs [55] to the development of definitive host stages particularly in the context of organism function and possible host-parasite interplay . In the case of PKA , consideration should be given to the importance of the regulatory subunits ( also highlighted in [25] ) and also AKAPs which permit targeting of PKA to certain sub-cellular locations . The current work , which includes a vital atlas of exclusively activated PKA in adult male and female S . mansoni , should prove invaluable for future studies into PKA function during worm development , pairing , and host-parasite interactions , as well as for studies into upstream effector mechanisms and downstream target molecules . | Schistosome blood flukes are formidable parasites . They can survive for many years as male-female pairs in the blood vessels of their vertebrate hosts where they copulate and produce large numbers of eggs that become lodged in tissues causing schistosomiasis . Over 200 million people are infected with schistosomes , mainly in tropical and sub-tropical countries; in terms of parasitic diseases , the socio-economic impact of human schistosomiasis is second only to malaria . Understanding how cellular mechanisms regulate schistosome form and function is a vital part of global research efforts on schistosomes , not least because identification of novel mechanisms might yield opportunities to develop new drugs against the parasite . Here we use a novel approach to provide a comprehensive atlas that displays the localization of an important protein ( protein kinase A ) when in an exclusively activated state within schistosomes . We show that this protein is activated within various tissues including those of the musculature and nervous system and that its activation in nerves visibly increases when paired adult male and female schistosomes separate . We also show that PKA plays a vital role in the co-coordination of schistosome muscular activity . Our findings offer valuable insight into this protein at the functional level and provide a much-needed physiological framework for further work on PKA in schistosomes , which has been highlighted previously as a potential drug target . | [
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| 2013 | Functional Mapping of Protein Kinase A Reveals Its Importance in Adult Schistosoma mansoni Motor Activity |
Rift Valley fever ( RVF ) is a mosquito-borne viral zoonosis caused by a phlebovirus and transmitted by Aedes mosquitoes . Humans can also be infected through direct contact with blood ( aerosols ) or tissues ( placenta , stillborn ) of infected animals . Although severe clinical cases can be observed , infection with RVF virus ( RVFV ) in humans is , in most cases , asymptomatic or causes a febrile illness without serious symptoms . In small ruminants RVFV mainly causes abortion and neonatal death . The distribution of RVFV has been well documented in many African countries , particularly in the north ( Egypt , Sudan ) , east ( Kenya , Tanzania , Somalia ) , west ( Senegal , Mauritania ) and south ( South Africa ) , but also in the Indian Ocean ( Madagascar , Mayotte ) and the Arabian Peninsula . In contrast , the prevalence of RVFV has rarely been investigated in central African countries . We therefore conducted a large serological survey of rural populations in Gabon , involving 4 , 323 individuals from 212 randomly selected villages ( 10 . 3% of all Gabonese villages ) . RVFV-specific IgG was found in a total of 145 individuals ( 3 . 3% ) suggesting the wide circulation of Rift Valley fever virus in Gabon . The seroprevalence was significantly higher in the lakes region than in forest and savannas zones , with respective rates of 8 . 3% , 2 . 9% and 2 . 2% . In the lakes region , RVFV-specific IgG was significantly more prevalent in males than in females ( respectively 12 . 8% and 3 . 8% ) and the seroprevalence increased gradually with age in males but not in females . Although RVFV was suggested to circulate at a relatively high level in Gabon , no outbreaks or even isolated cases have been documented in the country . The higher prevalence in the lakes region is likely to be driven by specific ecologic conditions favorable to certain mosquito vector species . Males may be more at risk of infection than females because they spend more time farming and hunting outside the villages , where they may be more exposed to mosquito bites and infected animals . Further investigations are needed to determine the putative sylvan cycle of RVFV , including the mosquito species and the reservoir role of wild animals in the viral maintenance cycle .
Rift Valley fever virus ( RVFV ) is a mosquito-borne RNA virus belonging to the Phlebovirus genus of the Bunyaviridae family . RVFV infects both humans and livestock [1] . Although severe clinical cases can be observed , infection with RVF virus ( RVFV ) in humans is , in most cases , asymptomatic or causes a febrile illness without serious symptoms . Some patients may develop serious complications , including meningoencephalitis ( about 1% ) , hemorrhagic disorders ( 1% ) and ocular disorders ( retinitis and uveitis in 12% and about 30% respectively in Saudi Arabia ) [2] , [3] , [4] , [5] . The case fatality rate varied widely between different epidemics but ranged between 1% to 13% . RVFV induces abortion and stillbirth in small domestic ruminants , and has a major socio-economic impact in African countries [6] , [7] . RVFV is transmitted by Aedes mosquitoes , but humans can also be infected through direct contact with blood ( aerosols ) or tissues ( placenta , stillborn ) of infected animals [8] , [9] . RVFV was first isolated in Kenya in 1930 [10] and is now known to be widespread in many African countries , especially in non-forested regions . Until the 1970s , RVF was mainly reported in southern and eastern Africa ( mainly Kenya ) , where it was considered as an animal disease , despite sporadic human cases [11] . After the 1970s , explosive outbreaks occurred in human populations throughout Africa , and principally in Egypt ( 1977–78 , 1997–98 ) [2] , [12] , [13] , Senegal and Mauritania ( 1987–1988 ) [14] , [15] , [16] , Kenya , Somalia and Tanzania , ( 1997–1998 , 2006–2007 ) [17] , [18] , Chad ( 2004 ) [19] , Sudan ( 2008 ) [20] and South Africa ( 2010 ) [21] , and also in the Arabian Peninsula ( 2000–2001 ) [22] , Mayotte and Madagascar ( 2007–2008 ) [23] , [24] , [25] . In east Africa , RVF outbreaks coincided with heavy rainfall and local flooding , which can lead to expansion of vector populations [26] , [27] . RVFV has been detected in many wild animal species ( ungulates in Kenya , bats in Guinea , small vertebrates in Senegal and South Africa ) , but it is not known whether they serve to maintain the virus in the ecosystem during inter-epidemic periods , or whether they contribute to amplifying outbreaks [28] , [29] , [30] , [31] . Although the RVFV cycle in savannas regions is now better understood , possible sylvan cycles in forested regions have not been explored [32] . In forested central Africa countries , no RVF outbreaks have been described , although RVFV-specific antibodies have been detected in wild animals and humans living in forested areas of Central African Republic ( CAR ) [28] , [29] , [30] , [33] , [34] , [35] , [36] and RVFV has been isolated from humans and wild mosquitoes ( Aedimorphus and Neolaniconion ) in the same regions [37] , [38] . In Gabon , one of the most densely forested countries of central Africa , RVFV-specific antibodies were episodically detected in humans [35] but no systematic investigation has been conducted . We therefore undertook a large serological survey of RVFV in Gabon , focusing on human rural populations .
Gabon is divided into nine provinces . Three-quarters of this country surface is covered by forest . Discontinuous areas of savannas are also found , mainly in the south and south-east of Gabon . In total , we collected 4323 serum samples from 212 villages ( Figure 1 ) , representing 10 . 7% of all villages in Gabon . No samples were collected in towns or cities . The required sampling population was calculated on the basis of on an estimated RVFV seroprevalence of 5% , and the villages were randomly selected ( drawn lots , using a manual method ) in the nine provinces . In each village , volunteers were interviewed and sampled . The survey was divided into nine time periods , corresponding to the survey of each province . We visited Estuaire province in July 2005 , Moyen Ogooué in January 2006 , Woleu-Ntem in April 2006 , Ngounié in June 2006 , Nyanga in January 2007 , Haut-Ogooué in April 2007 , Ogooué Ivindo in June 2007 , Ogooué Lolo in September 2007 and Ogooué Maritime in May 2008 . The villages were classified as located in forests , savannas , or lakes zones ( or Lakeland ) based on reference maps of Gabon . Forest regions were defined as dense and continuous forest , savannas as steppes or forest gallery , and the lakes zone as forested swamps , lagoons and lakes . In Gabon , the most humid region is the lakes zone with an annual rainfall about 2 , 000 mm and a tropical transitional humid climate . The following data were collected for each participant , using a standard questionnaire: demographics ( age , sex , marital status ) , socio-environmental conditions ( main occupation , contact with the forest , contact with animals , eating habits ) and health ( physical examination , last disease and symptoms ) . CIRMF ( Centre International de Recherches Médicales de Franceville ) has approved this study and the research . Informed consent was written and was obtained from all participants to this survey . A special authorization was delivered by Le Secrétaire Général du Ministère de la Santé Publique , lettre 00093/MSP/SG/SGAQM du 15/03/2006 . Blood samples were collected directly in 7-ml EDTA Vacutainer tubes . Serum was separated each evening by centrifugation ( 2000 g ) , stored at −20°C in cryovials ( VWR , Prolabo , France ) and transported to CIRMF ( Centre de Recherches Médicales de Franceville ) for laboratory analysis . The sera were tested with the RVFV sandwich enzyme-linked immunoassay ( ELISA ) developed and extensively validated by the National Institute for Communicable Diseases , Sandringham , South Africa [39] . The test uses RFVF antigen obtained from the whole virus to detect anti-RVFV IgG antibodies in human samples . Briefly , ELISA plates ( Maxisorp , Nunc , Denmark ) were coated with mouse anti-RVFV capture antibodies diluted 1∶10 000 in PBS overnight at +4°C . RVFV antigen diluted 1∶500 in 2% skimmed milk in PBS was then added to the wells . A mock antigen diluted in the same conditions was used as a control . The test and control sera were diluted 1∶400 . Four high positive controls , two low positive controls and two negative controls were used for each plate . The specific activity of each serum ( net optical density - OD ) was measured by subtracting the OD of the sample and control wells . The mean net OD was calculated for the high positive control serum and the reactivity of each serum was calculated as percentage positivity ( PP ) of the high positive control serum , as follows: PP serum = 100* net OD serum/mean net OD high positive control . Sera with PP values≥18 were considered positive and those with values of 17 or below were considered negative . This ELISA method has been validated against a serum neutralisation test [39] . Statistical analysis was used to analyze the distribution of RVFV-specific IgG positive samples among the sampled population , and to determine risk factors and association between factors . We used comparisons of means or frequencies across the two groups ( cases and non-cases ) with Chi-Square test , T test , or non-parametric tests by MC simulation , and we analyze the association between RVFV-IgG and age with linear regression . We calculate odds ratios ( OR ) for exposure factors , and adjusted OR with possible confounding factors ( age , gender , ecosystem ) , with adjusted Mantel-Haenszel Chi-square test for significance . Multivariate models stratified by ecosystem were constructed ( logistic regression ) , which included univariate analysis of risk factors with significance level of ≤0 . 10 , and the backwards stepwise elimination procedure was applied . Odds Ratios ( OR ) and exact 95% confidence intervals ( CI ) were used to access the association between risk factors and RVFV IgG seroprevalence . Statistical significance at 0 . 05 risk was used in tests and for confidence intervals ( CI ) . Finally , we used 0 . 005 for risk factors analysis to take in account a Bonferroni correction for multitesting . We used STATA 10 . 0 software ( College Station , Texas USA ) , Epi Info software ( 6 . 04 , Epiconcept ) , SavGIS software ( 9 . 05 , IRD ) .
A total of 4323 villagers were interviewed with a mean age of 46 . 9 years ( range 15–90 years ) ( Table 1 ) . Females comprised 52 . 9% and males 47 . 1% of the total . Seventy six percent of the study participants lived in forested areas , 13% in savanna , and 11% in the lake region . The overall RVFV-specific IgG seroprevalence was 3 . 3% , with 4 . 3% in males and 2 . 5% in females . Seroprevalence was highest in the lake area ( 8 . 3% ) , followed by the savanna ( 2 . 9% ) , and the forest ( 2 . 2% ) . Hunters had the highest seroprevalence of those tested at 4 . 4% , followed by 3 . 5% in farmers and 2 . 4% for other professions combined . In the 212 surveyed villages ( Figure 1 ) , the RVFV IgG seroprevalence rates varied from 0 to 38% . Three levels of seroprevalence rates were defined: low ( 0–4% ) , intermediate ( 5–15% ) and high ( >15% ) ( Figure 2A , 2B , 2C ) . So , 155 ( 73% ) villages were at low level , 51 ( 24% ) at intermediate level and six ( 3% ) at high level of RVFV IgG seroprevalence ( Table 2 ) . In forest and savanna areas , respectively 75% and 87% of the villages were at low level and respectively 24% and 13% at intermediate level . In the lakes region , the RVFV IgG prevalence was >5% in 14 villages ( 56% ) and >15% in 6 villages ( 24% ) . Two villages among them ( Pointe Elyse and Bordeaux ) had high rates , respectively 27% ( 4/15 ) and 38% ( 5/13 ) . Only the age , the gender and the ecosystem allowed rejecting the NULL hypothesis ( of no significant relationship with RVFV-specific IgG prevalence ) . In particular , no statistical significance was found according to the activity of the villagers . We calculate ORs with confidence intervals , and p-value of the Chi-Square in a case-exposure test . Results are shown in Table 3 . Age factor: the age mean in the RVFV IgG positive group was 50 . 7 and 46 . 8 in the negative one ( p<0 . 001 ) . We class the age in 5 categories , near quintiles ( [15–33[ , [33–44[ , [44–54[ , [54–61[ , [61–90] ) for ORs and risk analysis . A significant ( p = 0 . 00002 ) linear increase of RVFV IgG prevalence was noted between ordered age groups ( Figure 3 ) , even with a Bayesian adjustment ( EBE ) to reduce the variability difference between the groups . Gender factor: gender shows a strong relationship RVFV specific IgG . The prevalence was higher in the male group ( 4 . 32% , OR = 1 . 75 , [1 . 25 , 2 . 46] , Chi-Square = 11 . 05 , p = 0 . 00089 ) , than in the female group ( 2 . 50% , OR = 0 . 57 , [0 . 41 , 0 . 80] , Chi-Square = 11 . 05 , p = 0 . 00089 ) . In order to highlight a possible confounding factor , adjusted OR with ecosystem or age were performed . These ORs did not show a significant difference ( 1 . 68 and 1 . 59 for the males and 0 . 59 and 0 . 62 for the females ) , showing that age distribution in positive samples has same distribution whatever the gender . Ecosystem factor: ecosystem shows a strong OR difference ( Table 3 ) , with high risk in the lake ecosystem ( OR = 3 . 2 ) . Adjusted ORs with age and/or gender don't show any confounding factors ( Table 4 ) . In the forest and savanna areas , no significant relationship with RVFV-specific IgG prevalence and possible risk factors were found , except the activity ( to be hunter , in these two ecosystems ) . The results are shown in Table 5 . However , in the lake ecosystem , gender was a very high risk factor ( OR = 4 . 44 ) . Similarly than in the global population , the RVFV IgG prevalence rate increased with age . No other risk factor was highlighted . Finally , multivariate analyses were performed by ecosystem . Only in the lakes region , gender and age remained significantly associated with a positive RVFV- IgG , with respectively adjusted OR = 3 . 85 [1 . 75–8 . 33] , p = 0 . 001 and adjusted OR = 1 . 03 [1 . 01–1 . 059 , p = 0 . 01 ( Table 6 ) .
In this large serological survey , covering 10 . 7% of all Gabonese villages , we found an overall RVFV-specific IgG prevalence rate of 3 . 3% . This is surprisingly high for a country in which no RVFV outbreaks have ever been reported . As in Central Africa Republic , where no RVFV outbreaks have been described , successive serological surveys of people living in forested areas , in 1979–82 , 1984–85 and 1994–97 , showed RVFV-specific IgG prevalence rates ranging from 1 . 2% to 6 . 9% [33] , [35] , [36] . In countries with documented Rift Valley fever epidemics , the RVFV-specific IgG prevalence rates , measured in outbreak areas , were as high as 32% in Kenya in 1997 [17] , 22 . 3% in Senegal in 1989 [16] and 24 . 4% in Mauritania in 1998 [40] . During interepidemic periods in Kenya , the RVFV IgG prevalence rates ranged from 1% to 19% [9] , [41] . In Tanzania , a 2004 study showed a RVFV-specific IgG prevalence rate of 4% [42] . Thus , these data registered during interepidemic periods from epidemic countries are similar to those found in Gabon . This result strongly suggests widespread circulation of Rift valley fever virus in Gabon even if the epidemiological cycle and the modalities of this circulation remain unknown . Classically , the RVFV cycle involves domestic animals ( livestock ) as viral amplifying hosts before transmission to humans , as has been shown in many African countries [7] . However , in Gabon , cattle herds are rare ( except around major cities ) and , in the rural areas where our investigations were carried out , few domestic animals such as cows , sheep and goats were found . Thus , in Gabon , the RVFV cycle may involve wild rather than domestic animals . This is supported by the isolation of RVFV from a specific forest mosquito , Aedes ( Neomelaniconion ) gr . Palpalis [38] , in the Central African Republic and the detection of IgG in pygmies living in regions of this country where domestic animals are virtually absent [34] . One possible reason for the lack of reported RVF outbreaks or even isolated cases in Gabon is that cases of RVF might be attributed to malaria . Alternatively , less virulent strains may be circulating in Gabon . Although cross serological reactions with antibodies against unknown phleboviruses cannot be definitively ruled out , serological cross reactions against known phleboviruses are unlikely as the commercial ELISA method used in this survey has been extensively validated against a serum neutralisation test and was shown to be highly sensitive and specific for routine testing of human samples [39] . As RVFV transmission may vary with the local environment and vector distribution [41] , we analyzed the results according to the principal ecosystems found in Gabon . We found a significantly higher RVFV IgG prevalence in the lakes region ( 8 . 2% ) than in forest ( 2 . 9% ) and savanna ( 2 . 1% ) areas . The lakes region is the most humid region of Gabon and is mainly composed of swamps and forested lagoons . This ecological situation , with omnipresent surface waters , could favor various mosquito species ( or higher densities than elsewhere ) . A relationship has already been found between RVFV circulation and water resources in Kenya [41] , Egypt [42] , Senegal [43] and Saudi Arabia [44] . However , in Gabon , further investigation is needed to identify the mosquito species involved in RVFV transmission . In this ecosystem , the RVFV IgG prevalence was significantly higher ( OR = 4 . 44 , Table 4 ) in males ( 12 . 8% ) than females ( 2 . 8% ) , and increased regularly with age ( Table 6 , Figure 3 ) . In the lakes region , males spend a large part of the day outside their villages , engaged in agriculture or hunting ( or fishing ) . In contrast , females spend less time in agricultural activities and do not hunt; they remain indoors the rest of the time , cooking and taking care of infants . Thus , the higher RVFV IgG prevalence in males may be due to higher exposure to RVFV vectors or to infected animals . A similar gender difference was found in Kenya in 2006 , where males had an RVFV IgG prevalence rate more than three times higher than females [41] . The reasons of the RVFV IgG seroprevalence increase according to age are unknown . This could be compatible with a continuous exposure of Gabonese populations to RVFV but also to a higher rate of exposition to mosquito vector bites infected with RVFV of the older age groups . In conclusion , this first large serological survey of RVFV in central Africa strongly suggests that the virus circulates widely in Gabon , despite the lack of reported outbreaks . The overall high RVFV seroprevalence observed in Gabon suggests that human cases of RVF may occur but are either misdiagnosed or not reported . However , cross serological reactions with unknown phleboviruses cannot be ruled out . Further investigations are needed to isolate the Rift valley virus from human , animal or mosquito samples , to investigate the putative sylvan cycle of RVFV , and particularly to identify the mosquito species involved in human transmission and the potential role of wild animals as reservoir . | Rift Valley fever ( RVF ) is a disease transmitted by a mosquito bite ( Aedes ) . Humans can also be infected through direct contact with blood ( aerosols ) or tissues ( placenta , stillborn ) of infected animals . Although severe clinical cases can be observed , infection with RVF virus ( RVFV ) in humans in most cases causes a febrile illness without serious symptoms . In small ruminants RVFV mainly causes abortion and neonatal death . RVFV distribution has been poorly investigated in Central Africa . We conducted a large scale serological survey of RVF antibodies in rural populations in Gabon , involving 4 , 323 individuals from 212 randomly selected villages . The results showed an overall RVFV prevalence of 3 . 3% , with values of 2 . 9% in the forested zones , 2 . 2% in savannas and 8 . 3% in the lakes region . These findings strongly suggest for the first time the wide circulation of Rift valley fever virus in Gabon and the possible existence of a sylvan cycle of RVF virus in this country . The serological higher prevalence in the lake region suggests that this region is likely to have particular ecological conditions , especially mosquito vector species , favoring the circulation of this virus . In Gabon , human cases of RVF may occur but are either misdiagnosed or not reported . | [
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| 2010 | Rift Valley Fever Virus Seroprevalence in Human Rural Populations of Gabon |
The ~9 . 5 kilobase HIV-1 genome contains RNA sequences and structures that control many aspects of viral replication , including transcription , splicing , nuclear export , translation , packaging and reverse transcription . Nonetheless , chemical probing and other approaches suggest that the HIV-1 genome may contain many more RNA secondary structures of unknown importance and function . To determine whether there are additional , undiscovered cis-acting RNA elements in the HIV-1 genome that are important for viral replication , we undertook a global silent mutagenesis experiment . Sixteen mutant proviruses containing clusters of ~50 to ~200 synonymous mutations covering nearly the entire HIV-1 protein coding sequence were designed and synthesized . Analyses of these mutant viruses resulted in their division into three phenotypic groups . Group 1 mutants exhibited near wild-type replication , Group 2 mutants exhibited replication defects accompanied by perturbed RNA splicing , and Group 3 mutants had replication defects in the absence of obvious splicing perturbation . The three phenotypes were caused by mutations that exhibited a clear regional bias in their distribution along the viral genome , and those that caused replication defects all caused reductions in the level of unspliced RNA . We characterized in detail the underlying defects for Group 2 mutants . Second-site revertants that enabled viral replication could be derived for Group 2 mutants , and generally contained point mutations that reduced the utilization of proximal splice sites . Mapping of the changes responsible for splicing perturbations in Group 2 viruses revealed the presence of several RNA sequences that apparently suppressed the use of cryptic or canonical splice sites . Some sequences that affected splicing were diffusely distributed , while others could be mapped to discrete elements , proximal or distal to the affected splice site ( s ) . Overall , our data indicate complex negative regulation of HIV-1 splicing by RNA elements in various regions of the HIV-1 genome that enable balanced splicing and viral replication .
The HIV-1 genome contains a variety of RNA elements that have important cis-acting functions [1 , 2] . Some of these RNA sequences are multi-functional in that they lie in open reading frames and therefore encode proteins as well as performing functions as RNA that are critical during viral replication . Known cis-acting RNA elements in the HIV-1 genome that lie within protein-coding sequences include splice donors , acceptors , and branch points [3] , splicing regulatory elements that enhance or inhibit the use of proximal splice sites [4] , the Rev-responsive element [5 , 6] , the central polypurine tract and termination sequence [7] , the Gag-Pro-Pol ribosomal frameshift regulatory element [8] and components of the viral genome packaging signal [9 , 10] . It is not known whether the aforementioned represent a complete catalogue of cis-acting RNA elements , or whether additional RNA-based functionality exists in the HIV-1 genome . That additional RNA sequences function in cis may exist in the HIV-1 genome is suggested by studies employing chemical probing approaches . For example , SHAPE experiments indicate that individual nucleotides in HIV-1 RNA have widely divergent tendencies to be base-paired [11–13] . These findings , along with phylogeny-based approaches , strongly suggest that secondary structures form in HIV-1 RNA that are conserved between strains , and might therefore serve a function in HIV-1 replication [11 , 13] . One example of a recently suggested function for HIV-1 RNA secondary structure is the regulation of translational rate , whereby translation is periodically slowed to enable folding of one protein domain of the multidomain HIV-1 Gag protein before synthesis of the next [11] . However , no evidence for such a phenomenon was found in an analysis of SIVmac [14] . Despite the suggestion that novel RNA secondary structures may be important for HIV-1 replication , targeted mutagenesis of putatively important individual stem loops has not generally yielded evidence that is strongly supportive of a role for these potential structures in HIV-1 replication [15] . Conversely , some studies in which portions of the HIV-1 genome were synonymously mutated have suggested a role for RNA ( as opposed to protein ) sequence or structure in HIV-1 replication [16 , 17] . However , the precise nature of defects induced by synonymous mutations are unclear , and possibly pleiotropic . RNA sequence and structure must play a key role in a particular aspect of HIV-1 replication , namely alternative splicing . Indeed , RNA secondary structure surrounding the major 5’ splice donor affects splicing [18 , 19] and SHAPE analysis revealed a novel stem loop that influences splicing [14 , 20] . Nevertheless , the regulatory mechanisms controlling alternative splicing are incompletely understood [4 , 21] . HIV-1 employs four salient splice donors ( D1 , D2 , D3 , D4 ) and eight acceptors ( A1 , A2 , A3 , A4a , b , c , A5 and A7 , S1 Table ) to generate a large number of mRNAs that enable expression of nine viral open reading frames in a temporally biphasic manner [22] . Additional , ‘cryptic’ splice sites may be used at very low frequency , and are not required for expression of the viral ORFs ( S1 Table ) . HIV-1 splicing is , by necessity , inefficient as a substantial fraction of the viral transcripts must remain unspliced so as to provide viral genomes and Gag-Pol mRNA [21 , 23] . Splice-site utilization is regulated by deviation from consensus splicing signals as well as regulatory elements consisting of exonic and intronic splicing silencer ( ESS and ISS ) elements , and exonic splicing enhancer ( ESE ) elements [4 , 21 , 23–32] ( S1 Table ) . All HIV-1 splicing involves D1 , while splice acceptor sites 5’ to each initiation codon are used to generate mRNAs encoding the HIV-1 proteins Vif ( A1 ) , Vpr ( A2 ) , Tat ( A3 ) Rev ( A4a , b , c ) , Vpu/Env ( A4a , b , c and A5 ) and Nef ( A5 and A7 ) ( Fig 1 ) [3 , 4] . In addition , some HIV-1 mRNAs include short noncoding exons ( SX1 ( A1-D2 ) ) and ( SX2 ( A2-D3 ) ) 5’ to the expressed open reading frame . The relative frequencies that the various splice sites are used , which can be measured using next-generation sequencing approaches [20 , 33] , likely contributes to ensuring that the optimal levels of viral proteins are synthesized for viral replication . To identify and catalog regions of the HIV-1 genome that contain critical but unidentified cis-acting RNA elements that impact splicing and other functions , we employed a global synonymous mutagenesis strategy . Sixteen mutant proviruses , each containing blocks of synonymous mutations covering nearly the entire HIV-1 protein coding sequence , were examined . Despite encoding identical proteins , and despite the fact that all known cis-acting sequences were maintained in an intact form , some viruses were completely incapable of a spreading infection . Others mutants displayed apparently normal fitness . Some defective mutants , termed Group 3 mutants , displayed near normal splicing but were defective in spreading replication assays . In these cases , second site revertants could not be recovered , and we have recently demonstrated that these defects are associated with dinucleotide compositional changes that confer sensitivity to zinc finger antiviral protein ( ZAP ) [34] . In this study , we focused on Group 2 mutants that displayed aberrant splicing . We found that replication competence could be recovered by second-site revertant point mutations that were often , but not always , proximal to splice sites . Sequences responsible for aberrant splicing were sometimes diffusely distributed , but sometimes could be mapped to discrete elements of 20–120 nucleotides and suggest the existence of novel forms of splicing-suppressive RNA signals . Overall , this study demonstrates that uncharacterized elements in the HIV-1 genome determine the fate and splicing of HIV-1 RNA and thus the ability of HIV-1 to replicate .
To determine whether there are important , undiscovered cis-acting elements in the HIV-1 genome , a mutant HIV-1 sequence was designed that contained a maximum number of synonymous mutations in the open reading frames while leaving known RNA elements that are important for virus replication intact ( S1 Table ) . Mutations were designed so as to maximize the probability of disrupting base pairing in which a given nucleotide might participate . Thus , where multiple synonymous mutation possibilities were available , transversion mutations ( purine to pyrimidine or vice versa ) were preferred over transition mutations . To avoid the creation of new splice acceptors and donors , no new AG or GU dinucleotides were introduced . Moreover , sequences encoding overlapping open reading frames were not altered , and all known RNA elements that control HIV-1 splicing , gene expression and reverse transcription ( S1 Table ) remained intact in the mutant viral genome . This designed HIV-1 sequence contained 1 , 976 synonymous mutations ( S2 Table , S1 Data , S2 Data ) and was divided into 150–500 nucleotide blocks , that were synthesized separately . Each synthetic mutated fragment was introduced into a replication competent HIV-1 proviral plasmid ( HIV-1NHG ) [35] that carried GFP in place of the nonessential gene Nef ( S2 Table , S1 Data , S2 Data . ) . Thus , sixteen different mutated proviral plasmids , designated A through P , with each carrying a mean of ~125 synonymous mutations were generated ( Fig 1A ) . Each of the synyonymously mutated HIV-1NHG proviral plasmids was transfected into 293T cells and the infectious virion yield was determined in a single-cycle infection assay in MT4 cells ( Fig 1B ) . Many of the mutants yielded WT , or close to WT , levels of infectious virions in this transfection/titration assay format . However , mutants A , B , I , and J yielded between 5-fold and 1000-fold fewer infectious virions ( Fig 1B ) . Western blot analysis of the transfected 293T cells and extracellular virions showed that mutants A and B expressed additional Gag protein species of unexpected sizes , and mutant B displayed a particle release defect , possibly a consequence of the expression of the aberrant Gag protein ( Fig 1C ) . Mutants I and J displayed reduced Gag , Pol , Env and Vif protein , and slightly elevated GFP levels . Mutant J also grossly overexpressed the Vpr protein ( Fig 1C ) . We next examined whether each of the mutants could replicate in MT4 and CEM T-cell lines ( Fig 2 and S1 Fig ) . Seven of the mutants ( D , E , F , G , H , N and P ) replicated with WT , or close to WT , kinetics while eight other mutants , ( A , B , I , J , K , L , M , and O ) were replication defective , or highly impaired , in both cell lines ( Fig 2 and S1 Fig . ) . Mutant C was somewhat impaired in MT4 cells , but replicated with close to WT kinetics in CEM cells . Thus , an apparent discrepancy was evident in the ability of some of the mutants to generate infectious virions in 293T cells , versus their ability to generate a spreading infection in T-cell lines . The viral mutants were designed to avoid altering RNA sequences in the HIV-1 genome that are known to be important for replication , including those that participate in or regulate splicing ( S1 Table ) . Nevertheless , the aberrant pattern of viral protein expression in two of the synonymously mutated viruses ( I , J ) , and the appearance of novel Gag-related protein species in two others ( A , B ) , suggested that HIV-1 splicing might have been perturbed in at least some of the mutant viruses ( Fig 1C ) . Therefore , we next used two approaches to determine whether the mutations affected splicing in each of the mutant viruses . We used a recently described Primer ID-based deep sequencing approach [20] to globally quantify the relative utilization of the various splice donors and acceptors in the mutant viruses ( Fig 3A–3E ) . We also used a fluorescent primer-based PCR-PAGE assay to more conveniently , albeit less quantitatively , track the generation of the major mRNA species in the canonical spliced 1 . 8 kb class of HIV-1 mRNAs ( Fig 3F ) . These two assays yielded results that were in good agreement ( see below ) . Of the canonical splice acceptors in the central portion of the genome ( A1 , A2 , A3 A4a , b , c , and A5 , Fig 3A ) , the WT HIV-1NHG most frequently spliced to A5 , with lower levels of splicing to A1 , A2 , A3 , A4a , b , c ( Fig 3A , 3C and 3F ) . A splicing defect was observed for mutants A and B , which contained synonymous changes toward the 5’ end of the HIV-1 genome , within gag . For each of these mutants , the relative levels of canonical splice site utilization were only marginally perturbed , but the Primer ID-based sequencing assay revealed that cryptic splice sites near the 5’ end of the genome were activated ( Fig 3B , 3C and 3D ) . These mutants were designated Group 2a . For mutant A , a cryptic splice acceptor at position 955 ( A955 ) and a donor at position 1169 ( D1169 ) were activated , neither of which was used at a measurable level in WT HIV-1NHG ( Fig 3A and 3B ) . While A955 was used rarely ( ~1% of 1 . 8 kb mRNAs ) in mutant A , ~14% of the 1 . 8 kb mRNAs were spliced using D1169 . ( Fig 3B ) . Splicing events involving D1169 were selective with respect to which of the downstream acceptors were used: A3 , A4 , or A5 , were used as acceptors for D1169 but A1 or A2 were not . MaxEnt scoring , which employs an in silico analysis tool that predicts the intrinsic splicing efficiency of splice acceptors and donor sequences [36] , indicated that our mutagenesis increased the score of the cryptic acceptor A955 from -9 . 88 to 1 . 75 , suggesting that it became a stronger splice acceptor ( S3 Table ) . Thus , the minimal activation of A955 ( used in 1% of spliced reads ) in mutant A , could possibly be explained by a direct effect of the mutations . However , the predominant defect in mutant A was the activation of D1169 , which lies outside the mutated region ( Fig 3A ) and whose MaxEnt score was not altered . Thus , activation of D1169 could not be due to increased intrinsic efficiency of this cryptic donor , but rather due to some other mechanism acting via RNA sequences distal to D1169 . For mutant B , the major perturbation was activation of a cryptic splice acceptor at position 1321 ( A1321 , Fig 3A and 3B ) . This splicing event , involving ~12% of mRNAs in the 1 . 8 kb class , appeared to enable a cascade of further alternative splicing events , 50% of which subsequently involved cryptic splice donor activation at either D1509 or D1725 , outside the B mutant region ( Fig 3B ) . However , the most obvious outcome of the aberrant splicing event was the generation of a truncated ~40 kD Gag protein ( Fig 2B ) . This protein would be the expected translation product of an mRNA in which a D1- A1321 splice , and no further splicing , had occurred . Specifically , translation initiation at the second Met codon in the gag gene would generate a truncated ~40 kD Gag protein lacking MA , that likely accounts for the aberrant band on the cell-associated Gag western blot as well as the reduced particle yield from cells transfected with the B mutant proviral plasmid ( Fig 1C ) . For mutant B , the activation of A1321 may be due to a direct effect of the mutations increasing its MaxEnt score from -2 . 94 to 7 . 03 , again suggesting it became a stronger splice acceptor ( S3 Table ) . The other cryptic sites activated , D1509 and D1725 , are both outside of the mutated region in mutant B , and their use was likely secondary to activation of A1321 . Viruses containing mutations in the central portion of the genome , specifically mutants I , J and K , exhibited a different type of splicing defect , and were designated Group 2b . Specifically , mutants I , J and K exhibited increased direct splicing to canonical splice acceptors , A1 , A2 and A3 respectively , at the expense of direct splicing to downstream ( 3’ ) acceptors ( Fig 3C ) . In the case of mutant I , the primary defect ( increased use of A1 ) was accompanied by increased use of the proximal downstream splice donor ( D2 ) as well as a downstream acceptor–donor pair ( A2 and D3 ) and thus the abundant inclusion of short exons ( SX ) 1 and 2 ( SX1 = [A1-D2] and SX2 = [A2-D3] ) into spliced viral mRNAs ( Fig 3D ) . Some elevation of the use of upstream cryptic splice sites in mutant I ( D3569 , D3969 , D4641 and A4834 ) also generated low levels of novel transcripts ( Fig 3F ) . For mutant J , overuse of A2 ( which is positioned 3’ to SX1 ) was accompanied by overuse of proximal downstream splice donor D3 and thus overrepresentation of SX2 = [A2-D3] into spliced viral mRNAs ( Fig 3D ) . Additionally , some utilization of cryptic splice donors ( D5052 , D5434 , and D5478 ) generated low levels of novel transcripts ( Fig 3F ) . For mutant K , overuse of A3 ( which is positioned 3’ to SX1 and SX2 ) did not result in the more frequent inclusion of these short exons ( Fig 3D ) . Overall therefore , it appeared that one consequence of the overuse of a given splice acceptor ( A1 or A2 ) , was a resultant overuse of the proximal downstream splice donor ( D2 or D3 ) , consistent with an ‘exon definition’ model of splicing control . This overuse of canonical splice acceptors in I , J and K resulted in aberrant representation of particular viral mRNAs . Among the 1 . 8 kb class of mRNAs , for WT HIV-1 , Nef2 was the dominant mRNA species in both splicing assays ( Fig 3E and 3F ) . Conversely , in mutant I , Nef5 was the dominant mRNA , while in mutant J , Nef4 , Tat3 and Vpr1 were overrepresented ( Fig 3E and 3F ) . These changes were likely responsible for the overexpression of GFP ( in the nef position ) and/or Vpr in these mutants ( Fig 1C ) . For mutant K , Tat1 mRNA was over-represented ( Fig 3E and 3F ) , but the overall levels of protein expression were not greatly affected in transfected cells . For other replication defective mutants ( L , M , and O ) that we termed Group 3 , the relative uses of splice sites appeared normal , despite obvious replication defects ( Fig 2 , Fig 3B–3E ) . These viruses appeared to express a normal complement of viral proteins in transfected cells ( Fig 1C ) . Overall , therefore , analyses of viral replication and RNA splicing led to the classification of the synonymously mutated viruses into three groups ( Fig 4A ) : Group 1 mutants exhibited near WT fitness , Group 2 mutants exhibited replication defects accompanied by perturbed RNA splicing , while Group 3 mutants had profound replication defects in the absence of obvious splicing perturbation . The three phenotypes were caused by mutations that exhibited a clear regional bias with respect to their distribution along the viral genome ( Fig 4A ) . Specifically , Group 1 viruses carried mutations throughout the pro ( D ) and pol ( E to H ) genes or in the 3’ portion of the env gene ( N , P ) , and replicated indistinguishably from HIV-1NHG . Conversely , Group 2 viruses with obvious splicing defects carried mutations in two distinct genomic regions: Group 2a viruses ( A , B ) carried mutations toward the 5’ end of the genome , within gag , while Group 2b viruses ( I , J , K ) carried mutations or in the central portions of the genome , within the accessory genes ( Fig 4A ) . Group 3 viruses ( L , M , O ) that were defective but exhibited near-normal splicing carried mutations in the env gene ( Fig 4A ) . We next explored the discrepancy in the abilities of certain mutant viruses to yield infectious virions after transfection of 293T cells , but were unable to spread in MT4 cells ( Fig 2 ) . We examined this discrepancy by carrying out experiments that analyzed a single cycle of replication in MT4 cells . Specifically , we infected cells with each of the mutant viruses at an M . O . I . of 1 and quantified infectious virion release ( Fig 4B ) as well as levels of full length unspliced viral mRNA , during the first cycle of infection ( Fig 4C ) . These experiments revealed significant deficiencies in infectious virion yield from infected cells for mutants A , B , K , L and M , that were not evident or less evident when virions were generated after transfection of 293T cells with a plasmid containing full length viral DNA ( Fig 4B , Fig 1B ) . Moreover , these deficiencies , and the inability of each mutant virus to support a spreading infection , correlated with reduced levels of unspliced HIV-1 RNA in infected cells ( Fig 4A and 4C ) . These observations suggest that the underlying defect in each of the replication defective mutants is a deficit in maintaining the level of unspliced RNA . This deficiency likely result from excessive or aberrant splicing for Group 2 mutants . Conversely we have recently shown that at least one of the Group 3 viruses was sensitized to the antiviral protein , ZAP , through the cumulative effect of incidentally included CG dinucleotides during the synonymous mutagenesis [34] . It therefore appears likely that the deficit in unspliced RNA can be overcome , for some mutants , through the overexpression that results from transient viral DNA transfection in 293T cells . However , each of the mutants with reduced levels of unspliced RNA in single cycle-infected cells was profoundly defective in a spreading replication assay ( Fig 4A ) . To determine the sequence elements responsible for the perturbations in splicing in Group 2a and Group 2b viruses we took two approaches . First , we attempted to derive second-site revertant viruses through passage of each viral mutant in MT4 cells . Second , we applied a mapping approach , in which each block of mutations in mutants A , B , I , J and K was divided into roughly equally sized component segments ( S2 Table ) , and the splicing properties of each secondary mutant re-examined . Through an iterative process , sometimes combining mapping and second-site revertant derivation , we could determine the nature of the defects in each Group 2 mutant and map the responsible cis-acting sequences . For mutants A and B in which cryptic splice sites within the gag gene were activated by the silent mutations , we first attempted to derive revertant viruses by passage in MT4 cells . For both mutants A and B , passage quickly yielded viruses that replicated more rapidly than the parental mutants viruses ( Fig 5A and 5B , S2 Fig ) . In the case of mutant A , passage in MT4 cells yielded a revertant virus that replicated well , albeit with delayed kinetics relative to WT HIV-1NHG and contained two nucleotide substitutions relative to the A mutant parent . One of these mutations ( C819T ) was responsible for the revertant replication phenotype ( Fig 5A ) . The C819T mutation was synonymous , and while it occurred at a position that differs from the WT in mutant A , the reversion was not to the WT sequence ( WT = G819 , mutant A = C819 , revertant = T819 ) . Thus , if position G819 in the WT virus was involved in a hypothetical RNA secondary structure that was perturbed or induced in mutant A ( C819 ) , then the C to T substitution in the revertant would not be expected to affect the perturbation of that secondary structure . The C819T revertant largely corrected the predominant splicing defects in mutant A , reducing the use of the cryptic splice acceptor ( A955 ) from ~ 1% to ~0 . 1% and the cryptic splice donor ( D1169 ) from ~14% to <1% ( Fig 5C ) . Since the reversion mutation C819 was distal to the cryptic splice sites ( ~140 and ~350 nucleotides 5’ to A955 and D1169 , respectively , Fig 5D ) the mechanism by which it exerts its effect was unclear . Notably , the revertant mutation occurred within a few nucleotides of the reported secondary structure that includes the HIV-1 packaging sequence , and D1 ( which is at position 743 ) . Thus , it may be that the revertant mutation acts by modulating the secondary structure surrounding D1 , rather than on the cryptic A955 and D1169 that were activated in mutant A . For mutant B , passage in MT4 or CEM cells yielded two different replicating revertant viruses each of which contained a single nucleotide substitution relative to the B mutant ( G1326A in MT4 cells and T1311C in CEM cells ) ( Fig 5B , S2 Fig ) Both revertant mutations were synonymous , at positions that differed in WT and mutant B viruses . Both mutations were proximal to the cryptic splice acceptor ( A1321 ) that was activated by the B mutations ( Fig 5E ) . Analysis of the G1326A revertant in the NGS splicing assay indicated reduced use of the cryptic acceptor A1321 , D1509 , and D1725 ( from 12% , 2 . 9% , and 3 . 9% to <1% respectively Fig 5C ) . Given the proximity of the reversion mutations to the silenced cryptic splice site , it is likely that these mutations act directly to reduce splicing factor binding , and thereby reduce the use of the cryptic A1321 . However , only the T1311C mutation had a marginal effect on the predicted strength of A1321 , reducing the MaxEnt score from7 . 03 to 6 . 09 ( S3 Table ) , while G1326A had no effect on the MaxEnt score , yet this mutation abolished the use of A1321 as an acceptor . Mutations in mutant B may have created or revealed a splicing factor binding site that was otherwise limiting for the use of the cryptic A1321 in a manner that was reversed by the G1326A and T1311C revertant mutations . To map mutations in A and B that were responsible for activating cryptic splice sites D1169 and A1321 respectively , we generated a set of mutant viruses ( AA , AB , AC , AD , AE and BA , BB , BC , BD , BE ) that contained subsets of the synonymous mutations present in mutants A and B ( Fig 5D and 5E ) . We also designed a fluorescent primer-based PCR-PAGE assay in which a PCR primers were positioned to conveniently and specifically monitor the major aberrant splicing event in mutants A and B which , as expected , yielded PCR product consistent with splicing at the respective cryptic splice sites ( D1169 and A1321 , respectively ) ( Fig 5F and 5G ) . Surprisingly , none of the secondary mutants containing subsets of the A and B mutations recapitulated the effects of the A and B mutations ( Fig 5F and 5G ) . Thus , the activation of the cryptic splice sites by the Group 2a mutants A and B was the result of multiple synonymous changes in those mutant viruses . Additionally , the apparent inability of MaxEnt scoring to consistently predict cryptic site utilization in the context of these mutants indicated that splicing defects could not be due solely to direct enhancing effects of mutations on specific cryptic sites . For mutant I , which exhibits overuse of A1 and A2 , as well as the corresponding donors ( D2 and D3 ) positioned immediately 3’ to A1 and A2 , ( Fig 6A ) we failed to obtain revertant replication competent viruses , even after extended passage . Therefore , we divided the I segment into 5’ and 3’ halves and generated two derivative mutant viruses ( IA and IB ) each of which had approximately half the of the synonymous mutations that were present in I ( Fig 6A ) . Both IA and IB mutants also exhibited splicing defects that were primarily manifested as overuse of A1 , but these defects were less complete , in that some degree of direct splicing to downstream acceptors ( e . g . A5 ) was present in both IA and IB ( Fig 6B ) . Notably , IA exhibited oversplicing at A1 and ( unlike the parent mutant I ) the cryptic splice donor D2b [37] . Conversely IB exhibited direct oversplicing at both A1 and , to some degree , to A2 but not D2b ( Fig 6B ) . Both IA and IB could replicate with delayed kinetics compared to HIV-1NHG , and extended passage of IA and IB yielded point mutation revertants that could replicate with kinetics close to those of HIV-1NHG ( Fig 6C and 6D , S2 Fig ) . In the case of IA , a T4904A mutation occurred in the A1 polypyrimidine tract ( Fig 6A and 6C ) . This caused a profound reduction of splicing at A1 and likely as a consequence , reduction of the inclusion of SX1 ( A1-D2 ) in spliced RNAs ( Fig 6B ) . Use of the cryptic D2b site was also abolished in the IA ( T4904A ) revertant . In fact , other than underuse of A1 and reduced inclusion of SX1 , the IA ( T4904A ) revertant had a near normal splicing pattern Thus , activation of canonical and cryptic downstream splice donors in mutant IA appeared to be secondary to activation of A1 . For mutant IB , the situation was more complex; a revertant ( G4912A ) was recovered after passage ( S2 Fig ) , precisely at the A1 acceptor that abolished the use of A1 , and consequently the inclusion of SX1 into spliced mRNA ( Fig 6A , 6B and 6D ) . However , significant overuse of A2 , and consequent inclusion of SX2 ( A2-D3 ) remained evident in the IB ( G4912A ) revertant ( Fig 6B ) . Indeed , overuse of A2 was more prominent in the IB ( G4912A ) revertant than in IB , perhaps because of the functional removal of A1 ( Fig 6B ) . It was therefore apparent that native sequences within IA result in suppression of splicing at A1 , while sequences within IB cause suppression of splicing at both A1 and A2 . To map elements within IA and IB that control splicing at A1 and A2 , we used the fluorescent PCR-based assay to analyze the pattern of splicing for viruses containing subsets of the IA and IB mutations . For IA , analysis of viruses containing smaller component mutant elements ( IC and ID , Fig 6A ) revealed that aberrant splicing was conferred only by the ID element ( Fig 6E ) . Thereafter , when ID was subdivided into ID1 and ID2 , it was evident that the controlling element resided primarily within ID2 ( Fig 6E ) . Thus , this analysis revealed a 48 nucleotide sequence that appeared to suppress splicing at A1 . Notably , a novel ESS/ESE element , termed ESS2b/ESE2b was recently identified that nearly precisely coincides with ID2 [32] , indicating that our approach has the potential to identify novel splicing regulatory signals . Notably , the unmutated IA segment contained short runs of three G’s ( G3 ) that have been reported to constitute hnRNP binding sites which suppress the activation of the cryptic splice donor D2b [37] . The mutations in IA disrupted two of these G3 runs ( Fig 6A ) and D2b was used in ~17% of IA spliced reads . The revertant mutation IA ( 4904 ) was able to inhibit activation of D2b from 17% to 2% ( Fig 6B ) , suggesting that the activation of D2b is predominately a secondary effect of A1 overutilization . Nevertheless , one of the G3 motifs coincides with the ID2 segment and therefore disruption of the G3 motifs may also contribute to the activation of the cryptic D2b site . For IB the situation was more complex , as mutations in this segment control splicing at both A1 and A2 . Nevertheless , subdivision of the IB mutations into components IE and IF ( Fig 6A ) , revealed that the majority the effect of IB mutations were conferred by mutations in IF . However , IF did not exhibit as prominent a degree of perturbation as IB ( Fig 6E ) . Even though the splicing of mutant IE appeared normal , mutations in IE made some contribution to the defects present in IB ( Fig 6E ) . Subdivision of IF into IF1 and IF2 yielded viruses with a normal pattern of splicing ( Fig 6F ) . Thus , it was evident that multiple sequences acting together in IB , distributed over IE , but primarily concentrated in IF1 and IF2 , regulate splicing at A1 and A2 and their overall contributions could not be mapped through this approach to a single small candidate regulatory element . For mutant J , which exhibited overuse of A2 and D3 we also failed to obtain revertant replication competent viruses , even after extended passage . Therefore , we divided the J segment into 5’ and 3’ halves and generated two derivative mutant viruses ( JA and JB , Fig 7A ) . Although there was some degree of splicing perturbation in JB ( Fig 7B ) , this perturbation was modest compared to J and JA . Moreover , JB was only marginally delayed in spreading replication assays compared to HIV-1NHG . ( Fig 7C ) . Therefore , we did not attempt to select JB revertants . Conversely , JA recapitulated the splicing perturbation observed in J , and was replication defective ( Fig 7B and 7D ) . Extended passage of mutant JA yielded a revertant mutation ( G5463A ) that enabled replication ( Fig 7D , S2 Fig ) . Notably , the reversion mutation was precisely at , and inactivated donor D3 ( Fig 7A ) , but also abolished splicing to A2 ( Fig 7B ) . The enhancing effect of D3 on A2 splicing has previously been demonstrated [38] . Interestingly , even though D3 appeared to be required for splicing at A2 only a fraction of RNAs that are spliced to A2 are also spliced at D3 . It was notable that the JB mutants as well as the JA ( G5463A ) revertant exhibited some oversplicing to A1 ( and therefore elevated inclusion of SX1 ) even though the J mutant sequences were distal ( ~440 to 890 nucleotides ) to A1 ( Fig 7A and 7B ) . To map elements within JA that control splicing , we used the fluorescent PCR-based assay for the 1 . 8 kb HIV-1 mRNAs to analyze viruses containing subsets of the JA mutations . We divided the JA mutant segment into 5’ and 3’ halves in two derivative mutant viruses ( JC and JD , Fig 7A ) which both exhibited some degree of perturbed splicing ( Fig 7E ) . Further subdivision of JC into JC1 , JC2 and JC3 clearly suggested that the 20 nucleotide JC2 segment contained an element whose mutation was primarily responsible for the perturbed splicing in JC ( Fig 7E ) , but further division of 20 nucleotide JC2 yielded two mutant segments ( JC2A and JC2B ) both of which cause perturbed splicing to nearly the same degree as the J , JA , JC and JC2 mutant segments from which they were derived ( Fig 7F ) . Division of the JD segment into JD1 and JD2 clearly revealed another element within the 46 nucleotide JD1 segment , that when mutated yielded a oversplicing pattern similar to that of the J mutant virus ( Fig 7E ) . Thus , multiple mutations within the JA fragment , contained within the segments JC2 and JD1 were capable of causing oversplicing defects similar to those observed in the J mutant . For mutant K ( Fig 8A ) , which exhibits overuse of A3 ( Fig 8B ) , it proved straightforward to recover a revertant mutant virus through passage that corrected the splicing defect and replicated well ( Fig 8B and 8C , S2 Fig ) . This revertant contained two mutations , one of which ( C5774T ) was sufficient to restore replication to near WT kinetics ( Fig 8C ) . This functional reversion mutation was 3 nucleotides from A3 . Notably the K ( C5774T ) revertant not only corrected overuse of A3 but exhibited a splicing pattern that was nearly indistinguishable from that of HIV-1NHG ( Fig 8B ) . This was surprising because previous studies have reported that CAG and TAG are used at a similar efficiency as 3’ splice acceptors in the context of HeLa cell nuclear extracts [39] . The remarkable diminution of splicing in the K ( C5774T ) reversion mutant suggests that in the context of A3 , the TAG is far less well utilized than CAG . Further , the A3 MaxEnt score of is increased from 9 . 76 to 10 . 05 in the context of the K ( C5774T ) reversion mutant , predicting that A3 is a stronger acceptor in K ( C5774T ) . However , experimentally the reverse is the case , demonstrating the limitation of both in silico and in vitro analyses to predict splicing phenotypes in the context of a full-length HIV-1 construct in a living cell . To map sequences within the K mutant that were responsible for causing oversplicing , we divided the K mutant segment into two halves ( KA and KB , Fig 8A ) and analyzed the pattern of 1 . 8 kb mRNAs using the fluorescent primer PCR assay . This analysis revealed that mutations responsible for A3 overuse resided in KA ( Fig 8D ) . Then , further subdivision of KA ( into KC and KD ) revealed that KD contained the controlling element ( s ) ( Fig 8D ) . Finally , subdivision of KD ( into KD1 and KD2 ) showed that KD2 contained RNA sequences whose mutation caused oversplicing ( Fig 8E ) , but further subdivision of KD2 ( into KD2A and KD2B ) showed that mutations in both of these KD2 components contributed its effect ( Fig 8F ) . Thus , a 23-nucleotide element ( KD2 ) positioned >100nt from A3 contained an RNA element whose sequence influences splicing at A3 .
Through a global synonymous mutagenesis experiment we found that extensive portions of the HIV-1 genome could be synonymously mutated without major effects on viral replication ( Group 1 mutants ) . In particular , synonymous mutations throughout the majority of the pol gene had a minimal or no effect on viral fitness , such that their effect was not measurable in our assays . Given the extent of the mutations that were introduced , and the improbability that most RNA secondary structures would be preserved in our mutants , it seems unlikely that undiscovered specific RNA secondary structures essential for replication exist in portions of the viral genome covered by Group 1 mutants . Even among mutants that were replication defective , Group 2 mutants could be restored to replication competence through single nucleotide reversion mutations that suppressed the utilization of cryptic or canonical splice sites , whose use was enabled or elevated in the global mutagenesis experiment . Again , this argues against the notion that undiscovered , specific RNA structures that are essential for replication are prevalent in Group 2 mutants , with the exception of those that regulate splicing . An important caveat to this conclusion is that replication was measured in permissive cell lines in the absence of competition . It is possible , even likely , that mutants or revertants with WT or near WT replication dynamics , have modest fitness deficits that would be evident in a more stringent environment or a competitive replication assay . For example , some of the HIV-1 accessory genes are important in vivo , but non-essential in vitro , therefore defects in their expression would be expected to have minimal effects on replication in our assays . Thus , while we can reasonably conclude that RNA secondary structures in Group 1 and Group 2 revertant mutants are non-essential for replication per se , it is nevertheless possible that RNA structures play an accessory role in regulating or fine-tuning the levels or fates of mRNAs encoding certain accessory proteins . Nevertheless , synonymous mutations in some portions of the HIV-1 genome caused profound , near-lethal defects in these highly permissive T-cell lines ( Group 2 and Group 3 mutants ) . These mutations therefore perturbed essential , non-coding features of the HIV-1 nucleotide sequence . One noncoding feature of the HIV-1 genome that appeared important for replication that was uncovered by our Group 2a mutants was suppression of cryptic splice sites near the 5’ end of the RNA genome . For these mutants , the magnitude of the deviation from WT sequence appeared important for cryptic site activation . Simply subdividing the mutant sequence blocks into two mutant blocks approximately equal lengths ( e . g . mutants AA , AB and BA , BB ) reverted the mutant splicing phenotype . This finding suggests that activation of the existing cryptic splice sites resulted from multiple perturbations to the gag nucleotide sequence and that changes in predicted splice site strength were not sufficient to explain their activation . Moreover , in the case of mutant A , a single nucleotide revertant mutation that occurred distal to the activated cryptic splice sites corrected the splicing defect without affecting their MaxEnt scores . These findings might be best explained by the existence of multiple elements in the gag gene ( secondary structures or protein binding sites ) that act redundantly to suppress cryptic splice site utilization . The fact that the revertant mutation for mutant A occurred at a position proximal to an existing RNA structure that includes D1 [40 , 41] , may suggest a role for an extended secondary structure involving the 5’ leader and the gag gene in suppressing the utilization of potential cryptic splice sites in gag . The potential for the 5’ leader to form alternative structures that could affect splicing appears to be finely tuned [42] , and therefore could possibly be perturbed by distal mutations in gag . Clearly , further work will be required to understand how the WT noncoding RNA sequence is selected to avoid utilization of cryptic splice sites . A detailed analysis of Group 2b mutants , that targeted the central portion of the genome , revealed several elements whose perturbation resulted in dramatic overuse of the canonical splice acceptors A1 , A2 , and A3 . These mutant elements that resulted in oversplicing could be mapped to sequences of various lengths . Such sequences constitute candidate individual or clustered ESS elements . When combined with existing knowledge of splicing regulation , at A1 , A2 and A3 [4 , 21 , 23–32] , these and previous findings suggest a highly complex regulatory network of functional inputs that govern alternative splicing of the HIV-1 genome , as depicted in ( Fig 9 ) . The phenotypes of some mutant and revertant viruses ( i . e . viruses with perturbed splicing whose replication was recovered by mutations at splice sites ) is consistent with the notion that exon definition ( i . e . coordinated recognition of a 5’ splice acceptor and a 3’ splice donor by the splicing machinery [43] ) plays a central role in the regulation of HIV-1 alternative splicing . For example , the mutant ( IA ) that exhibits elevated use of A1 and the downstream donor D2 ( as well as the cryptic donor D2b ) while the revertant ( IA ( T4904A ) ) occurred at A1 and reduced utilization of D2 , and D2b as well as A1 . Similarly , in IB , that exhibits elevated use of both A1 and A2 , both downstream donors D3 and D2 are also overused . In this case , the revertant mutation ( IB ( G4912A ) ) at A1 caused abolition of splicing at both A1 and D2 while splicing at A2 and D3 remain elevated . Consistent with the notion that splicing at A1 and D2 are coordinated , previous work has shown that the efficiency with which D2 is recognized can affect the frequency of splicing at A1 [44] . Similar communication occurs between A2 and D3 . Indeed , for a mutant ( JA ) that exhibited profound oversplicing at A2 and D3 , a reversion mutation ( JA ( G54653A ) ) that inactivated D3 also caused abolition of splicing at A2 . Because not all splices at A2 lead to splicing at D3 , this observation suggests that recognition of D3 by the splicing machinery is required for splicing at A2 even when D3 is not utilized , as has previously been suggested [23 , 38] . Overall therefore , a major determinant of the use of a given acceptor or donor in the HIV-1 genome was the use of a downstream donor or upstream acceptor , respectively . Our data is also consistent with the notion that splice acceptor sites compete with each other , with ‘cascading’ effects based on exon definition . For example , the mutant ( JA ) under-utilizes A1 and over-utilizes A2 . Its revertant ( JA ( G54653A ) ) that abolishes splicing at both D3 and A2 , causes over-utilization of A1 . In a reciprocal example of A1-A2 competition , abolition of A1 utilization in the revertant IB ( G4912A ) , was accompanied by increased splicing at A2 . Acceptor competition was also evident in the context of the mutant K , which exhibited oversplicing at A3 at the expense of splicing at A1 , A2 , A4b and A5 . In this case , the reversion mutation at A3 in K ( C5774T ) led to restoration of WT splicing frequency at all other sites in the central portion of the HIV-1 genome . Overall therefore , a key regulator of the use of a particular splice site is the presence and utilization of other splice sites , through coordinated recognition of acceptor and donor sites , along with competition between acceptor sites . Thus , disruption of the splicing regulatory signals whose existence is indicated herein can have complex effects on overall splicing , through the propagation of their effects from one splice site to another . Clearly the overall effect of these perturbations is best appreciated in the nextgen sequencing assay with all splice sites represented in a viral construct . That being the case , the RNA sequence elements that we have identified as apparent regulators of splice acceptor utilization ( Fig 9 ) could work directly or indirectly . Specifically , they could act to directly inhibit access of the splicing machinery to the affected splice acceptors or indirectly through splice donors , inhibiting acceptor utilization by inhibiting exon definition . It is also possible that our mutations affect the ability of splice sites to effectively communicate with each other in the context of exon definition . Existing splicing regulatory sequences have been reported to exert their effect through binding of hnRNP or SR proteins , or through the formation of RNA secondary structures [45] . The sequences that we have identified are of varying sizes; some elements that had major effects on splicing appear sufficiently small ( e . g . JC2 and KD2 ) to constitute specific protein binding sites . However , these elements did not appear to be enriched in canonical hnRNP binding motifs , as might be expected for splicing silencers [45] . Some of the effects on splicing that we found in our mutants , particularly within the I and J fragments , appeared complex and not easily mapped to small discrete elements . Perhaps these effects are the result of combinatorial inputs from multiple binding sites or secondary structures that could act to occlude splice sites , or spatially separate 3’ donors from 5’ acceptors thereby inhibiting exon definition . Further work will be required to determine precisely how these elements inhibit HIV-1 splicing . Perturbation of balanced splicing did not always lead to abolition of HIV-1 replication . For example , the mutants JB and JA ( 5463A ) had perturbed splicing ( overuse of A1 for JA , and abolition of SX2 utilization for JB ) but their replication was only slightly delayed compared to WT . Similarly the IB ( G4912A ) revertant virus had near WT replication but completely lacked splicing to A1 and therefore abolished inclusion of SX1 . These perturbations would be expected to lead to underexpression or overexpression of Vif and Vpr , neither of which are essential for replication in the particular cell type used in our experiments . Thus , the requirement for a particular balance of HIV-1 mRNAs could be highly context dependent . In our experiments , replication defects that resulted from over-splicing were likely the result of depletion of the pool of unspliced RNA , thus leading to lower levels of synthesis of Gag , Pro , Pol , and other viral proteins , and lower levels of viral genomes for packaging . Thus , a key role of splicing inhibitory signals in the HIV-1 genome is to maintain the unspliced RNA pool , as well as adequate levels of necessary viral proteins . Overall , our global synonymous mutagenesis experiment has revealed several RNA elements whose native sequence is important for HIV-1 replication . In particular , we have identified several RNA elements in the HIV-1 genome whose native sequence appears to be important for suppression of canonical splice sites , regulation of alternative splicing and maintenance of unspliced transcript levels . Additionally , our analysis revealed that some as yet unidentified property of RNA sequences in the gag gene suppresses utilization of cryptic splice sites . Understanding how RNA sequence affects splicing in the context of HIV-1 may give insights into the general mechanisms by which alternative splicing is regulated and how splicing regulation evolves , as well as opportunities to intervene therapeutically in HIV-1 infection .
293T cells ( ATCC ) were grown in DMEM with 10% fetal bovine serum ( Sigma ) . MT4 cells ( NIH AIDS Reagent Repository ) were cultured in RPMI supplemented with 10% fetal bovine serum . HIV-1NHG and mutant derivatives containing a reporter GFP in place of nef were produced by transfection of 293T cells with the proviral plasmid using PEI . Virus titers were determined by FACS analysis of target MT4 cells . Dextran sulfate was added to inhibit reinfection at 18 hours post infection and the number of infected cells determined by FACS analysis of GFP expression 2 days post infection . For spreading replication infections , 2x105 MT4 cells in 2 mL of media were infected at an MOI of 0 . 002 . Aliquots of infected cells were withdrawn each day , fixed in 4% PFA and the proportion of infected cells determined by FACS analysis of GFP expression . For single cycle infections , cells were infected with HIV-1NHG or mutants thereof , at an MOI of 1 . 0 , and harvested 2 days post infection for western blot and qPCR analysis . Serial passage of mutant viruses was started by infecting a culture of 5x105 MT4 cells at an MOI of 0 . 002 and time points were fixed in 4% PFA . Upon infection of the entire culture , 200 μL of cell free supernatant was filtered and used to inoculate a new culture of 5x105 MT4 cells . After the final passage , the cells were collected and the DNA was extracted ( Qiagen Tissue Mini Kit ) , then the mutated region was sequenced to determine the revertant mutation that had occurred . The mutated regions of the HIV-1 genome were synthesized ( Genewiz ) and cloned into HIV-1NHG using restriction digest sites that were proximal to the mutated regions ( S2 Table ) . Division of the original mutants blocks into two new derivative mutants was achieved using overlap extension PCR based approaches with mutant and WT templates . Revertant mutations acquired through passage of the virus were reconstituted into the original mutant provirus from which they arose through site directed mutagenesis and overlap extension PCR . RNA was extracted from infected cells using Trizol and reverse transcribed using the SuperScript III reverse transcriptase ( ThermoFisher ) . The resulting cDNA was used as a temple for quantitative real-time PCR using the ABI Fast RT-PCR system along with the Fast Start TaqMan Probe master mix . Unspliced viral RNA was detected using a forward primer: 5’-GGACTTGAAAGCGAAAGGGA-3’ , a reverse primer: 5’-TCTCTCTCCTTCTAGCCTCCG-3’ and a TaqMan probe 5’-GGGCGGCGACTGGTGAGT-3’ targeting the major splice donor D1 . Serial tenfold dilutions of known copy numbers of HIV-1NHG plasmid was used to generate a standard curve . Cells were normalized for cell number , lysed in SDS sample buffer , separated by electrophoresis on NuPage 4–12% Bis-Tris gels ( Novex ) and blotted onto nitrocellulose membranes ( GE Healthcare ) . Blots were probed with an HIV-1 anti-capsid antibody ( 183-H12-5C ) obtained from the NIH AIDS reagent repository , a GFP antibody ( G1546 , Sigma ) , and HIV-1 anti-Env gp120 antibody ( 12-6205-1 , American Research Products ) . RNA from 293T cells transfected with mutant provirus was extracted using the Nucleospin RNA extraction kit ( Machery Nagel ) . RNA was reverse transcribed using SuperScript III reverse transcriptase ( ThermoFisher ) with gene specific primers for either fully spliced ( 8483R: 5’-CCGCAGATCGTCCCAGATAAG-3' and partially spliced ( 6223R: 5'-CAAGTGCTGATATTTCTCCTTCAC -3' ) mRNA classes . The cDNA templates were then used in a 10μL PCR reaction with fluorescent reverse primers specific to the splice class ( labelled at their 5’ ends with IRD800 ) and a forward primer position 5’ to the major splice donor ( 499F: 5' -CTGAGCCTGGGAGCTCTCTGGC-3' ) and run for 25 cycles with an annealing temperature of 54°C . Alternatively , to determine use of the activated cryptic splice site in mutant A , a forward primer , positioned 5’ to the mutations ( 763F 5’- TGACTAGCGGAGGCTAGAAGGAGAGAG -3’ ) and the fluorescent reverse primer for the fully spliced class ( 8483R ) were used in a PCR reaction with the cDNA templates . To determine use of the activated cryptic splice site in mutant B , the forward primer 499F and a fluorescent reverse primer 5’ to the mutations in B ( 1557R 5’- GATAGGTGGATTATGTGTCATCC -3’ ) were used in a PCR reaction with the cDNA template . Then , 10μL of 2x TBE-Urea sample buffer was added to the PCR reaction which was then run on a 6% TBE-Urea gel for 90 minutes at 180V ( Novex ) . A LI-COR Odyssey scanner was used to detect fluorescent signals directly from the gels . Determination of splice site utilization using the Primer ID-based deep sequencing assay was done substantially as described with minor modifications [20] . Briefly , RNA was extracted from cells transfected with HIV-1NHG or mutants thereof using the RNeasy Plus minikit ( Qiagen ) . Primers used for cDNA synthesis were GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNNNNNNNCAGTTCGGGATTGGGAGGTGGGTTGC for 1 . 8 kb spliced transcripts and GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNNNNNNNGCTACTATTGCTATTTGTATAGGTTGCATTACATG for 4 kb spliced transcripts . Indexed primers were obtained from Integrated DNA Technologies Custom Oligos . Total cell RNA ( 8 μg ) was subjected to cDNA synthesis , purification and cleanup and an initial PCR amplification . An aliquot of the product from this first PCR was used as the template for the second PCR to add the Illumina adapter and bar codes to allow multiplexing in the Illumina sequencing reaction . PCR products were visualized on a 2% agarose gel and then cleaned . Libraries were mixed/multiplexed and sequenced using the 300-base paired-end read for the Illumina Miseq platform . Reads were sorted using the Illumina bcl2fastq pipeline ( v . 1 . 8 . 4 ) to separate the multiplexed samples . Subsequent Data analysis and splicing quantification were done using the previously described in-house pipeline [20] written in Ruby and adapted to accommodate the mutated sequences . This program uses the combined sequence information from the paired-end reads to identify splice site usage and transcript type . Reads are condensed by Primer ID to prevent skewing in the PCR steps . Cryptic alternative donor and acceptor splice sites were identified using a program that compares data reads to a reference sequence and identifies the base where a splice discontinuity occurs and the base it splices to . | In addition to encoding viral proteins , the HIV-1 genome contains sequence elements that act at the level of RNA to enable replication . We undertook an experiment to discover new RNA elements that act in this way by altering nearly the entire coding sequence of the viral genome so as to change the RNA sequence without changing protein sequences . This experiment uncovered two classes of defective mutants . One class had profound defects in RNA splicing , the other had no obvious defects in splicing . Through an analysis of the splicing-defective mutants , we found several previously RNA sequences in the viral genome that affected splicing , enabling a nearly complete catalogue of signals that regulate HIV-1 alternative splicing in infected cells to be derived . Because these newly described sequences lack sequence motifs that are known to bind to canonical splicing-regulatory proteins , they may function through novel mechanisms . | [
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| 2018 | Global synonymous mutagenesis identifies cis-acting RNA elements that regulate HIV-1 splicing and replication |
Thousands of human genes contain introns ending in NAGNAG ( N any nucleotide ) , where both NAGs can function as 3′ splice sites , yielding isoforms that differ by inclusion/exclusion of three bases . However , few models exist for how such splicing might be regulated , and some studies have concluded that NAGNAG splicing is purely stochastic and nonfunctional . Here , we used deep RNA-Seq data from 16 human and eight mouse tissues to analyze the regulation and evolution of NAGNAG splicing . Using both biological and technical replicates to estimate false discovery rates , we estimate that at least 25% of alternatively spliced NAGNAGs undergo tissue-specific regulation in mammals , and alternative splicing of strongly tissue-specific NAGNAGs was 10 times as likely to be conserved between species as was splicing of non-tissue-specific events , implying selective maintenance . Preferential use of the distal NAG was associated with distinct sequence features , including a more distal location of the branch point and presence of a pyrimidine immediately before the first NAG , and alteration of these features in a splicing reporter shifted splicing away from the distal site . Strikingly , alignments of orthologous exons revealed a ∼15-fold increase in the frequency of three base pair gaps at 3′ splice sites relative to nearby exon positions in both mammals and in Drosophila . Alternative splicing of NAGNAGs in human was associated with dramatically increased frequency of exon length changes at orthologous exon boundaries in rodents , and a model involving point mutations that create , destroy , or alter NAGNAGs can explain both the increased frequency and biased codon composition of gained/lost sequence observed at the beginnings of exons . This study shows that NAGNAG alternative splicing generates widespread differences between the proteomes of mammalian tissues , and suggests that the evolutionary trajectories of mammalian proteins are strongly biased by the locations and phases of the introns that interrupt coding sequences .
The split structure of eukaryotic genes impacts gene expression and evolution in diverse ways . Most directly , the presence of introns enables multiple distinct mRNA and protein products to be produced from the same gene locus through alternative splicing , which is often regulated between tissues or developmental stages [1] , [2] . Alternative inclusion or exclusion of exons—“exon skipping”—can generate protein isoforms with distinct subcellular localization , enzymatic activity or allosteric regulation , and differing , even opposing , biological function [3]–[5] . Splicing is often regulated by enhancer or silencer motifs in the pre-mRNA that are bound by splicing regulatory proteins that interact with each other or with the core splicing machinery to promote or inhibit splicing at nearby splice sites [6] . Such enhancer and silencer motifs are common throughout constitutive as well as alternative exons and their flanking introns [7]–[9] . In turn , the presence of splicing regulatory motifs in exons , and their higher frequency near splice junctions , impacts protein evolution . For example , the frequencies of single nucleotide polymorphisms ( SNPs ) and amino acid substitutions are both reduced near exon-exon junctions relative to the centers of exons as a result of selection on exonic splicing enhancer motifs [10] , [11] . Thus , a gene's exon-intron structure and its evolution are intimately linked . Alternative 3′ and 5′ splice site use , in which longer or shorter versions of an exon are included in the mRNA , are among the most common types of alternative splicing in mammals [1] and can generate protein isoforms with subtly or dramatically differing function . For example , production of the pro-apoptotic Bcl-xS or the anti-apoptotic Bcl-xL protein isoforms is controlled through regulated alternative splice site usage [12] . Binding of splicing regulatory factors between the alternative splice sites or immediately adjacent to one site or the other can shift splicing toward the ( intron- ) proximal or distal splice site [6] , [13] , [14] , providing a means to confer cell type-specific regulation . The distance between the alternative splice sites can vary over a wide range , from hundreds of bases to as few as three bases in the case of NAGNAG alternative 3′ splice sites . NAGNAG alternative splicing ( Figure 1A ) has been observed in vertebrates , insects , and plants , and is known to be very common . Bioinformatic analyses of expressed sequence tag ( EST ) databases have identified thousands of examples [15]–[18] . However , most of the mechanisms known to regulate other alternative 3′ splice site pairs , particularly those that involve binding of regulatory factors between the sites , or much closer to one site than the other , cannot apply to NAGNAGs because of the extreme proximity of the two sites . Thus , regulation of NAGNAGs is more difficult to envisage . Furthermore , analyses of select genes using PCR and capillary electrophoresis approaches reached differing conclusions about NAGNAG tissue specificity [15] , [19] , [20] , and several authors have argued that NAGNAG splicing is purely stochastic , is not evolutionarily conserved , and is not physiologically relevant [21] , [22] . However , analyses of NAGNAG splicing at a genome-wide scale have been hampered by the impracticality of distinguishing such similar isoforms by microarray hybridization and the insufficient depth of EST databases for assessment of tissue specificity . In order to assess the abundance and potential regulation of NAGNAG splicing events genome-wide , we analyzed polyA-selected RNA-Seq data generated using the Illumina HiSeq platform from 16 human tissues at depths of ∼8 Gbp per tissue , similarly deep RNA-Seq data that we generated from eight mouse tissues , and data generated by the modENCODE consortium across a developmental time course in Drosophila . NAGNAG isoforms can be uniquely distinguished by short reads that overlap the splice junction , and the quantity of data available from each tissue in human and mouse typically represented at least 80-fold mean coverage of the transcriptome , a depth sufficient to detect potential tissue-specific differences in many cases . Sequence features were identified which can shift splicing toward the proximal or distal NAG , providing clues to regulation . We also analyzed the impact of NAGNAGs on exon evolution , obtaining evidence that NAGNAGs dramatically accelerate addition and deletion of sequence at the beginnings of exons .
Our initial analyses used the Illumina Body Map 2 . 0 dataset of polyA-selected RNA-Seq data from 16 human tissues ( adipose , adrenal , brain , breast , colon , heart , kidney , liver , lung , lymph node , ovary , prostate , skeletal muscle , testes , thyroid , and white blood cells ) sequenced at depths of ∼80 million paired-end 2×50 bp reads per tissue . This sequencing depth generates ∼8 Gbp of data , representing >80-fold coverage of the human protein-coding transcriptome . Enumerating all possible NAGNAG splicing events , we mapped both ends of each read against NAGNAG splice junctions ( Figure 1A ) . Isoform ratios were estimated across all tissues as “percent spliced in” ( PSI or ψ ) values ( Figure 1B ) , representing the fraction of mRNAs that use the intron-proximal splice site , thereby including the second NAG in the mRNA . The reliability of such RNA-Seq-based estimates of isoform abundance has been established previously [23] . Using a conservative approach that has comparable power to detect each of the major types of alternative splicing events , we estimated that NAGNAGs comprise slightly more than 20% of reading frame-preserving alternative splicing events in coding regions , making NAGNAGs the most common form of protein-producing alternative splicing after exon skipping ( Figure 1C ) . In all , more than 2 , 000 NAGNAG events were detected in protein-coding regions of human genes where both isoforms were expressed at ≥5% in at least one tissue . Strikingly , 73% of these NAGNAGs showed evidence of tissue-specific regulation ( p<0 . 01 by multinomial test ) . Furthermore , approximately 42% were “strongly regulated , ” with changes in ψ of at least 25% between tissues ( Table S1 ) . For example , a NAGNAG in the gene encoding FUBP1 , a transcriptional regulator of MYC , undergoes dramatically different splicing between kidney and lymph node ( Figure 1B ) . Here , we report absolute rather than relative differences in splicing levels , e . g . , a change from 10% to 35% between tissues is considered an increase of 25% , not 250% , and the largest difference in ψ between tissues is defined as the “switch score” [1] . Other genes containing NAGNAGs with switch scores of 50% or more included HOXD8 , CAMK2B , ATRX , CAPRIN2 , and MLLT4 ( a complete list of human genes containing alternative NAGNAGs , sorted by switch score , is provided in Table S2 ) . Technical replicates—sequencing of the same RNA-Seq libraries with 75 bp single-end reads at depths of ∼50 million reads per tissue—yielded similar estimates of NAGNAG abundance and regulation ( Table S3 ) . Regulation that contributes to fitness is expected to be evolutionarily conserved . A previous study reported the existence of selection against alternatively spliced NAGNAGs in coding sequences [24] . Nevertheless , some NAGNAGs are quite deeply conserved , e . g . , a NAGNAG that generates an arginine insertion/deletion in a RNA-binding domain of the splicing factor PTBP2 ( also known as nPTB or brPTB ) . Both isoforms of this NAGNAG event are observed in ESTs from human , mouse , and chicken , and the potential for alternative splicing is conserved at the sequence level to lizard ( Figure 1D ) . Consistent with this example , a previous analysis of EST databases suggested that a subset of alternatively spliced NAGNAGs are under purifying selection in vertebrates [25] . We systematically assessed the global conservation of NAGNAG isoform levels using RNA-Seq data generated from eight mouse tissues ( brain , colon , kidney , liver , lung , skeletal muscle , spleen , and testes ) . Restricting to the set of NAGNAGs which were alternatively spliced in human ( both isoforms expressed at ≥5% in at least one tissue ) , we found that NAGNAGs which were strongly regulated were approximately 10 times more likely than unregulated NAGNAGs to exhibit alternative splicing in their mouse orthologs , and vice versa ( Figure 1E ) . This large and consistent increase in conservation of alternative splicing with increasing switch score suggests that regulated NAGNAGs are much more likely to contribute to organismal fitness , and therefore to be selectively maintained , than are alternatively spliced events which do not exhibit tissue specificity . If NAGNAG alternative splicing were selectively neutral , then we would not expect to see a correlation between the observed degree of tissue specificity in one species and conservation of alternative splicing in the other species . NAGNAG isoform levels were very well correlated between biological replicates , consisting of individual mice of strains C57BL/6J and DBA/2J , whose genomes differ to an extent similar to that of unrelated humans ( r = 0 . 96 , Figure 2A ) , demonstrating the robustness and reproducibility of these RNA-Seq-based estimates of NAGNAG ψ values . Similar numbers of alternatively spliced NAGNAGs were detected in mouse as in human , with 28% of alternatively spliced NAGNAGs in mouse exhibiting evidence of tissue-specific regulation and 8% being strongly regulated across the eight tissues studied ( Table S4 ) . Many orthologous NAGNAGs in human and mouse exhibited tissue-specific regulation in both species , e . g . , NAGNAGs in FUBP1 , CAMK2B , CAPRIN2 , and ATRX ( a complete list of alternative NAGNAGs in mouse is provided in Table S5 ) . The higher fraction of regulated NAGNAGs detected in the human data probably results from a combination of factors , including the greater number of tissues sampled ( Figure S1 ) , the diverse genetic backgrounds of the human samples , and intrinsically higher read coverage variability in the human RNA-Seq data used . Comparing technical replicates of human tissues , which capture variability in sequencing , we estimated false discovery rates ( FDRs ) for discovering strongly regulated NAGNAGs ranging from ∼0 . 8% to ∼13 . 3% , with a mean FDR of 4 . 4% ( Figure S2 ) . In contrast , comparing biological replicates of mouse tissues , which capture all major sources of variability ( tissue collection , library preparation , sequencing , and individual-specific splicing differences ) , we estimated FDRs ranging from 0 . 6% to 1 . 9% , with a mean of 1 . 1% ( Figure S3 ) . Using these estimated FDRs , and extrapolating the mouse data to 16 tissues ( Figure S1 ) , we estimated that between 12% and 37% of NAGNAGs are strongly regulated across tissues in mammals , making strong regulation a fairly common occurrence—though somewhat less common than for other types of splicing events . The relatively small differences between samples of the same tissue from mice whose genomes differed to an extent comparable to that of unrelated humans ( Figure 2A ) suggested that inter-individual variation contributed less than other sources of variation ( e . g . , tissue-specific differences ) to the variations observed between the human libraries . Orthologous human and mouse NAGNAGs exhibited high quantitative conservation of isoform levels . This was particularly true when the difference between the proximal and distal 3′ splice site scores—using a method that scores the strength of the polypyrimidine tract and AG region—was conserved ( Spearman's ρ = 0 . 67 , Figure 2B ) . The correlation decreased somewhat in cases where the differences in 3′ splice site scores were less conserved ( ρ = 0 . 54 , p = 0 . 013 for test of equality of correlation using the Fisher transformation; Figure S4 ) , suggesting that changes in relative 3′ splice site strength may contribute to species-specific differences in NAGNAG splicing . Notably , many NAGNAGs with diverged splice site scores were alternatively spliced in one species but constitutively spliced in the other , suggesting relatively rapid evolution of 3′ splice site positions . To better understand how NAGNAG splicing is regulated , and which sequence regions might be involved , we examined sequence conservation of flanking intronic and exonic regions for NAGNAGs grouped by switch score using alignments of the genomes of placental mammals . Tissue-specific NAGNAGs exhibited markedly increased sequence conservation in the upstream intron ( Figure 2C–D ) , with little or no increase in other analyzed regions . The consistent increase in conservation in the upstream intron with increasing switch score provides further evidence that these regulated NAGNAGs contribute to organismal fitness , and is consistent with previous observations that alternatively spliced NAGNAGs have higher upstream sequence conservation than constitutive 3′ splice sites [26] . Enumerating NAGNAGs in introns of the fly Drosophila melanogaster , and comparing isoform usage across 30 developmental time points ( embryo to adult ) using RNA-Seq data from the modENCODE consortium [2] , we identified over 500 NAGNAGs in coding regions of Drosophila genes where both isoforms were expressed at ≥5% in at least one developmental time point . Of these , 14% were developmentally regulated , with 5% being strongly regulated as defined above . As in mammals , more highly regulated fly NAGNAGs were associated with increased sequence conservation within and upstream of the 3′ splice site ( Figure 2E ) . The consistent location of the sequence conservation signal for regulated NAGNAGs in mammalian and insect genomes ( Figure 2C–E ) suggested that the region ∼50 bp upstream of the NAGNAG motif , encompassing the competing 3′ splice sites themselves , may contain most of the regulatory information that governs NAGNAG alternative splicing . The extensive tissue-specific regulation observed in mammals and developmental regulation seen in flies may indicate that regulated NAGNAG alternative splicing is widespread in metazoans . The increased divergence in isoform usage observed for NAGNAGs that had undergone divergence in 3′ splice site score difference ( Figures 2B , S4 ) suggested that relative splice site strength is a major determinant of NAGNAG quantitative isoform usage . Supporting this hypothesis , previous EST-based analyses have demonstrated that splice site strength impacts whether or not a NAGNAG will be alternatively spliced [21] , [27] . To explore the relationship between splice site strength and quantitative isoform levels , rather than simply the presence or absence of alternative splicing , we created a biophysical model wherein the probabilities of using the proximal and distal splice sites are proportional to and , respectively , where the parameter determines the inherent preference for using the intron-proximal splice site and is a scaling factor for the splice site scores . This simple model , containing just two free parameters , accurately predicted mean isoform usage across human tissues ( Figure 3A ) , suggesting that relative 3′ splice site strength is the primary determinant of basal NAGNAG isoform levels . The fitted value provides a quantitative measurement of preference for the proximal splice site in NAGNAG 3′ splice site recognition , predicting that the distal splice site of a NAGNAG must typically be bit stronger than the proximal splice site in order to be spliced with equal efficiency . Analysis of mouse NAGNAGs yielded similar values of the Q and B parameters ( Figure S5 ) , supporting the robustness of these estimates . This preference for the proximal site was obvious even after controlling for the identity of the −3 bases ( the Ns of the NAGNAG ) ( Figure 3B ) , which are known to be important determinants of NAGNAG isoform choice [18] , [26] , [27] . Preference for the proximal splice site is consistent with models of 3′ splice site recognition that involve scanning or diffusion from an upstream branch point [28] , [29] . While the mean ψ value was accurately predicted by our model , the variability around the mean was substantially higher than expected based on measurement noise ( Figure 3A ) . This observation is consistent with the concept that splice site strength determines the basal levels of the two NAGNAG isoforms , but the presence of regulatory sequence elements not captured by the 3′ splice site score , and variation in the levels of associated trans-acting factors , modulates the isoform ratios that occur in different tissues . The variability in NAGNAG splicing observed above implies that features outside of splice site strength and the −3 base must also be involved in determining isoform usage . For example , the NAGNAG in the splicing factor PTBP2 ( Figure 1D ) represents an exception to the pattern observed above: the −3 bases ( CAGAAG ) predict predominant proximal splice site usage , since C is strongly favored over A and is also proximal , but roughly equal proportions of both isoforms are expressed across all tissues studied ( Figure S6 ) . This observation led us to wonder whether other aspects of this 3′ splice site , e . g . , the relatively short and distally located polypyrimidine tract and the relatively distal location of the putative branch point ( Figure 1D ) might favor use of the distal NAG in this and other cases . While many analyses support the importance of the −3 base combination in NAGNAG alternative splicing [18] , [26] , [27] , there is less consensus in the literature about the relevance of other major elements of the 3′ splice site , including the polypyrimidine tract and branch site . Molecular genetics experiments demonstrated that mutating sequences near the polypyrimidine tract and branch site influenced alternative splicing of specific NAGNAGs [30] , [31] , but two computational studies that used machine-learning approaches [27] , [32] concluded that neither of these elements significantly influenced NAGNAG splicing globally . Notably , the experimental studies [30] , [31] measured quantitative isoform ratios , as we do in this study , while the machine-learning studies [27] , [32] simply classified NAGNAGs as constitutively or alternatively spliced . In order to dissect features that impact NAGNAG isoform choice , controlling for the effect of the −3 bases , we considered the large class of NAGNAGs with favored ( C or T ) nucleotides at both −3 bases ( YAGYAGs ) . We found that exons that predominantly used the proximal splice site ( “proximal major” YAGYAGs ) had substantially distinct nucleotide preferences from those that predominantly used the distal site ( “distal major” YAGYAGs ) ( Figure 3C ) , consistent with the experimental results of Tsai et al . [30] , [31] , who found that modifying the sequence upstream of the 3′ splice site influenced NAGNAG splicing . For example , distal major YAGYAGs tended to have shorter , more distal , polypyrimidine tracts than proximal major YAGYAGs ( Figure 3D ) , implicating polypyrimidine tract length and location in control of NAGNAG splicing . The proportion of CT/TC dinucleotides in the polypyrimidine tract was ∼25% higher for distal major YAGYAGs ( Figure 3E ) , suggesting the possible involvement of CU/UC-binding factors such as those of the PTB family [33]—some of which are tissue-specifically expressed—in promoting use of distal NAGs . The location of the first upstream AG was also shifted several bases downstream in distal major YAGYAGs compared to other 3′ splice sites ( Figure 3F ) , suggesting that the branch site is located further downstream in this class and that use of a distally located branch site favors use of the distal YAG , perhaps because the distance to the 3′ splice site is more optimal . Strongly regulated YAGYAGs had features that were intermediate between the extremes found for proximal major and distal major YAGYAGs , such as polypyrimidine tracts of intermediate length ( Figure 3D ) , suggesting that the presence of intermediate features facilitates regulation . Increased regulation was also associated with reduced 3′ splice site strength and greater similarity in strength between the competing sites ( Figure S7 ) , consistent with previous studies of other types of alternative splicing [34] . The −4 base , four nucleotides upstream of the 3′ splice site , is not generally considered to be important in splicing ( with rare exceptions [35] ) . This position contains little or no information in alignments of constitutive 3′ splice sites [36] , although a previous machine-learning analysis of features distinguishing between constitutively and alternatively spliced NAGNAGs included the −4 base in their classifier [27] . Our quantitative analysis strongly supported a special role in NAGNAG regulation for this canonically unimportant position . For distal major YAGYAGs , the −4 position ( here referring to the position four nucleotides upstream of the intron-proximal splice site ) had the highest information content of any position upstream of the YAGYAG ( Figure 3C ) ; furthermore , the −4 base was more conserved in distal major and strongly regulated YAGYAGs than for other classes of 3′ splice sites ( Figure S8 ) . Of the observations in Figure 3 , the two that seemed most compelling were the preference for pyrimidines at the −4 position and the more distal positioning of branch points in YAGYAGs that favored the distal splice site . To test the predicted role of the −4 base in regulation of NAGNAG splicing , we used a minigene reporter based on the NAGNAG in PTBP2 , whose splicing alters an exon coding for the RRM4 RNA binding domain ( Figures 1D , 4A ) . As predicted based on the data in Figure 3C , mutation of the −4 base ( T in the wildtype ) to A or G resulted in a substantial shift in splicing toward use of the proximal NAG , while mutation to C had no effect ( Figure 4B ) . These observations confirm that presence of a pyrimidine at the −4 position favors use of the distal NAG , even though no sequence preference was observed at this position in constitutive splice sites ( Figure 3C ) . Presence of a pyrimidine at the −4 position of a NAGNAG might function to shift the location of binding of U2AF65 downstream by a base or more from its normal position , which might then result in preferential binding of U2AF35 to the downstream NAG , though this will require further study . We also tested the role of the branch point in NAGNAG splicing by manipulating the branch site to 3′ splice site distance in this reporter , either in a context in which the inferred native branch point sequence ( BPS ) was intact or in a context in which the native BPS had been replaced by the previously mapped BPS of IGF2BP1 intron 11 ( Figure 4A ) . With the native BPS present , an increase of just four bases in the BPS-3′ splice site distance was sufficient to cause a substantial shift in splicing towards the proximal NAG , with little or no additional shift resulting from addition of three more bases ( Figure 4C ) . In the context of the exogenous IGF2BP1 BPS , a somewhat higher basal level of proximal splice site usage was reduced by deletion of six bases , with deletion of three bases producing a modest change ( Figure 4D ) . These data indicate that the BPS plays a significant role in NAGNAG splicing and confirm that shorter BPS-3′ splice site distances can shift splicing toward the distal NAG . Together , our analyses of proximal/distal major splicing suggested that NAGNAG 3′ splice sites afford broad scope for evolutionary tuning of isoform ratios , even in cases where the sequence of the second NAG is constrained by selection on the encoded amino acid . For example , mutations affecting the upstream −3 and −4 bases , the polypyrimidine tract , or the location of the branch site could all potentially modulate the ratio of the two isoforms across a range from predominantly proximal to predominantly distal isoform usage , which might facilitate evolutionary addition and deletion of single codons at 3′ splice junctions . A previous study observed reduced frequencies of amino acid substitutions near exon-exon junctions relative to the centers of exons , presumably resulting from purifying selection acting on exonic splicing enhancer motifs [10] , [11] . By contrast , when we examined exon length changes in alignments of orthologous human and mouse coding exons ( Figure 5A ) , we observed a striking 18 . 5-fold enrichment for gain/loss of exonic sequence at 3′ splice sites relative to flanking positions ( Figure 5B; assignment of gaps is illustrated in example alignments in Figure S9 ) . No particular enrichment for gain/loss of exonic sequence was observed at the 5′ splice site , suggesting that increased addition/deletion of exonic sequence is associated with properties of the 3′ splice site itself , rather than being a generic feature of exon boundaries . This pattern was not changed when restricting to constitutive splice junctions ( Figure S10 ) . A majority of the changes plotted in Figure 5B involved gain/loss of precisely three bases , and restricting to changes of exactly this size yielded a similar degree of enrichment at the 3′ splice site ( Figure 5C ) . While gain/loss of exonic sequence is normally attributed to insertions or deletions ( “indels” ) in the genome , the increased frequency of changes at the 3′ splice site suggested a prominent role for an alternative mechanism involving genomic substitutions that give rise to three base shifts in exon boundaries without insertion or deletion of genomic DNA . For example , creation of a NAG motif immediately upstream of a 3′ splice site NAG by mutation would be expected to commonly shift splicing upstream by three bases ( resulting in exonization of three bases of intron ) or generate an alternatively spliced NAGNAG that could subsequently lose splicing at the downstream NAG through mutation . Alternatively , a mutation creating an immediately downstream NAG—or a mutation that weakened the upstream NAG relative to a pre-existing downstream NAG—could result in either alternative splicing or loss of three bases of exonic sequence . As outlined in Table S6 , both of these scenarios could arise frequently by single base substitutions , which occur at a rate that is an order of magnitude higher than the rate of genomic indels [37] . Consistent with this substitution/exaptation model and the finding that many NAGNAGs are alternatively spliced in the Drosophila lineage , we observed similar enrichment for gain/loss of three bases of exonic sequence at the 3′ splice site when comparing orthologous D . melanogaster and D . yakuba coding exons ( Figure 5D ) . Notably , the enrichment of three base gaps at the 3′ splice site was 3-fold weaker in comparisons of Caenorhabditis elegans and C . briggsae exons ( Figure 5E ) . NAGNAG alternative splicing is reported to occur rarely in nematodes due to a highly constrained 3′ splice site motif [15] . We confirmed the rarity of NAGNAG alternative splicing in C . elegans using RNA-Seq data from 14 developmental time points and conditions generated by the modENCODE consortium . Enumerating NAGNAGs in introns of C . elegans coding genes , we detected alternative splicing ( both isoforms expressed at ≥5% in at least one developmental time point ) for only 18% of NAGNAGs with favorable pyrimidine bases at both −3 positions based on RNA-Seq read depths slightly below those used in human . By contrast , 50%–85% of human , mouse , and Drosophila YAGYAGs were detected as alternatively spliced , suggesting that NAGNAG alternative splicing is substantially rarer in worms than in other metazoans . This decrease in abundance mirrors the 3-fold weaker enrichment of three base gaps at 3′ splice sites observed in worms ( Figure 5E ) . Sequence motif analyses further implicated NAGNAG splicing in the exon length changes observed at exon boundaries . Classifying the borders of orthologous mouse and rat exons as unchanged , expanded , or contracted ( comparing to human , cow , chicken , and/or Xenopus laevis as outgroups ) , we observed evidence of residual NAGNAG motifs in exons with altered boundaries ( Figure 5F ) . Specifically , exons expanded in mouse or rat exhibited a consensus NAG at exonic positions +1 to +3 , and contracted exons exhibited a consensus NAG at intronic positions −6 to −4 . The presence of this residual sequence motif provides further evidence that a substantial portion of exon length changes observed between orthologous mammalian exons derive from splicing-mediated shifts in exon boundaries rather than genomic indels . Likely because of subsequent selection to optimize the polypyrimidine tract , the residual NAG signal was weaker for contracted than for expanded exons . Consistent with these findings , we observed a strong association between gain/loss of three bases in the rodent lineage and presence of a NAGNAG in orthologous human exons . Exons that expanded or contracted in rodents were 7 . 5-fold more likely to have a NAGNAG in the orthologous human exon than were exons with unchanged boundaries ( Figure 5G ) . Further subdividing these exons according to the splicing pattern of the NAGNAG in human , we observed that rodent exons orthologous to alternatively spliced human NAGNAGs were ∼9 times more likely to have gained/lost exonic sequence than those orthologous to constitutively spliced human NAGNAGs ( Figure 5H ) . These analyses implicate NAGNAG alternative splicing as a very common evolutionary intermediate in the gain and loss of single codons from exons . This model , where frequent alternative splicing at the 3′ splice site leads to gain/loss of exonic sequence , is expected to play out very differently at 5′ splice sites . Competing 5′ splice sites are most frequently four bases apart [22] , resulting in a frame-shift which is likely to render one of the protein products non-functional and potentially target the mRNA for nonsense-mediated decay . Although common , competing 5′ splice sites separated by four bases are therefore unlikely to lead to accelerated exon length changes and we observed no significant increase in exon length changes at the 5′ splice site ( Figure 5A ) . Most three base changes to mRNAs probably minimally affect RNA-level properties such as message stability . However , insertion/deletion of a single amino acid residue can have a profound impact on protein function . For example , deletion of a single codon can alter protein degradation , subcellular localization , DNA binding affinity , or other protein properties [38] , [39]; can cause diseases including cystic fibrosis and Tay-Sachs disease [40] , [41]; and can even rescue a disease-related phenotype [42] . Insertion or deletion of a codon in a protein structural motif with a periodic hydrogen bonded structure such as a beta sheet or coiled coil domain might have a disproportionate effect on protein structure by altering the hydrogen bonding of a large number of downstream residues . The codon-level effects of NAGNAG splicing are largely determined by intron “phase” ( position relative to the reading frame ) [15] . Considering the spectrum of codons that occurred opposite three base gaps at the beginnings of exons ( corresponding to the peak in Figure 5C ) , we observed a highly non-random distribution that strongly favored glutamine , alanine , glutamate , and serine and disfavored most other residues including cysteine , phenylalanine , and histidine relative to the background ( Table S7 ) . Distinct and far stronger biases were observed when grouping introns by phase . These biases occurred in a pattern consistent with frequent origin via exaptation of NAGNAGs ( Figure 5I ) . For example , glutamine ( mostly coded by CAG ) was the most commonly added residue at the end of “phase 0” introns , for which the first three bases of the downstream exon form a codon . Serine ( mostly AGY ) and arginine ( mostly AGR ) were the most commonly added residues at the boundaries of phase 2 introns , for which the AG of an added NAG would form the first two bases of a codon . These biases contributed to a strong enrichment observed for gain/loss of predicted phosphorylation sites at 3′ splice sites ( Figure S11 ) . Together , the analyses in Figure 5 demonstrate that gain and loss of residues along proteins occurs in a strongly biased manner , with a highly accelerated rate and biased codon spectrum at the beginnings of exons that is likely driven by genomic substitutions that alter NAGNAG motifs or their splicing patterns . These observations suggest that the evolutionary trajectories of proteins in metazoans are shaped to a surprising extent by the specific locations and phases of introns that interrupt their coding sequences .
Mapped sequence reads from the human and mouse RNA-Seq experiments are located in NCBI's GEO database ( accession number GSE30017 ) . The complete Body Map 2 . 0 sequence data are in the ENA archive with accession number ERP000546 ( available at http://www . ebi . ac . uk/ena/data/view/ERP000546 ) . These data are also accessible from ArrayExpress ( ArrayExpress accession: E-MTAB-513 ) . The Body Map 2 . 0 data were generated by the Expression Applications R&D group at Illumina using the standard ( polyA-selected ) Illumina RNA-Seq protocol from total RNA obtained commercially ( Ambion ) using the HiSeq 2000 system . We downloaded D . melanogaster ( “Developmental Stage Timecourse Transcriptional Profiling with RNA-Seq” ) and C . elegans ( “Global Identification of Transcribed Regions of the C . elegans Genome” ) RNA-Seq data from the modMINE ( http://intermine . modencode . org/ ) website of the modENCODE consortium . For the C . elegans data , we restricted to 36 bp reads for consistency with other analyses . We used the set of splicing events from [1] to identify skipped exons , alternative 3′ splice sites ( >3 nt apart ) , alternative 5′ splice sites , and mutually exclusive exons in the human ( GRCh37 , or hg19 ) and mouse ( NCBIM37 , or mm9 ) genomes ( Figure 1C ) . We enumerated all possible NAGNAGs in the human genome by finding all 3′ splice sites in these alternative splicing events and the Ensembl [43] and UCSC [44] annotation databases and then searching for NAGNAG motifs . We classified splice junctions as constitutive if they did not overlap any alternative splicing event present in the databases described above . Mouse tissues from a 10-wk-old male were extracted immediately after death and stored in RNAlater per the manufacturer's instructions ( Ambion ) . Tissue was lysed in Trizol and RNA was extracted with Qiagen miRNeasy mini columns . Using 5 µg of total RNA , we performed polyA selection and prepared strand-specific libraries for Illumina sequencing following the strand-specific dUTP protocol [45] and using the SPRIworks Fragment library system ( Beckman Coulter ) . We obtained final insert sizes of approximately 160 bp . We sequenced these libraries using the Illumina HiSeq 2000 and the GAIIx machines . For each NAGNAG , we extracted the sequence flanking the proximal and distal 3′ splice sites and used Bowtie [46] version 0 . 12 . 7 to map reads to these two sequences . We required that short reads have at least 6 nt on either side of the splice junction ( an “overhang” of 6 nt ) , and furthermore that there be no mismatches within the overhang region . In order to eliminate errors in read mapping due to non-unique splice junctions , we restricted the set of NAGNAGs enumerated across the genome to the subset of NAGNAGs for which all 36-mers mapping to either splice site did not map to the genome or any other splice junction ( we used 36-mers because they were the shortest reads analyzed in our experiments ) . We then computed ψ values as ( number of reads mapping to the proximal splice junction ) / ( number of reads mapping to either the proximal or distal splice junction ) . For all bioinformatics analyses , we only analyzed the subset of tissues for which a particular NAGNAG had a total of at least 10 reads in order to control for variation in junction coverage due to gene expression differences . We experimented with requiring different levels of junction coverage ( 10–100 reads per NAGNAG ) and confirmed that our conclusions were insensitive to the chosen cutoff . We identified alternatively spliced events as those for which both isoforms were expressed at ≥5% in at least one sample ( restricting to tissues for which a particular NAGNAG had ≥10 reads ) , and identified regulated events as those with p≤0 . 01 by the proportion or z-test ( prop . test in R [http://www . R-project . org/] ) . As described in the text , when computing the fraction of regulated NAGNAGs , we only considered NAGNAGs which were alternative spliced by these criteria ( both isoforms expressed at ≥5% in at least one sample ) . For Figure 1C and Tables S2 , S3 , we re-mapped the reads using TopHat [47] version 1 . 1 . 4 and restricted to uniquely mapping reads with an overhang of 6 nt and no mismatches in the overhang region . Using only reads mapping to the two 3′ ( skipped exons , NAGNAGs , alternative 3′ splice sites , and mutually exclusive exons ) or 5′ ( alternative 5′ splice sites ) splice sites of each event , we computed ψ values and identified alternative spliced and regulated events as described above . We estimated false-discovery rates as the fraction of events which were differentially expressed between technical ( human ) or biological ( mouse ) replicates identified using the procedure described above for regulated events . Briefly , for each tissue and pair of replicates , we restricted to the set of NAGNAGs which were alternatively spliced in at least one of the replicates and computed the fraction of these NAGNAGs which were differentially expressed with p≤0 . 01 between the replicates . We estimated mean FDRs for human ( 4 . 4% ) and mouse ( 1 . 1% ) by taking a weighted average over tissues , where we weighted the FDR computed for each tissue by the number of alternatively spliced NAGNAGs analyzed for that tissue . The fraction of strongly regulated NAGNAGs increased essentially linearly with the number of tissues considered for both human and mouse ( Figure S1 ) . We expect this trend to continue as the number of mouse tissues increases , as it does for the human data . Accordingly extrapolating the mouse data to 16 tissues with a linear fit and subtracting the mean FDR of 1 . 1% , we estimated that at least 12% of alternatively spliced mouse NAGNAGs are strongly regulated , providing a lower bound on the fraction of strongly regulated NAGNAGs in mammals . We used the human data to compute a corresponding upper bound of 37% by subtracting the mean FDR of 4 . 4% from the observed fraction of strongly regulated NAGNAGs ( Figure S1 ) . For each NAGNAG event , the probabilities of using the proximal and distal splice sites are proportional to and , where and are the proximal and distal splice site scores . The probability of using the proximal splice site is therefore . We fit the parameters and as follows: For each NAGNAG , we computed the mean ψ ( averaging over tissues ) . We then binned NAGNAGs according to their splice site score differences , using a bin size of 3 . 25 bits and a bin increment of 0 . 5 bits , and computed the median ψ for each bin . We fit a straight line to the six bins flanking the point where ψ = 50% and estimated the parameters as and based on a first-order Taylor expansion . We performed a whole-genome alignment of human and mouse using Mercator ( http://www . biostat . wisc . edu/~cdewey/mercator/ ) and FSA [48] , and identified orthologous NAGNAGs as those for which both the 5′ splice site and competing 3′ splice sites were orthologous according to the corresponding sequence alignment . For the Drosophila analysis , we used a previously described D . melanogaster–D . yakuba whole-genome alignment [49] . For all sequence conservation analyses , we downloaded phastCons scores [50] from the UCSC annotation databases [44] . We used phastCons46 ( placental mammals ) for human , phastCons30way ( placental mammals ) for mouse , and phastConst15way for D . melanogaster . Segments of PTBP2 intronic sequence containing the NAGNAG were cloned into a modular splicing reporter [51] upstream of the IGF2BP1 exon using SacI and XhoI restriction enzyme sites . Forward and reverse oligonucleotides ( below ) were mixed in equimolar ratios , annealed , and double-digested with SacI and XhoI , or in some cases the oligonucleotides were ordered with desired restriction site overhangs , and ligated into the pGM4G9 minigene . For constructs analyzing the effects of distance to the native PTBP2 branch point , the vector ( IGF2BP1 ) branch point sequence was first mutated by site-directed mutagenesis ( TCATTGA was deleted , immediately upstream from the SacI restriction site ) prior to insertion of the PTBP2 3′ splice site . All minigene reporters ( 0 . 5 µg ) were transfected into HEK293T cells using Lipofectamine 2000 ( Invitrogen ) . RNA was isolated 18–24 h post-transfection with RNeasy Mini Kits ( Qiagen ) . RT-PCR was performed with a fluorescent primer ( NAGNAG_Forward: 5′ 6FAM- TCTTCAAGTCCGCCATGC and NAGNAG_reverse: 5′ AGTCAGGTGTTTCGGGTGGT ) . The proximal ( 63 nucleotides ) and distal ( 60 nucleotides ) isoforms were resolved on a 10% TBE gel and detected with a Typhoon 9000 scanner ( GE Healthcare ) . Proximal and distal isoforms were quantified with ImageJ software . Primers: PTB2_For: cagtgtctaattttataattttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTC; PTB2_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaattataaaattagacactgagct; BPS+4_For: cagtgtctaattttataaataattttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTC; BPS+4_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaattatttataaaattagacactgagct; BPS+7a_For: cagtgtctaattttataaataaatattttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTC; BPS+7a_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaatatttatttataaaattagacact gagct; BPS+7b_For: cagtgtctaatttttttataattttttttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTC; BPS+7b_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaaaaaattataaaaaaattagacactgagct; −4A_For: cagtgtctaattttataattttgttacagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTC; −4_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgtaacaaaattataaaattagacactgagct; −4G_For: cagtgtctaattttataattttgttgcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTc; −4G_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgcaacaaaattataaaattagacactgagct; −4C_For: cagtgtctaattttataattttgttccagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTc; −4C_Rev: TCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctggaacaaaattataaaattagacactgagct; IGF2BP1BPS_For: gcgagctcttataattttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTCTCGAGCGG; IGF2BP1BPS_Rev: CCGCTCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaattataagagctcgc; BPS-3_For: gcgagctctaattttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTCTCGAGCGG; BPS-3_Rev: CCGCTCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaattagagctcgc; BPS-6_For: gcgagctcttttgtttcagAAGATTGCACCACCCGAAACACCTGACTCCAAAGTTCGTATGGTTCTCGAGCGG; BPS-6_Rev: CCGCTCGAGAACCATACGAACTTTGGAGTCAGGTGTTTCGGGTGGTGCAATCTTctgaaacaaaagagctcgc . We restricted all analyses to “singleton orthologs , ” genes without paralogs and with unambiguous orthology assignments in all species considered for each analysis , annotated in Ensembl [43] and queried with PyCogent [52] . For each gene , we required that the longest annotated coding sequence have the same number of exons in all species , and performed all subsequent analyses using this longest coding sequence . For each longest coding sequence , we extracted pairs of consecutive exons , concatenated them , and then aligned them to their corresponding orthologous sequences using FSA [48] . In order to control for alignment error , we required that alignment sequence identity be greater than 70% and that the total inserted sequence be no longer than 20% of the length of the shortest exon . Furthermore , if gaps in an alignment could be moved to lie at exon-exon boundaries rather than within exonic sequence while preserving the alignment quality ( number of exact matches ) , then we modified the alignment accordingly , as FSA is unaware of exon structures . This modification affected only a small fraction of alignments , and our results in Figure 5 are unchanged without this modification . We classified orthologous mouse and rat exons as unchanged , expanded , or contracted based on comparison with an outgroup ( human , cow , chicken , Xenopus laevis , or Danio rerio , in that order , until an informative comparison was found ) . For each exon in each class , we extracted the corresponding intronic sequence and created a sequence logo using WebLogo ( Figure 5F–H ) [53] . For analyses of amino acid sequences in Figure 5I , we compared the amino acids gained or lost in alignments with gaps of three bases at the 3′ splice site . If the next gain/loss was a single amino acid ( for example , if the human peptide was SR and the mouse peptide was R ) , then we counted only the single amino acid which was inserted ( S ) ; if the gain/loss was two amino acids ( for example , if the human peptide was SR and the mouse peptide was K ) , then we counted both amino acids which were inserted ( SR ) . For Figure S11 , we used a BioPerl module [54] to query Scansite [55] to predict phosphorylation sites ( medium stringency ) in the translated longest annotated coding sequence , and plotted the location of predicted phosphorylation sites which were gained/lost in human and mouse . Unless otherwise described , all plots in Figure 5 were created with matplotlib ( http://matplotlib . sourceforge . net/ ) . | In order to translate a gene into protein , all of the non-coding regions ( introns ) need to be removed from the transcript and the coding regions ( exons ) stitched back together to make an mRNA . Most human genes are alternatively spliced , allowing the selection of different combinations of exons to produce multiple distinct mRNAs and proteins . Many types of alternative splicing are known to play crucial roles in biological processes including cell fate determination , tumor metabolism , and apoptosis . In this study , we investigated a form of alternative splicing in which competing adjacent 3′ splice sites ( or splice acceptor sites ) generate mRNAs differing by just an RNA triplet , the size of a single codon . This mode of alternative splicing , known as NAGNAG splicing , affects thousands of human genes and has been known for a decade , but its potential regulation , physiological importance , and conservation across species have been disputed . Using high-throughput sequencing of cDNA ( “RNA-Seq” ) from human and mouse tissues , we found that single-codon splicing often shows strong tissue specificity . Regulated NAGNAG alternative splice sites are selectively conserved between human and mouse genes , suggesting that they are important for organismal fitness . We identified features of the competing splice sites that influence NAGNAG splicing , and validated their effects in cultured cells . Furthermore , we found that this mode of splicing is associated with accelerated and highly biased protein evolution at exon boundaries . Taken together , our analyses demonstrate that the inclusion or exclusion of RNA triplets at exon boundaries can be effectively regulated by the splicing machinery , and highlight an unexpected connection between RNA processing and protein evolution . | [
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| 2012 | Alternative Splicing of RNA Triplets Is Often Regulated and Accelerates Proteome Evolution |
Animals need to continuously adjust their water metabolism to the internal and external conditions . Homeostasis of body fluids thus requires tight regulation of water intake and excretion , and a balance between ingestion of water and solid food . Here , we investigated how these processes are coordinated in Drosophila melanogaster . We identified the first thirst-promoting and anti-diuretic hormone of Drosophila , encoded by the gene Ion transport peptide ( ITP ) . This endocrine regulator belongs to the CHH ( crustacean hyperglycemic hormone ) family of peptide hormones . Using genetic gain- and loss-of-function experiments , we show that ITP signaling acts analogous to the human vasopressin and renin-angiotensin systems; expression of ITP is elevated by dehydration of the fly , and the peptide increases thirst while repressing excretion , promoting thus conservation of water resources . ITP responds to both osmotic and desiccation stress , and dysregulation of ITP signaling compromises the fly’s ability to cope with these stressors . In addition to the regulation of thirst and excretion , ITP also suppresses food intake . Altogether , our work identifies ITP as an important endocrine regulator of thirst and excretion , which integrates water homeostasis with feeding of Drosophila .
Maintenance of homeostasis is based on ingestion and metabolism of water and nutrients in a manner that reflects the internal needs of the animal , but the precise regulatory mechanisms are incompletely understood [1] . Despite the strong evolutionary conservation of the main pathways underlying energy homeostasis [2–5] , there is a considerable diversity in the strategies involved in the maintenance of water balance [6 , 7] . In insects , this variability arises mainly from the diversity of their habitats and life history strategies . For example , some blood-sucking insects are able to ingest a blood meal that exceeds their body volume up to twelve-fold; their feeding is hence coupled to massive post-prandial diuresis of the excessive water and ions [8] . However , in most of the non-blood sucking terrestrial insects , water conservation is more important than water secretion [1 , 9] . Studies on water balance in insects have historically focused mainly on the hormonal regulation of water excretion . These studies investigated the correlations between the hormone titers and diuresis , and analyzed the effects of injections or in vitro applications of the tested compounds ( reviewed e . g . in [8–11] ) . These works contributed to a better understanding of water regulation at the level of fluid secretion by the Malpighian tubules and water reabsorption in the hindgut ( reviewed e . g . in [8–11] ) . Later , development of genetic tools for Drosophila allowed analysis of diuretic hormones by direct genetic manipulations [12–14] . However , no anti-diuretic hormone has been identified in Drosophila until now . Drosophila is under laboratory conditions raised on media that provide both nutrients and water , and flies therefore do not regulate food and water intake independently . Nevertheless , insects , including Drosophila , can sense water [15 , 16] and exhibit hygrotactic behavior [17 , 18] . If given the opportunity , flies differentiate between food and water sources , and are able to seek and drink free water [19 , 20] , or ingest media rich in water but devoid of nutrients [21] . Recently , a small group of neurons were identified in the Drosophila brain that antagonistically regulate thirst and hunger [22] . These neurons sense osmolarity cell-autonomously with the cation channel Nanchung , and internal nutrients indirectly via Adipokinetic hormone signaling [22] . Although several hormones have been shown to regulate feeding and satiety ( reviewed in [23–27] ) , no endocrine regulator of thirst has been identified in Drosophila so far . The mechanisms that orchestrate water sensing , water-seeking behavior and conservation of water remain unclear . We hypothesized that these processes are likely coordinated by endocrine signaling . Physiological roles of Drosophila hormones are mostly well characterized ( reviewed e . g . in [23] ) ; one of the few exceptions is Ion transport peptide ( ITP ) , which belongs to the family of crustacean hyperglycemic hormones ( CHH ) [28 , 29] . CHHs promote water reuptake and hence , act as an anti-diuretic hormones in crustaceans [30] . The locust homolog of ITP promotes water reabsorption by acting on chloride channels in the hindgut [31 , 32] . Drosophila has a single ITP gene that gives rise to an amidated ITP hormone and to two longer forms called ITP-like peptides [28 , 29] . The functions of Drosophila ITP have not been investigated so far , except for a study that has shown a role of ITP in modulation of evening activity by the circadian clock circuitry [33] . The findings from the crustacean [34] and locust [31 , 32] members of the CHH family suggest that Drosophila ITP might be involved in the regulation of water balance as well . Here , we tested this hypothesis by investigating the effects of gain- and loss-of-function of ITP on key aspects of water homeostasis , such as body water content , desiccation and osmotic stress resistance , food and water intake , and excretion . Our work identified master regulatory roles of ITP in water homeostasis of Drosophila; ITP levels increase under desiccation stress and protect the fly from water loss by increasing thirst , reducing excretion rate , and promoting ingestion of water instead of food . Altogether , our work identifies the first anti-diuretic and drinking-promoting hormone in Drosophila , which also coordinates water balance with feeding behavior .
As the first step towards understanding the potential role of ITP in water homeostasis of Drosophila , we investigated whether expression of this gene reflects changes in the body water . We exposed standard ( w1118 ) flies to a short-term ( 6 h ) desiccation , which was sufficient to reduce body fluids ( Fig 1A ) , and monitored expression of the ITP gene ( CG13586 ) by quantitative PCR . Using a primer pair that covers all 5 known transcripts of the gene , we showed that desiccation stress increases expression of the ITP gene ( Fig 1B ) , suggesting a role of ITP in water homeostasis . We confirmed that the transcriptional increase involves also the RE transcript ( Fig 1C ) , the only transcript that gives rise to ITP ( FlyBase FB2017_06 ) , considered to be the only functional peptide produced by the ITP gene [28] . It has been shown that an ITP mutation is embryonically lethal [35] , and RNAi driven by the ubiquitous daughterless-GAL4 ( da-GAL4 ) also resulted in considerable developmental lethality ( Fig 1D ) . Therefore , to investigate the role of ITP in water balance , we used the GeneSwitch system [36 , 37] , which allowed circumventing the developmental lethality of ITP and studying the gain- and loss-of function of ITP specifically during the adult stage . In addition , this system enabled investigation of genetically identical animals , thereby avoiding any confounding effects of genetic backgrounds . The system is switched on by feeding flies the drug RU-486 , which in itself does not affect water balance ( S1 Fig ) . The expression pattern of ITP is complex and involves several distinct neuron types in the central nervous system and periphery , but the hormone is supposed to be released into the hemolymph [28 , 29] . Therefore , we used the ubiquitous daughterless-GeneSwitch ( daGS ) [38] driver for both RNAi ( ITPi ) and over-expression of ITP . The daGS-driven over-expression of ITP resulted in increased ( Fig 1E ) , and RNAi in decreased water content ( Fig 1F ) , demonstrating that ITP has anti-diuretic function . We reproduced this effect also using an independent RNAi line targeting an alternative part of the ITP transcript ( S2 Fig ) , and confirmed that ITP has anti-diuretic activity in female flies as well ( S3 Fig ) . However , despite their higher initial water content , animals with increased ITP levels were more sensitive to desiccation ( Figs 1G and S4 ) , with their survival reduced by over 30% . Interestingly , animals with reduced ITP levels had moderately increased sensitivity to desiccation as well ( Figs 1H and S4 ) , suggesting that survival under arid conditions depends on a tightly regulated expression of ITP . Taken together , these experiments revealed that ITP codes for a hormone that is regulated by internal water content and has an anti-diuretic function . Next , we asked if ITP regulates the response to desiccation , or whether it determines desiccation resistance only by influencing the initial water content prior to the desiccation . The daGS>ITPi manipulations from the experiments described above could not answer these questions , because they resulted in reduced body water already before the onset of desiccation . Thus , we looked for a weaker genetic manipulation of ITP , which would allow testing the desiccation resistance without affecting the initial water content . Using an ITP-specific antibody , we confirmed previous results [28 , 29] showing that the gene is expressed in the neurosecretory cells of the brain termed ipc-1 and ipc-2 , in the interneurons termed ipc-3 and ipc-4 , in the abdominal ganglion cells ( iag cells ) , and in the lateral bipolar dendrite neurons ( LBD neurons ) of abdominal segments A7/A8 ( Figs 2A , 2B and S5 ) . To achieve a weaker genetic manipulation of ITP , we used the Impl2-GAL4 driver , which targets only a subpopulation of the ITP-producing neurons: the neurosecretory neurons in the brain ( ipc-1 cells and the ipc-2a cells ) and the LBD neurons in the periphery ( Figs 2A , 2B and S5 ) . To avoid potential developmental effects , we took advantage of the TARGET switch ( temporal and regional gene expression targeting , [39] ) , by which the temperature sensitive tubGAL80ts allows switching on the RNAi specifically in the adult flies . Although we did not test the RNAi efficiency in a cell-autonomous manner , the Impl2-based TARGET effectively decreased the global ITP mRNA ( Fig 2C ) . Consistently , with targeting only a limited number of ITP-expressing neurons , the effect on the global ITP mRNA was approximately 20% weaker than the effect of the ubiquitous daGS-driven ITPi ( Fig 2C ) . Importantly , the ITPi driven by Impl2-based TARGET was not sufficient to impair body fluids ( Figs 2D and S6 ) . Thus , this driver allowed us to disentangle the effect of ITP on water storage before the onset of desiccation from its role during the desiccation exposure . ITPi driven by the Impl2-based TARGET resulted in a reduced survival under desiccation ( Figs 2E and S6 ) , suggesting that ITP is required to cope with the desiccation stress via an additional mechanism , not only by regulating water storage prior to desiccation . An effect on desiccation survival , similar to Impl2-driven ITPi , was obtained also by daGS-driven ITPi , when the system was switched by a low RU-486 dose . This low dose ( 50 μM ) was not sufficient to affect the body water content ( Fig 2F ) , but was sufficient to reduce survival upon desiccation stress , although to a lower extent than the standard dose of 200 μM RU-486 ( Fig 2G ) used in the rest of the GeneSwitch-based experiments . Thus , ITP regulates desiccation survival not only by accumulating proper levels of body water prior to the desiccation challenge , but it is also required to cope with the arid conditions . Regulation of water balance is important especially under ionic stress . Therefore , we monitored ITP expression after feeding on a medium containing 4% NaCl , using a primer pair that covers expression of all 5 known transcripts of the gene ( Fig 3A ) , and a primer pair specific for the ITP-RE transcript , the only transcript that gives rise to ITP [28] . Osmotic stress indeed increased expression of ITP-RE ( Fig 3B ) . However , this treatment also reduced the amount of body water ( Fig 3C ) and hence we cannot differentiate whether the increase in ITP expression was driven by the changes in the osmolarity or the volume of body fluids . Genetic over-expression of ITP decreased survival during osmotic stress ( Fig 3D ) , without affecting the osmolarity-induced changes in the body water ( Fig 3E and S1 Table ) . Similar to ITP over-expression , ITPi driven by the daGS and the Impl2-GAL4 lines resulted in a weak , but statistically significant reduction of osmotic resistance ( Fig 3F and 3H ) , suggesting that both up-and down-regulations of ITP impair osmotic tolerance . The daGS-driven ITPi reduced water levels to an extent comparable to that seen under osmotic stress ( Fig 3G ) . Subsequent exposure to osmotic stress did not decrease the body water of the daGS>ITPi flies any further ( Fig 3G and S2 Table ) . The weaker Impl2-driven ITPi neither affected water content nor its reduction by osmotic stress ( Fig 3I and S3 Table ) , suggesting that ITP is required to cope with osmotic stress independently of the regulations of water content . Taken together , we show that survival under osmotic challenge requires tight regulation of ITP expression , as both up- and down-regulation of this gene resulted in a reduced survival on a food medium with a high salt content . Next , we investigated the functional mechanism by which ITP regulates water balance . Under standard laboratory conditions , Drosophila obtains water from the food . Thus , we first asked whether ITP regulates food consumption . We tested whether ITP manipulations affect frequency of eating , measured as propensity to start spontaneous feeding . We transferred fed flies to fresh food supplemented with blue dye , which allows monitoring the time when animals initiate feeding ( Fig 4A ) . Neither ITP over-expression nor ITP RNAi affected the propensity of flies to start spontaneous feeding ( Fig 4B and 4C ) . Subsequently , we measured the total volume of food consumed ( Fig 4D ) , using a modification of the capillary feeding ( CAFE ) assay [40 , 41] . This assay revealed that ITP is an anorexigenic factor; an increase in ITP reduced the volume of consumed food ( Fig 4E ) , whereas ITP RNAi increased the total food intake ( Figs 4F and S7 ) . These experiments indicate that ITP is a negative regulator of food intake . Thus , increased water levels in the daGS>ITP and reduced levels in the daGS>ITPi animals suggest that ITP acts downstream of feeding to conserve body water . The ureter of Drosophila feeds into the hindgut , and water that is not re-absorbed by the hindgut epithelium is excreted by the same route as the feces [9] . Thus , we investigated whether the ITP manipulations affect excretion . Since our previous experiments ( Fig 4B and 4C ) had shown that genetic manipulations of ITP do not affect the propensity to initiate feeding , we monitored the speed of food transit throughout the digestive tract as the time from initiation of feeding until excretion of the blue dye in the feces ( Fig 5A ) . We transferred flies on the food with blue dye , and measured the time-dependent increase in the blue-dyed feces . The ITP gain-of-function reduced the speed of the food transition throughout the digestive tract ( Fig 5B and S4 Table ) , whereas ITP RNAi increased it ( Figs 5C and S8 and S5 Table ) . Subsequently , we tested whether ITP regulates also the frequency of the defecation events . Thus , we continuously fed flies with the blue-dyed food for two days and observed defecation events under conditions when intake and excretion of the dye were at equilibrium . The frequency of defecation events was decreased by ITP over-expression ( Fig 5D ) , and increased by ITPi ( Fig 5E ) . Hence , deficiency for ITP leads to a phenotype reminiscent of human diarrhea . The size of individual feces was reduced by both manipulations of ITP ( Fig 5F and 5G ) . Nevertheless , we were not able to detect significant differences in the color intensity of feces that might be indicative of differences in the water content ( S9 Fig ) . Altogether , the above experiments indicate that ITP regulates the rate of excretion . Deficiency in ITP results in a faster transit through the digestive tract and an increased number of defecation events , reminiscent of diarrhea , a common cause of dehydration in humans . Under standard experimental conditions , flies obtain water from their food , and the classical food intake assays do not distinguish between thirst and hunger . To differentiate the role of ITP in water versus food intake , we modified a recent method by Lau et al . [19] . We reared flies on a medium poor in water ( ‘dry food’ ) , and provided access to a separate , blue-dyed source of water ( Fig 6A ) . Flies with increased ITP levels started to drink faster than controls ( Fig 6B ) , and vice versa , ITPi resulted in a delayed time to the onset of water intake ( Fig 6C ) . In order to test whether ITP also regulates the total volume of ingested water , we modified the CAFE assay monitors from the food intake experiment ( Fig 4D ) ; water was provided in microcapillaries in the presence of food poor in water ( Fig 6D ) . Consistent with their increased propensity to start drinking , ITP over-expressing flies also drank more ( Fig 6E ) , whereas ITPi flies drank less water than controls ( Figs 6F and S10 ) . These experiments revealed that ITP is the first known hormonal regulator of thirst in Drosophila . In summary , in this study we identified ITP as a neuroendocrine factor central to regulation of water homeostasis . ITP increases in response to hypovolemia , and triggers drinking , while repressing feeding and water excretion , promoting thus conservation of water resources and protection from dehydration ( Fig 7 ) .
With the colonization of dry land and evolution of terrestrial life , conservation , rather than elimination of water became the main challenge for the maintenance of water homeostasis [42] . Despite the differences in the organization of the endocrine systems , the main principles of fluid homeostasis are the same in vertebrates and invertebrates; these include thirst , compensation for the feeding-induced increase in osmolarity by water intake , and water re-absorption by the excretory systems [1 , 9 , 10 , 42] . In humans , water homeostasis is regulated primarily by an osmostat located in the hypothalamus [43] . This osmostat increases water levels by triggering thirst , and reduces the water loss by inducing release of the anti-diuretic hormone vasopressin [43] . In addition to the regulation by osmolarity , thirst is also induced by the changes in the blood volume both via vasopressin [44 , 45] and the renin-angiotensin system [42 , 46] . Even though thirst and water retention are physiologically coupled , their regulation occurs independently [43 , 47] . We show here that these regulations are simplified in Drosophila , where the same hormone promotes thirst , reduces appetite , and increases water storage . Thus , ITP acts as a functional analog of both vasopressin and renin-angiotensin . Interestingly , like the vasopressin [44 , 45] and renin-angiotensin system [42 , 46] , also ITP is regulated by body water content . Over-expression of ITP increases water content by 4 . 5% , whereas RNAi dehydrates the fly by 3 . 3% . The physiological consequences of such mild changes of water levels are not known in Drosophila , but for comparison , in human patients , loss of as little as 2% water significantly impairs cognitive abilities [48] , and liquid overload and hypervolemia represent harmful conditions as well [49] . Our findings show that knockdown of ITP leads to increased water excretion similar to human disorders caused by defective water re-absorbance in kidney , such as diabetes insipidus [43 , 50] . Conversely , ITP over-expression results in increased water retention reminiscent of the human syndrome of inappropriate anti-diuretic hormone secretion ( SIADH ) [43] . ITP manipulations may thus become useful tools to induce and study pathologies associated with these human disorders in Drosophila . ITP is the first identified hormone that regulates drinking in Drosophila . Thus , it acts as a functional analog of the renin-angiotensin system of mammals . Similar to the renin-angiotensin system , ITP is most likely activated by hypovolemia . The neural circuits that control drinking and are regulated by ITP , however , remain to be investigated . Neurons that repress drinking in Drosophila have already been identified in the suboesophageal zone [22] . These neurons are regulated cell autonomously by an ion channel that senses osmolarity [22] . ITP-knockdown flies do not have the drive to drink despite their state of dehydration , whereas ITP over-expressing flies drink despite their excessive water content . Thus , unlike the Nanchung-expressing repressors of drinking [22] , the ITP-regulated neurons are not regulated by the volume of body water , but rather by ITP itself . In insects , primary urine is produced by the Malpighian tubules that are functional analogs of mammalian kidneys [9] . Water enters the lumen of these tubules by passive diffusion along the ionic gradient maintained by the vacuolar V-H+-ATPase [9] . The function of the Malpighian tubules is hormonally regulated by diuretic hormones [9] , which in Drosophila include products of the genes capa [13 , 51] , DH31 [52] , DH44 and leucokinin [14] . Urine then enters the hindgut , where it mixes with the gut contents . Importantly , considerable parts of the water and ions are subsequently re-absorbed in the ileum and rectum [9 , 32 , 53] . Here , we show that ITP reduces excretion of water by reducing the defection rate . Thus , it is likely that Drosophila ITP promotes water reabsorption in the hindgut similar to its homologs in the desert locust Schistocerca gregaria [31 , 32] or in the European green crab Carcinus maenas [34] . It is noteworthy that ITP-expressing neurons in the abdominal ganglia innervate Drosophila hindgut [29] , suggesting that in addition to the hormonal regulation [29] , the hindgut may also be regulated by ITP in a paracrine fashion . In crabs and in the red flour beetle Tribolium castaneum¸ CHH- or ITP-producing endocrine cells , respectively , have even been detected in gut epithelia [34 , 54] . Thus , whether produced in the neurosecretory cells or in the endocrine cells of the gut , the actions of CHHs and ITPs on the hindgut appear to be evolutionarily conserved . In mammals , an increase in osmolarity due to food intake results in postprandial thirst , and conversely , dehydration inhibits feeding when water is not available [55] and this is likely also the case in Drosophila . Our findings of the ITP-driven positive regulation of water intake , concomitant with a negative regulation of feeding likely represents another level of regulation of thirst and hunger , acting in parallel to that of the four drink-repressing neurons in the suboesophageal zone [22] . Whereas many terrestrial arthropods frequently experience arid conditions , salt stress is not very common in non-blood feeding terrestrial insects . Nevertheless , desiccation and salt stress resistance have been traditional tests in the studies of Drosophila diuretic hormones . RNAi against diuretic hormones increases desiccation resistance , as shown for capa [13] , DH44 [14] and leucokinin [12] genes . However , it remains unclear whether these hormones contribute to the natural response to the desiccation and osmotic stress . For example , desiccation does not change expression of diuretic hormones DH44 and leucokinin [14] . In contrast , ITP seems to be a natural component of the desiccation and osmotic stress responses , since both stressors trigger an increase in ITP expression . The role of ITP in thirst , hunger and excretion suggest that the ITP-regulated changes in behavior and physiology represent natural responses to cope with the reduction of body water . Consistently , knockdown of ITP reduces survival under desiccation and osmotic stress . However , it is unclear why over-expression of ITP reduces resistance to desiccation and osmotic stress . The UAS-GAL4 based manipulations may increase ITP levels far beyond the physiological range , which—although not lethal under standard feeding—might reduce survival under stressful conditions . Given the role of ITP in the ion transport across the hindgut epithelia of locusts [31 , 32] , it is tempting to speculate that a similar mechanism exists in Drosophila . In such a scenario , the non-physiological doses of ITP might considerably increase osmolarity of hemolymph . This would be toxic when feeding on a food medium with a high salt content , as well as under desiccation conditions ( which further increase osmolarity ) . Although ITP has been known for a long time [56] , its function has remained enigmatic in Drosophila . Our pioneering work on its roles in Drosophila physiology suggests that ITP codes for a master regulator of water balance , which also integrates the water homeostasis with energy metabolism . Thus , our study not only shows that this member of the CHH family has an evolutionarily conserved anti-diuretic role in Drosophila as it has in other arthropods [34] , but also reveals novel functions of this peptide family in food and water intake . It remains to be investigated to what extent these roles are conserved in other insect species or even in crustaceans , but the strong evolutionary conservation of the gene structure [30] suggests that this might be the case . It is possible that the fly ITP regulates , in addition to its here-described role in water balance , other processes that are known to be CHH-regulated in crustaceans [34] . For example , the high developmental lethality of ITP RNAi , together with the previously described lethality of ITP mutants [35] imply that Drosophila ITP plays a critical role during development , perhaps analogous to the role of CHHs in crustacean molting [34] . Although identification of the cellular sources of ITP that are responsible for the here-described functions of this hormone was beyond the scope of this manuscript , the expression pattern of the gene already provides some tempting hints . Previous in situ-hybridizations and immunohistochemistry experiments based on a locust anti-ITP antibody showed that Drosophila ITP is expressed in several neuronal types [28 , 29] . Here , using an antibody specific to Drosophila ITP , we confirmed that these cells include ipc-1 and ipc-2a neurosecretory neurons in the brain , ipc-3 and ipc-4 interneurons , three pairs of iag cells in the abdominal ganglia , and the LBD neurons in abdominal segments A7 and A8 . As described previously [28 , 29] , although ITP is expressed in several interneurons , the most prominent cells of the brain that express ITP are the neurosecretory protocerebral ipc-1 and the ipc-2a neurons , which send axons towards neurohemal release sites in the corpora cardiaca , corpora allata , and aorta . Our experiments based on the Impl2 driver showed that a proper response to desiccation and osmotic stress requires production of ITP in the ipc-1 neurons , ipc-2a neurons , or LBD neurons , or in their combination . The ITP production in these cells becomes nevertheless critical only under desiccation and osmotic stress . In contrast to the global manipulations , ITPi targeted to these neurons is not sufficient to impair water balance under standard conditions . Thus , water content is regulated either via ITP produced by cells outside of the Impl2 expression pattern , or the ITP-producing neurons are redundant in their ability to produce sufficient ITP to maintain water homeostasis under standard conditions . Altogether , additional cell type-specific manipulations are required to differentiate whether thirst , excretion and food intake are regulated by specific neurons , or whether different ITP-producing neurosecretory cells act redundantly to produce sufficient amount of the hormone to regulate physiology of the fly . Another key step towards understanding the ITP actions is the identification of the hitherto unknown Drosophila ITP receptor . This will facilitate cell- and tissue-specific manipulations to unravel the neural circuit ( s ) responsible for the roles of ITP in the control of thirst and hunger , and allow more detailed studies of the peripheral roles of ITP in defecation and water excretion .
Flies were reared under a 12 h light–12 h dark cycle on a standard Drosophila medium consisting of 6 g agar , 50 g yeast , 100 g sugar , 5 . 43 mL propionic acid , and 1 . 3 g methyl 4-hydroxybenzoate per 1 L of medium . Adult flies were collected within 24 h after eclosion , flipped on fresh media , and housed in groups of around 50 females + 50 males per vial . Flies for the TARGET experiments developed at 18°C and on the third day after eclosion were transferred to 29°C for the RNAi induction . Flies for the GeneSwitch experiments developed at 25°C on standard medium , and were kept from the third day after adult eclosion on a standard medium supplemented with RU-486 and reared further at 25°C . All GeneSwitch experiments were conducted with 0 and 200 μM RU-486 , and experiments described in Fig 2F and 2G were performed also with 50 μM RU-486 . After the switch induction , both TARGET and GeneSwitch flies were flipped every second day onto fresh media . If not stated otherwise , male flies were used for experiments 6–7 days after the induction of the transgene expression . Controls for the non-GeneSwitch experiments were generated by crossing the UAS and GAL4 lines to the w1118 strain . Experiments on the desiccation and osmotic stress–induced changes in the ITP expression were performed on the w1118 strain . The list of used fly stocks is available in the S1 File . Viability was expressed as egg-to-adult survival , i . e . as the percentage of eggs that gave rise to adult flies . Three independent egg collections ( each at least 120 eggs ) were tested for each genotype . Eggs were counted , allowed to develop at 25°C at 12 h light/12 h dark cycle on standard medium , and eclosed flies were collected and counted . Water content was expressed as percentage of fresh body weight . Flies were weighed using a Mettler MT5 analytical microbalance ( Mettler Toledo ) . Fresh weight was determined , then flies were desiccated for 2 days at 65°C and weighed again . The amount of water was calculated as the difference between the fresh and the dry weight , and expressed as % of the fresh body weight . At least 5 replicates ( each consisting of 5 flies ) were tested per treatment / genotype . Desiccation resistance was estimated as survival of flies in empty vials without any water source . Experiments were done in 3–4 replicates . TARGET-based experiments took place at 29°C , GeneSwitch-based experiments took place at 25°C . Osmotic stress resistance was determined as survival of flies on food medium containing 4% NaCl . Experiments were done in triplicates . TARGET-based experiments took place at 29°C , GeneSwitch-based experiments took place at 25°C . The food contained the same concentration of RU-486 ( 200 μM ) or ethanol vehicle control as during the pre-feeding period . The volume of ingested food was measured by a modification of the CAFE assay [40] in a feeder device constructed out of 24-well-plates , similar to the one described before [41] . Capillaries with food ( Hirschmann minicaps , 5μ ) were exchanged daily . Food intake of at least 15 animals per treatment was measured during 3 days , and corrected for the evaporation rate . The liquid food contained the same concentration of RU-486 ( 200 μM ) or ethanol vehicle control as during the pre-feeding period . Flies were transferred into a vial with a drop ( approximately 0 . 2 mL ) of food medium containing 0 . 5% Brilliant Blue ( Sigma ) , and the proportion of flies that started feeding ( blue dye was observable in their body after inspection under a stereomicroscope ) was counted 1 h , 1 . 5 h and 3 h after the transfer . Flies were separated into the tested groups 1 day before the experiment to avoid potential interference of CO2 anesthesia with the food intake . Each time point was tested in 4 replicates , each consisting of at least 16 flies . Flies were transferred into vials with a drop ( approximately 0 . 2 mL ) of food medium containing 0 . 5% Brilliant Blue ( Sigma ) and allowed to feed continuously . The cumulative numbers of feces that contained the blue dye were counted in the vial every hour , until 6 h after the switch to the blue-dyed medium . Feces were counted in three replicates , each vial containing 20 flies . For testing the statistical significance by two-way ANOVA , the number of new feces that were deposited within the given period was used . Excretion rate was measured as the number of defecation events ( number of feces ) per fly per hour . Flies were fed for 48 h on standard food ( with or without RU-486 ) with 0 . 5% Brilliant Blue ( Sigma ) . Flies were subsequently transferred into a new vial with a small drop of colored food , and the number of feces produced per fly per vial was counted . Experiments were performed in three replicates , each consisting of 20 flies . Flies were fed for 48 h on standard food ( with or without RU-486 ) with 0 . 5% Brilliant Blue ( Sigma ) . Subsequently , a new transparent plastic lid was put on top of the vials , and feces collected on this lid within 2 . 5 h and were photographed using Leica WILD M32 stereomicroscope with Leica DFC290 camera . The area and lightness were measured using the T . U . R . D . software [57] . Flies were separated into tested groups 1 day before the experiment to avoid potential effect of CO2 exposure on the water intake . Flies were transferred into vials containing approximately 2 mL of the water-deprived food , and after 30 min into new vials with 2 mL of the water-deprived food and a 0 . 2 mL of a water-rich agar droplet ( 0 . 6% agarose , 0 . 5% Brilliant Blue ) and allowed to eat and drink . Flies that started to drink were identified based on the blue color in their abdomina after inspection under a stereomicroscope . The proportion of flies that started to drink was checked 1 h , 1 . 5 h and 3 h after transferring flies to the water source . Experiments were done in triplicates , and at each time point , at least 42 flies were tested . Water-deprived food medium contained 75% less water and agar than the standard medium , consisting of: 0 . 6 g agar , 20 g yeast , 40 g sugar , 0 . 54 mL propionic acid , 0 . 13 g methyl 4-hydroxybenzoate and 1 mL of 20 mM RU-486 or ethanol per 100 mL of medium . The capillary drinking assay was performed in a device similar to the CAFE assay feeder , with the following modifications: the bottom of each chamber contained approximately 0 . 8 mL of water-deprived food with 200 μM RU-486 or ethanol as a vehicle control . Water-deprived food medium contained 75% less water and agar than the standard medium , as described above . Flies were allowed to drink water from the capillaries . To make the measurements of the ingested water easier , water was colored with 0 . 05% Brilliant Blue ( Sigma ) . The volume of ingested water was measured over one day , and corrected for the evaporation rate . At least 18 flies were tested for each genetic manipulation . Adult flies were dissected in ice-cold Drosophila Ca2+ free saline . After removing wings and legs , brain-thoracic/abdominal ganglia complexes were quickly excised from head and thorax . All preparations were fixed overnight in Zamboni's fixative overnight at room temperature , washed and treated as described in detail earlier [29] . The only modifications concerned the use of two different primary and secondary antibodies always at the same time of incubations . Primary antibodies were a polyclonal rabbit anti-DrmITP diluted 1:10 , 000 [33] and a monoclonal mouse anti-GFP ( against Jelly fish GFP; Invitrogen ) diluted 1:1 , 000 . Secondary antibodies were goat anti-rabbit Alexa 546 and goat anti-mouse Alexa 488 , respectively ( Invitrogen ) , both diluted 1:1 , 000 . Preparations were imaged with a Zeiss LSM 780 confocal microscope by use of 10× or 20× objectives . Confocal images were processed with Zeiss ZEN software , version 8 . 1 2012 , for maximum intensity projections of z-stacks . Brightness and contrast was adjusted using Corel Photopaint X7 during plate-mounting using Corel Draw X7 . RNA was extracted using the Zymo Research QuickRNA MicroPrep kit according to the manufacturer’s instructions . cDNA was synthesized by the QuantiTect Reverse Transcription Kit ( Qiagen ) using 1 μg of the total RNA . Quantitative real-time PCR was performed using SensiFAST SYBR Hi-ROX Kit ( Bioline ) and StepOne Real-Time PCR System ( Applied Biosystems ) . Expression levels were normalized to Actin 5C ( Act5C ) . Information on the primers is available in the S1 File . Measurement variables were analyzed by two-tailed Student’s t-test , one-way or two-way ANOVA . Nominal variables were analyzed by two-tailed Fischer’s exact test . Survival data were analyzed by log-rank test . P values are indicated by asterisk symbols ( * P < 0 . 05 , ** P < 0 . 01 , *** P < 0 . 001 ) . Error bars represent SEM . Data on the measurement variables were analyzed using Excel or PAST [58]: http://palaeo-electronica . org/2001_1/past/issue1_01 . htm . Survival data were analyzed using PAST . Data on nominal variables were analyzed by Graphpad QuickCalcs ( https://www . graphpad . com/quickcalcs/ ) . | Maintenance of energy and water balance is necessary for survival of all organisms . Even a mild dehydration triggers thirst , reduces appetite , and decreases diuresis ( water excretion ) , thereby promoting conservation of water resources and survival under arid conditions . Homeostasis is regulated primarily by endocrine systems that utilize neuropeptides and peptide hormones . Whereas hormonal mechanisms that regulate the water balance in humans are relatively well understood , much less is known about these regulations in the fruit fly Drosophila melanogaster . Here , we describe the first thirst-promoting and anti-diuretic hormone of Drosophila , encoded by the gene Ion transport peptide ( ITP ) . We show that ITP increases upon dehydration , and protects the animal from loss of body water by promoting thirst and repressing excretion . ITP also suppresses feeding , and can thus be considered as a master regulator integrating water and energy balance . | [
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| 2018 | The thirsty fly: Ion transport peptide (ITP) is a novel endocrine regulator of water homeostasis in Drosophila |
Several meiotic processes ensure faithful chromosome segregation to create haploid gametes . Errors to any one of these processes can lead to zygotic aneuploidy with the potential for developmental abnormalities . During prophase I of Drosophila male meiosis , each bivalent condenses and becomes sequestered into discrete chromosome territories . Here , we demonstrate that two predicted condensin II subunits , Cap-H2 and Cap-D3 , are required to promote territory formation . In mutants of either subunit , territory formation fails and chromatin is dispersed throughout the nucleus . Anaphase I is also abnormal in Cap-H2 mutants as chromatin bridges are found between segregating heterologous and homologous chromosomes . Aneuploid sperm may be generated from these defects as they occur at an elevated frequency and are genotypically consistent with anaphase I segregation defects . We propose that condensin II–mediated prophase I territory formation prevents and/or resolves heterologous chromosomal associations to alleviate their potential interference in anaphase I segregation . Furthermore , condensin II–catalyzed prophase I chromosome condensation may be necessary to resolve associations between paired homologous chromosomes of each bivalent . These persistent chromosome associations likely consist of DNA entanglements , but may be more specific as anaphase I bridging was rescued by mutations in the homolog conjunction factor teflon . We propose that the consequence of condensin II mutations is a failure to resolve heterologous and homologous associations mediated by entangled DNA and/or homolog conjunction factors . Furthermore , persistence of homologous and heterologous interchromosomal associations lead to anaphase I chromatin bridging and the generation of aneuploid gametes .
There are several critical steps that chromosomes must undergo as they transition from their diffuse interphase state to mobile units that can be faithfully transmitted to daughter cells . In the germline , faulty segregation leading to the creation of aneuploid gametes is likely a leading cause of genetic disease , miscarriages , and infertility in humans [1] . Some steps that promote proper segregation are universal to all cell types undergoing cell division . Chromosomal “individualization” is necessary to remove DNA entanglements that likely become introduced naturally through movements of the threadlike interphase chromatin [2] . Topoisomerase II ( top2 ) contributes to individualization with its ability to pass chromosomes through one another by creating and resealing double strand breaks [3] . The necessity of top2's “decatenation” activity to chromosome individualization becomes clear from fission yeast top2 mutants and vertebrate cells treated with a top2 inhibitor , where mitotic chromosomes appear associated through DNA threads [4] , [5] . Another step that occurs prior to chromosome segregation is chromosome “condensation , ” entailing the longitudinal shortening from the threadlike interphase state into the rod like mitotic chromosome [2] . Condensation is necessary due to the great linear length of interphase chromosomes that would be impossible to completely transmit to daughter cells . Because chromosome individualization and condensation appear to occur concurrently , it has been inferred that both are promoted by the same catalytic activity . In support of this idea , the condensin complexes have been implicated in chromosome individualization [6] and condensation [7] , suggesting a molecular coupling of both processes . The condensin I and II complexes are thought to be conserved throughout metazoa , each utilizing ATPases SMC2 and SMC4 , but carrying different non-SMC subunits Cap-H , Cap-G , Cap-D2 or Cap-H2 , Cap-G2 , and Cap-D3 , respectively [7]–[9] . In vitro , condensin I is known to induce and trap positive supercoils into a circular DNA template [10]–[12] . Current models to explain condensin I chromosome condensation highlight this activity as supercoiling may promote chromatin gathering into domains that can then be assembled into a higher order structure [13] . Condensin complexes may also promote condensation and individualization through cooperating with other factors , such as chromatin-modifying enzymes [14]–[17] and top2 [15] , [18]–[22] . While the effect of condensin mutations or RNAi knockdown on chromosome condensation is variable depending on cell type and organism being studied , in most if not all cases , chromatin bridges are created between chromosomes as they segregate from one another [7] . This likely represents a general role of the condensin complex in the resolution of chromosomal associations prior to segregation . While the second cell division of meiosis is conceptually similar to mitotic divisions where sister chromatids segregate from one another , the faithful segregation of homologous chromosomes in meiosis I requires several unique steps . It is essential for homologous chromosomes to become linked to one another for proper anaphase I segregation [23] and most often this occurs through crossing over to form chiasmata [24] . As recombination requires the close juxtaposition of homologous sequences , homologs must first “identify” one another in the nucleus and then gradually become “aligned” in a manner that is DNA homology dependent , but not necessarily dictated by the DNA molecule itself . Eventually , the homologous chromosomes become “paired , ” which is defined as the point when intimate and stable associations are established . The paired state is often accompanied by the laying down of a proteinaceous structure called the synaptonemal complex between paired homologous chromosomes , often referred to as “synapsis” [25] , [26] . Importantly , the recombination mediated chiasmata can only provide a linkage between homologs in cooperation with sister chromatid cohesion distal to the crossover [27] . Drosophila male meiosis is unconventional in that neither recombination [28] nor synaptonemal complex formation occur [29] , yet homologous chromosomes still faithfully segregate from one another in meiosis I . Two proteins have been identified that act as homolog pairing maintenance factors and may serve as a functional replacement of chiasmata . Mutations to genes encoding these achiasmate conjunction factors , MNM and SNM , cause homologs to prematurely separate and by metaphase I , they can be observed as univalents that then have random segregation patterns . It is likely that MNM and SNM directly provide conjunction of homologs as both localize to the X–Y pairing center ( rDNA locus ) up until anaphase I and an MNM-GFP fusion parallels this temporal pattern at foci along the 2nd and 3rd chromosomes [30] . While MNM and SNM are required for the conjunction of all bivalents , the protein Teflon promotes pairing maintenance specifically for the autosomes [31] , [32] . Teflon is also required for MNM-GFP localization to the 2nd and 3rd chromosomes [30] . This suggests that Teflon , MNM , and SNM constitute an autosomal homolog pairing maintenance complex . A fascinating aspect of Drosophila male meiosis is that during prophase I , three discrete clusters of chromatin become sequestered to the periphery of the nuclear envelope's interior . Each of these “chromosome territories” corresponds to one of the major chromosomal bivalents , either the 2nd , 3rd or X–Y [33]–[36] . A study of chromosomal associations within each prophase I bivalent demonstrated that the four chromatids begin in close alignment . Later in prophase I , all chromatids seemingly separate from one another , but the bivalent remains intact within the territory [36] . It has therefore been proposed that chromosome territories may provide stability to bivalent associations through their sequestration into sub-nuclear compartments [36] . Here we document that Drosophila putative condensin II complex subunits , Cap-H2 and Cap-D3 , are necessary for normal territory formation . When they are compromised through mutation , chromatin is seemingly dispersed throughout the nucleus . We propose that the consequence of this defect is failure to individualize chromosomes from one another leading to the introduction and/or persistence of heterologous chromosomal associations into anaphase I . This underscores the role of chromosome territory formation to prevent ectopic chromosomal associations from interfering with anaphase I segregation . Cap-H2 is also necessary to resolve homologous chromosomal associations , that like heterologous associations , may be mediated by DNA entanglements and/or persistent achiasmate conjunction as anaphase I bridging is rescued by teflon mutations . This highlights condensin II mediated chromosome individualization/disjunction in meiosis I and its necessity to the creation of haploid gametes .
Faithful chromosome segregation is necessary to organismal viability , therefore it is not surprising that in Drosophila , homozygous lethal alleles exist in the following condensin subunits: SMC4/gluon , SMC2 , Cap-H/barren , and Cap-G [19] , [37] , [38] . It has however been reported that one mutant Cap-D3 allele , Cap-D3EY00456 ( Figure S1 ) is homozygous viable , yet completely male sterile [39] . We have confirmed the necessity of Cap-D3 to male fertility as both Cap-D3EY00456 homozygous and Cap-D3EY00456/Cap-D3Df ( 2L ) Exel6023 males were completely sterile when mated to wild-type females . Furthermore , males trans-heterozygous for strong Cap-H2 mutations ( Figure S1 ) were also male sterile as no progeny were derived from crosses of Cap-H2Z3-0019/Cap-H2Df ( 3R ) Exel6159 , Cap-H2TH1/Cap-H2Df ( 3R ) Exel6159 , and Cap-H2TH1/Cap-H2Z3-0019 to wild-type females . A third allele , Cap-H2Z3-5163 ( Figure S1 ) , was found to be fertile as a homozygote and in trans-combinations with Cap-H2Z3-0019 , Cap-H2Df ( 3R ) Exel6159 , and Cap-H2TH1 alleles . To determine whether the primary defect leading to loss of fertility in Cap-H2 mutant males is pre or post copulation , Cap-H2Z3-0019 homozygous mutant and heterozygous control siblings were engineered to carry a sperm tail marker , don juan-GFP , and aged in the absence of females to allow sperm to accumulate in the seminal vesicles . In contrast to Cap-H2Z3-0019 heterozygous control males where the seminal vesicles fill with sperm , those from Cap-H2Z3-0019 homozygous males were seemingly devoid of sperm as no DAPI staining sperm heads or don juan-GFP positive sperm tails were detectable ( Figure 1A and 1B ) . The lack of mature sperm in the seminal vesicles confirmed that sterility in Cap-H2 mutant males is attributed to a defect in gamete production . To test whether a Cap-H2 mutant allelic combination that is male fertile , Cap-H2Z3-0019/Cap-H2Z3-5163 , has a decreased fertility , males of this genotype and heterozygous controls were mated to wild-type females and the percent of eggs hatched was quantified . There was no significant difference in male fertility between Cap-H2Z3-0019/Cap-H2Z3-5163 and Cap-H2Z3-5163/+ males ( Figure 1C ) . However , the introduction of one mutant allele of another condensin subunit , SMC408819 , to the Cap-H2 trans-heterozygote led to a substantial decrease in fertility relative to the SMC408819/+; Cap-H2Z3-5163/+ and SMC408819/+; Cap-H2Z3-0019/+ double heterozygous controls ( Figure 1D ) . This suggests that Cap-H2 is functioning in the Drosophila male germline as a member of a condensin complex along with SMC4 during gametogenesis . Given the well-documented roles of condensin subunits in promoting chromosome segregation [7] , we reasoned that a possible cause of fertility loss in Cap-H2 and Cap-D3 mutants is through chromosome missegregation in the male germline . Male gametogenesis begins with a germline stem cell division . While one daughter maintains stem cell identity , the gonialblast initiates a mitotic program where 4 synchronous cell divisions create a cyst of 16 primary spermatocytes that remain connected due to incomplete cytokinesis . These mature over a period of 3 . 5 days , undergo DNA replication , and subsequently enter meiosis [34] . To test whether chromosome segregation defects occur during gametogenesis of Cap-H2 mutants , i . e . during the mitotic divisions of the stem cell or gonia or from either meiotic divisions , genetic tests were performed that can detect whether males create an elevated level of aneuploid sperm . In these “nondisjunction” assays , males are mated to females that have been manipulated to carry a fused , or “compound” , chromosome . Females bearing a compound chromosome and specific genetic markers are often necessary to determine whether eggs had been fertilized by aneuploid sperm . Importantly , in nondisjunction assays , fertilizations from aneuploid sperm generate “exceptional” progeny that can be phenotypically distinguished from “normal” progeny that were created from haploid sperm fertilizations . Sex chromosome segregation was monitored as previously described for mutants in the ord gene [40] , with males bred to carry genetic markers on the X and Y chromosomes . These y1w1/y+Y; Cap-H2Z3-0019/Cap-H2Z3-5163 and corresponding Cap-H2 heterozygous controls males were crossed to females bearing compound X chromosomes ( C ( 1 ) RM , y2 su ( wa ) wa ) . As shown in Table 1 , no significant amount of exceptional progeny were generated from Cap-H2 mutant males . It is important to point out that the lack of significant sex chromosome segregation defects found in these nondisjunction assays with a likely weak Cap-H2 male fertile mutant may be misleading . In fact , sex chromosome segregation defects are observed cytologically in stronger Cap-H2 mutant backgrounds that could not be tested with nondisjunction assays because of their sterility ( see below ) . Fourth chromosome segregation was assayed as described previously for teflon mutants [32] , with males carrying one copy of a 4th chromosome marker mated to females bearing compound 4th chromosomes ( C ( 4 ) EN , ci ey ) . As with the sex chromosome segregation assays , 4th chromosome segregation did not differ substantially between the Cap-H2Z3-0019/Cap-H2Z3-5163 and heterozygous control males ( Table 2 ) . The possibility remains that this hypomorphic Cap-H2 allelic combination is not strong enough to reveal 4th chromosome segregation defects . Like sex chromosomes , 4th chromosome segregation abnormalities were observed cytologically in stronger male sterile mutants ( see below ) . Effects on second and third chromosome segregation were assayed with the use of females carrying either compound 2 ( C ( 2 ) EN , b pr ) or compound 3 ( C ( 3 ) EN , st cu e ) chromosomes . Interestingly , both the 2nd and 3rd chromosomes had a heightened sensitivity to Cap-H2 mutation as Cap-H2Z3-0019/Cap-H2Z3-5163 males created an elevated level of exceptional progeny ( Tables 3 and 4 ) . In both cases , the exceptional class most over represented were those from fertilization events involving sperm that lacked a 2nd ( nullo-2 ) or 3rd ( nullo-3 ) chromosome . Nullo progeny can be created from defects in either meiotic division . For example , the reciprocal event of incorrect cosegregation of homologs during meiosis I is one daughter cell completely lacking that particular chromosome . Similarly , nullo sperm can be created from meiosis II defects where sister chromatids cosegregate . To address whether meiotic I and or II segregation defects occur , males in the 2nd chromosome assays were bred to be heterozygous for the 2nd chromosome marker brown ( bw1 ) . If both 2nd homologous chromosomes mistakenly cosegregate in meiosis I , then a normal meiosis II will generate diplo-2 sperm that are heterozygous for the paternal male's 2nd chromosomes ( bw1/+ ) . Additionally , a normal meiosis I followed by a faulty meiosis II where sister chromatids cosegregate would generate diplo-2 sperm homozygous for the paternal male's 2nd chromosomes ( bw1/bw1 or +/+ ) . There was a trend toward an elevated level of the bw1/+ exceptional class from both Cap-H2Z3-0019/Cap-H2Z3-5163 and Cap-H2Z3-0019/+ males . This suggested meiosis I nondisjunction that possibly occurs even in Cap-H2 heterozygous males . Furthermore , there may also be a slight increase in meiosis II nondisjunction as the bw1/bw1 class is elevated in the Cap-H2 trans-heterozygous and heterozygous males . The Cap-H2 allelic combination utilized in these genetic nondisjunction assays is likely weak in comparison to others where males are completely sterile . Therefore , the elevated frequency of exceptional progeny from 2nd and 3rd chromosome assays relative to the sex and 4th may only represent a heightened sensitivity of these chromosomes rather then a role for Cap-H2 specifically in 2nd and 3rd chromosome segregation . In fact , defects in sex and 4th chromosome segregation were observed in stronger male sterile Cap-H2 mutants ( see below ) . One possible explanation for a major autosome bias in our nondisjunction assays may be related to the greater amount of DNA estimated for the 2nd ( 60 . 8 Mb ) and 3rd ( 68 . 8 Mb ) relative to the X , Y , and 4th chromosomes ( 41 . 8 , 40 . 9 , and 4 . 4 Mb , respectively ) [41] . Thus , perhaps larger chromosomes require more overall condensin II function to promote their individualization or condensation and are therefore more sensitive to Cap-H2 dosage . While plausible , if sensitivity to Cap-H2 mutation were purely due to chromosome size , it is difficult to explain why a more significant level of XY nondisjunction did not occur given that they are ∼70% the size of the 2nd and 3rd . An alternative hypothesis involves the fact that 2nd chromosome conjunction may occur at several sites or along its entire length [42] , whereas XY bivalent pairing is restricted to intergenic repeats of the rDNA locus [43] , [44] . This suggests that more total DNA is utilized for conjunction of the 2nd chromosome relative to the sex bivalent . Assuming the 3rd and 4th chromosomes maintain homolog pairing like the 2nd , then the relative amount of DNA utilized in conjunction is as follows: 3rd>2nd>4th>XY . Given that this closely parallels the trend of sensitivity to Cap-H2 mutation in the nondisjunction assays , it suggests that chromosomes which utilize more overall DNA in pairing/pairing maintenance activities require a greater dose of functional Cap-H2 for their proper anaphase I segregation . This points toward a role for Cap-H2 in the regulation of homolog conjunction/disjunction processes . We next addressed this hypothesis through cytological analyses of meiotic chromosome morphology in Cap-H2 mutant backgrounds . In prophase I stage S2 ( Figure 2A ) , nuclei appear to commence the formation of chromosome territories . By mid-prophase I stage S4 , territory formation is more evident ( Figure 2B ) and in late prophase I , stage S6 nuclei exhibit three discrete chromosome territories seemingly associated with the nuclear envelope ( Figure 2C ) [35] . Each of the three chromosome territories corresponds to the 2nd , 3rd , and sex chromosomal bivalents and are thought to have important chromosome organizational roles for meiosis I [33]–[36] . In male sterile mutants of the genotype Cap-H2Z3-0019/Cap-H2TH1 , chromosome organizational steps throughout prophase I are defective , as normal territory formation is never observed in 100% of S2 , S4 , and S6 stages ( n = 100 nuclei of each stage ) . Instead , chromatin is seemingly dispersed within the nucleus ( Figures 2D–2F ) . Male sterile Cap-D3EY00456 mutants mimic these defects ( Figure 2G–2I ) , suggesting that Cap-D3 and Cap-H2 function together within a condensin II complex to facilitate territory formation . No prophase I defects were observed in Cap-H2Z3-0019/Cap-H2Z3-5163 males , although subtle morphological changes may be difficult to detect . To establish possible roles for Cap-H2 and Cap-D3 in prophase I chromosome organization , it is important to outline the two general processes that must occur for proper territory formation . One is to gather or condense bivalent chromatin into an individual cluster . The second is to sequester each bivalent into a discrete pocket of the nucleus . Condensin II may perform one or both tasks , for example , perhaps chromatin is dispersed throughout the nucleus in the Cap-H2/Cap-D3 mutants because of faulty condensation . Alternatively , or in addition to , sequestration of chromatin into territories may be a primary defect in Cap-H2/Cap-D3 mutants . During late prophase I of wild-type primary spermatocytes , chromosomes from each territory condense further and appear as three dots corresponding to the 2nd , 3rd and sex bivalents . This stage , referred to as M1 of meiosis I , may be morphologically abnormal in strong Cap-H2 mutants because it was not detected in our studies ( n>50 testes ) . This is likely because these mutants fail to form normal chromosome territories . Proceeding further into meiosis , metaphase I is signified by the congression of the three bivalents into one cluster at the metaphase plate ( Figure 3A ) . Despite not forming normal chromosome territories and possibly never reaching normal M1 chromosomal structure , there were no unusual features detected in Cap-H2 male sterile metaphase I figures ( Figure 3D ) . Although subtle changes to chromosome morphology would not be detectable , it can be concluded that by metaphase I , gross chromosomal condensation occurs at least somewhat normally in Cap-H2 strong mutant males . This raises the interesting possibility that a gradual prophase I chromosome condensation is catalyzed by condensin II components in the course of chromosomal territory formation and culminates at M1 . Next , a second condensation step to form metaphase I chromosomes occurs , which is only partially dependent or completely independent of condensin II components . Perhaps condensin I or some other factor is the major player for metaphase I chromosome assembly or compensates for condensin II loss . In contrast to metaphase I , anaphase I is clearly not normal in Cap-H2 mutants , where instead bridges are often found between segregating sets of chromosomes ( Figure 3E , 3F , and 3H ) . The frequency of these bridges occurs in a manner that matches other phenotypic trends , found in 30 . 4% of the anaphase I figures for sterile Cap-H2Z3-0019/Cap-H2TH1 males ( n = 102 anaphase I figures ) , 11 . 5% for Cap-H2Z3-0019/Cap-H2Z3-5163 males that are fertile yet undergo 2nd and 3rd chromosome loss ( n = 78 ) , and never in the wild-type ( n = 90 , Figures 3B , 3C , 3G ) . As with territory formation , Cap-H2 is likely functioning along with Cap-D3 because in two cysts observed from Cap-D3EY00456 homozygous males , 7 of 20 anaphase I figures were bridged ( Figure 3I ) . This anaphase I bridging most likely represents a failure to resolve chromosomal associations prior to segregation as chromatin appears to be stretched between chromosomes moving to opposing poles . To gain further insight into why anaphase I bridges are created in Cap-H2 and Cap-D3 mutants , a chromosome squashing technique was employed that enables the visualization of individual anaphase I chromosomes ( Figure 4A ) . With this method , the 4th chromosomes are easily identified because of their dot like appearance . Centromere placement enables the identification of the sex chromosomes , where on the X it is located very near the end of the chromosome ( acrocentric ) and on the Y is about a quarter of the length from one end ( submetacentric ) . The 2nd and 3rd chromosomes are indistinguishable from one another because of their similar size and placement of the centromere in the middle of the chromosome ( metacentric ) . Whereas bridged anaphase I figures were never observed in wild-type squashed preparations ( n = 14; Figure 4A ) , bridging occurred in 40 . 5% of those from Cap-H2Z3-0019/Cap-H2TH1 mutant males ( n = 42; Figure 4B–4F ) . The chromosome squashing method was utilized to determine the nature of anaphase I bridges , and interestingly , it was concluded that bridging exists between both homologous and heterologous chromosomes ( Figure 4 ) . Of the total anaphase I figures from Cap-H2Z3-0019/Cap-H2TH1 testes , 21 . 4% appeared to have anaphase I bridging that existed between homologous chromosomes ( Figure 4B and 4C ) . A FISH probe that recognizes 2nd chromosome pericentromeric heterochromatin was used to distinguish 2nd and 3rd chromosomes and demonstrates that linkages in Figure 4B ( inset ) are between the 3rd chromosomes , perhaps at regions of shared homology . Furthermore , despite not finding 4th chromosome segregation defects in nondisjunction assays ( Table 1 ) , the 4th chromosome was bridged in 4 . 8% of anaphase I figures ( Figure 4C ) . This suggests that chromosome 4 becomes sensitive to further loss of Cap-H2 function in the stronger Cap-H2Z3-0019/Cap-H2TH1 mutant background . Persistent associations between homologous chromosomes in anaphase I may be explained by a failure to individualize paired homologs from one another prior to anaphase I entry . It is probable that DNA entanglements normally exist between paired homologous chromosomes as they are likely raveled around one another rather then simply aligned side by side in a linear fashion . Therefore , individualization failure in Cap-H2 mutants may allow entanglements to persist into anaphase I . Cap-H2 may mediate homolog individualization in prophase I , where bivalents do not appear to condense properly in Cap-H2 mutants ( Figure 2 ) . Another plausible scenario is that Cap-H2 functions to antagonize achiasmate homolog conjunction mediated by teflon , MNM , and SNM at some point prior to anaphase I entry . The other 19% of anaphase I figures that were bridged ( n = 42 ) in the Cap-H2Z3-0019/Cap-H2TH1 mutant involve heterologous chromosomes ( Figure 4D ) and cases where bridging is so substantial that its chromosomal nature could not be determined ( Figure 4E and 4F ) . The observed X–Y linkage in Figure 4D is consistent with the XY pairing site , or “collochore , ” and occurs in wild-type preparations [45] . The other linkage is an atypical heterologous association occurring between the Y and one of the major autosomes ( 2nd or 3rd ) . We speculate that the substantially bridged images in Figure 4E and 4F are comprised of associations between heterologous and/or homologous chromosomes . Figure 4F is particularly interesting because the 4th and sex chromosomes appear to have segregated normally , yet the major autosomes remain in an unresolved chromosomal mass . This pattern fits the trend of the nondisjunction studies , where the 2nd and 3rd chromosomes had a heightened sensitivity to Cap-H2 mutation . Because the 4th chromosome naturally tends to be separated from other prometaphase I to anaphase I chromosomes , it was often easily observed to be involved in heterologous chromosomal associations ( Figures 4G , 4H , and 5A' ) . These appear as threads and occurred in 42 . 5% of metaphase and anaphase I figures ( n = 40; Figures 4G and 5A' ) . Interestingly , 4th-to-heterolog threads were also observed in the wild-type , although at a lower frequency of 19% ( n = 21 , Figure 4H ) . Persistent associations between heterologous chromosomes such as that observed in figure 4D and inferred to exist within 4E and 4F may be traced to failed territory formation in Cap-H2 mutant prophase I . Perhaps interphase chromosomes are naturally entangled with one another and the Cap-H2/Cap-D3 mediated nuclear organization steps that occur during territory formation effectively detangle and individualize them into discrete structures . Alternatively , Cap-H2/Cap-D3 mediated chromosome territory formation may act to prevent the establishment of heterologous entanglements . These are plausible scenarios given that failed territory formation in Cap-H2/Cap-D3 mutants seemingly leads to persistent intermingling of all chromosomes . Such an environment could provide a likely source of heterologous chromosomal associations . Heterologous associations involving the 4th chromosome may also be entanglements that persist and/or were initiated through failure in territory formation . These cannot however be completely attributed to loss of Cap-H2 function because they were observed in the wild-type ( Figure 4H ) . The anaphase I bridging in Cap-H2 mutant males is one likely source for their elevated amount of nullo-2 and nullo-3 sperm ( Tables 3 and 4 ) . Chromatin stretched between daughter nuclei may occasionally lead to the creation of sperm lacking whole chromosomes or variable sized chromosomal regions . Bridged anaphase I images in Figure 4 represent likely scenarios where chromosome loss would occur and furthermore , visualization of the post-meiotic “onion stage” from Cap-H2 mutants is consistent with chromosome loss . With light microscopy , white appearing nuclei within the onion stage are nearly identical in size to the black appearing nebenkern , which represents clustered mitochondria ( Figure 4I , arrows ) . In onion stages from Cap-H2Z3-0019 homozygotes , micronuclei are often observed which may be the manifestation of chromatin lost through anaphase I bridging ( Figure 4I , arrowheads ) . The associations that create anaphase I bridging between chromosomes moving to opposing poles may also be capable of causing improper cosegregation of homologs . In fact , 9 . 5% of squashed anaphase I figures ( n = 42 ) are of asymmetrically segregating homologs that were never observed in the wild-type ( n = 14 ) . These are consistent with failure in homolog disjunction and subsequent cosegregation to one pole ( Figure 5A–5D ) . These may also be the consequence of associations between heterologous chromosomes that lead to one being dragged to the incorrect pole . As an expected outcome of cosegregation in meiosis I , aneuploidy in prophase II and anaphase II figures was also observed ( Figure 5E–5G ) . Such events likely explain the slight increase in diplo-2 sperm that were heterozygous for the male's 2nd chromosomes ( bw1/+ in Table 3 ) . The also provide a likely source for the elevated amount of nullo-2 and nullo-3 sperm ( Tables 3 and 4 ) . While the prevalence of meiotic anaphase I bridging is likely a major contributor to the observed 2nd and 3rd nondisjunction , it cannot be ruled out that the preceding stem cell and gonial mitotic divisions are also defective and lead to aneuploid sperm . This exists as a formal possibility , yet aneuploid meiotic I cells were not observed in squashed Cap-H2 mutant anaphase I figures where all chromosomes could be distinguished ( n = 10 ) . This suggests that pre-meiotic segregation is unaffected . Similarly , anaphase II defects could have contributed to the elevated nullo-2 and nullo-3 sperm and perhaps the slight increase in bw1/bw1 progeny that would have been generated from meiosis II nondisjunction ( Table 3 ) . In fact , anaphase II bridging was observed in 8 . 7% of Cap-H2Z3-0019/Cap-H2TH1 anaphase II figures ( n = 69 , Figure 6 ) , 2 . 1% of those from Cap-H2Z3-0019/Cap-H2Z3-5163 males ( n = 47 ) , and never in the wild-type ( n = 66 ) . Anaphase II defects may occur because of a specific role of Cap-H2 in meiosis II , or alternatively , anaphase II bridging could be attributed to faulty chromosome assembly or individualization in meiosis I . The protein Teflon is implicated in the maintenance of Drosophila male meiosis I autosome conjunction as teflon mutants lose autosomal associations prior to anaphase I [31] . To investigate whether persistent associations between homologous chromosomes in anaphase I of Cap-H2 mutants ( Figure 4B and 4C ) are Teflon dependent , teflon mutations were crossed into a Cap-H2 mutant background and the frequency of anaphase I bridging was assessed . While 30 . 4% of anaphase I figures from Cap-H2Z3-0019/Cap-H2TH1 males were bridged ( n = 102 ) , bridging existed within only 10 . 8% of anaphase I figures from tefZ2-5549/tefZ2-5864; Cap-H2Z3-0019/Cap-H2TH1 males ( n = 74 , p<1×10−6 , X2 ) ( Figure 7A ) . Furthermore , in squashed preparations anaphase I bridging was decreased from 40 . 5% in Cap-H2Z3-0019/Cap-H2TH1 males ( n = 42 ) to 25 . 6% in the tefZ2-5549/tefZ2-5864; Cap-H2Z3-0019/Cap-H2TH1 double mutants ( n = 43 , p<0 . 05 , X2 ) . The ability of teflon mutations to rescue Cap-H2 mutant anaphase I bridging suggests that Cap-H2 functions to antagonize Teflon mediated autosome conjunction . This may entail deactivation of an achiasmate conjunction complex consisting of MNM , SNM , and perhaps Teflon , at some point prior to the metaphase I to anaphase I transition . Consistent with this hypothesis , the percent of anaphase I figures where homologous chromosomes appeared to be bridged were decreased from 21 . 4% in the Cap-H2Z3-0019/Cap-H2TH1 mutants ( n = 42 ) to 9 . 3% in tefZ2-5549/tefZ2-5864; Cap-H2Z3-0019/Cap-H2TH1 males ( n = 43 , p<0 . 1 , X2 , Figure 7B ) . As an important alternative to Cap-H2 functioning to antagonize an achiasmate homolog conjunction complex , it may be that wild-type Teflon exacerbates DNA associations between chromosomes . For example , perhaps Teflon linked homologs are now particularly prone to becoming entangled . Under this scenario , teflon mutations may decrease the opportunity for DNA entanglements to be introduced between homologs because of their spatial distancing from one another during late prophase I to metaphase I . Given the formal possibility of both models , we conclude that Cap-H2 functions to either remove teflon dependent conjunction and/or to resolve chromosomal entanglements between homologs . The remaining bridged anaphase I figures from squashed preparations in tefZ2-5549/tefZ2-5864; Cap-H2Z3-0019/Cap-H2TH1 males were uninterpretable making it impossible to assess whether Cap-H2 mutant heterologous anaphase I bridging was also rescued by teflon mutation . However , 4th-to-heterolog threads were greatly suppressed by teflon mutations , decreasing from 42 . 5% ( n = 40 ) to only 6% ( n = 50 , p<0 . 00001 , Figure 7C ) . This is a surprising result given that Teflon has been described as a mediator of associations between homologous chromosomes . One plausible explanation is that Teflon can exacerbate heterologous chromosomal associations . This may occur when Teflon establishes autosomal conjunction in a prophase I nucleus where territory formation had failed . Cap-H2 may also antagonize a Teflon mediated autosomal conjunction complex that might mistakenly establish conjunction between heterologs when territories do not form . As described above , completely male sterile Cap-D3 and Cap-H2 allelic combinations exist and Cap-H2 mutant males lack mature sperm in their seminal vesicles ( Figure 1A and 1B ) . One possible explanation for this result is that chromosome damage created during anaphase bridging in the Cap-H2 mutants causes spermatogenesis to abort . This scenario seems less likely because tefZ2-5549/tefZ2-5864 rescued Cap-H2Z3-0019/Cap-H2TH1 anaphase I bridging to levels near that of fertile Cap-H2 mutants , yet tefZ2-5549/tefZ2-5864; Cap-H2Z3-0019/Cap-H2TH1 males were still found to be completely sterile . This points toward another function for Cap-H2 in post-meiotic steps of spermatogenesis . Figure 8 illustrates a working model of condensin II in Drosophila male meiosis to resolve both heterologous and homologous chromosomal associations . We speculate that these associations likely consist of DNA entanglements that naturally become introduced between interphase chromosomes due to their threadlike nature ( Figure 8A ) . The studies herein identified a function for condensin II during prophase I , when paired homologous chromosomes become partitioned into discrete chromosomal territories [33]–[36] . We propose that condensin II either promotes this partitioning , by actively sequestering bivalents into different regions of the nucleus , or functions to perform prophase I chromosome condensation . It is important to stress that in both scenarios , the role of condensin II mediated territory formation is to ensure the individualization of heterologous chromosomes from one another ( Figure 8B , large arrows ) . When sequestration into territories and/or condensation of the bivalents do not take place , i . e . in the condensin II mutants , individualization does not occur , heterologous entanglements persist into anaphase I , and chromosomes may become stretched to the point where variable sized chromosomal portions become lost ( Figure 8E ) . Persistent heterologous entanglements may also lead to one chromosome dragging another to the incorrect pole ( not shown ) . Despite what appears to be failed chromosome condensation in prophase I of Cap-H2 mutants , by metaphase and anaphase I no obvious defects in chromosome condensation were observed ( Figures 3 , 4 , 5 , and 7 ) . This suggests that sufficient functional Cap-H2 is present in this mutant background to promote metaphase/anaphase I chromosome condensation . Alternatively , perhaps another factor fulfills this role and/or compensates for condensin II loss . This parallels Cap-G mutants , where embryonic mitotic prophase/prometaphase condensation was abnormal , yet metaphase figures appeared wild-type [38] . In Drosophila , mutant and RNAi knockdown studies of condensin complex subunits in mitosis lead to a range of phenotypes , from complete failure in condensation [46] to seemingly normal axial shortening , but failure in chromatid resolution [37] , [39] . The variable phenotypes produced from these studies may reflect differences in cell type specific demand for condensin subunit dosage/activity . Anaphase I figures of Cap-H2 mutants also revealed persistent entanglements between homologous chromosomes that may be at regions of shared homology . We suggest that the paired state of homologs initiates or introduces the opportunity for DNA entangling between homologs and that condensin II functions to resolve these prior to segregation . A likely scenario is that this occurs during prophase I , where chromosome condensation appears abnormal in Cap-H2 and Cap-D3 mutants . Perhaps condensin II mediated prophase I condensation functions to individualize intertwined homologous chromosomes prior to segregation ( Figure 8B , small arrows ) . It is also plausible that condensin II homolog individualization continues up until anaphase I . We have found that mutations in teflon , a gene required for autosomal pairing maintenance , were capable of suppressing anaphase I bridging in Cap-H2 mutant males . Specifically , both homologous and heterologous chromosomal bridging were decreased in the teflon/Cap-H2 double mutant . This may occur because Teflon is capable of exacerbating DNA entanglements , if for example persistent homolog conjunction provides more opportunity for entanglements between homologs to be introduced . Teflon may also exacerbate entanglements between heterologous chromosomes . This might be especially true in a Cap-H2 mutant background with failed territory formation , as Teflon mediated autosomal conjunction may augment the extent of entangling . It is also plausible that Cap-H2 acts as an antagonist of Teflon mediated autosomal conjunction . Perhaps autosomal homologous associations persist into anaphase I of Cap-H2 mutants because a homolog conjunction complex was not disabled prior to the metaphase I to anaphase I transition . However , Cap-H2 as an antagonist of Teflon cannot explain persistent heterologous associations into anaphase I , unless Teflon is capable of mistakenly introducing conjunction between heterologous chromosomes . The opportunity for this might exist in a Cap-H2 mutant prophase I nucleus where heterologs continue to intermingle because of failed territory formation . An interesting result in our course of studies was the heightened amount of chromosome 2 and 3 nondisjunction in weaker male fertile Cap-H2 allelic combinations , whereas the sex and 4th chromosomes were unaffected . This is reminiscent of mutants from several other genetic screens that only affected the segregation of specific chromosomes or subsets [32] , [47]–[51] . However , given that sex and 4th chromosome segregation defects were observed in the stronger male sterile Cap-H2 mutant background , we propose that condensin II functions upon all chromosomes , yet the 2nd and 3rd require the greatest functional Cap-H2 dose for their proper segregation . This sensitivity of the 2nd and 3rd chromosomes may be due to their greater total amount of DNA utilized in homolog pairing and pairing maintenance activities . For example , perhaps longer stretches of paired DNA are more prone to entanglements or require more achiasmate conjunction factors and therefore necessitate higher levels of Cap-H2 individualization or disengagement activity . As an interesting corollary to support this theory , weak teflon mutations only lead to 4th chromosome missegregation , while the other autosomes segregate normally [31] . This suggests that the 4th chromosomes are more sensitive to Teflon dosage because of their fewer sites of conjunction . The majority of the data provided in this manuscript were on our studies of mutant Cap-H2 alleles , however , we found that a homozygous viable Cap-D3 mutant also failed to form normal chromosomal territories and exhibited anaphase I chromosome bridging . This provides support that these two proteins are functioning together within a condensin II complex . It is important to point out however , that to date there is no data in Drosophila to support that these proteins physically associate with each other or with other condensin subunits , namely SMC2 and SMC4 ( a Drosophila Cap-G2 has yet to be identified with computational attempts ) [8] . At this point in our studies of putative condensin II subunits in disjunction of achiasmate male homologous chromosomes , we cannot distinguish between possible scenarios that Cap-H2 and Cap-D3 act to disentangle chromosomes through individualization activity , that they function as antagonists of Teflon dependent achiasmate associations , or a combination of both activities . The fact that Teflon mutations do rescue Cap-H2 anaphase I bridging defects is an especially intriguing result as it points toward a molecular mechanism for Cap-H2 as an antagonist of achiasmate associations . While three genes have been found to promote achiasmate conjunction ( teflon , MNM , and SNM ) , no factors have been identified that act to negatively regulate conjunction and allow homologs to disengage at the time of segregation . Interestingly , one conjunction factor , SNM , is orthologous to the cohesin subunit Scc3/SA that appears to be specialized to engage achiasmate homologs [30] . Condensin has been shown to antagonize cohesins in budding yeast meiosis [52] and mitotic human tissue culture cells [53] . This raises the possibility that a conserved molecular mechanism exists for condensin II as a negative regulator of SNM in Drosophila male meiosis . The investigation of Teflon , MNM , and SNM protein dynamics in a Cap-H2 mutant background will be an important set of future studies to help decipher the function of Cap-H2 in achiasmate segregation mechanisms . Homologous chromosomal individualization in meiosis I has been previously documented as a condensin complex catalyzed activity in C . elegans as homologs remained associated in hcp-6/Cap-D3 mutants even in the absence of recombination and sister chromatid cohesion [6] . Here we demonstrated that condensin subunits are also required to individualize heterologous chromosomes from one another prior to anaphase I . As discussed above , this is likely through condensin II mediated chromosome organizational steps that occur during prophase I territory formation . This suggests that Drosophila males carry out territory formation to disfavor associations between heterologs , while also enriching for interactions between homologs . This model is particularly interesting as it may point toward an adaptation of Drosophila males to ensure meiotic I segregation in a system lacking a synaptonemal complex and recombination .
To visualize sperm head ( DAPI ) and tail ( don juan-GFP ) content in the seminal vesicles , males were restricted from females for ten days , then testes were dissected and fixed as previously described for whole mounted ovaries [54] . Meiotic microtubules were detected with rat anti-alpha tubulin antibodies ( Serotec , MCA78G and MCA77G ) at 1∶40 each and a FITC-conjugated donkey anti-rat secondary ( Jackson ImmunoResearch , #112-095-167 ) at 1∶200 . Immunofluorescence was conducted following protocols 5 . 2 and 5 . 6 from ref [55] , with the addition of two extra final PBS washes , the second to last containing 100 ng/ul DAPI . DAPI stained chromosome squashes were prepared as detailed in protocol 1 . 9 , method #3 w/o steps necessary for immuno-detection from ref . [56] . Testes were opened to release cells while in fixative on a siliconized coverslip prior to lowering a non-siliconized slide and squashing . Subsequent FISH to anaphase chromosome spreads was conducted as detailed in protocol 2 . 9 in ref . [57] . An ( AACAC ) 6 oligonucleotide end labeled with terminal deoxytransferase ( Roche 03333566001 ) and reagents provided in the ARES Alexa Fluor 546 DNA labeling kit ( Invitrogen A21667 ) were utilized to fluorescently detect 2nd chromosome pericentromeric heterochromatin . All imaging was performed with a Zeiss Laser Scanning Microscope , LSM 510 Meta , and the acquisition software LSM 510 Meta , version 4 . 0 . Images in figure 1 were captured with a Plan-Apochromat 20×/0 . 8 objective at an image bit depth of 8 bit . All other images were acquired with a Plan-Apochromat 63×/1 . 4 Oil DIC objective at an image bit depth of 8 bit . Appropriate filters and dichroic mirrors for fluorochromes DAPI , Alexa Fluor 546 , and FITC were used where applicable . To test for male fertility , 10 mutant males were crossed to 20 wild-type ( Oregon R ) virgin females and monitored frequently for the presence of larvae . To score fertility over time of the Cap-H2 trans-heterozygous and heterozygous control males , 10 , 1–4 day old males were placed with 30 , 1–5 day old virgin females in containers with grape juice agar plates and wet yeast . Flies were transferred to new plates every 24 hours for 4 days , but on the 4th , 8th , and 12th days , only males were kept and placed with a new batch of 1–5 day old virgin females . This scheme was carried out over a period of 16 days and in triplicate . For the SMC4; Cap-H2 double mutant studies , the strategy is as detailed above , except only 20 virgin females were used for each brood . To score hatch rates , the percent of eggs that hatched ( n = 200 total eggs/plate ) was scored from randomly selected regions of each plate 48 hours after parents were removed . Five Cap-H2Z3-0019/Cap-H2Z3-5163; spapol/+ males were crossed to fifteen C ( 4 ) EN , ci ey females at 25°C on standard fly food . As controls , the same experimental design was carried out with Cap-H2Z3-0019/TM6B , Hu; spapol/+ or Cap-H2Z3-5163/TM6B , Hu; spapol/+ males . Males and virgin females were 2–3 days old and the experimental cross was done in replicate , while the controls were only performed once . Parents were twice flipped into a new bottle after three days and then discarded from their final bottle after three days . Progeny were scored on the 13th , 15th , and 18th day after parents were placed into the bottle . Because the 4th chromosome in these females is attached , they produce eggs that either carry the compound C ( 4 ) EN , ci ey chromosome ( diplo-4 ) or no 4th chromosome ( nullo-4 ) . The fertilization of nullo-4 eggs by normal haploid sperm creates nullo-4/+ and nullo-4/spapol progeny . Both of these will develop into very small flies ( Minute ) from only carrying one 4th chromosome , with the latter also spapol . When normal haplo-4 sperm fertilize C ( 4 ) EN , ci ey/0 eggs , C ( 4 ) EN , ci ey/+ or C ( 4 ) EN , ci ey/spapol progeny are produced . These both appear wild-type from the wild-type alleles of ci and ey on the paternal 4th and wild-type spapol on the C ( 4 ) EN chromosome . There are two exceptional classes from male chromosome missegregation events that are detectable with this assay . The first is when nullo-4 sperm fertilize C ( 4 ) EN , ci ey/0 eggs to produce ci ey offspring . The second are sperm diplo-4 and homozygous for spapol fertilizing nullo-4 eggs to create spapol offspring . The following exceptional classes go undetected with this assay because they are phenotypically wild-type: +/+ , spapol/+ , spapol/spapol sperm that fertilize C ( 4 ) EN eggs or +/+ , spapol/+ and all triplo-4 and tetra-4 sperm possibilities that fertilize nullo-4 eggs . Therefore , the % 4th chromosome nondisjunction is likely an underestimate . This assay was adapted from that described in ref . [32] . Ten males , that were 2–3 days old , were crossed to 17 virgin females that were 0–3 days old at 25°C . Males each carried a Y chromosome with an X translocation containing the wild-type yellow gene . Females carried an attached X chromosome: C ( 1 ) RM , y2 su ( wa ) wa . In this assay , the viable offspring from sperm bearing the normal sex chromosome content , either one X or one Y , will be y1w1/nullo-X ( yw , XO male ) or y+Y/C ( 1 ) RM , y2 su ( wa ) wa ( y+ , XXY female ) ( nullo-X/Y and triplo-X are lethal combinations ) . If exceptional classes of sperm are created that are diplo-X , XY , XXY , or lack either sex chromosome entirely ( nullo-X or nullo-Y ) , then yellow white females , white males , white females , or yellow females will be produced , respectively . With this assay it cannot be determined whether offspring carry an extra Y chromosome . This experiment is adapted from that detailed in ref . [40] . The line C ( 2 ) EN , b pr carries second chromosomes that are fused , referred to as “compound” chromosomes , that segregate together as a unit and therefore gametes are created that are either nullo-2 or diplo-2 . Because any chromosome 2 content other than diplo-2 is lethal , viable offspring only occur from the fertilization of nullo-2 eggs by diplo-2 sperm or diplo-2 eggs by nullo-2 sperm . Therefore , if any offspring are created when crossing males to C ( 2 ) EN , b pr virgin females , then chromosome mis-segregation had occurred in the generation of male gametes . The males used in this experiment were heterozygous for a mutant allele of brown ( bw1 ) that is an insertion of a 412 retrotransposable element into the brown gene . In this assay , there are four classes of sperm that can successfully fertilize eggs from C ( 2 ) EN bearing females that can then develop into adult flies: nullo-2 , diplo-2 ( bw1/bw1 ) , diplo-2 ( bw1/+ ) , and diplo-2 ( +/+ ) . Progeny from nullo-2 sperm fertilizing diplo-2 eggs have the b pr phenotype . Those from bw1/bw1 sperm fertilizing nullo-2 eggs have the bw phenotype . Progeny from bw1/+ and +/+ sperm fertilizing nullo-2 eggs both appear wild-type . To distinguish between these two wild-type phenotypic classes , a PCR test was developed that could detect the presence of the bw1 mutant allele by utilizing the 412 element insertion in the brown gene . Thus , with forward primer tattatctgagtgagttttctcgag that anneals to the 412 element and reverse primer ttcacccacatcatcctcat that anneals to the brown gene , a 874 bp PCR product is generated only from bw1/+ and never from +/+ flies . Furthermore , with forward primer ggtgatctgcaattagggat and the same reverse primer as above ( ttcacccacatcatcctcat ) , an ∼571 bp fragment amplifies from the wild-type brown locus within both bw1/+ and +/+ flies , and serves as a positive control . Wild-type in these assays was the parental line from the Z3-0019 and Z3-5163 backgrounds [58] crossed to Oregon R ( bw1/+; st1/+ ) . Similarly , Cap-H2 heterozygous males were generated from a cross to Oregon R . Ten 1–3 day old males were crossed to twenty 1–5 day old virgin C ( 2 ) EN , b pr females at 25°C . This was replicated 19 times for the bw1/+; Cap-H2Z3-0019/Cap-H2Z3-5163 males , 12 for bw1/+; Cap-H2Z3-0019/+ , 20 for bw1/+; Cap-H2Z3-5163/+ , and 15 for bw1/+; st1/+ . The parents were kept in the original vial for a total of 5 days , flipped to a new vial for 5 more days , and then discarded . The progeny were scored on the 13th , 15th , and 18th day after parents were placed together into a vial . Like the second chromosome , any chromosome 3 content other than diplo-2 is lethal , so viable offspring only occur from the fertilization of nullo-3 eggs by diplo-3 sperm or diplo-3 eggs by nullo-3 sperm . This experiment was therefore set up in the same way as the 2nd chromosome nondisjunction tests , except that C ( 3 ) EN , st cu e females were used , three replicates were performed , parents were kept in vials for 3 days and flipped twice , and these crosses were done at room temperature ( 21–23°C ) . In this assay , there are four classes of sperm that can successfully fertilize eggs from C ( 3 ) EN bearing females that can then develop into adult flies: nullo-3 , diplo-3 ( heterozygous for paternal 3rd chromosomes ) , diplo-3 ( homozygous for one of the paternal 3rd chromosomes ) and diplo-3 ( homozygous for the other paternal 3rd chromosome ) . The Cap-H2Z3-0019 chromosome is marked with ru , h , st , sr , e , and ca , while the Cap-H2Z3-5163 chromosome is marked with only st . Using Cap-H2Z3-0019/Cap-H2Z3-5163 males as an example , the following describes how nullo-3 and the three different diplo-3 progeny classes were distinguished . Progeny from nullo-3 sperm fertilizing C ( 3 ) EN , st cu e , eggs have the st cu e phenotype . Those from diplo-3 , Cap-H2Z3-0019/Cap-H2Z3-0019 , sperm fertilizing nullo-3 eggs would be ru h st sr e ca . The progeny from diplo-3 , Cap-H2Z3-0019/Cap-H2Z3-5163 and Cap-H2Z3-5163/Cap-H2Z3-5163 , sperm fertilizing nullo-3 eggs both develop into st animals . These were distinguished by crossing to ru h st Cap-H2Z3-0019 st e ca/TM6B , Hu Tb e ca flies and scoring F2 progeny . The percentage of bridged anaphase I figures where chromosomes are oriented such that their identity is unambiguous is low . Additionally , anaphase I chromosomes quickly decondense upon entry into telophase I , reducing the overall frequency of anaphase I figures where chromosomes can be observed . Thus , the stronger Cap-H2Z3-0019/Cap-H2TH1 allelic combination was analyzed to increase the likelihood of visualizing interpretable bridged figures . Bridges were scored as homologous when they appeared to connect morphologically similar chromosomes , based on size and centromere location ( see text ) that appeared to be segregating away from one . It was concluded that the 4th chromosome was involved in a heterologous association during meiosis I when a DAPI staining thread extended to another non-4th chromosome . Thus , images were only scored when this thread clearly was connected to a heterolog , or the other 4th was present and it was clear that it did not participate in the thread . In the wild-type figures where 4th chromosome threads were observed , it could not be concluded whether the thread extended to another 4th or a heterolog . The data in figure 7C for the wild-type may therefore be an overestimate of 4th-to-heterolog threads because threads may actually connect homologs . bw; st Z3-0019/TM6B , Hu Tb e ca and bw; st Z3-5163/TM6B , Hu Tb e ca were obtained from Charles Zuker [58] and were identified in a previously detailed genetic screen [59] . A recombinant chromosome of the Z3-0019 line , ru h st Cap-H2Z3-0019 sr e ca/TM6B , Hu Tb e ca was used for all experiments herein . The Cap-H2TH1 allele was found on the Df ( 3L ) W10 bearing chromosome during the course of complementation studies that will be described elsewhere . The deficiency Df ( 3L ) W10 was recombined away from the Cap-H2TH1 bearing chromosome and instead ru h st Cap-H2TH1 Sb[sbd-2]/TM6B , Hu Tb e ca was utilized in these studies . The stocks SMC4k08819 , spapol , C ( 2 ) EN , b pr , C ( 3 ) EN , st cu e , Df ( 3R ) Exel6159 , Cap-D3EY00456 , and Df ( 2L ) Exel7023 were obtained from the Bloomington stock center . John Tomkiel provided the following stocks: cn tefZ2-5549 bw/CyO , cn tefZ2-5864 bw/CyO , and y w sn; C ( 4 ) EN , ci ey . The don juan-GFP/CyO and C ( 1 ) RM , y2 su ( wa ) wa were received from Terry Orr-Weaver . | Some of the processes that ensure proper chromosome segregation take place upon the chromosomes themselves . The chromosomes of Drosophila males undergo an interesting and relatively enigmatic step before entering meiosis , where each paired homologous chromosome becomes clustered into a discrete region of the nucleus . In this article , we provide evidence that improper chromosomal associations are resolved and/or prevented during this “chromosome territory” formation . This was uncovered through the study of flies mutant for Cap-H2 , which have abnormal territory formation and improper chromosomal associations that persist into segregation . Another important process that chromosomes undergo in meiosis is the pairing and physical linking of maternal and paternal homologs to one another . Linkages between homologs are essential to ensure their proper segregation to daughter cells . In contrast to meiosis in most organisms , linkages between homologs in male Drosophila are not recombination mediated . Here , we provide evidence that Cap-H2 may function to remove Drosophila male specific linkages between homologous chromosomes prior to anaphase I segregation . When chromosomal associations persist during segregation of Cap-H2 mutants , the chromosomes do not detach from one another and chromatin is bridged between daughter nuclei . The likely outcome of this defect is the production of aneuploid sperm . | [
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| 2008 | Condensin II Resolves Chromosomal Associations to Enable Anaphase I Segregation in Drosophila Male Meiosis |
Wnt signaling plays critical roles in dorsoventral fate specification and anteroposterior patterning , as well as in morphogenetic cell movements . Dishevelled proteins , or Dvls , mediate the activation of Wnt/ß-catenin and Wnt/planar cell polarity pathways . There are at least three highly conserved Dvl proteins in vertebrates , but the implication of each Dvl in key early developmental processes remains poorly understood . In this study , we use genome-editing approach to generate different combinations of maternal and zygotic dvl mutants in zebrafish , and examine their functions during early development . Maternal transcripts for dvl2 and dvl3a are most abundantly expressed , whereas the transcript levels of other dvl genes are negligible . Phenotypic and molecular analyses show that early dorsal fate specification is not affected in maternal and zygotic dvl2 and dvl3a double mutants , suggesting that the two proteins may be dispensable for the activation of maternal Wnt/ß-catenin signaling . Interestingly , convergence and extension movements and anteroposterior patterning require both maternal and the zygotic functions of Dvl2 and Dvl3a , but these processes are more sensitive to Dvl2 dosage . Zygotic dvl2 and dvl3a double mutants display mild axis extension defect with correct anteroposterior patterning . However , maternal and zygotic double mutants exhibit most strongly impaired convergence and extension movements , severe trunk and posterior deficiencies , and frequent occurrence of cyclopia and craniofacial defects . Our results suggest that Dvl2 and Dvl3a products are required for the activation of zygotic Wnt/ß-catenin signaling and Wnt/planar cell polarity pathway , and regulate zygotic developmental processes in a dosage-dependent manner . This work provides insight into the mechanisms of Dvl-mediated Wnt signaling pathways during early vertebrate development .
The specification of the dorsoventral ( DV ) axis is tightly linked to , and ultimately determines , the processes that establish the anteroposterior ( AP ) pattern in vertebrate embryos . A large body of work has elucidated the induction and patterning processes underlying DV and AP polarities [1 , 2] . It is well established that , in Xenopus and zebrafish , maternal canonical Wnt/ß-catenin signaling is activated on the future dorsal side following fertilization , and ß-catenin is absolutely required for the establishment of the primary DV asymmetry through cooperation with other factors , such as members of the Nodal family [1–5] . During gastrulation , dorsolateral cells converge toward the dorsal midline , while dorsal midline cells undergo extension along the AP axis . These movements , called convergence and extension ( CE ) , not only provide the driving force for gastrulation , but also make an important contribution to the elongation of AP axis . The conserved non-canonical Wnt/PCP ( planar cell polarity ) signaling plays a key role in CE movements in all vertebrates [6–13] . Thus , disruption of Wnt/ß-catenin and Wnt/PCP signaling pathways can result in severe defects in the formation of embryonic axes . Dishevelled ( Dvl ) is a key intracellular signaling molecule that mediates the activation of both Wnt/ß-catenin and Wnt/PCP pathways during early development [6 , 8 , 14–18] . Functional analyses in Xenopus suggest that Dvl2 ( also called Xdsh ) exhibits dorsalizing and neuralizing activity [14] , and controls polarized cell behaviors that are required for CE movements during gastrulation [15] . However , its requirement for the activation of maternal Wnt/ß-catenin signaling in dorsal axis specification has not been clearly established , and remains largely enigmatic [17] . Simultaneous depletion of maternally expressed dvl2 and dvl3 from Xenopus oocytes using morpholino antisense oligonucleotides has no obvious effect on the expression of maternal Wnt/ß-catenin target genes and the specification of dorsal axis [19] . This negative result may be due to the presence of stored punctae of Dvl proteins in the oocyte cortex , which translocate to the dorsal region soon after fertilization [20] , or due to the insufficiency of maternal dvl mRNA depletion . Therefore , appropriate genetic approaches will be necessary to determine whether Dvls function upstream of ß-catenin in early dorsal fate specification . There are three dvl genes ( dvl1 , dvl2 and dvl3 ) in human , mouse , and Xenopus , and at least five in zebrafish [21 , 22] . They are highly conserved and broadly expressed throughout early development [21 , 23] . Extensive analyses of mutant phenotypes in mice have uncovered both unique and redundant functions among the three Dvl genes [24–27] . Although single mutants for these mouse Dvl genes generally survived to adulthood , abnormal neural tube closure and defective organogenesis were observed in different combinations of double mutants , which also cause embryonic lethality [25–27] . This , combined with the development in utero , make it less convenient to assay Dvl implication in early axis patterning and morphogenetic processes . In Xenopus , triple knockdown of dvl1 , dvl2 and dvl3 led to CE defects [21] , which were similar as those resulted from overexpression of Xdd1 , a Dvl2 ( Xdsh ) mutant lacking the PDZ domain [28] . Interestingly , Dvl1 and Dvl2 were found to be involved in neural crest specification and somite segmentation , while Dvl3 was required to maintain muscle gene expression [21] . These observations reveal a distinct requirement of Dvl proteins for Wnt signaling in regulating the expression of developmental genes . However , at present , the relative contribution of individual Dvl protein in CE movements that are dependent on the Wnt/PCP pathway has not been clearly determined , making this important question quite open for further investigation . One of the critical functions of Wnt signaling during early development is the requirement of zygotic Wnt/ß-catenin pathway for ventroposterior development in Xenopus and zebrafish embryos . In contrast to maternal Wnt/ß-catenin signaling that specifies dorsal fate , zygotic Wnt/ß-catenin signaling inhibits anterior development by activating the expression of different target genes that specify ventral and posterior tissues [1–5] . How different dvl genes are implicated in this process also remains poorly understood . Another difficulty in studying Dvl function is the presence of abundant maternal dvl transcripts , as shown both in Xenopus [14 , 19] , and in zebrafish [29 , 30] . These maternal products play an important role to support early developmental processes , and when translated into proteins and stored during oogenesis , could not be targeted by knockdown approaches . Also , zygotic homozygous mutants often do not survive to fertile adulthood to produce maternal and zygotic ( MZ ) mutant embryos for the analysis of maternal gene function . Taken together , it is clear that the maternal and zygotic contributions of dvl genes in different patterning and morphogenetic processes remain elusive , and merits further investigation . In the present study , we take advantage of the genome-editing approach to generate different combinations of maternal and zygotic dvl mutants in zebrafish . Transcriptomic analysis revealed that , among the five dvl genes , only dvl2 and dvl3a are maternally and abundantly expressed , whereas the transcript levels of the other three dvl genes ( dvl1a , dvl1b , dvl3b ) are negligible [29] . By creating targeted mutations , we find that MZdvl2 mutants display most severe CE and craniofacial defects , but with correct AP patterning . In dvl2 and dvl3a double mutants , dvl3a dosage exerts a permissive effect on the loss of dvl2 in CE movements and posterior development . By further targeting the wild-type ( WT ) allele in triallelic mutant embryos to generate mosaic germline transmissible double homozygous adults , we obtained MZdvl2;MZdvl3a offspring . These mutants show correct dorsal fate specification , but display severe CE defects and develop trunk and posterior deficiencies , as well as cyclopia . These observations indicate that Dvl2 and Dvl3a may be not required for maternal Wnt/ß-catenin pathway activation . Instead , Dvl2 plays a major role in Wnt/PCP and zygotic Wnt/ß-catenin signaling . Our findings thus help to better understand the function of Dvl proteins in different patterning and morphogenetic processes .
We used transcription activator-like effector nucleases ( TALENs ) genome-editing approach to generate mutant lines for the five zebrafish dvl genes ( dvl1a , dvl1b , dvl2 , dvl3a , dvl3b ) . All indel mutations led to premature stop codons in the transcripts , and resulted in proteins truncated either at the DIX or the PDZ domain ( S1–S5 Figs ) . Analysis of the phenotypes at different stages indicated that , except for dvl2 , maternal and zygotic mutants for the other dvl genes developed normally , and could survive to adulthood and were fertile ( Fig 1 and S6 Fig ) . Because dvl2 and dvl3a represent the most abundantly expressed maternal transcripts ( S7 Fig ) , we focused our analyses on these two mutant lines . Compared with WT embryos at different stages ( Fig 1A–1C ) , Zdvl2 mutants showed weakly reduced AP axis extension at 11 . 5 hpf ( hours post-fertilization ) , as judged by the degree of the angle between the anterior end and posterior end , with vertex at the geometric center of the embryo ( Fig 1M ) . This defect was largely recovered at 30 hpf ( Fig 1E ) . At 5 dpf ( days post-fertilization ) , Zdvl2 mutants displayed essentially a normal AP axis , except for the presence of a smaller gas-filled swim bladder ( Fig 1F ) . However , only about half of these Zdvl2 mutants could survive to adulthood , and only about two-third of the survived adult female fish could spawn , whereas all male Zdvl2 mutants were not fertile due to the absence of courtship behavior . To obtain MZdvl2 embryos , we crossed female dvl2-/- fish with male dvl2+/- fish . At 11 . 5 hpf , about half of the resulting embryos showed more severe axis extension defect ( Fig 1G and 1M ) . At 30 hpf , they exhibited a shortened AP axis , associated with a reduced yolk extension , which are characteristics of defective CE movements ( Fig 1H ) . At this stage , MZdvl2 mutants also displayed an obvious cyclopic phenotype ( compare insets in Fig 1B and 1H ) . At 5 dpf , although MZdvl2 mutants had a similar length of AP axis as WT embryos , abnormalities in the head region were clearly apparent . These include severe craniofacial defects , and cyclopia or fused eyes ( compare insets in Fig 1C and 1I ) . In particular , pharyngeal arches were not correctly positioned , and eventually protruded outward ( Fig 1I ) . Alcian blue staining of larval head cartilages indicated that , among other abnormalities , the pair of trabeculae was fused and the ethmoid plate was absent ( Fig 1N ) . All these phenotypes are reminiscent of impaired Wnt/PCP signaling and defective extension of axial tissues , which are frequently observed in other Wnt/PCP-specific mutants , such as trilobite/vangl2 , and slb/wnt11 [31–33] . Most strikingly , the protrusion outward of pharyngeal cartilages is much similar as the “bulldog” facial phenotype described in slb/wnt11 mutant [32–34] . A more detailed analysis of these late phenotypes is beyond the scope of this study , but it will be interesting for future work . Due to these defects , MZdvl2 mutant embryos could not survive beyond 5 dpf . Genotyping by allele-specific PCR ( S1 Table and S8 Fig ) of large numbers of severely affected embryos derived from three independent fish pairs confirmed that they were indeed MZdvl2 mutants ( Fig 1O and 1P ) . In contrast to MZdvl2 mutants , the late phenotype of MZdvl3a mutants was indistinguishable from that of WT embryos ( Fig 1J–1L ) , although statistical analysis revealed a weakly reduced AP axis extension at 11 . 5 hpf ( Fig 1M ) . These results show that both maternal and zygotic Dvl2 make an important contribution to CE movements . They also suggest that deficiency of maternal and zygotic Dvl2 or Dvl3a is not sufficient to affect DV and AP patterning . By RT-PCR analysis , we found that maternal dvl2 mutant transcripts were subjected to nonsense-mediated mRNA decay ( NMD ) at cleavage stages , which was further confirmed by in situ hybridization . Maternal dvl3a mutant transcripts also underwent NMD , but to a lesser extent . We then checked whether there was a mutual compensation between dvl2 and dvl3a . No significant change in the level of maternal dvl2 transcripts was found in MZdvl3a mutants , whereas the level of maternal dvl3a transcripts showed a weak decrease in Mdvl2 mutants ( S9 Fig ) . We also attempted to verify if Dvl2 or Dvl3a protein was absent in the mutants by western blotting using available antibodies , but failed to detect any specific signal either in WT or in mutant embryos . Alternatively , to examine whether dvl2 and dvl3a mutant transcripts may be translated by using an alternative ATG or by a stop codon bypass mechanism , we cloned WT and mutant dvl2 and dvl3a coding sequences upstream of myc sequences , and injected the corresponding mRNA ( 100 pg ) into zebrafish embryos . Analysis by western blotting did not detect any signal in embryos injected with these mutant mRNAs ( S10A and S10B Fig ) . This suggests that dvl2 and dvl3a mutant transcripts should not be translated into proteins . To analyze the function of Dvl2 and Dvl3a dosages in AP axis extension and patterning , we first generated double heterozygous dvl2+/-;dvl3a+/- mutants by crosses between female Zdvl2 and male Zdvl3a . Compared to WT embryos ( Fig 2A–2C ) , dvl2+/-;dvl3a+/- mutants showed weak , but significant delay in axis extension at 11 . 5 hpf ( Fig 2D and 2P ) . They were completely normal at 30 hpf and 5 dpf ( Fig 2E and 2F ) , and developed to fertile adults , indicating that Wnt/ß-catenin signaling was not affected , and Wnt/PCP signaling was weakly affected during gastrulation . We then intercrossed dvl2+/-;dvl3a+/- fish to analyze the phenotypes of triallelic mutants . Similar as dvl2+/-;dvl3a+/- mutants , dvl2+/-;Zdvl3a mutants displayed weak axis extension defect at 11 . 5 hpf ( Fig 2G and 2P ) , and slightly shortened AP axis at 30 hpf ( Fig 2H ) . These mutants also recovered to a nearly normal phenotype at 5 dpf ( Fig 2I ) . However , the extent of axis extension delay was more pronounced in Zdvl2;dvl3a+/- mutant embryos at 11 . 5 hpf ( Fig 2J and 2P ) , which further developed a shortened AP axis at 30 hpf ( Fig 2K ) , and a compressed head at 5 dpf ( Fig 2L ) . These embryos , which were grouped as type I , presented a more severely affected phenotype than Zdvl2 mutants and did not survive to adulthood . This suggests that removal of one dvl3a allele could enhance CE defects in Zdvl2 mutants . Since dvl2+/-;Zdvl3a mutants could survive to adulthood and were fertile , the male fish could be used for generating Zdvl2;Zdvl3a double mutants , after crosses with female dvl2+/-;dvl3a+/- fish . We found that double Zdvl2;Zdvl3a mutants displayed more strongly affected axis extension at 11 . 5 hpf ( Fig 2M and 2P ) , and developed a shortened AP axis , with a reduced yolk extension at 30 hpf ( Fig 2N ) . The distance between the two eyes was also reduced ( compare insets in Fig 2B and 2N ) . All these phenotypes are suggestive of impaired CE movements . These embryos , grouped as type II , further developed bent axis and craniofacial defects at 5 dpf ( Fig 2O ) . From three independent fish pairs , we found that the occurrence of the defective axis extension phenotype in the resulting offspring was quite reproducible because similar proportions of type I ( 13% ) and type II ( 15% ) mutant embryos have been obtained ( Fig 2Q and 2R ) , which could be expected from the crosses between dvl2+/-;dvl3a+/- and dvl2+/-;Zdvl3a fish . Since both head and tail were present in zygotic double homozygous mutants , this indicates that half of the maternal Dvl2 and Dvl3a products should be largely sufficient to support AP patterning . Since AP patterning does not seem to be affected in Zdvl2;Zdvl3a mutants , we examined the maternal contribution of Dvl2 and Dvl3a in this process . Intercrosses between dvl2+/-;Zdvl3a triallelic fish could generate three types of mutant offspring , including MZdvl3a , dvl2+/-;MZdvl3a , and Zdvl2;MZdvl3a . Analysis of the phenotypes followed by genotyping indicated that , compared to WT embryos ( Fig 3A–3C ) , dvl2+/-;MZdvl3a mutants displayed mild axis extension defect at 11 . 5 hpf ( Fig 3D ) . At 30 hpf , these embryos were short in length , but AP patterning was not affected because head and tail regions were correctly formed ( Fig 3E ) . At 5 dpf , they completely recovered to a normal phenotype ( Fig 3F ) , and eventually developed to fertile adult . This indicates that , in the absence of Dvl3a , maternal and zygotic Dvl2 product derived from one allele is sufficient for AP patterning , although Wnt/PCP pathway activation is reduced at early stages . Strikingly , Zdvl2;MZdvl3a mutants displayed strong axis extension defect at 11 . 5 hpf ( Fig 3G ) , indicating severely impaired Wnt/PCP signaling . At 30 hpf and 5 dpf , axis extension and AP patterning defects became particularly prominent . These mutants displayed a reduced head size with cyclopia or fused eyes ( Fig 3H and 3I ) . This phenotype is likely caused by perturbed midline development , and is a fish-specific consequence independent of the affected genetic pathway [35] . Cardiac edema was also evident at 5 dpf ( Fig 3I ) . Importantly , Zdvl2;MZdvl3a mutants exhibited a severely reduced body length , with posterior truncation ( Fig 3H and 3I ) . We then intercrossed three independent dvl2+/-;Zdvl3a fish pairs , and found that the appearance of severe axis extension defect associated with posterior deficiency was quite reproducible in the resulting offspring , with an average of 24 . 6% that corresponds to the Mendel inheritance ( Fig 3J ) . Genotyping of these severely affected embryos confirmed that they were indeed Zdvl2;MZdvl3a mutants ( Fig 3K ) . This analysis indicates that , in the MZdvl3a background , the reduction of maternal Dvl2 dosage along with the absence of zygotic Dvl2 products strongly impairs Wnt/PCP-dependent axis extension , and leads to defective posterior patterning . Zdvl2;Zdvl3a double mutants could not survive to adulthood , this prevented us from obtaining maternal and zygotic double mutants to fully address the maternal contribution of Dvl2 and Dvl3a in DV and AP patterning . To circumvent this obstacle , we used a strategy to generate mosaic dvl2-/-;dvl3a-/- fertile adult fish , by disrupting the remaining dvl2 WT allele in dvl2+/-;dvl3a-/- embryos derived from intercrosses between triallelic dvl2+/-;dvl3a-/- fish ( Fig 4A ) . To obtain viable mosaic adult fish , low amounts of the original dvl2 TALENs mRNAs ( 100 pg each ) were injected in these triallelic mutant embryos at 1-cell stage . The resulting adult female fish were crossed with male dvl2+/-;dvl3a-/- fish , and genotyping of the offspring by allelic-specific PCR followed by sequencing was performed to screen female fish carrying a new germline transmissible dvl2 mutant allele ( Fig 4B ) . Those positive female fish were designated as mdvl2+ ( - ) /-;dvl3a-/- , for mosaic dvl2 homozygous genotype . By this approach , if mutations of the remaining dvl2 WT allele occur in some germ cells , MZdvl2;MZdvl3a offspring could be obtained through crosses between female mdvl2+ ( - ) /-;dvl3a-/- fish and male dvl2+/-;dvl3a-/- fish ( S11 Fig ) . Indeed , this strategy has allowed us to generate rather efficiently mdvl2+ ( - ) /-;dvl3a-/- fish . Among a total of 45 female adults tested , 7 female fish produced offspring with a varied proportion of extremely severe phenotype . At 11 . 5 hpf , these embryos displayed most strongly reduced axis extension associated with sharply widened paraxial mesoderm ( Fig 4C–4F ) . At 30 hpf , they exhibited severe trunk and posterior deficiencies , but the head region was still present ( Fig 4G and 4H ) . At 5 dpf , all these embryos developed cyclopia or fused eyes ( S12 Fig ) . Other siblings either presented posterior truncation , which were genotyped as Zdvl2;MZdvl3a mutants ( Fig 4I ) , or developed relatively normally , which were genotyped as dvl2+/-;MZdvl3a triallelic mutants or MZdvl3a mutants . These embryos could be expected from the crosses , since a high proportion of germ cells with one dvl2 WT allele should be still present in mdvl2+ ( - ) /-;dvl3a-/- fish . We then genotyped all the most severely affected embryos to confirm the presence of separate mutations in the dvl2 alleles . As expected , analysis of the sequencing chromatograms revealed that , in addition to the original mutated allele that has a deletion of 5 nucleotides , different new indels were also detected in these embryos ( Fig 4J ) . It is unlikely that the re-injected dvl2 TALENs could target the original mutant allele in dvl2+/-;dvl3a-/- embryos , since we verified that they had no effect in dvl2-/- embryos , and it has been shown the TALEN pair had no gene modification activity when separated by 11 nucleotides or less [36] , which is the case for the original dvl2 mutant allele . Thus , we can conclude that the extremely severe defects were specific to MZdvl2;MZdvl3a mutants , and were caused by the deficiency of maternal and zygotic Dvl2 and Dvl3a products . When the phenotypes of offspring from three independent crosses between a fixed pair of female mdvl2+ ( - ) /-;dvl3a-/- and male dvl2+/-;dvl3a-/- fish were analyzed , we reproducibly obtained most severely affected embryos , although the proportion varied among fish pairs , or between crosses from a fixed fish pair ( Fig 4K ) . Some female mdvl2+ ( - ) /-;dvl3a-/- fish ( #2 , #25 , #39 ) produced a relatively high proportion of MZdvl2;MZdvl3a mutant embryos , ranging from 10% to 24% , depending on the crosses . Thus , by generating mosaic double mutants , we revealed a maternal requirement for Dvl2 and Dvl3a in AP patterning , and in axis extension . An implication of Dvl in activating maternal Wnt/ß-catenin signaling for dorsal fate specification has been unclear in Xenopus [17 , 19 , 20 , 28] , and this issue has not been addressed in zebrafish . Unlike the maternal effect mutant , ichabod , that reduces ß-catenin2 transcripts and results in severe dorsal and anterior deficiencies during early development [37 , 38] , anterior structures such as eyes were present in MZdvl2;MZdvl3a mutants , although they were fused or cyclopic . This implies that dorsal and anterior fate specification should not be disrupted in the absence of maternal and zygotic Dvl2 and Dvl3a products . To further test this possibility , we examined the expression of dorsal mesoderm genes , goosecoid and chordin , and the pan-mesoderm gene , tbxta ( ntla ) , at dome stage by in situ hybridization . Since only a small proportion of MZdvl2;MZdvl3a embryos was present in the offspring , and no phenotype difference could be observed among siblings at early stages , all the offspring from the crosses between female mdvl2+ ( - ) /-;dvl3a-/- and male dvl2+/-;dvl3a-/- fish were collected , and divided into three parts for hybridization with each probe . Following in situ hybridization , the embryos were individually imaged and genotyped by sequencing ( Fig 5A ) . The experiment was performed using three independent fish pairs and the results did not reveal any obvious difference in the expression patterns of goosecoid , chordin , and tbxta between WT embryos and MZdvl2;MZdvl3a mutants ( Fig 5B–5F , 5H–5L and 5N–5R ) . By contrast , in the embryos injected with 2 ng ß-catenin2 morpholino ( ß-cat2MO ) , goosecoid expression was strongly reduced , chordin expression was absent , but tbxta expression was not affected ( Fig 5G , 5M and 5S ) , confirming that maternal ß-catenin2 is required for dorsal fate specification . Together with phenotype analysis , this result suggests that maternal Dvl2 and Dvl3a may be not required for dorsal axis specification in zebrafish . The negative result could be interpreted by the presence of residual amounts of other Dvl proteins . To test this possibility , we injected 2 ng of each dvl1a , dvl1b , and dvl3b morpholino oligonucleotides ( referred to as dvl1a/1b/3bMOs ) in 1-cell stage offspring derived from the crosses between female mdvl2+ ( - ) /-;dvl3a-/- fish and male dvl2+/-;dvl3a-/- fish . In situ hybridization analysis of the expression of dorsal organizer genes goosecoid and chordin was performed at dome stage . The results showed that simultaneous knockdown of dvl1a , dvl1b , and dvl3 in WT or MZdvl2;MZdvl3a embryos did not change the expression of goosecoid and chordin , compared to the embryos injected with 2 . 5 ng control morpholino ( Fig 6A–6D and 6F–6I ) . Consistent with this observation , analysis by immunocytochemistry indicated that the nuclear accumulation of endogenous ß-catenin in dorsal marginal cells was comparable between WT and MZdvl2;MZdvl3a embryos at high stage ( compare Fig 6E and 6J ) . Similarly , injection of dvl1a/1b/3bMOs had no effect on axis extension in WT embryos ( Fig 6K , 6L , 6P , 6Q and 6T ) , and did not aggravate the defective phenotype of MZdvl2;MZdvl3a mutants ( Fig 6M–6O and 6R–6T ) . Moreover , quantitative RT-PCR analysis at different stages , and analysis by RNA sequencing at 12 hpf all showed that there was no upregulation of dvl1a , dvl1b and dvl3b transcripts in MZdvl2;MZdvl3a mutants ( S13 Fig ) . These results suggest that Dvl1a , Dvl1b , and Dvl3b could not compensate for the loss of Dvl2 and Dvl3a in early dorsal fate specification and in axis extension . To further confirm the absence of Dvl activity in MZdvl2;MZdvl3a early embryos , we injected synthetic wnt8a mRNA ( 50 pg ) in 1-cell stage offspring obtained as above . In situ hybridization analysis of goosecoid and chordin ectopic expression clearly showed that overexpression of Wnt8a was able to strongly induce ectopic expression of these genes in WT embryos ( Figs 7A , 7B , 7G and 6H ) . However , reducing Dvl dosage progressively decreased the activity of Wnt8a to activate ectopic organizer gene expression ( Fig 7C , 7D , 7I and 7J ) . In particular , Wnt8a had no effect in Zdvl2;MZdvl3a or MZdvl2;MZdvl3a embryos , which showed similar goosecoid and chordin expression patterns as in WT embryos ( Fig 7E , 7F , 7L and 7M ) . The fact that Wnt8a failed to activate maternal Wnt/ß-catenin signaling leading to ectopic organizer gene expression suggests that Dvl activity should be absent in these mutants . Inhibition of zygotic Wnt/ß-catenin signaling , in particular Wnt8 , is known to cause dorsalization and anteriorization [1 , 3 , 5] . However , this does not seem to occur in MZdvl2;MZdvl3a embryos , since the expression domains of goosecoid and chordin at 60% epiboly did not show obvious lateral expansion , although lateral and ventral expression of axin2 was inhibited ( S14 Fig ) . This suggests that downregulation of Wnt8 and Dvl activity exerts distinct effects to restrict the organizer domain . To analyze how AP patterning is affected in MZdvl2;MZdvl3a mutants , we first performed in situ hybridization using two well characterized posterior markers , sp5l ( spr2 ) and tbx16l ( tbx6l ) , which mediate zygotic Wnt/ß-catenin signaling in posterior patterning [39–41] . At 12 hpf , the phenotypes specific to Zdvl2;MZdvl3a or MZdvl2;MZdvl3a mutants were easily discernible , which allowed us to select sufficient mutant embryos from different crosses . Confirmed by genotyping after in situ hybridization , Zdvl2;MZdvl3a mutants showed no obvious or only weak alternation in the expression of sp5l ( Fig 8A , 8A’ , 8C and 8C’ ) , but they displayed a markedly reduced expression of tbx16l ( Fig 8B , 8B’ , 8D and 8D’ ) . In MZdvl2;MZdvl3a mutants , however , the expression of sp5l was strongly decreased ( Fig 8E and 8E’ ) , and the expression of tbx16l was reduced to residual level ( Fig 8F and 8F’ ) . Consistently , TOPFlash luciferase assay revealed that there was approximately a 30% decrease of Wnt/ß-catenin transcriptional activity in Zdvl2;MZdvl3a mutants at 12 hpf , and about a 75% decrease in MZdvl2;MZdvl3a mutants ( Fig 8G ) . This decrease of reporter activity correlated well with a reduction of endogenous ß-catenin nuclear accumulation in ventral marginal cells at shield stage , when zygotic transcription has already started , as assayed by immunofluorescence staining ( Fig 8H and 8I ) . These observations strongly suggest that maternal Dvl2 and Dv3a play an important role in the activation of zygotic Wnt/ß-catenin signaling , and that Dvl2 may exert a predominant role . We further examined the expression of a panel of AP genes in MZdvl2;MZdvl3a mutants at 12 hpf by in situ hybridization . This indicated that the expression of otx1 ( forebrain and midbrain ) , otx2 ( forebrain , midbrain and midbrain-hindbrain boundary ) , and pax2a ( midbrain-hindbrain boundary , otic placode and pronepheric mesoderm ) was reduced , and widened mediolaterally due to impaired axis extension ( Fig 8J–8L ) . The expression of egr2b ( krox20 ) in rhombomeres 3 and 5 , and hoxb1b in the notochord and paraxial mesoderm was compressed and widened ( Fig 8M and 8N ) . Notably , the expression of hoxb1b in the tailbud was absent ( Fig 8N ) . The expression of axin2 in the neural plate , and of cdx4 in the posterior paraxial mesoderm was strongly inhibited ( Fig 8O and 8P ) . Analysis by RNA sequencing confirmed that axin2 , tbx16l , sp5l and cdx4 , which are zygotic Wnt/ß-catenin target genes in AP patterning [3 , 39–41] , as well as hoxb1b , were all downregulated ( Fig 8Q–8U ) . These indicate that AP patterning , and in particular posterior development , are strongly affected in MZdvl2;MZdvl3a mutants . To determine whether AP patterning defect in MZdvl2;MZdvl3a mutants was due to a decreased zygotic Wnt/ß-catenin signaling , offspring at 5 hpf derived from the crosses between female mdvl2+ ( - ) /-;dvl3a-/- fish and male dvl2+/-;dvl3a-/- fish were treated with LiCl , which activates Wnt/ß-catenin signaling downstream of Dvl . In situ hybridization was performed to examine the expression of axin2 and tbx16l at 12 hpf . Following genotyping , we found that MZdvl2;MZdvl3a mutants treated with LiCl displayed increased expression of axin2 and tbx16l , compared to untreated mutants ( Fig 8V and 8W ) . Injection of a low dose of synthetic mRNA ( 50 pg ) encoding the constitutively active ß-catenin into 1-cell stage embryos also rescued the expression of axin2 and tbx16l , but to a lesser extent ( S15A–S15F Fig ) , likely due to the mosaic distribution of injected mRNA . However , phenotypic examination indicated that this injection could effectively rescue tail development in Zdvl2;MZdvl3a mutants ( S15G–S15I Fig ) . These observations further demonstrate that Dvl2 and Dvl3a deficiency causes defective AP patterning by preventing zygotic Wnt/ß-catenin signaling . The most severely impaired axis elongation in MZdvl2;MZdvl3a mutants clearly suggests an important maternal contribution of Dvl2 and Dvl3a for CE movements . To further clarify this aspect and to determine Dvl dosages in Wnt/PCP signaling , we first compared the extent of axis extension defect between different mutants by phenotype analysis , and by simultaneous in situ hybridization using tbxta as a marker of the notochord , dlx3 as a marker of the neural plate borders , and ctslb ( hgg1 ) as a marker of the prechordal plate mesoderm . At 11 . 5 hpf , Zdvl3a , Zdvl2 or MZdvl3a mutants displayed weak , but obvious delay in neural plate convergence and notochord elongation ( Fig 9A–9D , 9I–9L and 9Q ) . However , MZdvl2 mutants showed more severe defect ( Fig 9E , 9M and 9Q ) . Thus , it is clear that either MZdvl2 or MZdvl3a mutants present more severely affected CE phenotypes than the respective zygotic mutants . The same situation was also observed in double mutants . MZdvl2;MZdvl3a mutants displayed most severely impaired neural plate convergence and axis extension , compared with Zdvl2;Zdvl3a and Zdvl2;MZdvl3a mutants ( Fig 9F–9H and 9N–9Q ) , indicating that maternal Dvl dosage is important in Wnt/PCP signaling . Dvl was shown to regulate cytoskeletal architecture and cell polarity upstream of Rac and Jun N-terminal kinase ( JNK ) both in Xenopus and zebrafish [42 , 43] . When the AP1 luciferase reporter was used to monitor JNK activation [44] , we found a 40% decrease of AP1 reporter activity in Zdvl2;MZdvl3a mutants , and a 60% decrease in MZdvl2;MZdvl3a mutants ( Fig 9R ) . Consistent with this result , analysis of cell polarity indicated that WT midline cells at 12 hpf were elongated mediolaterally , while MZdvl2;MZdvl3a mutant cells were rounded with a strongly reduced length to width ratio ( Fig 9S and 9T ) , which was much similar as dvl2 and dvl3a knockdown cells [43] . However , injection of synthetic mRNA ( 200 pg ) encoding a constitutively active JNK , along with synthetic mRNAs encoding Histone2B and membrane GFP in one blastomere at 64-cell stage , significantly rescued mediolateral elongation of fluorescently-labeled descendent MZdvl2;MZdvl3a mutant cells ( Fig 9S and 9T ) . This suggests that deficiency of Dvl2 and Dvl3a affects cell polarity by preventing at least partially JNK activation . Furthermore , we performed live time-lapse analysis of cell movement behaviors in Zdvl2;MZdvl3a and MZdvl2;MZdvl3a mutants , in comparison with the PCP mutant trilobite/vangl2 [45] . Since the AP axis was strongly shortened in these mutants , and the notochord became severely irregular , we examined the dorsal convergence behaviors of lateral cells . In WT embryos , these cells moved toward the notochord with regular trajectories ( Fig 9U and S1 Movie ) . In zygotic trilobite/vang2 mutants , lateral cells displaced along irregular trajectories and moved in more posterior direction ( Fig 9V and S2 Movie ) . Lateral cells in Zdvl2;MZdvl3a mutants moved similarly as in zygotic trilobite/vang2 mutants ( Fig 9W and S3 Movie ) . Strikingly , the directional movement of lateral cells in MZdvl2;MZdvl3a mutants was most severely affected . These cells moved along zigzagging trajectories , and displayed forward and back movements ( Fig 9X and S4 Movie ) , with a more strong increase in the ratio of trajectory distance relative to net distance toward the notochord ( Fig 9Y ) . These analyses strongly indicate an important maternal contribution of Dvl2 and Dv3a in Wnt/PCP signaling and CE movements . The fact that lateral cells in these mutants tend to move in more vegetal direction is likely due to an impaired AP patterning . Thus , it could not be excluded that the severely affected CE movements may result from the combined effects of defective Wnt/PCP and zygotic Wnt/ß-catenin signaling . We further demonstrated the maternal contribution of Dvl2 and Dvl3a in axis extension using maternal double mutants . By crossing female mdvl2+ ( - ) /-;dvl3a-/- fish with male WT fish , the resulting double heterozygous embryos that carry a new mutation in one dvl2 allele are maternal double mutants because of the absence of maternal Dvl2 and Dvl3a products ( see S11 Fig ) . These Mdvl2;Mdvl3a mutants displayed strongly impaired axis extension at 11 . 5 hpf . However , they only presented a slightly shortened AP axis at 30 hpf ( S16A–S16D and S16I Fig ) , suggesting that zygotic Dvl2 and Dvl3a can rescue axis extension at late stages . By contrast , Mdvl3a mutants or dvl2+/-;Mdvl3a mutants ( lacking half of the maternal Dvl2 products ) , obtained from the crosses between female dvl2+/-;dvl3a-/- fish and male WT fish , were either normal or displayed weak axis extension defect at 11 . 5 hpf , and were completely normal at 30 hpf ( S16E–S16I Fig ) . These observations indicate a predominant contribution of maternal Dvl2 to embryonic axis elongation , and Dvl3a exerts a permissive effect . Altogether , our results show that both maternal and zygotic Dvl2 and Dvl3a cooperate to orchestrate CE movements and AP patterning .
Dvl proteins play key roles in both Wnt/ß-catenin and Wnt/PCP signaling pathways , and they are highly conserved and broadly expressed during early development in all vertebrates . However , many aspects of their involvement in early developmental processes remain elusive and enigmatic . In this study , we resolved some of the unanswered issues through comprehensive mutational analyses . Our results demonstrate that the two most abundantly expressed Dvl proteins , Dvl2 and Dv3a , are not required for early dorsal fate specification , which is dependent on the activation of maternal Wnt/ß-catenin signaling . Instead , maternal and zygotic Dvl2 and Dvl3a , in particular Dvl2 , are important in CE movements , which are regulated by Wnt/PCP signaling , and in AP patterning . These findings help to clarify the implication of Dvl proteins in Wnt-regulated developmental events , and provide insight into the mechanisms underlying embryonic axis formation . The early development in many vertebrates and invertebrates is supported by maternal products accumulated during oogenesis , zygotic transcription does not occur until the start of mid-blastula transition [1–5] . Inactivation of key genes implicated in early developmental processes frequently leads to embryonic lethality , or unproductive adults , preventing the analysis of maternal gene function . The situation becomes more complex when multiple gene paralogs are expressed . This is particularly true for functional analyses of Dvl paralogs during early development . Before this work , no dvl mutant has been reported in zebrafish , and the maternal and zygotic functions of Dvl proteins are not clear . We have used TALENs genome-editing technology to generate single mutants for all five zebrafish dvl paralogs , as well as a panel of dvl2 and dvl3a triallelic and double homozygous mutants , and examined the maternal and zygotic contributions of Dvl2 and Dvl3a in embryonic patterning and morphogenetic movements . These mutants represent a valuable resource for the study of important developmental processes , which are dependent on the activation of the Wnt pathways . A significant finding is the absence of implication for maternal Dvl2 and Dvl3a in early dorsal fate specification . This suggests that they are not required for the activation of maternal Wnt/ß-catenin signaling . Indeed , the expression of the dorsal organizer genes , goosecoid and chordin , is not affected in these mutants , whereas it is strongly decreased in ß-catenin2 morphants . Moreover , the phenotypes of MZdvl2;MZdvl3a or Mdvl2;Mdvl3a mutants completely differ from those of zebrafish ichabod mutants , which display an absence of anterior structures caused by a reduced ß-catenin2 activity [37 , 38] . This further argues against a requirement of Dvl2 and Dvl3a in dorsal axis formation . Our results from mutational analyses are consistent with the observations showing that simultaneous knockdown of dvl2 and dvl3a in zebrafish does not apparently affect head formation [43 , 46 , 47] , but mostly affects AP axis elongation and tail development [43] . They are supported by functional studies in Xenopus , which show that depletion of maternally expressed Dvl2 and Dvl3 from oocytes also has no effect on dorsal fate specification [19] . There is a possibility that these negative outcomes could be due to the inefficiency of the approaches to inhibit endogenous Dvl function [20] . However , our genetic evidence now suggests that the activation of maternal Wnt/ß-catenin signaling is independent of Dvl2 and Dvl3a . Although it was extremely difficult to assay maternal Wnt/ß-catenin activity in MZdvl2;MZdvl3a mutants at blastula stages , the correct expression of dorsal organizer genes is suggestive of an unaffected maternal Wnt/ß-catenin signaling . Zebrafish genome contains at least five dvl genes , but transcriptomic analysis has revealed that dvl2 and dvl3a represent approximately 98% of total dvl transcripts from fertilization until before the end of gastrulation [29] , indicating that they are the major dvl genes expressed in the early embryo . It is unlikely that the loss-of-function of Dvl2 and Dvl3a could be compensated by other Dvl proteins , since they are expressed at an extremely low level , and their maternal and zygotic mutants do not result in any discernable phenotype at all stages examined . Moreover , our quantitative analyses indicate that the maternal and zygotic expression of dvl1a , dvl1b and dvl3b is not upregulated in MZdvl2;MZdvl3a mutants , and that simultaneous knockdown of these genes has no effect . A recent study indicates that Wnt/ß-catenin signaling is not affected in mouse ependymal cells lacking 5 of the 6 Dvl alleles [48] , there is thus a possibility that trace amounts of Dvl protein could be sufficient for dorsal fate specification . However , several studies in zebrafish suggest a dose-dependent function of maternal Wnt/ß-catenin activity in organizer formation . In ichabod embryos with reduced ß-catenin2 level , dorsal and anterior deficiencies occur with variable expressivity [37] , and knockdown of ß-catenin2 increases the severity of ichabod phenotypes [38] . By contrast , MZdvl2;MZdvl3a embryos displayed correct organizer gene expression at the onset of zygotic transcription , suggesting that maternal Wnt/ß-catenin signaling should not be affected by the deficiency of Dvl activity . Thus , our present results support the model that early dorsal axis formation is a consequence of dorsal accumulation of ß-catenin caused by asymmetrical translocation of vegetally localized dorsal determinants [1–5] . They suggest that Dvls may be dispensable for the activation of maternal Wnt/ß-catenin signaling in dorsal fate specification . Nevertheless , there may be a possibility that maternal Wnt/ß-catenin signaling is activated by other mechanisms that are independent of maternal Wnt ligand/receptor signaling . In Xenopus , Wnt11 was shown to be required for the activation of maternal Wnt/ß-catenin signaling in dorsal axis formation [49] . In zebrafish , vegetally localized maternal wnt8a is transported to the dorsal region and has been thought to play a role in specifying dorsal fate [50] . In this regard , it would be interesting to analyze the maternal effect following removal of the bicistronic wnt8 locus [51] . While this work was in progress , it was reported that maternal mutants for the two wnt8a open reading frames did not show axis formation defect [52] . Thus , our present results are consistent with this observation , and in particular , the trunk and posterior deficiencies in MZwnt8a mutants are much similar as those observed in our MZdvl2;MZdvl3a mutants . However , it is worth to mention that MZwnt8a mutants show dorsalized phenotype [52] , whereas MZdvl2;MZdvl3a mutants exhibit CE defects with strongly reduced ventroposterior gene expression , without obvious dorsalizing effect . There are at least two credible explanations that may account for these differences . First , zygotic Wnt8a only activates Wnt/ß-catenin signaling and participates in ventral and posterior tissue formation , but not in CE movements . Second , extracellular Wnt8a ligand should also function to antagonize organizer-secreted Wnt inhibitors during gastrulation , and its absence leads to an expansion of organizer activity [5] . However , the absence of maternal and zygotic Dvl2 and Dvl3a should not disturb this functional antagonism , thus keeping early dorsal fate unaffected . Also as a consequence , MZdvl2;MZdvl3a mutants do not show anteriorization at late stages . It is well established that Dvl plays a critical role in mediating the activation of Wnt/PCP signaling in CE movements during gastrulation [17] . Several studies in mice suggest that functional redundancy exists between Dvl proteins , and that the Wnt/PCP pathway during neurulation is more readily affected following reduction of Dvl dosage [25–27] . However , it is still unclear how Dvl dosage influences CE movements during gastrulation , and how Dvl proteins functionally interact in these processes in zebrafish . Our analyses by using single and double mutants clearly indicate that Dvl2 plays a predominant role in embryonic axis extension . There is only a partial functional redundancy between Dvl2 and Dvl3a , because MZdvl2 mutants display obvious CE defects , whereas MZdvl3a mutants are phenotypically normal . Nevertheless , reducing Dvl3a dosage in zygotic dvl2 mutants sensibly aggravates the defective CE phenotypes , indicating that Dvl3a exerts a permissive effect on Dvl2 in Wnt/PCP signaling . In mice , Dvl2-/-;Dvl3+/- triallelic mutants exhibit more severely shortened AP axis than Dvl3-/- or Dvl2+/-;Dvl3-/- mutants [26] . Taken together , our present results suggest that Dvl2 plays an important role in Wnt/PCP signaling during CE movements , which may be conserved in vertebrates . Interestingly , mutational analyses indicate that the absence of zygotic Dvl2 and Dvl3a function only results in moderate CE defects . However , removal of both maternal and zygotic Dvl2 and Dvl3a produces the most severely affected CE phenotypes , indicating clearly an important maternal contribution of these proteins in Wnt/PCP-mediated CE movements . This is further confirmed by analyzing the phenotypes of maternal dvl2 and dvl3a double mutants , which show severe axis extension defect during gastrulation . Thus , by comparison of the extent of axis extension defect between Zdvl2;Zdvl3a , Mdvl2;Mdvl3a , and MZdvl2;MZdvl3a at different stages , it can be concluded that , to a large extent , maternal Dvl2 and Dvl3a may be sufficient to activate Wnt/PCP signaling in CE movements during gastrulation , whereas zygotic Dvl2 and Dvl3a are implicated to a lesser extent . Consistently , MZdvl2;MZdvl3a mutants display strongly reduced ability to activate the AP1 reporter , which monitors JNK activation [44] , and the disrupted cell polarity can be rescued by a constitutively active JNK . However , the activity of the AP1 reporter was not completely blocked in MZdvl2;MZdvl3a mutants at late gastrula stages . This may be due to an independent activation of JNK signaling by other proteins such as the paraxial protocadherin that regulates morphogenesis and signals through the small GTPases RhoA and Rac1 to JNK [53 , 54] . Altogether , our analyses indicate that Wnt/PCP-mediated CE movements are particularly sensitive to both maternal and zygotic Dvl dosages . Another striking observation is the requirement for maternal Dvl function in AP patterning that is dependent on zygotic Wnt/ß-catenin signaling to activate region-specific gene expression . It is well established that an endogenous Wnt/ß-catenin signaling gradient , with highest activity in the posterior region , is important for AP patterning [3 , 5] . At present , there is only limited evidence implicating Dvl in AP axis specification . In Xenopus , overexpression study shows that graded amounts of Dvl elicit AP fates in the prospective ectoderm [55] , suggesting that Dvl dosage may be important to differentially activate zygotic gene expression along the AP axis . This is supported by the present observation showing an implication of Dvl2 and Dvl3a in this process in a dosage-dependent manner . We find that progressive reduction of Dvl dosage gradually elicits AP patterning defect , ranging from posterior deficiency to a complete lack of trunk and tail . The maternal contribution of Dvl2 and Dvl3a is clearly evident . While Zdvl2;Zdvl3a mutants display a relatively normal AP axis , Zdvl2;MZdvl3a mutants begin to show caudal truncation . The most severe defect of AP patterning in MZdvl2;MZdvl3a mutants is clearly caused by a strongly impaired Wnt/ß-catenin signaling , which results in a severely decreased expression of target genes . Maternal and zygotic Wnt/ß-catenin signaling has opposite functions in the specification of embryonic axes . While maternal Wnt/ß-catenin signaling specifies dorsal fate , zygotic Wnt/ß-catenin signaling induces ventroposterior mesoderm and inhibits anterior development [5] . This apparently contradicts with the requirement of maternal Dvl in AP axis specification , but it could be explained by the region-specific expression and regulation of other components of the Wnt/ß-catenin pathway . Following zygotic gene activation , the ventral region expresses several Wnt ligands . Thus , maternal Dvl proteins may serve to relay extracellular Wnt signals for region-specific activation of the pathway . Another possibility is that maternal Dvl may play a role in the specification of AP cell fates in the prospective ectoderm , as observed in Xenopus embryo [19 , 55] . In overexpression experiments , high levels of Xdsh ( Dvl2 ) activate posterior neural markers , whereas low levels induce the expression of anterior neural genes . Accordingly , in our zebrafish dvl mutants , deficiency of posterior and trunk tissues could be obtained by substantially reducing maternal Dvl dosage . In the present study , we have revealed a predominant role for Dvl2 dosage both in CE movements and in AP patterning , however , it is clear that Dvl3a also cooperates with Dvl2 in these processes . Our results are consistent with previous studies indicating that Dvl proteins differentially activate the Wnt pathways and regulate distinct developmental processes . Indeed , the three mammalian DVL proteins differentially mediate the activation of Wnt/ß-catenin signaling in cultured cells [56] . In Xenopus , knockdown of dvl1 and dvl2 causes severe neural crest defects , while knockdown of dvl3 affects muscle gene expression and sclerotome development [21] . In this regard , it is of interest to note that MZdvl2 mutants develop craniofacial defects that at least partially result from fusion or absence of neural crest-derived cartilages . In addition , the heart abnormality in dvl2 and dvl3a double mutants is consistent with previous observations showing that Dvl2 mutant mice display defects in cardiac neural crest development [25] . At present , it is still intriguing that why Dvl2 plays a major role both in Wnt/ß-catenin and Wnt/PCP signaling , and how it distinguishes these two pathways ? Our previous structural and functional analyses have provided some clues as to how Dvl2 activity in Wnt/PCP signaling is regulated by its C-terminus [57 , 58] . The present observations further indicate that the activaty of Wnt/ß-catenin or Wnt/PCP signaling during development may be regulated by Dvl dosages . In summary , our findings have uncovered , to a significant extent , the manner in which these Dvl proteins are implicated in regulating the activation of different Wnt signaling pathways . In particular , we clarified that they are not required for dorsal fate specification , and demonstrated that maternal and zygotic Dvl2 dosages , in cooperation with Dvl3a , play a predominant role in regulating important zygotic events , such as AP patterning and morphogenetic movements .
All experiments using zebrafish adults and embryos were performed according to the ARRIVE guidelines and approved by the Ethics Committee for Animal Research of Life Science of Shandong University ( permit number SYDWLL-2018-05 ) . Zebrafish adult of the AB strain were maintained at 28 . 5°C . The embryos were staged as described [59] , and for most experiments , were injected at 1-cell stage in the yolk using a PLI-100A Picoliter microinjector ( Harvard Apparatus ) . TALENs were assembled through Golden Gate Assembly [60] , using the Golden Gate TALEN and TAL Effector Kit ( cat#1000000016 ) from Addgene . TALEN repeat variable di-residues targeting sequences were cloned into modified pCS2+KKR and pCS2+ELD vectors [61] . Zebrafish wnt8a coding sequence was PCR-amplified and cloned in pCS2 vector . WT and mutant dvl2 and dvl3a coding sequences were cloned in pCS2MT vector such that the proteins are C-terminally myc-tagged . Constructs for Histone2B-RFP , mGFP , JNKK2-JNK1 ( encoding a constitutively active Jun kinase ) and ΔN-ß-catenin ( encoding a constitutively active ß-catenin ) have been previously described [43 , 62–64] . Capped mRNAs were synthesized from linearized plasmids by in vitro transcription using appropriate RNA polymerases . Translation-blocking morpholino antisense oligonucleotides against ß-catenin2 [38] , dvl1a ( 5′-AATCATTGACAGAAGAAGGAGCAAG-3′ ) , dvl1b ( 5′-GGTATATGATTTTAGTCTCCGCCAT-3′ ) , dvl3b ( 5′-TCTCCCTTCAGACAGCGACAATAAC-3′ ) , and standard control morpholino ( 5′-CCTCTTACCTCAGTTACAATTTATA-3′ ) were synthesized by Gene Tools , and suspended in sterile water . The two TALEN mRNAs were mixed at equal amounts ( 200 pg each ) and injected into 1-cell stage embryos . The targeting efficiency was determined by Sanger sequencing of PCR products amplified from genomic DNA extracted from 15 randomly selected F0 embryos at 24 hpf . When the result indicates a positive targeting effect , other embryos were reared to adulthood for outcross to screen F1 heterozygotes using genomic DNA extracted from the tail fin . To generate MZdvl2;MZdvl3a mutant lines , we first used germline replacement approach by transplanting blastoderm cells from Zdvl2 donors at dome stage into dvl2+/-;dvl3a-/- hosts . Since this only generated 21 male chimera fish , we next used a strategy to target the remaining dvl2 WT allele in dvl2+/-;dvl3a-/- embryos . The offspring obtained from crosses between dvl2+/-;dvl3a-/- carriers were injected at 1-cell stage with dvl2 TALEN mRNAs ( 100 pg each ) and raised to adulthood . Due to the mosaic distribution of TALEN mRNAs and incomplete targeting efficiency , mosaic fish with mixed dvl2+/-;dvl3a-/- and dvl2-/-;dvl3a-/- genotypes could be obtained , and denoted as mdvl2+ ( - ) /-;dvl3a-/- , for mosaic homozygous dvl2 mutations . Female mdvl2+ ( - ) /-;dvl3a-/- fish were then crossed with male dvl2+/-;dvl3a-/- , and the resulting offspring were screened by PCR using allele-specific primers ( S1 Table ) , followed by sequencing to detect de novo mutations in the dvl2 allele . The offspring that contained a new indel along with the original indel were MZdvl2;MZdvl3a mutants , and the parental fish were selected for further experiments . To obtain Mdvl2;Mdvl3a mutants , female mdvl2+ ( - ) /-;dvl3a-/- fish were crossed with male WT fish . The resulting dvl2 and dvl3a heterozygous offspring that carry either the original indel or a new indel in one dvl2 allele were devoid of maternal Dvl2 and Dvl3a gene products . Whole-mount in situ hybridization was performed as previously described [65] . The constructs for goosecoid , chordin , tbxta , dlx3 , ctslb , otx2 , pax2a and egr2b were reported previously [65 , 66] , and otx1 , axin2 , hoxb1b , cdx4 , sp5l , tbx16l and dvl2 constructs were generated by cloning PCR framents in pZeroBack/Blunt Vector ( Tiangen ) . They were labeled using digoxigenin-11-UTP ( Roche Diagnostics ) . Staining of embryos simultaneously hybridized with tbxta , dlx3 , and ctslb probes was performed using NBT/BCIP and Fast Red as substrates ( Roche Diagnostics ) , respectively . For immunostaining , the embryos were fixed in 4% paraformaldehyde at 4°C overnight , and washed with PBST ( PBS , 0 . 1% Triton X-10 ) , they were then incubated in mouse monoclonal anti-ß-catenin antibody ( 1/250 , Sigma-Aldrich , C7207 ) at 4°C overnight . After several washes in PBST , embryos at high stage were incubated with horseradish peroxidase conjugated secondary antibody ( 1/500 , INTERCHIM ) , followed by incubation in diaminobenzidine substrate , and embryos at shield stage were incubated with Alexa-488 conjugated secondary antibody ( 1/1000 , INTERCHIM ) , followed by confocal microscopic imaging ( Zeiss , LSM700 ) . Larvae at 5 dpf were fixed in 4% paraformaldehyde at 4°C overnight , then washed twice in PBS for 10 minutes . The larvae were incubated in alcian blue solution ( 0 . 37% HCl , 70% ethanol , 0 . 1% alcian blue ) for 6 hours , and washed in destaining solution ( 1% HCl , 70% ethanol ) . Following dehydration in ethanol , the larvae were cleared in benzyl benzoate , and imaged using an upright microscope ( Leica DM2500 ) . Embryos at 90% epiboly stage were mounted in a cavity microscope slide in 1% low-melting agarose as described [43] . Cell movements were recorded using an upright light microscope ( Leica , LM2500 ) equipped with a CCD digital camera ( Leica , IC180 ) , under differential interference contrast . The embryos were imaged every 30 seconds for a period of 60 minutes , and mutant embryos were then subjected to genotyping by Sanger sequencing . Time-lapse movies were generated using ImageJ software ( NIH Image ) . At 64-cell stage , a single marginal cell was injected with a mixture of Histone2B-RFP ( 50 pg ) and mGFP ( 100 pg ) mRNAs , with or without caJNK mRNA ( 200 pg ) . At 12 hpf , mosaically labeled embryos were dechorionated and placed on a microscope slide in a drop of Ringer’s solution . The yolk was removed , and the embryos were flat mounted with neuroectoderm facing upward . Following image acquisition using an upright fluorescence microscope ( Leica LM2500 ) , the embryos were subjected to genotyping . Total RNA was reverse transcribed using M-MLV reverse transcriptase ( Invitrogen ) . Semi-quantitative PCR was performed using gene-specific primers , with ß-actin as a loading control ( S1 Table ) . The intensity of PCR products was analyzed using the Lane 1D software ( Sagecreation ) . Quantitative PCR was performed using Quant qRT-PCR Kit ( Tiangen ) with gene-specific primers ( S1 Table ) . RNA sequencing was performed on Illumina HiSeq 2000 , using 12 hpf mRNA libraries constructed by TruSeq RNA Library Preparation Kit . The data were aligned and analyzed as described [65] . Zebrafish embryos were lysed in ice-cold lysis buffer ( 100 mM NaCl , 10 mM Tris-HCl , pH 7 . 5 , 5 mM EDTA , 1% Triton X-100 ) containing 1 x protease inhibitor cocktail ( Sigma-Aldrich ) . The samples were separated by polyacrylamide gel electrophoresis , transferred to nitrocellulose membrane , probed with anti-myc ( 1/1000 , Santa Cruz Biotechnology ) and anti-α-tubulin ( 1/1000 , GeneTex , GTX124303 ) antibodies , and detected using the Western-Lightning Plus-ECL substrate ( PerkinElmer ) . Single embryo was placed in an Ependorf tube containing 40 μl of lysis buffer ( 10 mM Tris-HCl , pH 8 . 0 , 2 mM EDTA , 0 . 2% Triton X-100 , 100 μg/ml proteinase K ) , and homogenized by pipetting . The tube was heated at 50°C for 2 hours , then at 94°C for 10 minutes . After a brief centrifugation , 1 μl of the solution was used for PCR reaction . WT and mutant embryos at 1-cell stage were injected with 50 pg TOPFlash or AP1 reporter DNA , along with 5 pg pRL-TK DNA as an internal control . Fifteen to twenty embryos at 12 hpf were manually dechorionated and lysed in 60 μl lysis buffer ( Promega ) . The lysate was clarified by centrifugation and luciferase activities were measured using the Dual-Luciferase® Reporter Assay System ( Promega ) , according to the manufacturer’s instruction . The values were normalized with respect to Renilla luciferase activities , and the value in control condition was set as 1 , and expressed as relative luciferase activity . All data were obtained from at least three independent experiments , and analyzed using paired Student’s t test . | The embryogenesis of most animals is first supported by maternal gene products accumulated in the oocyte , and then by the expression of genes from the zygote . In all vertebrates , there are at least three Dishevelled ( Dvl ) proteins , which play critical roles in normal development and human diseases . They are both maternally and zygotically expressed , and can activate the ß-catenin-dependent Wnt pathway that regulates gene expression and cell fate , and the ß-catenin-independent Wnt pathway that orchestrates cell movements . In zebrafish embryo , Dvl2 and Dvl3a are most abundant , but their functions are not fully understood . We find that maternally and zygotically expressed Dvl2 plays a predominant role in the elongation of the anteroposterior axis , and the expression of genes involved in the development of the posterior region . Dvl3a cooperates with Dvl2 in these processes . Analyses after loss-of-function of these genes indicate that deficiency of maternal and zygotic Dvl2 and Dvl3a results in embryos with cyclopia , craniofacial defects , and severe abnormality in the trunk and posterior regions . Many human birth defects and other diseases , like cancer , are attributed to the dysfunction of the Wnt pathways . Our results help to understand the mechanisms of Dvl-mediated Wnt pathway activation , and the causes of developmental disorders . | [
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| 2018 | Mutational analysis of dishevelled genes in zebrafish reveals distinct functions in embryonic patterning and gastrulation cell movements |
A cascade of alternative sigma factors directs developmental gene expression during spore formation by the bacterium Bacillus subtilis . As the spore develops , a tightly regulated switch occurs in which the early-acting sigma factor σF is replaced by the late-acting sigma factor σG . The gene encoding σG ( sigG ) is transcribed by σF and by σG itself in an autoregulatory loop; yet σG activity is not detected until σF-dependent gene expression is complete . This separation in σF and σG activities has been suggested to be due at least in part to a poorly understood intercellular checkpoint pathway that delays sigG expression by σF . Here we report the results of a careful examination of sigG expression during sporulation . Unexpectedly , our findings argue against the existence of a regulatory mechanism to delay sigG transcription by σF and instead support a model in which sigG is transcribed by σF with normal timing , but at levels that are very low . This low-level expression of sigG is the consequence of several intrinsic features of the sigG regulatory and coding sequence—promoter spacing , secondary structure potential of the mRNA , and start codon identity—that dampen its transcription and translation . Especially notable is the presence of a conserved hairpin in the 5’ leader sequence of the sigG mRNA that occludes the ribosome-binding site , reducing translation by up to 4-fold . Finally , we demonstrate that misexpression of sigG from regulatory and coding sequences lacking these features triggers premature σG activity in the forespore during sporulation , as well as inappropriate σG activity during vegetative growth . Altogether , these data indicate that transcription and translation of the sigG gene is tuned to prevent vegetative expression of σG and to ensure the precise timing of the switch from σF to σG in the developing spore .
Cells across all domains of life alter their phenotypes through global changes in gene expression . In bacteria , global changes in gene expression drive phenotypic changes critical for growth , development , and pathogenesis . For example , the ability of the human pathogen Chlamydia trachomatis to progress through its infectious cycle requires sequential transitions between three stage-specific networks of gene regulation [1] . Here , we study a switch in gene expression that occurs during the developmental process of spore formation by the soil bacterium Bacillus subtilis , a premier model system for studies of regulation [2 , 3] . At the onset of B . subtilis sporulation , which is triggered by nutrient depletion , the rod shaped bacterial cell divides asymmetrically , resulting in two daughter cells of unequal size and fate . The smaller “forespore” becomes the mature , dormant spore , while the larger “mother cell” aids the development of the forespore but ultimately dies . Initially these two cells lie side-by-side; subsequently , the mother cell membranes migrate around the forespore in a process called engulfment , resulting in the forespore being pinched off as a free protoplast within the mother cell cytoplasm ( Fig 1A ) . After engulfment , the forespore is encased in a protective peptidoglycan cortex and protein coat , and is then released into the environment as a mature spore upon lysis of the mother cell . The morphological events of sporulation are orchestrated by a complex gene regulatory network that coordinates the expression of hundreds of sporulation genes at the right time and in the right cell [3 , 4] . This gene regulatory network operates primarily to ensure the sequential and compartment-specific appearance of four sporulation sigma ( σ ) factors—σF , σE , σG , and σK—that bind and impart promoter specificity upon core RNA polymerase ( RNAP ) . Early in sporulation , following asymmetric division , σF and σE direct gene expression in the forespore and mother cell , respectively . Later , after the completion of engulfment , σG takes over for σF in the forespore and σK replaces σE in the mother cell ( Fig 1A ) . These two transitions , from σF- to σG-directed gene expression in the forespore and from σE- to σK-directed gene expression in the mother cell , are tightly regulated such that no temporal overlap between the activities of the early and late sigma factors can be detected [5] . However , the molecular mechanisms that control these global transitions in gene expression with such precision are not well understood . In this study , we sought to identify molecular mechanisms that help to orchestrate the switch from σF to σG in the B . subtilis forespore ( Fig 1A and 1B ) . The current model for the σF-to-σG switch , summarized here , is based on literature spanning several decades . To begin , σF is activated in the forespore soon after asymmetric cell division via a complex , but relatively well-characterized regulatory circuit [6] . In turn , σF directs the transcription of genes required for early forespore development as well as the gene sigG ( previously spoIIIG ) that encodes σG [7–9] . Any σG produced at these early times remains inactive; only after forespore engulfment is complete does σG become active and replace σF . The deactivation of σF is poorly understood , but involves the small protein Fin ( previously YabK ) as well as other unidentified mechanisms [10 , 11] . The subsequent activity of σG requires an intercellular channel apparatus comprised of the mother cell proteins SpoIIIAA-AH and forespore protein SpoIIQ ( reviewed in [12] ) . Interestingly , this SpoIIIAA-AH•SpoIIQ channel does not specifically regulate σG , but instead is required more generally to maintain forespore physiology and to support any forespore gene expression at late times , even that engineered to be directed by a heterologous phage RNAP [13 , 14] . Finally , once active , σG directs the transcription of genes required for late forespore development , as well as its own gene in an autoregulatory loop [9] , thus locking in the transition to the late program of developmental gene expression in the forespore . A major unanswered question regarding the switch from early to late gene expression in the forespore is how σG activity is delayed until the early , σF-directed phase of gene expression is complete . One protein that has been implicated in controlling early σG activity is the anti-sigma factor CsfB ( also called Gin ) , which is expressed under the control of σF at early times and is a potent antagonist of σG [15–18] . Deletion of csfB has been reported to cause premature activation of σG in a subset of sporulating cells [16] . But other studies have concluded that ΔcsfB does not alter the level nor timing of peak σG activity [19] , and ΔcsfB cells display no defect in spore formation [15] . These results suggest that CsfB-independent mechanisms must also be in place to restrict σG activity at early times . A second possible explanation for the delay in σG activation comes from reports that the transcription of sigG by σF is delayed by up to an hour relative to other σF target genes [20 , 21] . In addition , σF-dependent transcription of sigG , unlike that of other σF target genes , has been reported to require the forespore membrane protein SpoIIQ and the early-acting mother cell sigma factor σE [20 , 22 , 23] . These findings have led to speculation that a signaling pathway , perhaps involving SpoIIQ , specifically couples σF-dependent sigG transcription to the activation of σE in the mother cell [2 , 24] . Satisfyingly , a checkpoint mechanism such as this would account for the observed delay in sigG expression , given that both spoIIQ expression and σE activation require the earlier activity of σF [25–27] . To test this idea , several groups have monitored the timing of σG activation during sporulation of strains engineered to express sigG under the control of a more typical “early” σF-target promoter . The majority found no evidence of premature σG activity in such engineered strains [17 , 19 , 28] , although one study reported that if sigG expression was boosted ( by inserting three copies of the engineered sigG construct ) , inappropriate σG activity could be detected at early times [14] . This may hint at the importance of sigG expression levels , in addition to timing , in dictating the onset of σG activity . Overall , however , the regulation of sigG expression and its impact upon the timing of σG activity remains an open question . Here we report the results of a careful examination of sigG expression that calls into question long-standing assumptions about its regulation . We argue here that sigG transcription by σF is not delayed , nor does it require a specific intercellular signaling pathway originating in the mother cell . Instead , we propose a simpler model ( Fig 1C ) in which sigG is first transcribed by σF with normal timing , but at levels that are very low and , as such , difficult to detect . Subsequently , the majority of sigG transcription occurs later under the control of σG itself . We present evidence that the low-level expression of sigG at early times is a consequence of four intrinsic features of the sigG regulatory and coding sequences that dampen its transcription and translation: suboptimal spacing between the -10 and -35 sigG promoter elements , a suboptimal translational start codon , a hairpin in the 5’ leader sequence of the sigG mRNA that occludes the ribosome-binding site ( RBS ) , and a suboptimal 5’ coding sequence . Finally , we demonstrate that misexpression of sigG from regulatory sequences lacking these features results in inappropriate σG activity during vegetative growth and premature σG activity in the forespore during sporulation .
Before examining sigG expression during sporulation , we first determined the timing of the switch from σF to σG under our sporulation conditions using lacZ reporters fused to promoters under the exclusive control of each sigma factor . ( All lacZ reporter genes in this study were integrated at the non-essential amyE locus . ) As shown in Fig 1B , β-galactosidase production from a lacZ reporter fused to the σF-dependent spoIIQ promoter ( PspoIIQ ) [27] was first detected above background at approximately hour 1 . 5 of sporulation , peaked at hour 2 . 5 , and declined thereafter . In contrast , a lacZ reporter fused to the σG-dependent sspB promoter ( PsspB ) [29] turned on at hour 3 of sporulation , after which time β-galactosidase production continued to rise through hour 5 ( Fig 1B ) . Together , these findings indicate that under our sporulation conditions , σF is active from hours 1 . 5–2 . 5 , σG is active from hours 3–6 , and the switch from σF to σG occurs between hours 2 . 5–3 . To monitor sigG expression , we fused the entire sigG regulatory region ( -203 to +114 , in reference to a +1 transcription start site [9] ) , including the sigG promoter ( PsigG ) , ribosome binding site ( RBS ) , and first 28 codons , in-frame to lacZ ( see Fig 1D ) . ( In this and other experiments in this study , unless otherwise indicated , the native sigG gene was left intact , under the control of its wild-type regulatory sequences . ) As shown in Fig 2A and 2B , expression of this PsigG-sigG1-28-lacZ reporter gene was first detectable above background at very low , but statistically significant levels at hour 2 . 5 of sporulation , after which time its expression increased substantially . This profile indicates that the majority of sigG expression occurs during times when σG is active ( hours 3–6 ) , but that very low levels of sigG expression can be detected at slightly earlier times ( hour 2 . 5 ) . To improve our ability to detect low levels of PsigG transcription that may be occurring at early times of sporulation , we altered our lacZ reporter gene to optimize translation . First , we replaced the native sigG translation start codon , GUG ( GTG ) , with the more efficiently utilized start codon AUG ( ATG ) [30] . Consistent with modestly improved translation , the modified reporter gene displayed a 1 . 3-fold increase in β-galactosidase activity at sporulation hours 2 . 5–6 relative to the original reporter gene harboring the native GTG start codon ( Fig 2A ) . Notably , introduction of the ATG start codon also permitted the detection of weak , but statistically significant β-galactosidase activity at hour 2 of sporulation ( Fig 2B ) . Given that the native sigG gene in this experiment was expressed from its unaltered wild-type regulatory sequences ( including its suboptimal GTG start codon ) , any potential further amplification of sigG expression that might have resulted from enhanced σG autoregulation ( i . e . if GTG had been replaced with ATG at the native site ) was not measured in this assay . To further optimize translation , we turned our attention to the remaining 27 sigG codons ( codons 2–28 ) fused in frame to lacZ . The 5’ coding region can be a major determinant of translation efficiency , likely due to the presence of rare codons and/or the propensity of this region to form RNA secondary structures that impede translation initiation ( reviewed in [31] ) . Notably , the sigG 5’ coding sequence has the potential to form several hairpin structures ( Fig 1D , indicated by blue and purple arrows ) . As a first strategy to optimize the 5’ coding region of our PsigG reporter , we utilized the mRNA Optimizer tool [32] to redesign sigG codons 2–28 such that the potential for secondary structure was minimized without altering the encoded protein . ( See S1 Fig for the optimized sequence and its secondary structure potential; also , we note that this tool does not optimize for species-specific biases in codon usage . ) In a second approach , we altogether replaced sigG codons 2–28 with codons 2–8 of the highly expressed B . subtilis gene comGA , a strategy that has been demonstrated to significantly improve translation of another reporter gene [33] . As shown in Fig 2C and 2D , the resulting PsigG reporters with ATG start codons and optimized 5’ coding sequences were expressed more robustly ( ~1 . 5-fold and ~2-fold , respectively ) at sporulation hours 2–6 relative to the reporter harboring only the ATG alteration . Together , this data indicates that sigG translation is ordinarily dampened by a suboptimal start codon and suboptimal 5’ coding sequence . Moreover , the improved reporter gene expression supports the conclusion that PsigG is transcriptionally active both before and after the switch from σF to σG . In the generally accepted model for regulation of sigG expression , sigG is transcribed first under the control of σF ( albeit with unique regulation , see Introduction and below ) and then under the control of σG in an auto-regulatory loop . To determine the contribution of σF versus σG to the transcription of PsigG ( using our strongest reporter , PsigG-ATG-comGA2-8-lacZ , henceforth simply PsigG-lacZ ) , we deleted the gene encoding σG ( sigG ) alone or in combination with sigF , which encodes σF . We predicted that ΔsigG would eliminate any late PsigG activity that depends upon σG , leaving intact the σF-dependent contribution to PsigG expression , which would then be eliminated upon introduction of ΔsigF . However , we found that PsigG-lacZ expression was not at all reduced at any time by ΔsigG , but in fact was slightly stimulated at later times; yet as predicted , the ΔsigF ΔsigG double mutant displayed no detectable β-galactosidase activity at any timepoint ( Fig 3A ) . Close examination of the literature revealed that this effect of ΔsigG upon PsigG should not have been unexpected . Some of the earliest studies of sigG ( then called spoIIIG ) reported that PsigG activity was unaltered or even stimulated by sigG mutation [9 , 34 , 35] , although other studies observed modest or even significant decreases [20 , 36] . One interpretation of our results could be that all PsigG activity , at both early and late times during sporulation , is due exclusively to σF and not at all to σG . However , we find this to be an unsatisfactory interpretation given that σG has been shown to activate PsigG in vitro and in directed in vivo experiments [9 , 37 , 38] . Furthermore , there is no evidence that σF remains active at later times of sporulation during which σG is active; in fact , in one study , σF and σG activities could not be detected to overlap [5] . We instead suggest an alternative explanation for these data , namely that the late expression of PsigG-lacZ in ΔsigG cells is due to aberrant σF activity that is known to be unmasked in the absence of the late-acting sigma factor σG [13 , 15 , 39] . The cause of this aberrant activity is poorly understood but likely involves the σF inhibitor Fin , which is expressed partly under the control of σG , as well as other σG-dependent , Fin-independent mechanisms [10 , 11] . To demonstrate the likelihood that PsigG-lacZ is aberrantly activated by σF at late times in ΔsigG cells , we repeated the experiment presented for PsigG-lacZ ( Fig 3A ) , but with strains harboring the exclusively σF-dependent PspoIIQ-lacZ reporter gene . As shown in Fig 3B and as we have previously reported [13] , deletion of sigG unmasks a late phase of PspoIIQ-lacZ expression from hours 3–5 of sporulation . All PspoIIQ-lacZ expression was eliminated by the further introduction of ΔsigF ( Fig 3B ) , indicating that the early ( normal ) and late ( abnormal ) transcription of PspoIIQ-lacZ in ΔsigG cells is driven by σF . It therefore seems plausible that the sigG promoter , like the spoIIQ promoter , is subject to aberrant late transcription by σF in the absence of σG . Importantly , this could make it appear as if σF—and not σG—ordinarily drives PsigG transcription at later times during sporulation . As such , we conclude that the contribution of σF versus σG to the transcription of PsigG in wild type cells cannot be determined with confidence through the use of a ΔsigG mutant . Despite our inability to genetically dissect the contribution of σF versus σG to the activation of our PsigG-lacZ reporter , we reasoned that we could tentatively assign its early expression ( hours 2–2 . 5 ) to σF and its late expression ( hours 3–6 ) to σG , based on the timing of the switch from σF to σG under our conditions ( between hours 2 . 5 and 3 , Fig 1B ) . This simple interpretation is complicated , however , by the possibility that PsigG may not be a typical σF-dependent promoter . Unlike other σF-target promoters , σF-dependent transcription of PsigG has been reported to be delayed and to depend upon the mother cell sigma factor σE and the forespore protein SpoIIQ ( itself under the control of σF ) [20–23] . These data have been interpreted as evidence for an intercellular signaling pathway/checkpoint mechanism that specifically delays PsigG transcription by σF [2 , 24] . Upon closer examination of the original studies , however , we noted that the relevant experiments were performed in strains deleted for sigG to eliminate σG-dependent PsigG transcription . In light of our finding that PsigG may behave aberrantly in the absence of σG , as well as recent strides in our understanding of the function of SpoIIQ ( see below ) , we reasoned that these data and their interpretation warranted re-evaluation . To begin , we sought to reproduce these original findings with our PsigG-lacZ reporter . As shown in Fig 3C , PsigG-lacZ expression in ΔsigG cells was indeed significantly reduced at sporulation hour 3 and later when spoIIQ or sigE ( the gene encoding σE ) were deleted . Interestingly , however , β-galactosidase production at earlier times ( hours 2–2 . 5 ) , albeit relatively weak , was unaffected by deletion of spoIIQ or sigE ( Fig 3D ) . These data indicate that in ΔsigG cells , σF-dependent expression of PsigG at late times ( but not early times ) requires σE and SpoIIQ , a finding that is mostly consistent with previous studies [20 , 22 , 23] . In the years since these previous studies were performed , SpoIIQ has been determined to assemble with the eight mother cell proteins SpoIIIAA-AH into a channel apparatus that connects the forespore and mother cell at intermediate stages of sporulation ( reviewed in [12] ) . This SpoIIIAA-AH•SpoIIQ channel is generally required for any late gene expression in the forespore , including that normally directed by σG , abnormally directed by σF ( as in a ΔsigG mutant ) , or engineered to be directed by a heterologous RNAP [13] . We reasoned , therefore , that the σE- and SpoIIQ-dependence of late σF-dependent PsigG expression might simply be another example of late forespore gene expression requiring the SpoIIIAA-AH•SpoIIQ channel ( note that the spoIIIAA-AH operon is expressed under σE control ) . To investigate this possibility , we tested whether late PsigG activity in ΔsigG cells also depended upon the channel proteins SpoIIIAA-AH . As shown in Fig 3C , introduction of ΔspoIIIAA-AH caused PsigG-lacZ expression to be significantly reduced at late times ( hour 3 and later ) in a manner that was comparable to that observed with ΔsigE and ΔspoIIQ . And like ΔsigE and ΔspoIIQ , ΔspoIIIAA-AH did not alter β-galactosidase production at early times ( hours 2–2 . 5 ) ( Fig 3D ) . These findings therefore indicate that PsigG expression is dependent at late ( but not early ) times upon SpoIIQ , σE , and SpoIIIAA-AH . Although we cannot exclude the possibility of pleiotropic effects of these deletion mutants , we believe that the simplest interpretation of these data is that PsigG expression at late times is dependent—as appears to be any late forespore gene expression—upon the SpoIIIAA-AH•SpoIIQ channel . To further demonstrate the likelihood that PsigG is not subject to unique regulation by SpoIIQ , σE , and SpoIIIAA-AH , we repeated the experiment presented for PsigG-lacZ ( Fig 3C and 3D ) with strains harboring the σF-dependent PspoIIQ-lacZ reporter gene . As shown in Fig 3E and ( in part ) as we have previously reported [13] , the aberrant , late σF-directed expression of PspoIIQ-lacZ in ΔsigG cells was similarly dependent upon spoIIQ , sigE , and spoIIIAA-AH . ( The relatively higher residual expression in the ΔsigE mutant is likely due to the abnormal formation of two forespore compartments , each with active σF [40] . ) Also similar to our findings for PsigG-lacZ , these mutations did not alter PspoIIQ-lacZ expression at early times ( hours 2–2 . 5 ) ( Fig 3E ) . We therefore conclude that PsigG and PspoIIQ both require spoIIQ , sigE , and spoIIIAA-AH for σF-dependent expression at late ( but not early ) times in ΔsigG cells . Overall , these findings are consistent with the general dependence of late forespore gene expression upon the SpoIIIAA-AH•SpoIIQ channel and , conversely , cast significant doubt upon the conclusion that these proteins comprise a regulatory pathway that specifically delays sigG transcription . If PsigG transcription is not delayed by an intercellular regulatory pathway , then why is sigG not more robustly expressed at early times ? Bioinformatically the -10 and -35 elements of PsigG are recognized as a good match to the σF consensus ( Figs 1D and 4A ) [41] , and PsigG is readily transcribed by σF•RNAP in vitro [9] . The sigG RBS and its spacing from the start codon ( Fig 1D ) appear to be ideal for translation initiation [30] . And , as we have shown , the less common GTG start codon of sigG , as well as its native 5’ coding sequence , only modestly reduce its translation efficiency ( Fig 2B and 2D ) . We therefore reasoned that PsigG may be subject to a currently unknown , additional mode of negative regulation in vivo that weakens its expression at early times and , perhaps in turn , helps to properly time the switch to late , σG-directed gene expression in the developing spore . To identify novel mechanisms of sigG regulation , we first turned our attention to the PsigG -10 and -35 promoter elements . As shown in Fig 4A , there were two notable differences when we compared PsigG to several other promoters also recognized by σF in B . subtilis . First was that the spacing between the PsigG -10 and -35 elements is 14 nts , whereas the majority of promoters had a spacing of 15 nts . Second , the nucleotide at position -7 in PsigG is a T , whereas in the majority of promoters the equivalent position is an A or G . To test whether one or both of these unique features could explain the low level expression of PsigG , we constructed variants of our PsigG-lacZ reporter in which the spacing between the -10 and -35 elements was increased to 15 nt by insertion of single base pair ( 15ntPsigG-lacZ ) , or in which the T at position -7 was switched to A or G ( T→APsigG-lacZ or T→GPsigG-lacZ , respectively ) . As shown in Fig 4B , the 15ntPsigG-lacZ reporter displayed a notable increase in expression relative to the corresponding wild type reporter at both early and late times of sporulation . Interestingly , this stimulation was most pronounced at times corresponding to σF activity: at hours 2 and 2 . 5 , the 15ntPsigG-lacZ reporter displayed a nearly 3-fold increase in expression ( Fig 4C ) . In contrast , switching the T at position -7 to either A or G had very little effect on the extent of PsigG expression , although a slight , statistically-significant increase for the T→APsigG-lacZ reporter ( ~1 . 5-fold relative to the wild type PsigG-lacZ reporter ) was detected at hours 2 and 2 . 5 ( Fig 4D and 4E ) . Finally , we combined the 15nt alteration with the T→A or T→G mutations to test for a synergistic effect on PsigG activity , but no additional stimulation was observed ( S2 Fig ) . Together these findings suggest that the ability of σF to activate PsigG at early times is considerably diminished by the shorter spacing between the PsigG -10 and -35 elements , and is only modestly ( if at all ) affected by the identity of the nucleotide at position -7 ( T vs . A vs . G ) . Next , our attention was drawn to the possibility that nucleotides adjacent to the sigG RBS might also contribute to the low levels of sigG expression . This idea came from characterization of a PsigG-lacZ variant , originally constructed for another line of investigation , in which 12 nucleotides ranging from positions +10 to +30 , both upstream and downstream of the sigG core RBS , were randomly mutated ( Fig 5A ) . ( Note that the PsigG-lacZ reporter utilized here also harbored an ATG start codon in place of the native GTG start codon . ) As shown in Fig 5C , this PsigG+10→+30-lacZ reporter was expressed ~5-8-times more robustly than the corresponding wild type reporter at both early and late times of sporulation . To identify the nucleotides responsible for this effect , we constructed PsigG-lacZ variants harboring subsets of the original 12 mutations ( Fig 5A ) . Through this analysis , we discovered that the 6 alterations introduced at positions +10 through +15 accounted for the majority of the observed stimulation . β-Galactosidase production from PsigG+10→+15-lacZ was increased ~3-4-fold at both early and late times of sporulation compared to the corresponding wild type PsigG-lacZ , while all other mutations either had no effect or only modestly affected expression ( Fig 5C ) . Positions +10 through +15 are located downstream of the sigG transcription start site ( +1 ) and upstream of the core sigG RBS ( positions +19 to +23 ) ( Fig 5A ) . These nucleotides therefore might exert their inhibitory effect upon sigG expression at the level of transcription , mRNA stability , and/or mRNA translation . To distinguish these modes of regulation , we introduced the +10→+15 mutations into a transcriptional PsigG-lacZ reporter ( referred to here as PsigG-RBS-lacZ ) , in which lacZ was separated from the sigG RBS by a spacer and provided with an engineered , optimal RBS . As shown in Fig 5D , β-galactosidase production from the PsigG-RBS-lacZ reporter was unaffected by the +10→+15 mutations , arguing strongly for a role of these nucleotides specifically in the regulation of translation initiation at the sigG RBS . One mechanism for regulation of translation initiation involves the occlusion of the RBS by complementary base-pairing with adjacent nucleotides in the 5’ mRNA leader sequence ( reviewed in [42] ) . To determine whether this may be the case for sigG , we analyzed the sigG 5’ mRNA leader sequence for potential secondary structures . Strikingly , we found that a stem-loop structure comprised of five sequential base-pairs was predicted to form between the majority of the sigG RBS and upstream nucleotides ( Fig 5A and 5B ) , with a calculated free energy of -6 . 2 kcal/mol [43] . Interestingly ( and serendipitously ) , five of the six nucleotides altered in the +10→+15 mutant correspond exactly to the five nucleotides that participate in formation of this sigG “RBS hairpin” ( asterisks in Fig 5B ) . Re-analysis of the sigG+10→+15 5’ mRNA leader sequence for potential secondary structure confirmed that this mutant was no longer predicted to form this hairpin [43] . Together , these findings are suggestive of a model in which sigG translation is ordinarily dampened by a stem-loop structure that occludes the sigG RBS . To further confirm the role of the identified RBS hairpin in regulating sigG translation , we introduced mutations into our PsigG-lacZ reporter that were specifically designed to weaken/eliminate ( “mut7” ) or strengthen ( “mut2” ) the sigG RBS hairpin ( Fig 5E , right ) . As expected , the PsigGmut7-lacZ reporter , which retained the potential for only two complementary base pairs , was expressed more robustly , producing ~2-fold more β-galactosidase than the corresponding wild type reporter at both early and late times of sporulation ( Fig 5E ) . We do note that this effect was not as robust as the 3-4-fold stimulation we observed for the +10→+15 variant ( see Fig 5C ) . Although we cannot be certain , we speculate that this difference is not due to residual hairpin formation , but rather secondary consequences of these mutations ( such as decreased mRNA stability ) . In contrast , the “strengthened hairpin” PsigGmut2-lacZ reporter displayed very low levels of expression ( Fig 5E ) . Altogether , these findings indicate that the sigG RBS hairpin ordinarily dampens sigG translation by 2-4-fold and , when strengthened , can almost entirely block translation from the sigG mRNA . Our data reveal that expression of sigG is diminished by at least four mechanisms: ( i ) suboptimal spacing of the PsigG -10 and -35 elements , ( ii ) a hairpin in the sigG mRNA 5’ leader sequence predicted to occlude the RBS , ( iii ) a suboptimal GTG start codon , and ( iv ) secondary structure in the sigG 5’ coding sequence . To visualize the full extent of sigG inhibition by these mechanisms , we built a PsigG-lacZ reporter construct ( referred to as quadPsigG-lacZ ) that simultaneously removed or “repaired” all of these features . Strikingly , quadPsigG-lacZ was expressed ~4-6-fold more robustly than the original PsigG-sigG1-28-lacZ reporter ( Fig 6A and 6B ) . This effect is in line with what would be predicted by the individual effects of each alteration ( 4-13-fold ) , suggesting that these four features operate independently to modulate sigG expression during sporulation . Importantly , this 4-6-fold increase is almost certainly an underestimate of the actual increase that would result if the sigG gene itself ( as opposed to a lacZ reporter gene ) were misexpressed , given the amplification of sigG expression that would occur via σG autoregulation . We hypothesized that the four identified mechanisms of sigG negative regulation help to ensure the proper execution of the switch from σF to σG in the developing forespore , most likely by preventing premature σG activation . To test this prediction , we constructed a strain in which sigG itself ( i . e . not a lacZ reporter gene ) was expressed from regulatory sequences altered to remove or repair the four features that ordinarily reduce sigG expression ( these alterations were identical to those in the quadPsigG-lacZ reporter , see above ) . This engineered sigG gene , referred to as quadPsigG-sigG , was inserted at an ectopic locus in a strain deleted for the native sigG gene; a strain harboring sigG under the control of wild type regulatory sequences ( PsigG-sigG ) was constructed in an identical manner as a control . The resulting quadPsigG-sigG strain displayed no detectable defect in heat resistant spore formation relative to the wild type PsigG-sigG control strain ( S3 Fig ) , indicating that misregulation of sigG does not drastically compromise sporulation . To determine whether sigG misregulation interferes with the switch from σF to σG , we measured the activities of these two sigma factors during sporulation of the quadPsigG-sigG and wild type PsigG-sigG control strains using lacZ reporter genes . As shown in Fig 6C , we observed no detectable difference in the timing or extent of σF-dependent β-galactosidase production from a PspoIIQ-lacZ reporter . In contrast , σG-dependent PsspB-lacZ expression was significantly altered such that the quadPsigG-sigG strain displayed up to 100-fold more β-galactosidase activity at sporulation hours 0–3 , but appeared to be relatively normal thereafter ( Fig 6D ) . These results could be consistent with inappropriate early activation of σG in the forespores of quadPsigG-sigG cells during sporulation , but the presence of significant β-galactosidase activity at hour 0 ( i . e . prior to the onset of sporulation ) also suggests that σG may be inappropriately active in vegetative cells . To identify the sub-population ( s ) of quadPsigG-sigG cells exhibiting inappropriate σG activity , we monitored and quantified σG-dependent expression of a PsspB-gfp reporter gene in single cells by fluorescence microscopy during both vegetative growth and sporulation . As shown in Fig 7A and 7B , we detected GFP fluorescence significantly above background in 20% of vegetative quadPsigG-sigG cells , as compared to 0% of the wild type PsigG-sigG control cells . Inappropriate σG activity in a subset of vegetative cells has also been reported for cells lacking the σG inhibitor CsfB [16 , 44] . Consistent with these reports , introduction of a ΔcsfB deletion into our wild type PsigG-sigG control strain caused 1% of vegetative cells to display σG-dependent GFP expression ( Fig 7A and 7B ) . Interestingly , when the quadPsigG-sigG and ΔcsfB mutations were combined , the resulting double mutant exhibited aberrant σG activity in 40% of vegetative cells ( Fig 7A and 7B ) . The double mutant also appeared sickly: the intensity of GFP fluorescence per cell was lower than in the respective single mutants ( Fig 7A and 7B ) , and GFP protein aggregates accumulated in most cells ( Fig 7A ) . Last , it is worth noting that the quadPsigG-sigG ΔcsfB double mutant was difficult to construct and gave rise to very small colonies when grown on LB plates , unlike the two single mutants ( Fig 7C ) . Growth curve analysis in liquid LB media revealed that the double mutant had a significantly longer doubling time during log phase ( ~32 min vs . ~25 min for the corresponding wild type strain ) , and failed to reach the same maximal cell density during stationary phase ( S4 Fig ) . In contrast , growth of the two single mutants was indistinguishable from wild type with the exception of the quadPsigG-sigG strain , which had a slight but statistically significant increase in doubling time during exponential growth ( S4 Fig ) . Together , these data indicate that the sigG regulatory features identified in this study act synergistically with CsfB to prevent inappropriate σG activity during vegetative growth and that , in the absence of this regulation , vegetative cells are at a severe fitness disadvantage during both exponential growth and stationary phase . Next , we observed cells during sporulation to determine whether misexpression of sigG might also cause σG to become prematurely active in the forespore . As expected , σG-dependent expression of the PsspB-gfp reporter gene in wild type PsigG-sigG forespores was observed only after the process of forespore engulfment was complete , and was not detected in any mother cells ( Fig 8A–8C ) . In contrast , when sigG expression was misregulated in the quadPsigG-sigG mutant , 31% of forespores displayed aberrant σG activity prior to engulfment ( Fig 8A and 8B ) . Based on a previous report [16] , we anticipated that deletion of csfB in the wild type PsigG-sigG control strain might give rise to a similar effect . Indeed , we detected significant σG-dependent GFP fluorescence in 24% of ΔcsfB forespores that had not yet completed engulfment ( Fig 8B ) . In both cases , aberrant σG activity was specific to forespores , given that only 3% of quadPsigG-sigG mother cells and 1% of ΔcsfB mother cells displayed σG-dependent fluorescence ( Fig 8C ) . Unfortunately , due to the poor vegetative growth of the double quadPsigG-sigG ΔcsfB mutant ( Fig 7C ) , as well as the pre-existing σG activity in 40% of vegetative cells ( Fig 7A and 7B ) , we were unable to determine with confidence the combined influence of these mutations on premature σG activation during sporulation . Nevertheless , our findings reveal that proper regulation of sigG expression by the features identified in this study are required to prevent premature σG activation in the developing forespore .
The long-standing working model for regulation of sigG expression ( see , for example , [2 , 24] ) posits that an intercellular checkpoint mechanism , perhaps involving SpoIIQ , delays σF-dependent sigG expression until the early phase of σE-dependent , mother cell gene expression is underway . Here we present evidence that the data supporting this original working model ( see Introduction and Results ) can be reinterpreted in a manner that does not invoke the existence of an enigmatic , sigG-specific intercellular checkpoint . First , the apparently delayed activation of PsigG by σF can be ascribed to aberrant , late σF activity that is unmasked in the absence of σG ( note that the original experiments were performed in a ΔsigG genetic background ) [13 , 15 , 39] . Second , the dependence upon sigE and spoIIQ can be explained by the failure of these mutants to assemble the SpoIIIAA-AH•SpoIIQ channel , which is required for any late gene expression in the forespore , including that directed by aberrantly active σF [13] . As such , we propose here a new , simpler working model for sigG regulation ( Fig 1C ) . At early times , we posit that sigG is transcribed under the control of σF without delay , but at levels that are very low and therefore difficult to detect . Then , at later times , σG drives its own transcription in an autoregulatory loop , during which time the majority of sigG/σG is produced . We have established here that at least four features of the sigG regulatory and coding sequences , affecting both transcription and translation , account for its low-level expression: reduced spacing between the -35 and -10 promoter elements , a GTG start codon , a suboptimal 5′ coding sequence , and an RBS-sequestering hairpin located in the 5’ leader sequence . A collective take-home message from this and many other studies of sigG/σG is that this gene/protein is subject to layer upon layer of negative regulation , at the transcriptional , translational , and post-translational levels . While it is tempting to imagine that these layers of regulation evolved to ensure the precise timing of σG activation in the forespore during sporulation , it has become increasingly evident that they function in addition , or in some cases instead , to prevent aberrant σG activity in the mother cell and/or in non-sporulating , vegetative cells . Given that sigG is autoregulated , even very low levels of “leaky” expression could trigger a positive feedback loop of σG synthesis and activity . The anti-sigma factor SpoIIAB and the serine protease LonA , which inhibit σG post-translationally , counteract/eliminate σG produced in the mother cell and in non-sporulating cells , with no apparent role in σG regulation in the forespore [28 , 44 , 56–59] . In contrast , the anti-sigma factor CsfB post-translationally inhibits σG in the forespore ( at early times ) , mother cell , and in non-sporulating cells , an impressive feat that is accomplished via csfB regulatory sequences that permit transcription by σF ( at early times in the forespore ) , σK ( at late times in the mother cell ) , and σG itself ( in vegetative cells , but not in the forespore ) ( this study and [15 , 16 , 44 , 59 , 60] ) . Here we have found that the regulatory features that dampen sigG transcription and translation , like the aforementioned σG inhibitors , are not solely dedicated to preventing premature σG activity in the forespore . While misexpression of sigG indeed caused nearly one third of forespores to display premature σG activity , thus supporting our original hypothesis , it also led to aberrant σG activity in up to 20% of vegetative cells . ( We found no evidence for any effect in the mother cell . ) We do not currently know why the sigG/σG autoregulatory loop was triggered in this particular subpopulation of vegetative cells , but we speculate that it could be due to activation of PsigG by σF ( itself produced by leaky expression ) or spurious transcription through the sigG coding sequence . We do note that read-through transcription from the spoIIGA-sigE operon [9 , 52] , which resides upstream of the native sigG gene , could not have been a contributing factor given that sigG was located at an ectopic locus in our strain background . Strikingly , misexpression of sigG in cells also lacking CsfB led to a synergistic and severe effect on vegetative cells: nearly half of the population displayed aberrant σG activity and the strain was sickly and exhibited a small colony morphology , consistent with the documented toxicity of σG to vegetative cells [44 , 61] . Unfortunately , this toxicity was so severe that we were unable to assess synergistic effects in the forespore during sporulation . These results , along with the fact that early σG activity in the forespore ( i . e . in quadPsigG-sigG or ΔcsfB cells ) does not appear to be detrimental to the sporulation program ( this study and [15] ) , raise the speculative idea that the majority of sigG/σG regulation has evolved primarily to prevent ectopic σG activity in vegetative cells , and not to precisely regulate the switch from early to late gene expression in the developing spore . In this light , a key challenge for future studies will be to understand how σG escapes these many layers of inhibition in order to drive late gene expression in the developing spore .
All B . subtilis strains were derived from the laboratory strain PY79 [62] . Tables of strains , plasmids , primers , and synthetic DNA fragments used in this study , as well as details of strain and plasmid construction , are provided as supplementary material ( S1 , S2 , S3 , S4 Tables , S1 Text ) . Bacterial strains were propagated in lysogeny broth ( LB ) , either in liquid culture or on solid plates with 1 . 6% agar . When appropriate , antibiotics were included as follows: chloramphenicol ( 5 μg/mL ) , erythromycin plus lincomycin ( 1 μg/mL and 25 μg/mL , respectively ) , spectinomycin ( 100 μg/mL ) , kanamycin ( 5 μg/mL ) , and ampicillin ( 100 μg/mL ) . For growth curves , colonies grown overnight on LB agar from a frozen stock were inoculated directly ( to minimize selection of suppressors ) into 1 . 5 mL liquid LB in clear 12-well flat-bottom plates . Plates were incubated at 37°C with continuous shaking in a Synergy H1M plate reader ( BioTek , Inc . ) , and optical density at 600 nm ( OD600 ) was measured every 5 min . To quantify spore formation , cells were induced to sporulate by nutrient exhaustion in Difco sporulation medium ( DSM ) [63 , 64] . After 24 h at 37°C , the number of colony-forming units that survived heat treatment ( 20 min at 80°C ) was calculated . For all other experiments , sporulation was induced by the resuspension method [64 , 65] . β-Galactosidase activity was measured as previously described [13] and is reported in arbitrary units ( rate of substrate conversion normalized to cell density ) , unless otherwise noted . Cells harboring the σG-dependent PsspB-gfp reporter were collected during mid-log phase to observe vegetative cells , or at hour 3 of sporulation to observe forespores and their corresponding mother cells during late-engulfment . Harvested cells were resuspended in 1x phosphate-buffered saline ( PBS ) with 1 μg/ml FM 4–64 ( Molecular Probes ) to stain membranes , and spotted on thin 1% agarose pads with poly-L-lysine ( Sigma ) treated coverslips . Fluorescence microscopy was performed with a Nikon Eclipse Ti-U inverted microscope fitted with filter sets 49002 ( Chroma; excitation 470/40x , dichroic 495Ipxr , and emission 525/50m ) and 49017 ( Chroma; excitation 560/40x , dichroic 590Ipxr , and emission 590lp ) for GFP and FM 4–64 detection , respectively . Images were captured with an ORCA-Flash4 . 0 digital CMOS camera ( Hamamatsu Photonics K . K . ) using NIS-Elements Advanced Research software ( Nikon Instruments Inc . ) . For each strain , GFP fluorescence intensity above background was quantified for more than 500 vegetative cells and more than 200 late-engulfment forespores ( defined as cells that were more than halfway engulfed , but not fully engulfed as determined by FM 4–64 membrane staining ) and their corresponding mother cells , using the ImageJ software distributed within the Fiji package [66 , 67] . Cells to be quantified were obtained from two or three biological replicates ( experiments carried out on different days ) . For each replicate sample , at least three fields were selected at random from the same slide and images were acquired with identical settings . Cells with a net GFP fluorescence intensity more than three standard deviations higher than autofluorescence ( as determined by images collected in parallel from PY79 , the wild type parent strain lacking a gfp reporter ) , were considered to have detectable σG activity . Percentages were calculated based on the total number of cells or forespores quantified . Representative images had background subtraction applied , were false colored , and overlaid using the aforementioned ImageJ software . | Global changes in gene expression occur during normal cellular growth and development , as well as during cancer cell transformation and bacterial pathogenesis . In this study we have investigated the molecular mechanisms that drive the switch from early to late developmental gene expression during spore formation by the model bacterium Bacillus subtilis . At early times , gene expression in the developing spore is directed by the transcription factor σF; at later times σF is replaced by σG . An important , yet poorly understood aspect of this σF-to-σG transition is how σG activation is delayed until the early , σF-directed phase of gene expression is complete . Here we have carefully examined expression of the gene encoding σG , sigG , and found that its transcription and translation are ordinarily dampened by several features of its regulatory and coding sequences . Moreover , we have found that this “tuning” of sigG expression is required for proper timing of the switch to σG . These results reframe our understanding of how sigG is regulated during B . subtilis sporulation and , more broadly , advance our understanding of how global changes in gene expression can be precisely executed at the molecular/genetic level . | [
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| 2018 | Transcription and translation of the sigG gene is tuned for proper execution of the switch from early to late gene expression in the developing Bacillus subtilis spore |
The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes , and is traditionally divided into three components according to its chemical composition , i . e . actin , tubulin and intermediate filament cytoskeletons . Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type . Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton , but it also imposes additional challenges in the image processing stage , such as the presence of imaging-related artifacts and heavy blurring introduced by ( high-throughput ) automated scans . However , although there exists a considerable number of image-based analytical tools to address the image processing and analysis , most of them are unfit to cope with the aforementioned challenges . Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments ( at least in some finer or coarser scale ) . Based on this observation , we propose a three-steps actin filaments extraction methodology: ( i ) first the input image is decomposed into a ‘cartoon’ part corresponding to the filament structures in the image , and a noise/texture part , ( ii ) on the ‘cartoon’ image , we apply a multi-scale line detector coupled with a ( iii ) quasi-straight filaments merging algorithm for fiber extraction . The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise , artifacts and heavy blurring . Moreover , it provides numerous parameters such as filaments orientation , position and length , useful for further analysis . Cell image decomposition is relatively under-exploited in biological images processing , and our study shows the benefits it provides when addressing such tasks . Experimental validation was conducted using publicly available datasets , and in osteoblasts grown in two different conditions: static ( control ) and fluid shear stress . The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods .
Computer vision and image-analytical tools are essential in order to study the biology of cells [29] . Several approaches exist for the analysis of filamentous structures [19–21 , 30–36] , consisting in one or several sequential processing steps: pre-processing , filaments network segmentation , and individual fibers extraction . The filaments analysis problem can be solved in many different ways , as illustrated in Fig 2a . These solutions can be roughly organized in at least three main categories ( middle layers in Fig 2a and 2b ) : based on filaments directionality , based on filaments network , and based on single-filaments extraction . The processing path ( from the image to output ) will naturally determine the computational burden , accuracy and the amount of information to be extracted from the image . The strategy we followed is depicted in Fig 2b . In the first category , the filaments information is directly extracted from the pre-processed image [30 , 35] , sidestepping the foreground/background segmentation step . Segmentation-related errors were avoided at the cost of limiting the model applicability to directionality-related analysis [37] only . Networks-related approaches extract the filaments networks [19] after filtering , allowing to complement orientation information with motion and filaments density analysis [38] . One category of methods for the identification of fiber networks uses template matching [39] , where prior knowledge about the target is incorporated into 3D ( or 2D ) templates . These template-based detection methods are more selective and impose a similarity function . In [40] , actin filaments in cryo-ET data sets have been segmented using a stochastic template-based search , which combines a genetic algorithm and a bidirectional expansion strategy . Template matching methods does not resolve the possible template overlap when tracing converging filament branches , thus network junctions could be left undetected . Such problem has been solved in [41] where an active contour based method allowed increasing the segmentation robustness by incorporating prior information about the filament shape . Such approach explicitly models the linear nature of filaments . The overall strategy of the network-based approaches is to extract the whole curvilinear network at once . In contrast , our work aims at providing information at the single-fiber level , where individual filament segments [20 , 21 , 32] are extracted . This allows to perform further analysis [11 , 16 , 42–46] taking into account fibers position , orientation , and length . However , errors introduced during the different processing stages can accumulate and , unless appropriate computational steps are taken , later analysis ( statistical and other ) can be compromised .
MC3T3-E1 cells , established as an osteoblastic cell line , were provided from Sigma ( 99072810 ) . Osteoblasts were grown in alpha-modified minimal essential medium ( α-MEM; Life Technologies ) supplemented with 10% fetal bovine serum ( Life Technologies ) , 1% L-glutamin ( Life Technologies ) and 1% penicillin/streptomycin ( Life Technologies ) in an incubator at 5% CO2 heated at 37°C . The medium was changed twice weekly , and the cells were subcultured into 75 cm2 culture flasks by detaching them gently after a brief PBS rinsing step followed by Trypsin treatment once the cells were reaching subconfluency . For mechanical stimulation , MC3T3-E1 cells were plated into Ibidi μ-slide device ( μ-Slide I 0 . 4 Luer , ibiTreat from Ibidi ) at a concentration of 1 . 5 × 104 cells/mL . After overnight culture , the medium was replaced by α-MEM ( powder exempt of Bicarbonate , Life Technologies ) 10% FBS 1% L-glutamin 1% penicillin/streptomycin 25 mM HEPES ( Life Technologies ) adjusted to pH 7 . 4 for 4 hours in the incubator without CO2 at 37°C . The shear stress was induced by a fluidic system composed of two containers and a pump . This system , depicted in Fig 4a , permits a gravity driven constant flow of culture medium in the chamber containing the osteoblasts . The flow rate was adjusted by setting the container ( see Fig 4a nb 4 ) at the appropriate height above the osteoblasts . In our case , it provided a shear stress between 9 and 12 dyn/cm2 . This value corresponds roughly to the one to which endothelial cells are exposed to in our arteries . The direction of the shear stress flow is estimated around ≈ 80° as illustrated in Fig 4b . After stress exposure for 4 hours , the cells were rinsed in phosphate saline buffer ( PBS ) and chemically fixed with 4% paraformaldehyde ( PFA ) for 15 minutes . Before immunostaining the cells were rinsed twice in PBS . Eventually , the fixed cells were incubated with PBS 0 . 2% Triton X100 for 20 minutes , and exposed to Alexa Fluor 568 phalloidin ( Molecular Probes ) for 1 hour at room temperature . Finally the cells were rinsed again with PBS before a quick water rinse and coverage with cover glass ( by using polyvinylalcohol ( Sigma ) ) . A static osteoblast culture , in the same ibidi device , was performed and actin stained as control . Imaging was performed with an inverted Axiovert 200M system with a 40x Plan-Neofluor ( Carl Zeiss , Oberkochen , Germany ) . We used a motorized platform ( MS-2000 , Applied Scientific Instrumentation , with NanoDrive controller; Mad City Labs , Madison , WI ) to scan the sample . Acquisition was performed using a CoolSnap HQ2 camera ( PhotoMetrics , Tucson , AZ ) and the Multi-Dimensional Acquisition module of the software Metamorph ( Molecular Devices , Sunnyvale , CA ) . The problem of separating an image into different semantic constituent parts , ‘textures’ and ‘cartoon’ , can be addressed by several approaches . Variational calculus [24 , 25] , and sparse multi-source separation [23] are among the most popular methods and their success varies depending on the image nature . In this work , we follow the Starck’s [23] image decomposition which is based on the Basis-Pursuit denoising ( BPDN ) algorithm . The basic idea behind this algorithm is to choose two appropriate dictionaries , one for the representation of ‘textures’ , and the other for the ‘cartoon’ parts . Both dictionaries are to be designed such that each leads to sparse representations over the images it is serving , while yielding non-sparse representations on the other content type . In our problem , we model an imaged ( actin ) cytoskeleton f as a combination of two sources plus some additive noise: f = u f + v a + η ( 1 ) being va the background artifacts related content , and uf the filamentous elements; the noise is represented by η . In [23] the sparse source separation problem , for the image f , has been defined as: min λ a , λ f ‖ f - D a λ a - D f λ f ‖ 2 2 + γ ‖ λ a ‖ p + γ ‖ λ f ‖ p + δ ( ∇ λ a + ‖ λ a ‖ 1 ) ( 2 ) with γ > 0 , δ ≥ 0 and p ∈ {0 , 1} . The obtained sparse coefficients λf provides the fibers-related content uf = Df λf , referred to as fibers image in the rest of the paper , and λa provides the artifacts related content ua = Da λa , referred to as artifacts image; the norm p ∈ {0 , 1} determines the reguralizer type and the parameter γ regulates the solution coefficients . The reconstructed image f ^ = u f + v a is an approximation of f involving artifacts-related and fiber-related dictionaries , Da and Df respectively; the reminder f - f ^ is usually related to noise η . The definition of the dictionaries Da and Df is very much related to the nature of the different contents present in the image , therefore we considered different dictionaries based on fast transforms . For the artifacts related content we used an undecimated wavelet transform ( UDWT ) for modeling the dictionary Da , whereas for modeling the fibers dictionary Df , we used the curvelets transform . The latter being , as discussed in the introduction section , a natural choice for modeling curvilinear structures , while the wavelets transform allows modeling artifacts present in the images . Note that , in [23] the image component has been modelled by ridgelets and the texture ( artefact ) component by a Discrete Cosine Transform ( DCT ) . Ridgelets has been also used in [50] to represent global lines in images . Our implementation of the image decomposition is based on the MCALab library provided in [54] , running a maximum of 100 iterations . The parameter γ ( in Eq 2 ) was linearly decremented during the iterations and initialized accordingly . For all reported experiments , we set p = 0 ( in Eq 2 ) , namely a ℓ0-norm for the model definition , and δ = 3 . Results of the the image decomposition are illustrated in Fig 5 . We refer the reader to [50] for more details on the solution algorithm , including initialization and updates of the γ parameter ( in Eq 2 ) , and to the Supporting Information ( SN . 1 . 1 in S1 Text ) for a discussion on the impact of the different parameters values on the decomposition results . For the filaments network segmentation , we opted for a multi-scale line detector based on a structural element of different orientations and widths , representing the scales . The multi-scale line detector basically analyze each pixel’s neighborhood of uE at different scales by evaluating ( according to a score ) if such pixel is part of a line of certain width . Such evaluation is performed by computing a ‘line response’ for the width evaluation , but also for a length evaluation that will be used in the next processing step for individual lines segments detection , as illustrated in Fig 7 . The line response takes place at each pixel in the image , in a ( discrete ) set of possible orientations between 0 and 180 degrees , considering linear elements of size s ∈ [1 , W] , with W the expected fibers width ( this parameter is related to the magnification of the image ) . Then , the final multi-scale line response of each pixel ( x , y ) of the enhanced fibers image , uE , is provided as [34]: u G ( x , y ) = 1 W + 1 ∑ s = 1 W max θ ∈ { 0 , ⋯ , 180 } R ( u E ( x , y ) ; θ , s , W ) ∀ ( x , y ) ∈ Ω ( 4 ) where R ( . ) , applied on the fibers enhanced image uE , provides a score indicating how likely is certain point to be the center of a line passing though it with a certain width and a certain direction , by analyzing the neighboring points . The reader is referred to [34] for the details on how R ( . ) is computed . In our implementation , based on the source code provided by [34] , we retained the advantages of the multi-scale linear response image , uG , and at the same time introduced some modifications to obtain the final binary image , uB of the fibers-related pixels . For the latter , we applied on the uG gray-scale image a local thresholding algorithm to separate background and fibers pixels . For this purpose , we made use of the Wellner’s adaptive thresholding [55] were a median filtering provides an estimated local threshold . The final retained local threshold is a percentage b of the estimated threshold . The smaller b is the more line segments candidates are retained in the final binary image , representing a set of edge segments , which are clean , contiguous , i . e . , connected , chains of edge pixels . Fig 8 illustrates the output of this step . Note that the obtained edges have a width larger than 1 pixel . Several complex approaches of line grouping , such as the on in [56] , have been proposed within the computer vision community . However , for our purpose of fibers extraction , we developed a simple algorithm capable of iteratively extracting continuous linear segments ( denoted as filament segments ) by connecting the fixed-length segments extracted in the previous step . We first associate to a given filament segment all overlapping fixed-length segments oriented in the same direction ( same θ ) . By repeating this process , all the overlapping segments with the same orientation will be combined into a longer straight-line segment . We then connect segments according to their orientation difference up to a ‘curvature’ threshold θ < Tθ . In addition , when merging ( i . e . connecting ) the k-th and the i-th segments ( Fk⋃Fi ) , we discard all the pixels that are beyond the connection ( intersection ) point . The above described procedure is detailed in Algorithm ( 1 ) , and illustrated in Fig 9 . Algorithm 1 Fibers extraction θi: Indicates the orientation of extracted fiber Fi δ ( Fi ) : Indicates the length of extracted fiber Fi Input: Tθ as angle merging tolerance ( F , θ ) ← List of fixed-length extracted segments tuples ( Line segmentation section ) for ξ = 0⋯Tθ 0 for i = 1⋯ do S ← { k ∈ N + | k > i , - ξ ≤ θ k - θ i ≤ ξ , OverlapEndPoints ( Fk , Fi ) } if S ≠ ∅ then k ← S ( 1 ) ( F k , θ k ) ← ( F k ⋃ F i , δ ( F k ) θ k + δ ( F i ) θ i δ ( F k ) + δ ( F i ) ) end if end for end for Output: list of individual fibers ( F , θ ) Note: Fk⋃Fi combines the segments Fk and Fi discarding all the pixels located beyond the connection ( intersection ) point .
The cytoskeleton plays an important role in numerous physiological and pathological processes , its morphological characteristics are therefore of prime importance to understand numerous basic cellular phenomena , such as cellular adaptation to physical or chemical stress . However , cytoskeleton quantification and analysis is far from being straightforward , and sophisticated algorithms are required to fulfill the task . In this work we present a processing framework that efficiently detects cytoskeletal fibers and quantifies its morphological characteristics such as the number of filaments it contains , their length and orientation . The proposed model was tested on images of osteoblasts cultivated in shear stress and static ( control ) conditions . The detection of highly oriented actin fibers in shear stress cultivated cells corroborates what one would expect in such a condition , i . e . an alignment of the fibers with the direction of the flow . In addition , our algorithm successfully detected the perinuclear actin cap , a structure difficult to detect by other state-of-the-art methods , and that seems to play an important role in mechano- transduction [11 , 58] . It was also shown that the model separated filaments and imaging-related artifacts very efficiently , even in the presence of heavy blurring , a step that endowed the model with high sensitive detection capabilities . The proposed framework can be extended to extract 3D meshwork of actin filaments . Indeed , after the image decomposition and filament enhancements steps , a method such as the multiple Stretching Open Active Contours ( SOACs ) of [61] could be used for fibers tracing . | We propose a novel actin filaments cytoskeleton analysis framework that allows extracting quasi-straight individual fibers in a robust manner , and provides their respective position , orientation , and length as output . The proposed framework is defined as a three-steps processing sequence , that can explicitly cope with high-throughput imaging related issues , such as noise/artifacts presence and heavy blurring , and can similarly process artifacts-free and well-focused images . | [
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| 2016 | A Robust Actin Filaments Image Analysis Framework |
A substantial genetic contribution to systemic lupus erythematosus ( SLE ) risk is conferred by major histocompatibility complex ( MHC ) gene ( s ) on chromosome 6p21 . Previous studies in SLE have lacked statistical power and genetic resolution to fully define MHC influences . We characterized 1 , 610 Caucasian SLE cases and 1 , 470 parents for 1 , 974 MHC SNPs , the highly polymorphic HLA-DRB1 locus , and a panel of ancestry informative markers . Single-marker analyses revealed strong signals for SNPs within several MHC regions , as well as with HLA-DRB1 ( global p = 9 . 99×10−16 ) . The most strongly associated DRB1 alleles were: *0301 ( odds ratio , OR = 2 . 21 , p = 2 . 53×10−12 ) , *1401 ( OR = 0 . 50 , p = 0 . 0002 ) , and *1501 ( OR = 1 . 39 , p = 0 . 0032 ) . The MHC region SNP demonstrating the strongest evidence of association with SLE was rs3117103 , with OR = 2 . 44 and p = 2 . 80×10−13 . Conditional haplotype and stepwise logistic regression analyses identified strong evidence for association between SLE and the extended class I , class I , class III , class II , and the extended class II MHC regions . Sequential removal of SLE–associated DRB1 haplotypes revealed independent effects due to variation within OR2H2 ( extended class I , rs362521 , p = 0 . 006 ) , CREBL1 ( class III , rs8283 , p = 0 . 01 ) , and DQB2 ( class II , rs7769979 , p = 0 . 003 , and rs10947345 , p = 0 . 0004 ) . Further , conditional haplotype analyses demonstrated that variation within MICB ( class I , rs3828903 , p = 0 . 006 ) also contributes to SLE risk independent of HLA-DRB1*0301 . Our results for the first time delineate with high resolution several MHC regions with independent contributions to SLE risk . We provide a list of candidate variants based on biologic and functional considerations that may be causally related to SLE risk and warrant further investigation .
Systemic lupus erythematosus ( SLE ) is the prototypic systemic autoimmune disease characterized by autoantibody production and involvement of multiple organ systems . Although the etiology of SLE remains unknown , several lines of evidence underscore the importance of genetic factors , including a high sibling risk ratio ( λs = 8–29 ) , familial clustering , where approximately 10 to 12% of SLE patients have an affected first-degree relative , and higher concordance rates in monozygotic twins ( 24–69% ) relative to dizygotic twins and non-twin siblings ( 2–9% ) [1] , [2] , [3] . Similar to many other autoimmune diseases , genes within the major histocompatibility complex ( MHC ) on the short arm of chromosome 6p21 . 3 exhibit strong association with the risk for SLE . The MHC is a gene-dense region of the human genome spanning approximately 4 . 5 Mb and known to encode more than 180 expressed genes [4] , [5] . Forty percent of the expressed loci have functions related to immune activation and response . The class III region , with more than 55 expressed loci , is the most dense subregion of the MHC and the entire human genome [6] . Historically , interest in the MHC region for SLE has focused on the highly polymorphic HLA class I and II genes that encode membrane glycoproteins that present peptides for recognition by T lymphocytes , as well as genes within the HLA class III region , particularly the tumor necrosis factor and complement component C4 gene loci . Indeed , inherited deficiency of complement genes , particularly C4A ( null ) alleles , has long been recognized as strong , albeit rare , genetic risk factors for SLE [7] . Work by Graham and colleagues [8] , [9] examining ∼50 microsatellite markers across the MHC region highlighted the importance of HLA class II haplotypes involving the HLA-DRB1 and –DQB1 loci , particularly those corresponding to serologic types HLA-DR2 and DR3 ( DRB1*1501-DQB1*0602 and DRB1*0301-DQB1*0201 , respectively ) . More recently , genome wide association scans in SLE also underscore the importance of the MHC [10] , [11] , [12] . However , strong linkage disequilibrium ( LD ) between particular alleles within the MHC has interfered with disease variant identification and previous studies have not been able to distinguish between associated MHC variants . Long-range , extended or ‘ancestral’ haplotypes , sometimes spanning greater than 2 Mb , have been observed [8] , [13] . Recent studies in European-derived populations have examined the distribution of LD across the MHC and have suggested that SNPs can help dissect causal variation within this region [14] , [15] . A recent analysis of 314 UK SLE families examining 68 SNPs across the HLA class II and III regions and HLA-DRB1 suggested two distinct association signals centered on DRB1*0301 and the T allele of rs419788 in intron 6 of the class III region gene SKIV2L [16] . Of interest , the class III signal appears to exclude the TNF -308 promoter polymorphism , which has been a focus of prior work [17] . Herein we extend this work by characterizing a much larger collection of SLE cases ( and parents ) for 1 , 974 MHC genetic markers plus the HLA-DRB1 locus . We also characterized SLE cases for a set of ancestry informative markers ( AIMs ) to identify population outliers and assess the possible impact of substructure within the European population on our genetic association results . Further , we utilized several analytic strategies to determine whether multiple distinct alleles or haplotypes contribute to SLE risk .
Table 1 summarizes characteristics of the 1 , 610 Caucasian SLE cases and available parents ( n = 1 , 470 ) from the complete trio families included in this study . Clinical characteristics of the SLE cases are consistent with those reported for Caucasian patients , generally [18] . Table 2 summarizes association results for HLA-DRB1 alleles in SLE cases and non-transmitted control alleles from parents of the trio families [19] . Due to the highly polymorphic nature of this locus and prior evidence suggesting that multiple HLA-DRB1 alleles influence SLE risk , we employed a relative predispositional effects ( RPE ) analysis [20] ( see Methods ) . Results indicated that three specific HLA-DRB1 alleles were strongly associated with SLE risk: DRB1*0301 ( odds ratio , OR = 2 . 21 , p = 2 . 53×10−12 ) , *1401 ( OR = 0 . 50 , p = 0 . 0002 ) and *1501 ( OR = 1 . 39 , p = 0 . 0032 ) . The DRB1*0801 allele , which has been shown in previous work to be associated with SLE risk [8] was not significantly associated with SLE in the current RPE analysis , after accounting for testing of multiple DRB1 alleles ( full results shown in Table S1 ) . Figure 1 displays single marker association results for the 1 , 974 MHC SNPs ( out of 2 , 360 ) passing quality control filters ( see Methods ) in SLE cases compared to controls ( additional results are shown in Table S2 ) . We observed strong association signals across a broad region encompassing the HLA class I , III and II regions , with weaker evidence of association in the extended class I and II regions . The strongest evidence of association was observed at the multiallelic DRB1 locus ( global p = 9 . 99×10−16 and ORs as shown in Table 2 ) . The MHC region SNP demonstrating the strongest evidence of association with SLE was rs3117103 ( bp 32 , 457 , 535 ) , with OR = 2 . 44 and p = 2 . 80×10−13 . This SNP is flanking C6orf10 at a distance of 9 . 8 kb . Given the extensive LD across this region , we utilized conditional analyses to identify association signals that were not due to rs3117103 . Similarly , we repeated these analyses conditioning instead on the HLA-DRB1 locus , including the DRB1*0301 , *1401 and *1501 alleles . The rs number and map position for all SNPs associated with SLE using the conditional haplotype method ( CHM ) ( total = 171 SNPs with p<0 . 01 ) are provided in Table S3 . Following these conditional analyses , several analytic approaches were utilized to further define genetic variants with the most compelling evidence of association with SLE risk , as shown in Figure 2 . Figure 3 displays initial association results for the MHC region variants ( n = 11 , including HLA-DRB1 ) that were shown to be associated with SLE risk based on conditional haplotype and stepwise logistic regression analyses , with p<0 . 001 ( See Figure 2 and Methods ) . In addition to the HLA-DRB1 locus , there were several variants with evidence of association with SLE , including one variant in the extended class II region , five variants in HLA class II ( including HLA-DRB1 ) , one variant in HLA class III , two in HLA class I , and two in the extended class I region . Figure 4 shows an LD plot representing the correlation ( r2 ) among HLA-DRB1 and the aforementioned 10 associated SNPs that met our significance threshold of p<0 . 001 based on conditional analyses ( see Methods and Table S4 ) . There was very little evidence of LD among these 10 SNPs and HLA-DRB1 with the exception of SNPs within the MICB ( r2 = 0 . 58 ) , C6orf10 ( r2 = 0 . 53 ) and HLA-DQB2 loci ( r2 = 0 . 35 ) and strong LD between HLA-DRB1 and the C6orf10 locus ( r2 = 0 . 77 ) . To further evaluate the evidence supporting association of these 10 variants with SLE we performed transmission disequilibrium testing [21] , [22] among 650 complete trio families . These analyses were consistent with the prior results , including strongest evidence of association for rs3117103 ( OR = 2 . 44 , p = 1 . 58×10−14 ) . Additional details are shown in Table S5 . We also evaluated these 10 SNPs among a more homogeneous subset of cases ( and non-transmitted controls ) estimated by AIMs to have ≥90% Northern European ancestry . These results also supported our main analyses , with the strongest evidence of association for rs3117103 ( OR = 2 . 49 , p = 1 . 62×10−13 ) ( see Table S5 for additional details ) . Similarly , results for the top four DRB1 risk alleles did not change when analyses were repeated among the Northern European subset of cases and controls ( data not shown ) . A distinguishing feature of the MHC region is the extensive , long-range LD , which has been observed particularly among European-derived populations [15] and is haplotype specific [23] . Thus , to further assess the independence of the 11 variants ( including DRB1 ) and determine whether one or more extended haplotypes are strongly associated with SLE risk , we estimated haplotypes involving these variants and evaluated the evidence of association with SLE ( see Methods ) . When case and control chromosomes were considered together , a total of 171 unique haplotypes were observed . When MHC haplotypes were compared between SLE cases and controls , evidence of association was revealed for two common haplotypes . The haplotype with the strongest evidence of association with SLE contains the DRB1*0301 allele ( 15% transmitted vs . 6% non-transmitted haplotypes , OR = 2 . 63 , p = 8 . 32×10−15 ) , which represents ∼70% of the DRB1*0301 haplotypes . The other associated haplotype contains the *1501 allele ( 9% transmitted vs . 6% non-transmitted haplotypes , OR = 1 . 54 , p = 0 . 0025 ) ( data not shown ) . Finally , we considered our data both a ) excluding and b ) conditioning on haplotypes containing the SLE-associated DRB1 alleles . More specifically , we compared MHC SNP allele frequencies among cases vs . controls after sequentially removing haplotypes containing *0301 , *1501 , and then *1401 ( see Table 3 and Table S6 ) . After removing haplotypes containing these alleles , we observed no evidence for independent association of rs3117103 with SLE ( p>0 . 30; see Table 3 and Table S6 ) . However , four MHC region variants remained significantly associated with SLE , including rs362521 ( OR2H2 , extended class I region; p = 0 . 0060 ) , rs8283 ( CREBL1 , class III; p = 0 . 011 ) , rs7769979 and rs10947345 ( DQB2 , class II; p = 0 . 0030 and 0 . 0004 , respectively ) . Of interest , two HLA class II region SNPs at the DQB2 locus remained associated with SLE even in the absence of DRB1 risk alleles . This is also the first study to support a role in SLE for variants within the extended MHC class I region , near OR2H2 . Next , the CHM was used to investigate associations with the top 10 MHC region SNPs conditioned on haplotypes containing the SLE-associated HLA-DRB1 alleles ( see Table 4 and Table S6 ) . When conditioning on DRB1*0301 haplotypes , we observed significant association with SLE ( p = 0 . 0059 ) for a SNP at the MICB locus ( HLA class I ) . In contrast , conditioning on DRB1*1501 or DRB1*1401 revealed no evidence for other MHC effects on SLE risk , however limited power was present for conditional analyses involving DRB1*1401 given the infrequency of that allele in this dataset . Finally , logistic regression analyses involving the aforementioned five SNPs and HLA-DRB1 revealed evidence of independent association with SLE for all variants ( p-values = 0 . 003 to <10−6 ) with the exception of one of the DQB2 SNPs ( rs7769979 ) ( data not shown ) . In summary , we have characterized a large set of SLE cases and parents of European ancestry for the highly polymorphic HLA-DRB1 locus and ∼2 , 000 SNPs across 4 . 9 Mb of the MHC . We have employed methods to ensure that our main results are not due to confounding by population admixture or major substructure within the European population . Our results support the existence of multiple , independent association signals across this region as opposed to a single primary association signal ( with other effects explained by LD ) . Independent associations within extended class I and both class II and III regions were present , even after accounting for HLA-DRB1 effects . However , we cannot exclude the possibility that multiple associated variants within MHC haplotypes function together to influence SLE risk through co-regulation or other mechanisms . An important limitation of the current analysis is the lack of comprehensive coverage in the MHC region . For example , the MHC Panel Set of markers was not designed to directly assess rare variants or multi-allelic markers , such as insertion-deletion or copy number variants . In addition , we have not characterized these families for other classical HLA genes ( besides HLA-DRB1 ) , and in fact our results implicate a role for other HLA class II region genes . Thus , we cannot determine which , if any , of the associated variants we have identified represent underlying causal variants , versus proxies for causal variants as a result of LD . In order to begin to address this important question we identified SNPs and other variants tagged by the independently associated SNPs and summarized their known function , as well as what is known about these variants themselves ( see Table S7 ) . One of the novel loci highlighted in the current study is OR2H2 , within the extended class I region , which encodes olfactory receptor family 2 , subfamily H , number 2 . The olfactory receptor proteins are G-protein-coupled receptors that share structure with many neurotransmitter and hormone receptors [24] . To our knowledge , this gene has not been implicated in risk of SLE or other human autoimmune diseases . Our top SNP in this region tags three additional SNPs in a single locus , gamma-aminobutyric acid ( GABA ) B receptor , 1 ( GABBR1 , see Table S7 ) , which is also a neurotransmitter receptor [25] . Of relevance to this finding , recent chromosome 6 sequencing efforts have now fully characterized the extended MHC in humans [26] , spanning a total of 7 . 6 Mb and comprised of five subregions that include the original ‘classical’ MHC [27] . Of the 421 loci in this region , a total of 252 ( 60% ) are classified as expressed genes . Although our study included 305 SNPs within the extended class I MHC and 247 within the extended class II MHC , more work is needed to interrogate the entire extended MHC region . Our top SNP within the class III region was located in the 3′ untranslated region of CREBL1 , which encodes c-AMP responsive element binding protein-like 1 [28] . Of interest , we did not identify additional variants tagged by our top SNP in this region ( see Table S7 ) . Unfortunately , our MHC marker set did not include SNPs within or near the C4 locus , which has been previously associated with SLE . In particular , copy number variation at this locus has been strongly implicated in SLE risk . However , the two markers flanking the C4 locus did not reveal evidence for association with SLE in the current study ( rs389512 and rs1009382 , p>0 . 15 ) . Given the density of genes within the class III region and previous work suggesting a contribution of class III variants to SLE risk , further study of this region is clearly warranted , including direct assessment of C4 variation . We also demonstrated association of SLE with a variant near the 5′ end of the MHC class I chain-related B ( MICB ) gene , which encodes a heavily glycosylated protein that serves as a ligand for the NKG2D type 2 receptor [29] . Binding of this ligand activates the cytolytic response of natural killer cells . This finding is also of interest given previous evidence of association of the closely related MICA gene to SLE [30] , although results have been contradictory [31] . The MICB gene has not been previously implicated in SLE risk . All other variants tagged by our top SNP in this region are within or very near the MICB locus ( see Table S7 ) . Lastly , two HLA class II region SNPs at the DQB2 locus remained associated with SLE even in the absence of DRB1 risk alleles , and these two SNPs tag variants of at least four additional class II loci ( see Table S7 ) . However , our study lacked power to detect additional rare DRB1 associations ( beyond *0301 , *1501 and *1401 ) , thus it is possible that additional DRB1 effects explain , at least in part , the DQB2 association observed in the current study . We also sought to determine how our results compare to those reported by Fernando , et al . , based on their analysis of 68 SNPs in the HLA class II and III regions , plus HLA-DRB1 , in 314 UK SLE families [16] . Two distinct association signals centered on DRB1*0301 and the T allele of rs419788 in intron 6 of the class III region gene SKIV2L were observed . Five SNPs within this gene were included in our marker panel , including their top SNP , rs419788 . Four of the five SNPs were associated with SLE with p<0 . 01 in our single marker analyses , however , only two of these SNPs ( but not rs419788 ) met the significance threshold ( p<0 . 01 ) for our CHM analyses . Neither of these two SNPs were among the group of ten SNPs selected for further study based on conditional haplotype based analyses or stepwise logistic regression . We have not examined other , non-European populations in this study and this represents a very important focus for future work given the increased burden of SLE among those groups . Further , comparison of MHC association results across major ethnic groups , where LD patterns are substantially different , or the analysis of specific disease subtypes may improve our ability to localize causal variants within this region .
Written consent was obtained from all study participants and ethical approval for this study was obtained from the University of California , San Francisco Committee on Human Research . SLE trio families and cases were derived from three independent case series , including the UCSF Lupus Genetics Project collection , the Lupus Genetics Studies and Lupus Family Registry and Repository ( http://lupus . omrf . org/ ) at the Oklahoma Medical Research Foundation ( OMRF ) and the University of Minnesota SLE collection . SLE cases from trio families ( n = 735 ) were a subset of the total set of SLE cases ( n = 1 , 610 ) , as shown in Table 1 . Details of recruitment and enrollment procedures have been reported previously [32] , [33] , [34] . All subjects were self-reported Caucasian and all SLE cases met the American College of Rheumatology classification criteria for SLE [35] , [36] . Non-transmitted HLA-DRB1 and MHC SNP alleles ( described below ) from SLE trio family parents were used as ‘controls’ for all comparisons with SLE cases , as previously described [19] . DNA was extracted from whole blood using the Gentra Puregene Whole Blood Kit . DNA was obtained from buccal cells and extracted using the Gentra Puregene Mouse Tail Kit . A total of 50 ng of buccal cell or 25 ng of blood DNA ( when necessary ) was whole genome amplified ( WGA ) using the Sigma WGA2 Kit prior to genotyping . WGA samples were column purified using the Sigma Genelute PCR Clean-up Kit . DNA was also extracted from DNA Genotek Oragene saliva sample collection kits according to the manufacturer's protocol . Of the total 3 , 080 individual DNA samples used for genotyping in this study , 69% were derived from whole blood , 25% from buccal cells , and 6% from saliva; 72% of all samples ( including both blood and buccal ) were WGA prior to genotyping . | Systemic lupus erythematosus ( SLE ) is an autoimmune disease characterized by autoantibody production and involvement of multiple organ systems . Although the cause of SLE remains unknown , several lines of evidence underscore the importance of genetic factors . As is true for most autoimmune diseases , a substantial genetic contribution to disease risk is conferred by major histocompatibility complex ( MHC ) gene ( s ) on chromosome 6 . This region of the genome contains a large number of genes that participate in the immune response . However , the full contribution of this genomic region to SLE risk has not yet been defined . In the current study we characterize a large number of SLE patients and family members for approximately 2 , 000 MHC region variants to identify the specific genes that influence disease risk . Our results , for the first time , implicate four different MHC regions in SLE risk . We provide a list of candidate variants based on biologic and functional considerations that may be causally related to SLE risk and warrant further investigation . | [
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| 2009 | High-Density SNP Screening of the Major Histocompatibility Complex in Systemic Lupus Erythematosus Demonstrates Strong Evidence for Independent Susceptibility Regions |
Schistosomiasis mansoni is a debilitating and sometimes fatal disease . Accurate diagnosis plays a key role in patient management and infection control . However , currently available parasitological methods are laborious and lack sensitivity . The selection of target antigen candidates has turned out to be a promising tool for the development of more sensitive diagnostic methods . In our previous investigations , the use of crude antigens led to false-positive results . Recently , focus has been given to highly purified Schistosoma mansoni antigens , especially to circulating antigens . Thus , our main goal was to test different types of circulating cathodic antigen glycoprotein ( CCA ) , as “crude antigen , ” the protein chain of recombinant CCA and two individual peptides . These schistosome proteins/peptides were tested in a new diagnostic method employing immunomagnetic separation based on the improvement of antigen–antibody binding . Use of recombinant CCA as a diagnostic antigen allowed us to develop a diagnostic assay with high sensitivity and specificity with no false-negative results . Interestingly , the “crude antigen” worked as a good marker for control of cure after praziquantel treatment . Our new diagnostic method was superior to enzyme-linked immunosorbent assay in diagnosing low endemicity patients .
Schistosomiasis is a disease caused by one of six Schistosoma species , namely Schistosoma haematobium , S . guineensis , S . intercalatum , S . mansoni , S . japonicum , and S . mekongi [1] . Schistosomiasis occurs in the tropics and subtropics and is among the most important parasitic diseases worldwide , with a considerable socioeconomic impact [2] . Seventy-four countries are endemic , with roughly 120 million individuals being symptomatically infected and 20 million being severely affected [3] . Moreover , schistosomiasis represents an increasing problem in non-endemic areas , due to the growing number of immigrants and tourists [4]–[6] . Herein , diagnosis plays a crucial role in the monitoring of early infection as well as efficacy of treatment . Currently , the ‘gold’ standard remains the detection of S . mansoni eggs in stools . The Kato-Katz technique is the most widely used copromicroscopic method in epidemiological surveys [7] . However , because of low and sporadic egg production , the risk of having a large percentage of individuals who remain undiagnosed is considerable . Undiagnosed individuals remain infected and contribute to transmission of the disease [8] , [9] . Immunodiagnostic techniques are rapid , sensitive , convenient , and easy to apply , and hence they have been used to estimate infection rates with the goal of improving diagnosis in epidemiological surveys and identifying individuals to target for treatment [10]–[13] . Nonetheless , low specificity is frequently seen in immunodiagnostic assays , largely due to the use of crude antigens that contain many antigens that might be shared with unrelated pathogens . The systematic purification of antigens from Schistosoma spp . should allow the development of new anti-schistosome antibodies that might become valuable diagnostic tools [14] , [15] . Antigens excreted by adult worms into the circulation of the host , “circulating antigens” , have repeatedly been shown to be potent diagnostic target molecules [16]–[19] . Research on circulating antigens has focused on two genus-specific proteoglycan antigens derived from the schistosome gut: circulating anodic antigen ( CAA ) and circulating cathodic antigen ( CCA ) . Urine-dipstick diagnostic tests that detect schistosome CCA have the potential to provide more sensitive and rapid detection of for intestinal schistosomiasis in field-based surveys and they showed promising results in Africa [20] , although the tests are currently not suitable for rapid mapping of schistosomiasis in areas where S . mansoni and S . haematobium are co-endemic [21] . For this reason , defined diagnostic antigen ( s ) that increase sensitivity and specificity of serological assays and that can detect patients with low parasite loads would be of considerable benefit to schistosome control programs . In this regard , a new immunological assay , immunomagnetic separation ( IMS ) , was developed and refined by our group . A benefit of this approach is to effectively concentrate , rather than dilute , patient serum during incubation . We compared IMS to enzyme-linked immunosorbent assay ( ELISA ) using the same antigens in order to evaluate the effectiveness of this new approach . Therefore we assessed the sensitivity of different forms of CCA for their diagnostic potential . The antigens we focused on were: ( i ) “crude antigen” , ( ii ) protein chain of recombinant CCA ( CCAr ) , and ( iii ) two individual CCA peptides ( CCApep1 and CCApep2 ) . A prospective survey was performed with individuals from a low endemicity area for schistosomiasis mansoni . Final analyses were done by comparing IMS results to Kato-Katz and “Three Fecal Test” ( TF-Test ) , as parasitological assays . Here , we show that a well standardized immunological assay is sensitive and specific for the discrimination of low parasite load cases , by demonstrating that ( i ) the levels of parasite-specific immunoglobulin G ( IgG ) are significantly different from positive and negative individuals when IMS is performed with CCAr; ( ii ) IMS-CCAr achieved the most significant positivity ratio for diagnosis with no false-negative results; and ( iii ) IMS methodology was superior to ELISA in detecting the presence of schistosome infection in patients with low parasite loads .
This project was approved by the Ethical Research Committee of the Rene Rachou Research Center , Oswaldo Cruz Foundation for Animal Use ( CEUA L-0023/08 ) according to the International Guiding Principles for Biomedical Research Involving Animals developed by the Council for International Organizations of Medical Sciences ( CIOMS ) . The Ethical Research Committee of the Rene Rachou Research Center ( CEPSH/CPqRR 03/2008 ) and the National Brazilian Ethical Board ( 784/2008 , CONEP 14886 ) approved the human study . The study objectives were presented and explained to all participants and written informed consent was obtained through signing a form before admission to this study . Parents/guardians provided written consent on behalf of all child participants . A prospective study was performed in the communities of Buriti Seco and Morro Grande in Pedra Preta , a small village in a schistosomiasis-endemic area in the rural region of Montes Claros , Minas Gerais , southeast of Brazil [22] . This area was chosen based on the fact that the population had not been treated for schistosomiasis and also has a low population migration index . Schistosomiasis prevalence rate of 12% was reported in 2005 according to data provided by the Montes Claros Zoonosis Control Centre . The total amount of residents participating in the survey was 201 individuals ( 93 females and 108 males ) . Fifty-three healthy non-endemic area residents ( 35 females and 18 males , aged 22–65 years ) were selected to be used as negative control group . The volunteers presented no medical history of previous schistosomiasis . Paramagnetic microspheres ( 0 . 4 µm ) ( Estapor Microspheres , Merck; Lyon , France ) were sensitized with CCA ( 106 microspheres with 1 µg/ml of antigen/assay ) : ( i ) with “crude antigen”; ( ii ) CCAr; ( iii ) CCA pep1; and ( iv ) CCA pep2 . All incubation steps were performed under rotation to improve antigen-antibody binding . For sensitization step , antigens were diluted in 0 . 05 M carbonate-bicarbonate buffer pH 9 . 6 for 16 h at 4°C . Microspheres were washed four times with washing buffer using a 1 . 5 ml tube magnetic base ( Invitrogen; Grand Island , United States of America ) . Non specific-binding was blocked using 20% skim milk proteins in washing buffer at 4°C for 16 h . Microspheres were washed and maintained at 4°C . Prior to use , microspheres were washed , then 100 µl of a non-diluted serum sample were added in duplicate , followed by incubation at 37°C for 1 h . Microspheres were then incubated at 37°C for 1 h with 100 µl of peroxidase conjugated anti-human IgG Fc specific ( Sigma-Aldrich; St . Louis , United States of America ) diluted 1∶60 , 000 in washing buffer . Tubes were washed and 100 µl of substrate 3 , 3′ , 5 , 5-tetramethylbenzidine solution ( TMB/H2O2 ) ( Invitrogen; Grand Island , United States of America ) were added to each well and the reaction was stopped after 10 min . Using the magnetic base , supernatant were transferred to a microtiter plate and results were obtained as OD at 450 nm in a microplate reader ( Model 3550 , Bio-Rad Laboratories; Tokyo , Japan ) . ELISA were standardized based on a technique described elsewhere [31] after some modifications . Microtiter plates MaxiSorp Surface ( NUNC , Thermo Scientific; Roskilde , Denmark ) were coated with 100 µl per well of CCA diluted at 1 µl/ml in 0 . 05 M carbonate-bicarbonate buffer pH 9 . 6 for 16 h at 4°C . Plates were washed three times , then blocked by addition of 2 . 5% skim milk and incubating at 37°C for 1 h . Plates were washed , then 100 µl of individual serum sample diluted 1∶100 in 0 . 15 M phosphate buffer saline pH 7 . 2 were added in duplicate followed by incubation for 1 h . Plates were then washed and incubated with peroxidase conjugated anti-human IgG Fc specific ( Sigma-Aldrich; St . Louis , United States of America ) diluted in washing buffer at 1∶60 , 000 . 100 µl of substrate solution were added to each well and the reaction was stopped after 10 min of incubation and OD at 450 nm determined by microplate reader . The cut-off value of each ELISA method was determined by receiver operating curve ( ROC ) and they were defined as 0 . 250 for ELISA-crude Ag , 0 . 103 for ELISA-CCAr , 0 . 117 for ELISA-CCApep1 and , 0 . 166 for ELISA-CCApep2 ( A = 0 . 765 , 0 . 924 , 0 . 954 , 0 . 824 , respectively ) . Positive and negative controls were assayed for both techniques . Data derived from absorbance values were analyzed with Minitab software ( Minitab Inc , United States of America ) by Kolmogorov-Smirnov normality test . Normal distributed data were analyzed by Student's t test and non-normal distributed data were analyzed by Mann-Whitney test . Comparisons between methods were done by chi-square ( χ2 ) analysis ( p<0 . 05 as significance level ) . The sensitivity , specificity , cut-off values , and likelihood ratios were determined with Prism 4 . 0 software . Agreement between methods was measured using the Cohen coefficient [32] and analyzed according the Landis & Koch definition [33] , with software ComKappa 2 . 0: 1 . 00-0 . 81 almost perfect agreement; 0 . 80-0 . 61 substantial agreement; 0 . 60-0 . 41 moderate agreement; 0 . 40-0 . 21 fair agreement; 0 . 20-0 slight agreement; <0 poor agreement . CCA gene reference sequence was obtained from the database of the National Center for Biotechnology Information ( NCBI ) , GenBank AAB53003 . 1 .
Four different forms of CCA were obtained and tested in IMS assays . Initially , the “crude antigen” was obtained from adult worm extracts and the CCAr were induced in E . coli . Final products are shown on Figure 1 and both demonstrated a 30 kDa protein , correlating with previously reported characteristics of CCA [25] . Afterwards , two CCA peptides were synthesized based on predicted B cell epitopes . These four CCA were then tested as diagnostic assay candidates , using a monoclonal IgG1 antibody against S . mansoni CCA ( lot 5F4 . B4 , University of Georgia , Monoclonal Antibody Facility , Athens , United States of America ) . Results are shown on Figure 2 where a significant reaction was seen for all four CCA in comparison to bovine serum albumin ( BSA ) as our negative control . The prospective study involved the communities of Buriti Seco and Morro Grande in Pedra Preta , southeast Brazil . These communities are areas of low endemicity for schistosomiasis mansoni , and with low migration index and no history of previous treatment . Among the 201 individuals participating in the survey , 50 patients including adults and children were selected to provide serum samples ( 24 females and 26 males ) . These patients were first diagnosed by Kato-Katz and TF-Test and results showed a parasite load range between 1 and 555 EPG among the group . All patients were treated as recommended and they resubmitted stools for Kato-Katz testing 30 days post treatment when serum samples were obtained . Retreatment was done in all reinfection cases . The 50 serum samples selected from people of Pedra Preta , together with the healthy donors' serum samples were screened by IMS using the four different antigens described: sensitized with “crude antigen” ( IMS-crude Ag ) , CCAr ( IMS-CCAr ) , CCA pep1 and CCApep2 ( IMS-CCApep1 and IMS-CCApep2 ) . The 53 healthy donors were initially tested for antibodies to schistosomes by ELISA-SWAP and ELISA-SEA . Only one individual was reactive for both antigens and was not therefore removed from the healthy ( negative ) control group . Further , cut-off value , positivity ratio , sensitivity , and specificity of each IMS methodology were determined by ROC , which are represented in Figure 3 . IMS-crude Ag presented a sensitivity of 90% and a specificity of 92% for a cut-off value of 0 . 197 , which showed that the “crude antigen” might be considered a good marker for schistosome infection with five missing positive patients and four missing negative individuals . Moreover , IMS using CCAr showed an excellent result providing a sensitivity of 100% and specificity of 96% for a cut-off of 0 . 063 , where only two negative individuals presented false-positive results . Finally , IMS using CCA peptides showed similar effectiveness with the same sensitivity ( 80% ) and a specificity of 90% and 92% , respectively for cut-off values of 0 . 164 and 0 . 133 . When analyzing false-positive and false-negative results , we could see that the use of these 20 amino acids peptides decreased the diagnostic effectiveness with 10 false-negative results for both peptides , five for IMS-CCApep1 , and four for IMS-CCApep2 . The positivity ratios achieved by each IMS method were 91% ( 93/102 ) , 98% ( 100/102 ) , 85% ( 87/102 ) and 86% ( 88/102 ) , for IMS-crude Ag , IMS-CCAr , IMS-CCApep1 and IMS-CCApep2 , respectively . The positivity ratio achieved by IMS-CCAr was significantly higher than the other three IMS assays ( χ2 = 0 . 74 , p<0 . 05 ) . Figure 4 shows the individual OD for each positive and negative patient as determined by each IMS protocol . Not all the infected patients showed an adequate post treatment follow-up , since no eggs were found in any patient stools 30 days after drug administration . Forty-two of the 50 praziquantel-treated patients agreed to donate serum samples once more . Diagnostic results obtained by the four IMS protocols from both time points were compared with the purpose of detecting any differences in IgG antibody titers . From the observations in each period , 98% of the patients became negative via IMS-crude Ag ( 41/42 ) , whereas 81% became negative via IMS-CCApep1 ( 34/42 ) and 93% via IMS-CCApep2 ( 39/42 ) . IMS using CCAr identified 55% of the patients as negative for S . mansoni 30 days after treatment ( 23/42 ) , as shown in Figure 5 . In addition to the fact that IMS was standardized with non-diluted serum , the incubation steps were performed under rotation with the purpose of improving antigen-antibody binding and thus , diagnostic sensitivity . To test this hypothesis , “crude antigen” , CCAr , and CCA peptides were used in ELISA ( ELISA-crude Ag , ELISA-CCAr , ELISA-CCApep1 , and ELISA-CCApep2 ) and the results were compared to data obtained using IMS analysis . Significant differences were observed in the positivity ratios . Forty-five positive patients were correctly diagnosed by IMS-crude Ag , but only 35 were diagnosed by ELISA-crude Ag ( χ2 = 0 . 21 , p<0 . 05 ) . All the patients were positive for IMS-CCAr in comparison to 48 patients diagnosed by ELISA-CCAr ( χ2 = 0 . 48 , p<0 . 05 ) . On the other hand , IMS-CCApep1 and ELISA-CCApep1 presented no difference with 40 positive patients . However , comparing CCApep2 , statistical differences were detected with 40 patients diagnosed by IMS-CCApep2 and 37 by ELISA-CCApep2 ( χ2 = 0 . 21 , p<0 . 05 ) . Analysis of Cohen's kappa index showed a moderate agreement of 0 . 47 ( ±0 . 10 ) ( 69/102 ) between IMS-crude Ag and ELISA-crude Ag . The same agreement was found for IMS-CCApep2 and ELISA-CCApep2 that showed an agreement of 0 . 48 ( ±0 . 11 ) ( 66/102 ) . A better agreement was found for IMS-CCAr versus ELISA-CCAr and , IMS-CCApep1 versus ELISA-CCApep1 , which indicated a substantial agreement of 0 . 66 ( ±0 . 10 ) with a positivity of 84/102 and 0 . 70 ( ±0 . 11 ) with 75/102 , respectively . Data obtained from a prospective parasitological diagnosis with 18 slides of Kato-Katz plus TF-Test confirmed the low parasite load of residents of Pedra Preta that were infected by S . mansoni ( 1 to 555 EPG ) . Based on the World Health Organization thresholds [34] , most of the infected individuals ( 48/50 ) presented low parasite load ( 1–99 EPG ) . Hence , those individuals were subdivided into three groups: 1–10 EPG , 11–30 EPG , and >30 EPG in order to determine the sensitivity of each methodology used . Groups were examined by IMS and ELISA methods . Results are shown in Table 2 .
Population and treatment-based control programs have been successful in reducing the intensity of infection and severe morbidities associated with schistosomiasis . However , transmission remains active in highly endemic areas , and recurring low-level reinfection is likely to be associated with subtle but persistent morbidities [35]–[37] . Adequate case-finding is essential for the effective execution of control programs . Diagnosis has mainly depended upon finding eggs in patients' fecal samples . However , fluctuation in egg output and the chance of missing light infections necessitate repeated examinations [9] . Serologic testing has been used to enhance our ability to detect the disease in order to be more sensitive in demonstrating light infections [10]–[12] , [38] . This study applied a multievaluation approach , combining specific antibody detection for four different antigens with an investigation performed on parasitological data in efforts to produce a more field-applicable assay format . The identification and description of CCA as a constitutional glycoprotein from adult worms gut [39] has allowed the development of assays for detecting antibodies or circulating antigens in urine and serum samples of infected individuals [18] , [19] , [40]–[42] . When CCA was used in those assays , sensitivity was lower than expected which was partly explained as a consequence of low levels of circulating antigens being regurgitated by adult worms [43] , especially in patients with low parasite loads . To solve this problem , we standardized a method called IMS that uses paramagnetic beads in contact with non-diluted serum and is based on incubation steps performed under rotation , allowing an increased antigen-antibody binding . The IMS method was evaluated with four different CCA , including the “crude antigen” , the protein chain of CCAr and , also , two individual peptides of 20 amino acids . Since schistosomiasis epidemiological profiles show an increase in the number of low endemicity areas , the sensitivity of each IMS was validated with patients' samples from an endemic area in southeast Brazil , where most of them showed low parasite load based on 18 Kato-Katz thick smears plus TF-Test . Although “crude antigen” prepared from S . mansoni adult worm showed good results in IMS-crude Ag methodology , CCAr presented more significant results , especially when all the positive cases were properly detected with a sensitivity of 100% and , only two false-positive results , giving rise to a specificity of 96% ( χ2 = 0 . 74 , p<0 . 05 ) . Whereas IMS-crude Ag achieved 90% sensitivity and 92% specificity , with five false-negative and four false-positive results . Comparison between the positivity ratios revealed that IMS-CCAr was significantly superior to IMS-crude Ag for diagnosing low endemicity patients ( χ2 = 0 . 74 , p<0 . 05 ) . The prior structural difference between “crude antigen” and CCAr that justifies their specificity lays on the fact that CCA is present in “crude antigen” sample in its native form as a whole glycoprotein , while the recombinant CCA was expressed in E . coli and contains only the protein chain of the native CCA , which was not glycosylated . Native CCA glycoprotein contains 0-linked poly ( Lex ) carbohydrate chains with approximately 25 repeating units . Carbohydrate chains containing multiple Lex determinants have been identified on several glycolipids not only from schistosomes but also from other parasites [44] , [45] , from human adenocarcinomas [46] and also , circulating granulocytes carry relatively high abundance of branched N-linked polysaccharides having Lex repeating units [47] , [48] . Additionally , Lex sequence is particularly immunogenic , playing an important role during inflammatory processes , especially in granulocyte and monocyte adhesion processes and recruiting granulocytes to sites of inflammation [49] . It is conceivable that the use of the native CCA glycoprotein in schistosome diagnosis leads to false-positive results , when IgG antibodies against its most immunogenic portion ( the Lex units ) can be mistakenly detected . In contrast , the CCA protein sequence of 347 amino acids , obtained by recombinant expression , is exclusively found in the genus Schistosoma with no description in any other parasite or human proteins , confirmed by Blast search . The use of synthetic peptides corresponding to a single continuous epitope may increase the specificity of an immunoassay in the same way that monoclonal antibodies recognizing a single epitope do compared to polyclonal antiserum . Thus prediction of B cell epitopes was performed and the two best conformations were considered . Same identity analysis was done and both peptides were recognized by the CCA-specific monoclonal antibody . When evaluating each peptide for the diagnosis of S . mansoni using IMS methodology , data showed similar results for these two methods , as demonstrated by ROC . IMS-CCApep1 presented 80% of sensitivity and 90% of specificity with 10 missing positive cases and five missing negative individuals . IMS-CCApep2 showed the same sensitivity and four missing negative cases , leading to a specificity of 80% . Despite the possible advantage of increasing diagnosis specificity with individual peptides , our data did not show any disparity in specificity between IMS-crude Ag and IMS-CCApep1 or IMS-CCApep2 when similar identification of false-positive cases was found by the three methods . In a final comparison of the four antigens , CCAr continued to yield a higher positivity ratio of 98% compared to “crude antigen” ( 91% ) , CCApep1 ( 85% ) , and CCApep2 ( 86% ) ( χ2 = 0 . 74 , p<0 . 05 ) . Recombinant protein-based diagnosis offers important advantages because higher antigen concentrations can be used , and nonspecific moieties are not present in those proteins , as they may be in crude antigens or in native proteins . Nevertheless , due to the restricted amino acids sequence of a single peptide , the use of each sequence has been deemed impractical . This suggests that for peptides to be used a large pool of epitopes would be required to achieve wide population coverage and the cost would increase significantly . All the individuals who presented eggs in stools were treated , as recommended by the Brazilian Ministry of Health . These positive patients were invited to resubmit stool samples subjected to the Kato-Katz technique after 30 days of chemotherapy and none of them presented eggs in stool at that time . Forty-two patients were followed up by IMS methodology . A decrease was detected on IgG antibody level for some individuals in the four IMS assays . Forty-one patients presented low IgG levels for IMS-crude Ag , whereas only 23 presented for IMS-CCAr , 34 for IMSCCApep1 , and 39 for IMS-CCApep2 . Although the dynamics of the post-treatment antibody levels are variable [50] , IMS-crude Ag , IMS-CCApep1 , and IMS-CCApep2 results were considered extreme . The analysis of the control of cure of IMS-CCAr would benefit from patients' diagnosis in different time points for at least one year . The establishment of antibody titers timeline may help to distinguish the treatments that result in parasitological cure from those that are only partially successful . The distinctive patient who was positive for IMS-crude Ag was also positive for the other IMS methodologies showing no inconsistency on the diagnosis performed for IMS . The possibility that this patient may have been reinfected or presented immature worms at the moment of treatment cannot be excluded . No data involving the detection of CCA in serum of patients infected for less than 6 weeks have been published to date . However , CCA can be detected in mice 3 weeks post-infection , plus freshly transformed schistosomula , or isolated adult worms excrete CCA in vitro immediately after transformation [51] . Since other investigators have reported low sensitivity values for the detection of antibodies against CCA or the circulating antigen itself in urine and serum samples using immunological assays for S . mansoni diagnosis [19] , [41] , [42] , [52] , we compared our IMS data with results obtained by ELISAs that were standardized with the same CCA antigens . The main differences of these two methodologies were: ( i ) IMS was performed with non-diluted serum , while ELISA used only diluted samples ( 1∶100 ) , and ( ii ) incubation steps were done under rotation for IMS , different from the ELISA . As expected , the use of “crude antigen” , CCAr , and CCApep2 on IMS methodology showed significant higher sensitivity than ELISAs . Indeed , IMS-crude Ag were capable of detecting 10 extra positive patients than ELISA-crude Ag ( χ2 = 0 . 21 , p<0 . 05 ) , whereas IMS-CCAr and IMS-CCApep2 detected two and three extras positive patients , respectively than ELISAs ( χ2 = 0 . 48 and 0 . 21 , p<0 . 05 ) . The superiority of IMS in detecting positive cases was evident when low egg burden patients were divided into three groups based on parasite load . Interestingly , our data showed that IMS-crude Ag , IMS-CCAr and IMS-CCApep2 continued to show a higher sensitivity than ELISA even for patients with parasite loads as low as 1–10 EPG , and this observation was especially demonstrated by IMS-CCAr . Cohen's kappa index confirmed a moderate agreement between IMS and ELISA for CCA “crude antigen” ( 0 . 47±0 . 10 ) and CCApep2 ( 0 . 48±0 . 11 ) , and a substantial agreement for CCAr ( 0 . 66±0 . 10 ) and CCApep1 ( 0 . 70±0 . 11 ) . The present study was undertaken to develop an assay that might be more field applicable than the ELISA for testing of individuals from low endemicity areas . The comparison between different types of CCA allowed the evaluation of the specific capability of each assay in diagnosing positive and negative individuals and the occurrence of false-positive and/or false-negative results . IMS-CCAr presented the most significant positivity ratio for the primary diagnosis . Due to the restricted single epitopes of individual peptides , the 20 amino acids sequence used here showed no advantages in comparison to other antigens . Results revealed that the detection of specific IgG antibody against CCA antigens in serum may be used as an important tool for the diagnosis of S . mansoni for patients with low parasite load . | Schistosomiasis mansoni is a debilitating and sometimes fatal disease that affects many individuals in Africa and Brazil . Currently available diagnostic methods are not sensitive for patients with low parasite load which leads to underreported cases . The selection of target diagnostic antigen candidates is a promising tool for the development of a new and more sensitive assay . In this study , we focused on different types of circulating cathodic antigen ( CCA ) for development of an innovative assay . This new assay is called immunomagnetic separation and it uses magnetic microspheres attached to the antigens in order to improve the diagnostic sensitivity . Best results were found when we used the protein chain of the recombinant CCA showing high sensitivity and specificity with no false-negative results . Together , the use of “crude antigen” presented good results for the control of cure . Our new assay was superior to enzyme-linked immunosorbent assay in discriminating positive and negative cases , especially related to patients with low parasite load . | [
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| 2013 | New Approaches with Different Types of Circulating Cathodic Antigen for the Diagnosis of Patients with Low Schistosoma mansoni Load |
The fucosylated ABH antigens , which constitute the molecular basis for the ABO blood group system , are also expressed in salivary secretions and gastrointestinal epithelia in individuals of positive secretor status; however , the biological function of the ABO blood group system is unknown . Gastric mucosa biopsies of 41 Rhesus monkeys originating from Southern Asia were analyzed by immunohistochemistry . A majority of these animals were found to be of blood group B and weak-secretor phenotype ( i . e . , expressing both Lewis a and Lewis b antigens ) , which are also common in South Asian human populations . A selected group of ten monkeys was inoculated with Helicobacter pylori and studied for changes in gastric mucosal glycosylation during a 10-month period . We observed a loss in mucosal fucosylation and concurrent induction and time-dependent dynamics in gastric mucosal sialylation ( carbohydrate marker of inflammation ) , which affect H . pylori adhesion targets and thus modulate host–bacterial interactions . Of particular relevance , gastric mucosal density of H . pylori , gastritis , and sialylation were all higher in secretor individuals compared to weak-secretors , the latter being apparently “protected . ” These results demonstrate that the secretor status plays an intrinsic role in resistance to H . pylori infection and suggest that the fucosylated secretor ABH antigens constitute interactive members of the human and primate mucosal innate immune system .
In turbulent systems such as the oro-gastro-intestinal tract , adaptation to local niches requires microbial adherence properties to match host receptors and thereby stabilize microbial colonization or infection . H . pylori achieved this goal by developing the BabA and SabA adhesins , which bind to the host fucosylated blood group ( bg ) ABO antigens ( denoted the ABH antigens ) and sialylated Lewis antigens , respectively [1–5] . These adhesins are relevant to H . pylori pathogenicity since BabA-positive strains are frequently present in both peptic ulceration and gastric cancer [5–8] . H . pylori infection and associated gastritis induce expression of mucosal sialylated receptors for the SabA adhesin that also attract peripheral neutrophils to local areas of inflammation [2 , 9] . In turn , H . pylori SabA adhesin acts as a “selectin-mimic” in mediating binding to both sialylated epithelium and to neutrophils [2 , 10] . The A , B , and H antigens are complex fucosylated carbohydrates expressed on erythrocytes of all individuals of blood group A , B , or O , respectively . The common denominator is the Fucα1 . 2-glycan presented by all three ABH antigens , because the bone marrow ( from where the erythrocytes originate ) express the common H- ( fucosyl ) transferase . Lack of fucosylated ABH antigens on erythrocytes in circulation ( Bombay phenotype ) is exceedingly rare . The ABH antigens are also expressed along the oro-gastro-intestinal ( GI ) mucosal lining in individuals of “positive secretor status” ( secretor phenotype , Se ) [11 , 12] . This is because Se individuals express the Secretor- ( fucosyl ) transferase , which is the enzyme that produces the Fucα1 . 2-glycan structure , the hallmark of ABH antigens in saliva , and gastrointestinal mucus secretions and epithelium ( see Figure 1A for bg antigens ) . Due to the mucosal secretor-transferase , gastrointestinal epithelia of secretors express blood group O antigens [Lewis b ( Leb ) and H antigens] , which can be extended by a GalNAc- or Gal-residue into bg A or B antigens , respectively [12] ( Figure 1 ) . In contrast , individuals of non-secretor phenotype , Se0 , lack the secretor-transferase altogether , and make the shorter Lewis a antigen ( Lea ) [13] ( Figure 1 ) . A third and most recently described human secretor phenotype , the weak-secretor phenotype , Sew , is characterized by expression of both Lea and Leb antigens . The composite of Lea and Leb antigens is the consequence of a weak ( mutated ) form of the secretor transferase [11] ( Figure 1B and Figure 1C ) . A protective mucus layer comprised of mucin glycoproteins carrying multitudes of carbohydrate structures covers the mucosal surfaces . This glyco-mucus layer exhibits rapid turnover and is shed into the GI tract lumen together with scavenged and aggregated secretions , desquamated cells , and microorganisms . Mucins from both humans and Rhesus monkeys efficiently bind H . pylori via fucosylated carbohydrates [14 , 15] . Thus , fucosylated host secretions such as mucins , saliva , and milk , inhibit adherence of H . pylori and other microbial pathogens to the mucosal cell surfaces [16–18] . A majority of Caucasians ( 80% ) are secretors , whereas 20% of them are non-secretors , and weak-secretor individuals are rare or not yet discovered . In contrast , weak-secretor individuals are common among Chinese , Japanese , Polynesians , Australian aborigines , and African-Americans [11] . The skewed prevalence in secretor phenotypes suggests selection in response to specific types of infections or other environmental conditions . Indeed , Se0 individuals are more at risk for urinary tract infections [19 , 20]; Se0 subjects also express higher inflammatory reactivity and sialylated host antigens to H . pylori infection , which may explain the higher prevalence of peptic ulcer disease observed in those subjects [21–23] . In contrast , Se0 individuals are less likely to develop Norwalk virus–induced acute gastroenteritis due to the lack of mucosal Se-dependent ABH antigens that mediate mucosal adherence of virus particles in Se subjects [24] . Here , the dynamics of mucosal responses to H . pylori infection was studied in Rhesus monkeys because this animal is subject to natural H . pylori infection and exhibits human-like patterns of gastric glycosylation [15 , 25–27] . Furthermore , the complete description of the macaque genome is available for this important primate model [28] . After determining the ABO blood groups and secretor phenotypes of 41 Rhesus monkeys , we inoculated virulent H . pylori strains to representative secretor groups . During the persistent H . pylori infection that ensued , gastric mucosal fucosylation transiently decreased and sialylation reciprocally increased . In vivo H . pylori density , inflammation , and in vitro adherence of H . pylori to sialylated antigens were all lower among weak-secretors ( the common Rhesus monkey phenotype ) compared to regular secretors . We propose that mucosal glycosylation on GI cell surfaces and secretions as determined by secretor status together influence the course of H . pylori infection as part of the primate innate immunity .
Immunostaining of gastric biopsies from 41 Rhesus monkeys without H . pylori infection demonstrated that 28 animals ( 68% ) were of bg B and 13 ( 32% ) were of bg AB . Thus , mucosal bg B antigen was expressed in all 41 monkeys . The positive immunostaining of bg B and A antigens in the gastric epithelium also demonstrates that all animals were secretors ( Se ) of ABH-antigens and , hence , that non-secretors ( Se0 that lack ABH antigens in the GI mucosal lining ) were not represented in this large group of monkeys ( Figure 1 ) . In addition , 34/41 ( 83% ) animals expressed both Lea and Leb , an antigen combination that , in humans , is regarded as the characteristics of the weak-secretor phenotype ( Sew ) . Only seven monkeys did not express mucosal Lea and they were identified as regular Se . Thus , Sew status with the weak form of secretor transferase appears to be the predominant secretor phenotype in the Rhesus monkey . A total of ten animals , 3/7 Se ( bg B ) and 7/34 Sew ( bg B , Lea+ Leb+ ) animals , were selected for H . pylori inoculation experiments , and two virulent CagA and VacA positive H . pylori strains became predominant in most animals after a few months [29] . We determined that these two strains could bind both ABH and sLea/x antigens ( see Materials and Methods ) . Persistent high-grade infection was observed in 9/10 monkeys and only animal 86D02 ( Sew ) demonstrated low-grade infection ( H . pylori score of 1 , gastritis score ranging from 1 to 2 , but negative cultures , Table S1 ) . H . pylori infection and associated gastritis developed similarly in the antrum ( Figure 2A and 2C ) and in the corpus ( Figure S2A and S2C ) starting at day 7 after infection . In both regions of the stomach , H . pylori in vivo density score was 2-fold higher in Se than in the dominant Sew phenotype from 4 to 10 months after inoculation ( Figures 2B and S2B , P <0 . 05 ) . Consistent with the higher infection load , secretors had increased levels of gastritis compared to weak-secretors but the difference was significant only in the corpus ( Figures 2D and S2D ) . Interestingly , corpus gastritis scores increased significantly compared to pre-inoculation levels only in Se monkeys but not in Sew animals ( Figure S2D ) . Finally , H . pylori infection density and gastritis were strongly correlated ( Table 1A ) . In all seven Sew monkeys , both Leb and Lea antigens were expressed in surface epithelium before experimental infection ( Figure 3A ) . At 1–4 weeks after inoculation , Leb ( Figure 3B ) and/or Lea expression ( data not shown ) transiently decreased in 5/7 monkeys . However , by 2–4 months , expression of Leb/Lea returned to pre-inoculation levels or higher ( Figure 3C ) . Similarly , the fucosylated Lewis y ( Ley ) antigen expressed in the gastric glands of both Se and Sew non-infected animals initially decreased in response to inoculation , and then returned to baseline in 8/10 monkeys ( data not shown ) . Thus , H . pylori infection causes general suppression of fucosylation in the gastric mucosa , as reflected by the alterations in the different Lea , Leb , and Ley antigens ( this series of fucosylated Lewis antigens are described in Figure 1A ) . The mean percentage of sialyl-Lewis a ( sLea ) and sialyl-Lewis x ( sLex ) positive surface epithelial cells rapidly increased in all monkeys with established H . pylori infection ( Figure 2E and 2F in antrum and in Figure S2E and S2F in corpus ) . Sialyl expression is also illustrated in Figure 3D–3I . Expression of sLea was stronger than that of sLex , and the percentage of sLea positive cells rapidly increased 4-fold within a week of inoculation . Thus , a majority of surface epithelial cells ( 60% ) express strong sLea at 2 months both in antrum and corpus ( Figures 2E and S2E ) . Similarly , expression of sLex was strongest at 2 months ( Figure 2F ) . Expression of sialylated antigens later decreased in seven monkeys ( Figures 2E and S2E and also illustrated in Figure 3F and 3I ) . Importantly , sLea and sLex expression in surface epithelium of antrum correlated with both H . pylori density and gastritis scores ( see Table 1B ) . The observed shifts in glycosylation were not due to pre-dysplastic alterations of the gastric mucosa such as intestinal metaplasia , because the spatial distribution of expression patterns for the two common gastric mucins MUC5AC and MUC6 ( Figure S3 ) were unchanged during the 10-month period of experimental infection , with no aberrant MUC2 or atypical sulfo-mucins being detected ( data not shown ) . The significant correlation between H . pylori density and gastritis and the similarity in time course ( Figures 2A , 2C , S2A , and S2C ) suggest that the higher level of mucosal inflammation is a consequence of the higher infection load . Because of the exclusive binding properties of BabA ( for fucosylated antigens ) and SabA ( for sialylated antigens ) mediated binding properties [3 , 30] , H . pylori mutants were applied as lectin-like tools in an in vitro adherence assay to functionally map detailed shifts specific for secretor-dependent mucosal fucosylation and sialylation induced by H . pylori infection and the associated inflammatory responses . The ΔsabA ( BabA+ ) mutant bound fucosylated , secretor-dependent ABH/Leb antigens ( but not the shorter Lea or sialylated antigens ) , whereas the ΔbabA ( SabA+ ) mutant bound inflammation-associated sialylated ( but not fucosylated ) antigens [2 , 5 , 31] . Representative adherence patterns of binding to Rhesus monkey biopsies by the ΔsabA ( BabA+ ) and the ΔbabA ( SabA+ ) mutants are shown in Figure S1 . In histo-tissue sections of gastric mucosa , the ΔsabA ( BabA+ ) mutant binds to the foveolar epithelium cell surfaces and intra cellular mucins and , in addition , to the secreted fucosylated mucus layer . These binding tests demonstrate that BabA positive bacteria co-localize with the MUC5AC mucin ( compare Figure S3A and S3C ) . By comparison , the ΔbabA ( SabA+ ) mutant does not bind to the surface epithelium of non-infected Rhesus monkey gastric mucosa , and SabA-mediated binding instead colocalizes with the sialylated MUC6 mucin expressed in the deeper located glandular region ( compare Figures S3B and S3D ) . However , when the mucosa during infection has responded with expression of inflammation-associated antigens , the ΔbabA ( SabA+ ) mutant binds to the sialylated foveolar epithelium and mucus layer ( illustrated in Figure S1 ) . Importantly , in vivo , H . pylori binds to the intact apical cell surfaces and secreted extracellular mucins in mucus , whereas by the in vitro adherence assay and use of histo-tissue sections , the H . pylori bacterial cells also bind to intracellular mucins that have been exposed by histo sectioning of the mucosal cells and tissue . Thus , the in vitro adherence method provides an unique opportunity to investigate time-dependent changes in secretor-dependent mucosal glycosylation that occurs during H . pylori infection , i . e . , using small pinch biopsies collected at regular intervals during an extended study period and without sacrificing the animals . In the basal state , the BabA-positive ΔsabA mutant adhered in vitro to the non-inflamed , non-infected gastric mucosa of all ten Rhesus monkeys ( see Figures 4A and S3C ) , in accordance with the mucosal expression of secretor-dependent bg B antigen in all monkeys . Following H . pylori inoculation , the dynamics of glycosylation and inflammation were tightly correlated ( see Table 1 ) , as revealed by the rapid and transient decrease in BabA-mediated in vitro adherence that paralleled the expression patterns of fucosylated antigens ( Compare Figure 4A with Figure 3A–3C ) . Expression of secretor-dependent mucosal fucosylation involved in H . pylori adhesion remained strong and robust throughout the 10-month observation period in Sew subjects compared to the rapid initial decrease in fucosylation in Se individuals , as revealed by BabA-mediated in vitro adherence to fucosylated bg antigens ( Figure 4A ) . In addition , BabA-mediated in vitro adherence to fucosylated mucosa was 1 . 7 times higher in Sew than in Se monkeys ( P <0 . 001 ) and inversely correlated with in vivo H . pylori density , gastritis , and sLex expression ( see Tables 1D and 1E , respectively ) . Thus , Sew with strong and robust expression of gastric fucosylation have both lower H . pylori infection density and less gastritis compared to Se individuals ( Figures 4A , 2B , and 2D ) . The strong fucosylation in Sew monkeys , as revealed by the robust BabA-mediated in vitro adherence , reflects the high density of fucosylated mucins in the Sew gastric mucosa . Indeed , gastric mucins purified from healthy , non-infected Rhesus monkey bind H . pylori primarily via fucosylated structures ( Figure S3E ) , similarly to human mucins binding to H . pylori [4 , 14] . In contrast to BabA-mediated adherence , in vitro adherence to sialylated glycoconjugates by the SabA positive ΔbabA mutant was absent in the surface epithelium of healthy , uninfected mucosa . However , SabA-mediated adherence rapidly increased in response to inoculation , thus demonstrating induction of mucosal sialylation . SabA-mediated in vitro adherence to the surface epithelium correlated with both in vivo H . pylori density and gastritis ( Table 1D ) and with expression of the inflammation-associated sLex and sLea antigens ( Table 1E ) . Furthermore , SabA-mediated in vitro adherence to sialylated glycoconjugates was 1 . 9-fold higher in Se than in Sew monkeys ( Figure 4B ) . The rapid induction of SabA-mediated binding to the sialylated surface epithelium of the gastric mucosa ( Figure 4B ) demonstrates that Se monkeys react with stronger inflammatory response and higher recruitment of inflammatory cells , i . e . , gastritis , whereas the Sew animals with low grade infection and inflammation provides sparse mucosal sialylation and only modest recruitment of inflammatory cells . Fucosylation and sialylation levels follow opposite dynamics during the full 10-month observation period ( fucosylation in Figures 4A and 3A–3C; sialylation in Figures 4B and 3D–3I; Table 1C ) . Thus , Sew individuals are more robust in mucosal fucosylation and balanced in sialylation , which confers lower inflammatory level , lower gastritis , and lower H . pylori infection density as compared to Se individuals ( Figures 2B , 2D , S2B , and S2D ) .
The present study demonstrates that Sew individuals have robust mucosal fucosylation and lower mucosal inflammatory and sialylation responses to experimental H . pylori infection . Therefore , Sew monkeys would be expected to better tolerate persistent infections and to be more prevalent in regions with high incidence of this type of infections . Such a selection for specific blood group phenotypes is strongly suggested by our observation that all 41 macaques included in the study express blood group B antigens , especially since the blood group B is the least common worldwide of the ABO phenotypes . Interestingly , the ancestors of most macaques used in the present study originated from Northern India , which is the region with highest worldwide prevalence of bg B phenotype in humans [32 , 33] . The recent Rhesus macaque genome annotation revealed that the Indian and Chinese groups diverged some 160 , 000 years ago [34] . Interestingly , all the ABO blood groups are represented among macaques that moved to Southeast Asia/Thailand [35] , a region in which bg B is lower in humans [32] . The human and macaque paralleled demographic selection for high prevalence of Sew and bg B phenotype may result from selection by endemic infectious disease [36] as the two species often suffer from the same infectious diseases . Of particular relevance for the Indian subcontinent , bg B individuals are less likely to become infected by Vibrio cholera [37] . In addition , phylogenetic analysis suggests that bg B arose several times and independently from bg A , indicating that the genes of these blood group transferases are prone to convergent evolution [38] . The ABO blood groups were discovered over a century ago [39] , chemically described 50 year ago [40 , 41] , and cloned almost 20 years ago [42] . Similarly , the secretor system was described 60 years ago [43] , blood group antigens were characterized from human intestine over 30 years ago [44] and the Se transferase was cloned in 1995 [13] , but the biological and functional role of the ABO system has remained an enigma [45] . Here , we show that secretor phenotype determines the dynamics of mucosal glycosylation in response to H . pylori infection and conditions the nature of the host response . Thus , H . pylori infection is associated with an increase in sialylated mucosal antigens and a concurrent decrease in fucosylated mucosal antigens . The loss of fucosylation during acute H . pylori infection is probably a consequence of the fast induction in expression of inflammation-associated sialyl-transferases and the resulting competition for carbohydrate chains by glycosyl ( sialyl and fucosyl ) transferases ( Figure 1B ) . Competition between fucosyl- and sialyl-transferases for the same carbohydrate core chains was demonstrated by competition experiments where di-saccharides suppressed both sialylation and formation of selectin ( endothelial cell adhesion ) ligands on cancer cells [46] . The present series of results also reveals that , in contrast to Se monkeys , Sew individuals maintain strong and robust expression of fucosylated mucosal ABH antigens during H . pylori infection ( Figure 4A ) . Interestingly , intestinal mucosal glycosylation also becomes fucosylated in response to establishment of conventional bacterial flora in gnotobiotic mice [47] . The combined results suggest that mucosal fucosylation could be a mixed blessing for H . pylori . Indeed , large mucin molecules with fucosylated high-affinity binding sites for BabA could be exploited by H . pylori as in vivo binding sites , but they may also act as scouts of the host glycan innate immunity system . Thus , the mucosal fucosylation represents a protective scavenger factor that reduces infection density , especially since H . pylori infection also increases gastric mucus secretion [48] . The present demonstration that mucosal fucosylation in response to H . pylori infection reduces bacterial density and associated inflammation and , in particular , impacts on infection in Sew monkeys due to stronger mucosal fucosylation phenotype , strongly suggests that ABH secretor-dependent mucosal glycosylation modulates innate immunity responses and may contribute to variable risk of gastric disease .
The experiments were conducted according to the “Guide for the Care and Use of Laboratory Animals” [49] . All procedures involving animals were reviewed and approved by the USUHS animal care and use committee . The ancestors of 37 animals had been captured in India , while four were of mixed Chinese and Indian origin . The localization of Lewis antigens in these monkeys before H . pylori inoculation has been investigated [15] . H . pylori and H . heilmannii infection was eradicated in all monkeys 6 months before inoculation [29] . The ten male monkeys that were inoculated all originated from India and were 4–13 years old ( mean 7 . 1 ) . F754 , 86D02 , T4C , 8V5 , F436 , 82A49 , and 8PZ animals were Sew , and 86D06 , E6C , and 85D08 were Se . Seven low-pass H . pylori strains were cultured , characterized ( five were CagA+ and two were CagA- ) ( see Text S1 ) , and inoculated to monkeys as reported [29] . Their binding properties were analyzed by RIA [31]: J170 , J254 , J166 , and J258 bound both sLex and Leb , J282 bound Leb , and J178 bound sLex . Genta-stained sections were used to determine H . pylori density scores and the Sydney system was used to determine gastritis scores [29] . Sulfo-mucins were detected with high-iron diamine stain [17] . Immunohistochemistry was performed as described [15] ( see Text S1 ) . For quantitative histochemistry , tissue sections were stained simultaneously . Non-sialylated carbohydrate structures were quantified by visual estimation of intensity , whereas a program by J . Czege , USUHS , was used for sialylated antigens . The surface/foveolar epithelium , lamina propria and glands were separately outlined and the percentage area stained was average from three fields of view . The 17875/Leb-mutant was referred to as the ΔsabA ( BabA+ ) mutant or “BabA-positive mutant” , and the isogenic 17875babA1::kan babA2::cam deletion mutant was referred to as ΔbabA ( SabA+ ) mutant or “SabA-positive-mutant [2 , 5] . In vitro adherence was digitally quantified in a total of 1 , 200 mucosal zones ( 120 biopsies , ten pits/biopsy ) [31] ( see Text S1 ) . Data are reported as means ± SEM . Changes over time within animals were compared with a mixed-effects ANOVA model corresponding to a repeated-measures ANOVA model with time as a within-subject factor and secretor status as a between-subjects factor . Dunnett's post-hoc test was used to compare the average at each time point to the average before inoculation . For each pair of variables , three correlation coefficients were calculated by ANOVA with random effects ( see Protocol S1/Statistics ) . | The common ABO blood group antigen system was described in the early 20th century . In addition , it has been known for 60 years that the majority of individuals also express the corresponding ABO antigens ( carbohydrate identity tags ) in their saliva , tears , milk , and mucus secretions in the digestive tract . To this date , however , the biological function of the ABO blood group antigens has remained an enigma . Here , we show that the great majority of Rhesus monkeys are of blood group B and weak-secretors , i . e . , are similar to the human populations in South Asia from where these monkeys originate . This observation suggests that an evolutionary adaptation in digestive tract mucosal carbohydrate patterns to local environmental selection has occurred . In addition , we demonstrate that long-term infection by the “peptic ulcer bacterium” Helicobacter pylori induces mucosal carbohydrate patterns that change according to the individual secretor phenotype . The common weak-secretor monkeys were apparently “protected , ” as they had stable glycosylation , lower inflammation , and lower bacterial infection load , whereas the less common secretor animals had increased levels of inflammation-associated mucosal carbohydrate patterns and a transient decrease in the ABO blood group system type of carbohydrates . These novel observations suggest that the individual ABO blood group and secretor phenotype are part of human and non-human primate innate immunity against infectious disease . | [
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| 2008 | Role of ABO Secretor Status in Mucosal Innate Immunity and H. pylori Infection |
HSV-1 is the leading cause of sporadic encephalitis in humans . HSV infection of susceptible 129S6 mice results in fatal encephalitis ( HSE ) caused by massive inflammatory brainstem lesions comprising monocytes and neutrophils . During infection with pathogenic microorganisms or autoimmune disease , IgGs induce proinflammatory responses and recruit innate effector cells . In contrast , high dose intravenous immunoglobulins ( IVIG ) are an effective treatment for various autoimmune and inflammatory diseases because of potent anti-inflammatory effects stemming in part from sialylated IgGs ( sIgG ) present at 1–3% in IVIG . We investigated the ability of IVIG to prevent fatal HSE when given 24 h post infection . We discovered a novel anti-inflammatory pathway mediated by low-dose IVIG that protected 129S6 mice from fatal HSE by modulating CNS inflammation independently of HSV specific antibodies or sIgG . IVIG suppressed CNS infiltration by pathogenic CD11b+ Ly6Chigh monocytes and inhibited their spontaneous degranulation in vitro . FcγRIIb expression was required for IVIG mediated suppression of CNS infiltration by CD45+ Ly6Clow monocytes but not for inhibiting development of Ly6Chigh monocytes . IVIG increased accumulation of T cells in the CNS , and the non-sIgG fraction induced a dramatic expansion of FoxP3+ CD4+ T regulatory cells ( Tregs ) and FoxP3− ICOS+ CD4+ T cells in peripheral lymphoid organs . Tregs purified from HSV infected IVIG treated , but not control , mice protected adoptively transferred mice from fatal HSE . IL-10 , produced by the ICOS+ CD4+ T cells that accumulated in the CNS of IVIG treated , but not control mice , was essential for induction of protective anti-inflammatory responses . Our results significantly enhance understanding of IVIG's anti-inflammatory and immunomodulatory capabilities by revealing a novel sIgG independent anti-inflammatory pathway responsible for induction of regulatory T cells that secrete the immunosuppressive cytokine IL-10 and further reveal the therapeutic potential of IVIG for treating viral induced inflammatory diseases .
Herpes simplex virus ( HSV ) is the leading cause of sporadic encephalitis , which , although rare , can be fatal or result in severe neurological deficits in survivors [1] . We reported previously that dysregulated CNS inflammatory responses cause fatal HSE in 129S6 ( 129 ) mice . Most importantly , we showed that once CNS inflammation was initiated by HSV entry into the brainstem , inhibiting virus replication could not prevent development of fatal HSE [2] , [3] . Similar conclusions have emerged from studies with susceptible BALB/c mice [4] . IVIG comprises human polyclonal IgG derived from pooled plasma collected from thousands of healthy donors . Initially it was used to provide normal levels of circulating IgG as replacement therapy for primary and secondary immunodeficiencies [5] , [6] . IVIG has a broad repertoire of neutralizing antibodies for various pathogens and neutralization is commonly assumed to be the mechanism of protection in secondary immunodeficiencies [7] . Remarkably , early reports that IVIG was able to prevent fatal HSE in BALB/c mice independently of neutralizing activity , even when administered up to 48 h post infection ( pi ) , were not further investigated to elucidate the mechanism ( s ) of protection [8] , [9] . IVIG is a FDA approved treatment for immune thrombocytopenia ( ITP ) and Kawasaki's vasculitis , and dramatic response rates that exceed 80% have been observed for ITP . The use of IVIG for treating a variety of autoimmune and systemic inflammatory diseases has steadily increased to include not only antibody mediated diseases , but also disorders caused by dysregulated cellular immunity , such as multiple sclerosis ( MS ) , myasthenia gravis and graft versus host disease [10] , [11] . IVIG has been reported to prevent development of experimental autoimmune encephalitis ( EAE ) , an animal model of MS , by increasing both the frequency and suppressive activity of CD4+ T regulatory cells ( Tregs ) [12] , [13] . Nonetheless , despite intense study , IVIG's mechanism ( s ) of action remain enigmatic , as discussed in several recent reviews [11] , [14] , [15] , [16] , [17] , [18] . Based on studies in mouse models of ITP , serum induced arthritis and nephrotoxic nephritis , Ravetch and colleagues proposed a model to explain the sustained anti-inflammatory effects of IVIG . They proposed that IVIG interacted initially with a CSF-1 dependent ‘sensor’ cell , identified as a regulatory macrophage . The ensuing upregulation of the inhibitory FcγRIIb concomitant with down regulation of the activating FcγRIV has the net effect of raising the activation threshold of effector monocytes , thereby diminishing inflammation [16] , [19] . An important recent finding by this group was that a subset of IgG molecules sialylated on Asp297 in the Fc domain could recapitulate the protective effects of IVIG in the ITP , nephrotoxic nephritis and arthritis models [20] . Sialylated IgGs ( sIgG ) , which comprise 1–5% of IVIG , were effective at ∼10 fold lower concentration , which explains the requirement for high dose ( 1–2 g/kg ) IVIG [16] , [21] . We report here that low dose ( 150 mg/kg ) IVIG protected all 129 mice from fatal HSE . IVIG mediated protection against HSE depended on its immunomodulatory activity but was independent of HSV specific antibodies . We confirmed the anti-inflammatory activity of purified sIgG , which is accessed with high dose IVIG . Importantly , we showed that the non-sIgG fraction of IVIG , corresponding to low dose IVIG , mediated equally potent anti-inflammatory effects to prevent fatal HSE . IVIG devoid of sIgG induced a dramatic expansion of Tregs and ICOS+ CD4+ T cells . Protection against fatal HSE was critically dependent on IL-10 produced primarily by the ICOS+ CD4+ T cells that accumulated in the CNS of IVIG treated but not PBS treated control mice . Although , signaling via the inhibitory FcγRIIb contributed to IVIG induced suppression of CNS infiltration , the absence of FcγRIIb did not abrogate protection against fatal HSE , which is characterized by accumulation of pathogenic Ly6Chigh macrophages .
We investigated IVIG protection in a mouse model of HSE characterized by massive accumulation of macrophages and neutrophils in inflammatory lesions in the brainstem ( BS ) [3] using a dose of 3 . 75 mg IVIG/mouse , which was previously reported to protect all HSV infected BALB/c mice [9] . HSV inoculated 129 mice were injected i . p . with 3 . 75 mg IVIG 24 h pi and survival was compared to PBS treated control mice . While >90% of the control mice succumbed to HSE by 7–12 pi , all IVIG recipients survived ( Figure 1A ) . Protection against fatal HSE depended on the timing of IVIG administration [9] . Although , IVIG prolonged survival of infected 129 mice when given at 72 or 96 h pi , it nonetheless failed to prevent mortality ( Figure S1 ) . Because IVIG preparations typically contain <1% aggregates and 3–15% IgG-dimers [22] , [23] we evaluated the contribution of IgG-dimers and monomers to protection . When HPLC purified IVIG fractions were injected into mice 24 h pi , the monomeric , but not the dimeric , IgG fraction protected all mice from fatal HSE ( Figure 1A ) . Hence , IVIG protection resides exclusively within the monomeric IgG fraction . Purified F ( ab ) 2 and Fc fragments were injected into infected mice 24 h pi and the mice were monitored for survival . Neither the F ( ab ) 2 or Fc fragment preparations were protective when given at 4 mg/mouse . However , increasing the Fc fragment dose to 25 mg/mouse ( equivalent to high dose IVIG ) , protected ∼65% of mice ( Figure 1B ) . IVIG is rich in neutralizing antibodies specific for HSV-1 and 2 [24] , and that the F ( ab ) 2 fragments retained neutralizing activity yet failed to protect suggested that neutralization was dispensable for IVIG mediated protection [8] . To demonstrate conclusively that IVIG protection against HSE was independent of HSV neutralizing activity , infected mice were given IVIG devoid of neutralizing antibodies 24 h pi . As expected , IVIG adsorbed free of neutralizing , but not non-neutralizing antibodies , was still able to protect against fatal HSE ( Figure 1B and Table 1 ) . To determine a role for non-neutralizing antibodies , pooled sera collected from donors seronegative for both HSV-1 and HSV-2 was administered to infected mice 24 h pi . Although , the absence of HSV specific antibodies in seronegative IVIG reduced protection only slightly relative to IVIG , the effect was statistically significant ( Figure 1B , 70% versus 100% , p = 0 . 014 ) , which suggests that non-neutralizing HSV specific antibodies present in the HSV adsorbed sera have a minor role in protection . HSV infected mice were given deglycosylated IVIG or IVIG desialylated at either α2 , 3 or both the α2 , 3 and α2 , 6 linkages 24 h pi to determine the contribution of glycosylation , and more specifically sialylation , of IgG to protection in the HSE model . Deglycosylated IVIG failed to protect , consistent with glycosylation being mandatory for maintaining the functional integrity of the Fc domain , which is critical for protection by IVIG [25] . In contrast to the ITP and arthritis models , ≥80% of HSV infected mice treated with 3 . 75 mg IVIG desialylated with either α2 , 3 or α2 , 6 specific neuraminidases survived ( Figure 1C ) , despite complete desialylation ( Figure S2 ) . To determine if sIgG ( S+ IgG ) could prevent HSE induced mortality , mice were given sIgG purified on Sambucus nigra ( SNA ) lectin affinity columns . Notably , >75% of infected mice given 1 mg sIgG purified from IVIG ( HSV+S+ IgG ) survived compared to ∼45% of mice given sIgG purified from pooled seronegative sera ( HSV−S+ IgG ) ( Figure 1D ) . Protection declined with lower doses and a sIgG dose <0 . 5 mg failed to protect . Thus , sIgG can protect against fatal HSE when given at doses corresponding to high dose IVIG , much greater than that present in 3 . 75 mg IVIG . The non-sIgG ( S− ) fraction of IVIG also conferred statistically greater protection than that isolated from HSV seronegative IVIG when administered at 3 . 75 mg/mouse; >90% and ∼60% of mice survived , respectively ( Figure 1D ) . Cumulatively , these results reveal a novel potent sIgG independent anti-inflammatory pathway mediated by low dose IVIG . Massive CNS inflammation is the primary cause of fatal HSE in 129 mice , while C57B6 mice , which exhibit minimal CNS inflammation , are resistant to HSE [3] . Flow cytometric analysis of leukocyte CD45high infiltrates in the BS revealed that 129 mice had 75% CD45high infiltrates compared to 30% CD45high infiltrates for B6 mice at d12 pi ( Figure 2A ) . Although 129 mice cleared infectious virus from the BS and trigeminal ganglia ( not shown ) by d10 pi , they nonetheless failed to control CNS inflammation ( Figure 2B ) . Compared to control infected 129 mice , IVIG treated mice exhibited a dramatic reduction of infiltrating CD45high peripheral leukocytes at d6 , 8 and 12 pi ( Figure 2C–F ) . At d6 pi , CD45high infiltrates comprised ∼12% ( range 6–18% ) of total cells recovered from the BS of IVIG treated 129 mice compared to ∼30% ( range 22–42% ) in control 129 mice ( Figure 2C , E ) . By d8 pi , CD45high infiltrates comprised more than 55% ( range 45–62% ) of total BS cells in control mice compared to ∼24% ( range 20–28% ) in IVIG treated mice ( Figure 2C , E ) . The majority of cells that infiltrated the BS of control 129 mice were CD11b+ macrophages and neutrophils ( Figure 2E ) . The few surviving control mice exhibited even more pronounced inflammation ( ∼75% ) at d12 pi ( Figure 2A , C ) compared to protected IVIG treated mice ( ∼30% , Figure 2C ) . When presented as total cell numbers the striking difference in leukocyte infiltration is even more dramatic , with an initial 3-fold difference in total CD45high cells at d6 pi ( 5 . 5±2 . 6×104 in IVIG treated mice compared to 1 . 6±0 . 5×105 in controls ) that escalated to a massive 7-fold difference by d8 pi ( 1±0 . 2×105 in IVIG vs . 7 . 2±1 . 1×105 in controls; Figure 2C ) . The reduced CNS inflammation in IVIG treated 129 mice mirrored the leukocyte CNS infiltration observed in untreated resistant B6 mice that survive ( Figure 2A , C–F ) . It is also evident that IVIG's anti-inflammatory effects persist long-term ( Figure 2C ) . Leukocytes infiltrated the brain ( Figure 2D , F ) and spinal cord ( Figure S3 ) of control mice robustly by d6 pi , and by d8 pi there was a 10-fold increase in infiltrates within the brains of control mice ( 3 . 8±2×105 at d6 to 3 . 7±2 . 3×106 at d8 ) compared to only a marginal increase in total CD45high infiltrates in IVIG treated mice ( 2 . 5±1×105 at d6 to 4 . 5±2×105 total infiltrates at d8 ) . Thus , IVIG regulates inflammation by diminishing the infiltration of cells into the CNS of infected mice . Impaired anti-inflammatory activity was responsible for the failure of deglycosylated IVIG to protect 129 mice ( Figure 1C ) , as mice treated with 3 . 75 mg deglycosylated IVIG or Fc fragments had increased levels of CD45high infiltrating cells , similar to control infected 129 mice ( Figure 2G ) . We infer that the anti-inflammatory activity of sialylated Fc fragments accounted for survival of mice treated with 25 mg Fc fragments . Mice that were protected by treatment with desialylated IVIG , 3 . 75 mg of S− IgG or 1 mg S+ IgG isolated from either IVIG or HSV seronegative IVIG also had reduced levels of CD45high infiltrating cells , similar to IVIG treated mice ( Figure 2G ) . Vigorous inflammation in the CNS of 129 mice suggested the integrity of the blood brain barrier ( BBB ) might be severely compromised in infected control 129 mice but not in the IVIG recipients . Sodium fluorescein uptake assays performed to assess BBB integrity showed a >6-fold increase in uptake from d0 to 6 pi in BS of control mice in contrast to only a ∼2 . 5-fold increase for IVIG treated 129 mice ( Figure 2H and 2C–D ) , consistent with the dramatic reduction in CNS inflammation in IVIG treated compared to control 129 mice . Notably , IVIG was excluded from the CNS , at least in the first 72 h post infusion , as determined by micro-PET imaging and biodistribution of Cu64-labeled IgG ( Figure S4 ) ; hence , IVIG acted peripherally to modulate CNS inflammation . At both d6 and 8 pi , >70% of the CD45high leukocytes infiltrating the BS and brain of infected control 129 mice were Gr-1+ ( Table 2 and Figure 3A ) . IVIG treatment of infected mice dramatically reduced infiltration of this population to ∼40% of total CD45high cells at d8 pi , equivalent to a ∼20-fold reduction in total Gr-1+ cells ( Figure 3A , Table 2 ) . Gr-1+ cells are comprised primarily of Ly6G+ CD11b+ neutrophils and Ly6Chigh/int F480+ macrophages . Further analysis of the Gr-1+ cells in BS of IVIG treated mice revealed that although IVIG treatment reduced total numbers of Ly6G+ F480− SSChigh neutrophils within this population ( Table 2 ) , the most dramatic decline was observed within the Ly6Chigh inflammatory macrophage subset ( Figure 3B ) . Ly6Chigh F480+ SSC low macrophages , which comprised 50–60% of total CD45high cells in both BS and brains of control mice , were reduced to ∼15–17% in the IVIG treated group ( Figure 3B ) . Total percentages of F480+ macrophages were reduced from 66% in BS and 57% in brains of control mice to 28% and 30% in BS and brains , respectively , of IVIG treated mice , which constituted a 15-fold drop in total macrophage numbers in the CNS at d8 pi ( Figure 3B and Table 2 ) . The majority of Ly6Chigh macrophages isolated from BS of control mice expressed high levels of FcεR1 as compared to markedly diminished expression on the Gr-1+ subset present in the BS of IVIG treated mice ( Figure 3C ) . Thus , these data show early involvement of inflammatory Ly6Chigh macrophages in the disease process of HSE , confirming earlier results , which showed that anti-Gr-1 mAb mediated depletion of macrophages and neutrophils prolonged survival of 129 mice [3] , [26] . In addition to reducing the numbers of Ly6Chigh macrophages , IVIG treatment altered the composition of BS infiltrating cells: IVIG treatment reduced infiltration of macrophages and neutrophils that predominated the CNS of control 129 mice while it increased T cell infiltration . Whereas only 10% and 20% of brain and BS infiltrates were composed of T cells in control mice , T cells comprised 40% and 55% of the brain and BS infiltrates of IVIG treated mice , respectively ( Table 2 ) . These results emphasize IVIG's capacity to regulate not only the generation of pathogenic macrophages , but also to control infiltration of different immune cell subsets into the CNS of HSV infected mice . We analyzed MHC class I and II expression on microglia and macrophages by flow cytometry to determine their activation status . Although microglia in both control and IVIG treated mice expressed MHC class I at d6 pi , MHC class II expression was very low ( mean fluorescence intensity [MFI] = 0 , Table 2 ) . However , by d8 pi , MHC class II expression on microglia was greatly increased in the brain of control mice compared to IVIG treated mice ( 74% vs . 45% ) ( Figure 3D ) , and MHC class I expression also increased in brains and BS of both groups of mice ( Figure 3E ) . Surprisingly , both the fraction of microglia that expressed MHC class II and the MFI were increased in the BS of IVIG treated mice as compared to control mice ( 74% vs . 60% , MFI 150 vs . 57; Figure 3D ) . Thus , IVIG treatment enhances the activation state of macrophages and microglia in the CNS , as demonstrated by MHC I/II ( Table 2 , Figure 3 ) . Induction of MHC II expression on microglia in the CNS requires the presence of IFN-γ which is likely derived from T cells infiltrating the BS of IVIG treated mice ( Table 2 ) . To determine if macrophages in peripheral lymphoid tissues have a different activation or degranulation profile , splenocytes obtained from control and IVIG treated HSV infected mice at d6 pi were stimulated ex vivo for 4 h with or without heat killed HSV ( HK-HSV ) antigen in the presence of antibodies to CD107a/b ( LAMP-1 and 2 ) . The majority of splenic macrophages isolated from control infected mice degranulated spontaneously and expressed high levels of surface CD107a , even in the absence of HK-HSV stimulation ( Figure 4A ) , whereas , strikingly , those isolated from IVIG treated mice did not ( Figure 4A ) . To identify the highly activated cell subset ( s ) in spleens of infected 129 mice with this phenotype , cells were phenotypically distinguished by surface expression of Gr-1 , F480 and Ly6C . Both the Gr-1+ CD11b+ F480− SSChigh neutrophils and the mature non-inflammatory macrophages ( Ly6C− F480+ ) degranulated only in response to stimulation with HK-HSV antigen , whereas the Ly6Chigh inflammatory macrophages expressed high levels of CD107a without stimulation ( Figure 4B ) . Similarly , there were more spontaneously degranulating CD45high CD11b+ macrophages in the brain ( 32% vs . 16% ) and BS ( 74% vs . 51% ) of control mice compared to IVIG treated mice ( Figure 4C ) . Moreover , as macrophages were more prevalent in infected BS of control PBS treated mice than in IVIG treated mice , the total numbers of degranulating macrophages were greatly increased ( Table 2 and Figure 3 ) . Thus , macrophages in the CNS and lymphoid organs of IVIG recipients are anti-inflammatory , while those in control mice have a pathogenic phenotype . Modulation of FcR expression is one of many mechanisms proposed to explain IVIG's anti-inflammatory activity [27] , [28] . Therefore , we examined FcR expression on monocytes from lymphoid organs of control and IVIG treated mice . Surface Ly6Chigh expression facilitated discrimination of inflammatory from non-inflammatory CD11b+ F480+ macrophages ( Figure 5A ) . Splenic Ly6C− macrophages ( Figure 5A ) in control and IVIG treated mice expressed similar levels of the inhibitory FcγRIIb and activating FcγRIII receptors , as determined by CD16/32 reactivity . In contrast , the Ly6Chigh inflammatory subset , which was more prevalent in the spleens of control mice , expressed much lower levels of FcγRIIb/III compared to those in the spleens of IVIG treated mice . FcγRI expression on both subsets was similar in both groups ( Figure 5A ) . To determine a role for FcγRIIb/III receptors in IVIG's anti-inflammatory effects , CNS inflammation was analyzed at d8 pi in IVIG treated infected mice that were also treated with the 2 . 4G2 blocking mAb that inhibits FcγRIIb/III signaling [29] . Flow cytometry analysis revealed that the absence of FcγRIIb/III signaling compromised IVIG's ability to suppress CNS infiltration . With intact FcγRIIb/III signaling , there were ∼20–25% CD45high infiltrating cells in the CNS of IVIG treated mice ( Figure 2C and E ) . However , inhibition of FcγRIIb/III signaling resulted in increased CNS infiltration with ∼70% CD45high cells present in the BS of these mice ( Figure 5B ) . Interestingly , in wild-type ( WT ) mice , IVIG reduced macrophage influx while it increased infiltration of T cells ( Table 2 ) , whereas in the absence of FcγR signaling , CD11b+ F480− monocytes dominated BS infiltrates ( Figure 5B ) . The majority of monocytes , however , expressed intermediate levels of Ly6C and low levels of MHC II ( Figure 5B ) , indicating that they were not inflammatory . Thus , FcγR signaling is required for IVIG mediated suppression of CNS infiltration , but not for modulation of pathogenic inflammatory macrophages . To determine whether the inhibitory FcγRIIb receptor or the activating FcγRIII receptor was critical for suppression of inflammation , we compared CNS inflammation in infected IVIG treated BALB/c FcγRIIb knock-out ( KO ) mice at d6 pi to that in IVIG treated BALB/c mice and IVIG treated BALB/c mice given anti-FcγRIIb/III antibodies . IVIG suppressed CNS leukocyte infiltration in BALB/c mice as effectively as in 129 mice , but it failed to suppress CNS infiltration in either FcγRIIb/III depleted or FcγRIIb KO mice at d6 and 8pi ( Table 3 ) . Similar to results seen with 129 mice , the absence of FcγRII/III signaling skewed CNS infiltrates to predominantly CD11b+ monocytes rather than T cells ( Table 2 and Figure 5C ) , but , very few of these monocytes were Ly6Chigh , indicating they were not inflammatory ( Figure 5C ) . The results shown in Table 3 indicate that signaling via FcγRIIb rather than FcγRIII contributed to IVIG mediated modulation of CNS infiltration and modulation of the composition of cellular infiltrates . Surprisingly , despite unmitigated CNS leukocyte infiltration in IVIG treated 129 and BALB/c mice deficient in FcγRIIb signaling , all mice survived and showed no symptoms of encephalitis . T cells were predominant in CNS infiltrates of IVIG treated mice at d8 pi . IVIG expanded CD4+ Tregs that contributed to protection in two different models of pathogenic inflammation [12] , [30] . We used 129 FoxP3-GFP reporter mice to investigate the possibility that IVIG induced Tregs in the HSE model . Analysis of HSV infected , IVIG treated 129 FoxP3-GFP reporter mice revealed that the majority of the CD4+ T cells in the spleen were FoxP3+ CD25+ Tregs at d8 ( ∼40% ) and d18 pi ( ∼30% , Figure 6A ) . In contrast , the percentage of Tregs was only modestly increased in the spleens of infected PBS treated control mice ( ∼15% , Figure 6A ) and these Tregs were obviously ineffective in suppressing the exaggerated CNS inflammatory responses . To determine whether the S+ or S− IgG component of IVIG induced Tregs , we analyzed 129 FoxP3-GFP reporter mice treated separately with the two preparations at d6 pi ( Figure 6B ) . S− IgG dramatically expanded Tregs in the cervical lymph nodes ( CLN ) ( 20% ) and spleen ( 38% ) , while S+ IgG did not induce Tregs ( 3% in CLN and 12% in spleen ) . Interestingly , FoxP3+ CD4+ Tregs were not present in the infected BS of either IVIG treated or control mice , suggesting the Tregs function primarily in lymphoid organs ( Figure 6C ) . We used adoptive transfer of IVIG induced or control Tregs from infected or naïve mice to assess the contribution of Tregs to IVIG's anti-inflammatory effects , which facilitated assessing their intrinsic suppressive potential . FoxP3+ GFP+ Tregs isolated from the spleens of naïve , IVIG and PBS treated HSV infected FoxP3-GFP mice on d8 pi were adoptively transferred into naive 129 recipients that were challenged 24 h later with HSV . Most recipients of Tregs isolated from IVIG treated HSV infected FoxP3-GFP mice , but not control PBS treated or naïve mice , were protected long-term ( Figure 6D ) . This result shows that adoptively transferred IVIG induced Tregs prevented lethal HSE while Tregs from the PBS treated control or naïve mice were non-functional . We investigated the requirement for Tregs in protection afforded by IVIG by using anti-CD25 antibodies to deplete Tregs prior to HSV infection and during subsequent IVIG treatment . Unexpectedly , all Treg depleted mice survived HSV challenge , which indicated that Tregs were not essential for IVIG induced suppression of pathogenic CNS inflammatory responses ( Figure 6D ) . To determine if , in addition to Tregs , another T cell subset was involved in IVIG's anti-inflammatory effects , we analyzed spleen and CLN CD4+ T cells for expression of cell surface markers such as ICOS , GARP , CD103 and GITR that are characteristic of regulatory T cells . Only ICOS was dramatically up regulated on CD4+ T cells isolated from both CLNs and spleens of IVIG treated mice compared to control mice ( Figure 6E ) . Upregulation of ICOS expression occurred preferentially within the activated CD62Llow population ( spleen—IVIG: 62% vs . control: 43%; CLN—IVIG: 78% vs . control: 12% ) . Importantly , only ∼20–26% of the ICOS+ CD62Llow CD4+ T cells in the spleens of both groups of mice were FoxP3+ Tregs . Similarly , the majority of activated ICOS+ CD4+ T cells in the CLN of IVIG treated mice did not express FoxP3 , indicating that they were not Tregs , while about 46% of the corresponding activated CD62Llow ICOS+ CD4+ T cells in control mice were FoxP3+ Tregs . Importantly , ICOS+ FoxP3− CD4+ T cells were the major constituents of the CD4+ T cell population in the BS of IVIG treated mice but not of control mice at d6 pi ( Figure 6F ) . Moreover , both CD11c+ DC and F480+ macrophages isolated from the BS of IVIG treated 129 mice at d6 pi expressed higher levels of ICOS-L , the ligand for ICOS , compared to those from control 129 mice ( Figure 6G ) . These data show that IVIG elicits different populations of regulatory CD4+ T cells , and that whereas Tregs act primarily in peripheral lymphoid organs , ICOS+ CD4+ T cells may function both in the periphery and at sites of inflammation in the BS . To determine the mechanism ( s ) by which regulatory CD4+ T cells suppress inflammatory CNS responses , we used the RT2Profiler PCR array kit ( SABiosciences , Frederick , MD ) to compare the expression profiles of inflammatory genes in the BS of control and IVIG treated mice at d6 pi to those of uninfected mice . Expression of IFN-γ and TGF-β was moderately increased in BS of IVIG treated mice compared to uninfected mice , but not significantly compared to control PBS treated infected mice ( Figure 7A ) . In contrast , relative to control uninfected mice , the BS of IVIG treated infected mice showed an ∼80-fold increase in IL-10 mRNA expression compared to only an 18-fold increase in BS of control PBS treated mice . Concordant with the RT-PCR results , intracellular flow cytometric analysis following stimulation with PMA and ionomycin revealed significantly increased IFN-γ and IL-10 expression by CD45high cells , specifically the CD4+ T cells isolated from the BS of IVIG treated mice at d6 pi ( Figure 7B , S5 ) . Interestingly , both CD45int microglia and a population of CD45neg cells , possibly astrocytes , in the BS of both groups of mice secreted IL-10 , even without stimulation ( Figure S5 ) . Furthermore , by d14 pi there was an even greater increase in the percentages of IFN-γ and IL-10 secreting CD45high and CD4+ T cells in the BS of IVIG treated mice . Importantly , only the ICOS+ CD4+ T cells in both IVIG treated and control PBS treated mice secreted IL-10 following stimulation ( Figure 7C ) . ICOS+ cells dominated the CD4+ T cell population in the BS of IVIG treated mice ( 70% ) but not the BS of control mice ( 23% ) . IL-10 secreting cells comprised more than 50% of the ICOS+ CD4+ population in the BS of IVIG treated mice compared to ∼30% in the BS of control PBS treated mice ( Figure 7C ) . These results suggest that ICOS and IL-10 may orchestrate suppression of hyper-inflammatory immune responses in the CNS of infected mice . To determine whether IL-10 was critical for IVIG mediated protection against HSV induced CNS inflammation , we compared the outcome of infection in 129 WT and IL-10 KO mice treated with either IVIG or PBS at 24 h pi . As expected , the majority of 129 WT and IL-10 KO mice treated with PBS succumbed to HSE . While IVIG protected all 129 WT mice , only ∼45% of IVIG treated IL-10 KO mice survived to d25 pi ( Figure 7D ) and , of these mice , some exhibited symptoms of neurological sequelae , including altered gait , hunched back and weight loss . Having established a critical role for IL-10 in IVIG protection against fatal HSE , it was important to show that impaired protection of IL-10 KO mice by IVIG was due to impaired suppression of CNS inflammation . Comparison of inflammatory responses in the BS of infected 129 IL-10 KO and WT mice treated with IVIG revealed that in the absence of IL-10 , CNS inflammation continued unabated in the IL-10 KO mice ( Figure 7F and 2E ) . High levels of CD45high infiltrates ( 82% ) were present in the BS at d8 pi , the majority of which were inflammatory Ly6Chigh MHC II+ CD11b+ F480+ macrophages ( Figure 7F ) . Therefore , we concluded that IL-10 is essential for IVIG to suppress CNS inflammation and inhibit development of pathogenic Ly6Chigh inflammatory macrophages . Interestingly , in the absence of IL-10 , macrophages rather than T cells dominated immune cell infiltrates in the BS of IVIG treated 129 WT mice ( Figure 3B , 7F and Table 2 ) . Concordant with their inflammatory phenotype , the majority of macrophages isolated from the brain and BS of PBS and IVIG treated IL-10 KO mice degranulated , even in the absence of HK-HSV , confirming their pathogenic phenotype ( Figure 7G ) . Importantly , macrophages from IVIG treated IL-10 KO mice degranulated much more than macrophages isolated from IVIG treated 129 WT mice ( 62% vs . 16% for brain and 80% vs . 51% for BS , respectively ) ( Figures 4C and 7G ) . This effect was even more exaggerated if the increased numbers of macrophages in the BS of IL-10 KO mice compared to WT mice was considered ( Figures 3 and 7 ) . To determine if CD4+ T cells were compromised in IL-10 KO mice , we compared expression of ICOS in peripheral lymphoid organs to that in 129 WT mice treated with IVIG . Expression of both CD25 and ICOS was reduced in the CLN of IL-10 KO mice compared to 129 WT mice ( Figure 7E ) . Cumulatively , these results suggest that IL-10 secreting CD4+ ICOS+ T cells may drive suppression of inflammation and modulation of inflammatory macrophages . The potential regulatory role of IVIG induced ICOS+ CD4+ T cells in suppression of macrophage activation and CNS inflammation is currently under investigation .
We reported previously that HSE progression was not affected by Acyclovir inhibition of HSV replication after virus had entered the BS , which is consistent with immune mediated pathology rather than virus cytopathology causing death [3] . The number of clinical reports speculating on a role for immune pathology in HSE has also been increasing [31] , indicating increased interest in this idea . In our studies of IVIG mediated protection against fatal HSE , 100% of 129 mice treated with low dose ( 3 . 75 mg/mouse ) IVIG 24 h pi were protected from fatal HSE . This dose of IVIG is much lower than the 1–2 g/kg high dose typically used for treatment of autoimmune diseases [28] , [32] . The monomeric IgG fraction of IVIG was protective , while the dimeric IgG fraction comprised of idiotype-anti-idiotype pairs [23] , [33] was not . Purified Fc fragments have been shown to suppress ITP and Kawasaki disease in humans [18] . In our studies , at low dose ( 4 mg/mouse ) , Fc fragments failed to protect , whereas a high dose of Fc ( 25 mg/mouse ) conferred significant protection , analogous to results obtained for ITP , nephrotoxic nephritis and serum transfer arthritis in experimental mouse models given 25 mg IVIG/mouse [28] , [34] . Protection against fatal HSE by IVIG was independent of HSV specific antibodies and entry into the CNS during early acute infection . Rather , protection correlated with early and profound suppression of pathogenic inflammatory responses in the BS of infected IVIG treated 129 mice , which effectively prolonged the integrity of the BBB . In addition to markedly reducing CNS infiltration of CD45high Ly6Chigh inflammatory monocytes , IVIG treatment profoundly altered the phenotype of these cells . A particularly impressive manifestation of IVIG's potent immunomodulatory effects was the suppression of spontaneous and antigen induced degranulation by highly activated Ly6Chigh monocyte/macrophages isolated from spleen , CLN and BS of HSV infected mice . Thus , IVIG acted peripherally to modulate early innate responses emanating from both infiltrating and resident immune cells in the CNS to protect against fatal HSE . We infer that failure of IVIG to protect when given at later times after infection is likely due to CNS infiltration by non-modulated aggressive Ly6Chigh inflammatory monocytes prior to IVIG administration . Ravetch and colleagues have provided extensive data to show that purified sIgG , which is present at 1–3% in IVIG , afforded complete protection against ITP and arthritis when given at a 10-fold lower dose than the usual high dose of 1 . 0 g/kg , thereby explaining the need for high dose IVIG in order to access its anti-inflammatory activity [21] , [32] . They showed further that specific removal of α2 , 6 linked sialic acid residues on Asp297 in the Fc domain or deglycosylation , which impairs structural integrity of the Fc domain , both abolished IVIG's anti-inflammatory activity in ITP and arthritis models [21] , [32] , [35] . We speculated that the sIgG mechanism applied also to the HSE model . Deglycosylation of IVIG abrogated protection against fatal HSE , confirming the critical role of the Fc domain . In contrast , desialylation of IVIG did not significantly reduce protection against fatal HSE , contrary to results from the ITP and arthritis models [21] , [32] . Notably , when HSV infected mice were given escalating doses of affinity purified sIgG ranging from 100 ng to 1 mg , robust protection against fatal HSE ( ∼70% survival ) was observed only with doses >500 ng; drastically reduced survival was observed with doses less than 500 ng . Similar results were obtained with the purified Fc fragment . It was obvious from these results that sIgG could not be responsible for the protective effects of low dose IVIG , as it was present in too low an amount compared to the amount of purified sIgG required for robust protection . This important conclusion predicted that IVIG devoid of sIgG ( HSV+S− IgG ) would be protective , and indeed , >90% of infected mice given 3 . 75 mg of HSV+S− IgG survived . Furthermore , CNS inflammation in these mice was reduced to an extent comparable to that seen with IVIG or sIgG treated mice . Thus , we have identified a sIgG independent anti-inflammatory pathway mediated by low dose IVIG that is as potent as IVIG . HSV+S− IgG conferred statistically greater protection compared to HSV−S− IgG , which is consistent with greater protection afforded by HSV seropositive compared to seronegative IVIG . Experimentally , immune complexes ( ICs ) formed in vitro or in vivo can mimic the protective effect of IVIG in , for example , ITP and serum induced arthritis models [16] , [36] , [37] , [38] . Since IC formation would be expected in infected mice receiving HSV seropositive compared to seronegative IgG , we are investigating the potential role of ICs in mediating the anti-inflammatory effects of low dose IVIG . Several studies reported an indispensable role for expression of the inhibitory FcγRIIb on effector macrophages and monocytes in IVIG's anti-inflammatory effects [39] , [40] , [41] . IVIG markedly upregulated FcγRIIb expression on CD11b+ Ly6Chigh inflammatory monocytes in HSV infected mice , while expression of the activating FcγR1 was unaffected . Suppression of CNS infiltration was abolished by treating 129 and BALB/c mice with the 2 . 4G2 blocking mAb that targets FcγRIIb and FcγRIII or by genetic ablation of FcγRIIb signaling in BALB/c mice . Signaling via FcγRIIb in IVIG treated mice biased CNS infiltrates in favor of T cells rather than the predominant monocyte influx seen in PBS treated control mice . Remarkably , IVIG was still able to inhibit development of highly activated Ly6Chigh inflammatory monocytes in BALB/c mice deficient in FcγRIIb expression , and all mice survived despite high levels of cells infiltrating the CNS . An important finding in the HSE model was that following IVIG treatment , FcγRIIb signaling acted primarily to limit CNS infiltrates and modulate their composition , but was dispensable for modulation of monocyte activation . The observation of potent up regulation of IL-10 transcripts in the BS of IVIG treated infected mice revealed that IVIG induced functional Tregs . An important new finding was that the non-sIgG fraction of IVIG was primarily responsible for induction of Tregs . Regulatory T cell epitopes ( Tregitopes ) capable of inducing Tregs were recently identified in the Fc domain of IgG [42] . However , our results imply that IVIG induces Tregs independently of presentation of Tregitopes , since purified sIgG was unable to induce Tregs even though it should contain Tregitopes . IVIG has been reported to induce Tregs in various models of inflammatory disease [12] , [43] , which could account for its long-term protective effects , including in our HSE model . Using EGFP-FoxP3 reporter mice , we showed that IVIG significantly expanded FoxP3+ Tregs and that Tregs purified from infected IVIG treated , but not control PBS treated , EGFP-FoxP3 mice protected infected 129 recipients not treated with IVIG from fatal HSE . Thus , IVIG not only expands Tregs , but also augments their effector function . Unexpectedly , Treg depletion during infection of IVIG treated mice did not prevent anti-inflammatory responses or reduce survival . Possible explanations are that IVIG induced redundant regulatory cell populations , including , for instance , Tr1 regulatory T cells that secreted high levels of IL-10 [44] , [45] , [46] . We observed a dramatic expansion of ICOS+ FoxP3− CD4+ T cells in the spleen and draining CLN of IVIG treated , but not control PBS treated , infected mice , and these cells also localized to the BS . ICOS is expressed by Tregs and plays a role in their regulation but it is also expressed on other regulatory cells , such as Tr1 cells [47] , hence we are investigating the potential regulatory role of the ICOS+ CD4+ T cells induced by IVIG . The immunosuppressive cytokine IL-10 was essential for protection against pathogenic hyper-inflammatory responses in the CNS , since survival of infected IVIG treated 129 IL-10 KO mice was drastically reduced . Notably , IVIG failed to suppress CNS infiltration or spontaneous degranulation by splenic macrophages from IL-10 KO mice . Highly activated inflammatory Ly6Chigh macrophages were predominant in the extensive CD45high infiltrates in BS of infected IL-10 KO mice . In contrast , IVIG treated FcγRIIb KO mice survived , despite high levels of CNS CD45high infiltrates comparable to those in IL-10 KO mice , which implicates the Ly6Chigh inflammatory macrophages as being causally involved in the high mortality of IL-10 KO mice . Thus , these results emphasize that the quality of the inflammatory response rather than just its magnitude is the critical determinant of pathogenic outcome . Significant induction of IL-10 and IFN-γ expression by IVIG in primarily CD4+ T cells was evident early after treatment in the BS . Interestingly , ICOS+ CD4+ T cells that were predominant amongst CD4+ T cells in BS of IVIG treated , but not control , mice were the major producers of IL-10 . HSV infection of microglia , even though abortive , has been reported to induce robust expression of proinflammatory cytokines and chemokines . In vitro , exogenous IL-10 was shown to attenuate this response [48] , [49] , [50] . IL-10 can exert autocrine inhibitory effects on macrophages and dendritic ( DCs ) cells that inhibit development of TH1- and TH2-type responses [51] , while IL-10 produced by TH1 , TH2 and TH17 cells represents a feedback loop to regulate the effector functions of macrophages and DCs [52] . These important immunoregulatory functions of IL-10 fit well with its essential role in promoting balanced inflammatory responses in IVIG treated mice that favor clearance of HSV without bystander immune pathology . Cumulatively , these results show that IVIG induced IL-10 producing ICOS+ CD4+ T cells are required for long-term regulation of CNS inflammatory responses . One model to explain IVIG's anti-inflammatory activity proposes that sIgGs present at <3% in IVIG trigger anti-inflammatory responses upon binding SIGN-R1 expressed on sensor marginal zone splenic macrophages in mice [18] , [20] , [35] . Results from our studies of IVIG protection against fatal HSE induced by dysregulated CNS inflammation not only support , but extend the model by demonstrating existence of a second pathway induced by the non-sIgG fraction that was highly effective even with low dose IVIG . Whereas FcγRIIb was essential for prevention of ITP and arthritis by sIgG , it was not important for protection against fatal HSE by low dose IVIG ( i . e . , lacking sIgG ) . The non-sIgG pathway induced Tregs as well as ICOS+ CD4+ T cells that produced IL-10 , the latter being essential for the anti-inflammatory effects of low dose IVIG . However , Kaneko et . al . reported that desialylated IVIG failed to protect against serum induced arthritis , as inflammatory infiltration of the joints was not inhibited [21] . This discrepancy may be due to differences in the experimental models used . Alternatively , the non-sIgG fraction of IVIG may function optimally to elicit anti-inflammatory responses in the context of virus induced inflammatory diseases as opposed to autoimmune diseases . In sum , our results significantly advance understanding of IVIG's anti-inflammatory and immunomodulatory activities and reveal the potential utility of IVIG for treating viral infections in which excessive inflammatory responses contribute to disease pathology , such as in West Nile virus and highly pathogenic influenza virus infections .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All animal studies were conducted under a protocol approved by the Institutional Animal Care and Use Committee ( IACUC , Permit # A3001-01 ) of City of Hope to ensure the highest ethical and humane standards were followed . 129S6 WT ( Taconic , Hudson , NY ) , 129 FoxP3-GFP [53] and 129 IL-10 KO ( Jackson Laboratories , Bar Harbor , Maine ) mice were bred in the vivarium at City of Hope . Male mice of 6–8 weeks of age were infected with HSV 17+ strain . Mice were sedated with ketamine ( 60 mg/kg ) and xylazine ( 5 mg/kg ) prior to HSV inoculation by corneal scarification with 3200 PFU ( equivalent to 10 LD50 for the 129 WT strain ) as previously described [3] . Infected mice were monitored daily as previously described [3] . Pooled human serum was obtained from Sigma-Aldrich ( St . Louis , MO , USA ) and IVIG ( Carimmune , NF ) was obtained from CSL Behring ( King of Prussia , PA , USA ) . The recipient group was intraperitoneally ( i . p . ) injected with 0 . 5 ml of either human sera or IVIG ( 3 . 75 mg/mouse , as indicated ) 24 h pi . IVIG and various derivatives were administered 24 h pi unless otherwise stated . The dose of IVIG was 3 . 75 mg/mouse , based on an earlier study [9] . IVIG dose ranging studies demonstrated that 1 . 5 mg was the minimal protective dose for HSV infected 129 mice ( Figure S1 ) . For some experiments , HSV antibodies were removed from IVIG by adsorption with HSV infected monolayers of Vero cells . Adsorbed antibody was used after confirming the absence of neutralizing activity [8] . Desialylation and deglycosylation of IVIG was performed as described previously [32] . Briefly , for desialylation , IVIG ( 100 mg ) in sodium citrate buffer ( 0 . 05 M , pH 6 . 0 ) was incubated ( 37 C , 40 h ) with 700 units of recombinant α2 , 3 or α2 , 3/α2 , 6 neuraminidase ( Clostridium perfingens , New England BioLabs ) . For deglycosylation , IVIG ( 100 mg ) in sodium phosphate buffer ( 0 . 2 M , pH 8 . 5 ) was incubated ( 37°C , 48 h ) with 25 , 000 units of PNGaseF ( Flavobacterium meningosepticum , New England BioLabs ) . Monomer fractions of deglycosylated or desialylated IVIG preparation ( 3 mg/mouse ) purified by HPLC were used . For depletion of FoxP3+ CD4+ Tregs and FcγRIIb/II expressing CD11b+ cells , mice were injected with 4 doses of either anti-CD25 mAb PC61 ( 250 µg ) or anti-FcγRIIb/III mAb 2 . 4G2 ( 500 µg ) on days −2 , 0 , +1 , +2 pi . All mice were inoculated with HSV at day 0 pi and received either IVIG or PBS i . p . on day +1 pi . Depletion of cell subsets was confirmed by flow cytometry . CNS derived mononuclear cells were isolated as previously described [3] . Briefly , brains , BS and spinal cords were removed separately from mice perfused with PBS , minced and digested with collagenase and DNAse for 30 min prior to centrifugation ( 1250× g , 50 min ) on a two step Percol gradient [3] . Brain refers to the whole brain minus the brainstem . The enriched population contained CNS infiltrating CD45high cells , CD45int microglia and CD45neg CNS resident glial cells . CD45high cells comprised ∼ 5–8% of total mononuclear cells isolated from the BS of naïve 129 WT mice . Cell viability was greater than 95% as revealed by trypan blue staining . We confirmed that enzyme digestion did not affect expression of cell surface markers . Single cell suspensions isolated from either , brain , BS , spleen or CLN were blocked with a 10% Fmixture of normal mouse , rat and horse serum and rat anti-mouse CD16/32 ( 2 . 4G2 , BD PharMingen ) for 15 min prior to incubation with antibodies ( Abs ) to determine cell surface expression of various markers . Phycoerythrin ( PE ) , FITC or allophycocyanin conjugated Abs specific for F480 ( BM8 ) , CD11b ( M1/70 ) , Gr-1 ( RB6-865 ) , CD8 ( 53-6 . 7 ) , CD4 ( RM4-4 ) , ICOS ( 7E . 17G9 ) , CD62L ( MEL-14 ) , CD25 ( PC61 . 5 ) , IFN-γ ( XMG1 . 2 ) , IL-10 ( JES5-16E3 ) , FcεR1 ( MAR-1 ) , MHC class I ( 28-14-8 ) and class II ( M5/114-15 . 2 ) were obtained from eBioscience ( San Diego , CA ) . FITC , PE or PerCP conjugated Ly6-G ( 1A8 ) , Ly6-C ( AL-21 ) , CD45 PerCP ( 30-F11 ) , CD107a ( 1D4B ) and CD107b ( ABL-93 ) were obtained from BD PharMingen ( San Jose , CA ) . All isotype controls were obtained from eBioscience . In the Figure Legends and Results , all references to infiltrating cells or inflammatory cells from either BS or brain refer to mononuclear cells isolated from either compartment distinguished by their CD45high expression . F480+ macrophages were characteristically CD45high , CNS resident microglia CD45int and glial cells CD45neg . Activation of both cell subsets was determined by their mean fluorescence intensity of expression of MHC class II molecules . Efficiency of degranulation by macrophages was determined in vitro in the absence of antigen stimulation or following stimulation of cells for 5 h with heat-killed HSV in the presence of anti-CD107a/b antibodies to capture cell surface associated LAMPs . Resting macrophages did not express surface CD107a ( data not shown ) . Neutrophils were determined by their SSChigh , Gr-1high , Ly6-G+ , MHC II− , F480− phenotype . CD4+ Tregs were determined by reactivity to CD25 and FoxP3 GFP expression in the 129 FoxP3 GFP reporter mice . Cells were acquired on a Cyan ADP Analyzer ( Beckman Coulter , Fullerton CA ) and flow cytometry analysis was performed using Flowjo software ( Treestar Inc . ) . In Results , where percentage of cell subsets is calculated within specific populations , the specific population is gated and considered to be 100% , while cell subsets are expressed as a fraction thereof . For example , in Figure 2E , the top left plot shows BS mononuclear cells isolated from IVIG-treated HSV-infected mice at d6 pi . The CD45high infiltrating cells comprise 6 . 6% of total mononuclear cells . CD11b+ monocytes therefore comprise 55% ( 3 . 6% CD11b+ ÷ 6 . 6% CD45high ) of total CD45high infiltrating cells within the BS . To determine the integrity of the BBB after HSV corneal infection , mice were injected i . p . with 10% sodium fluorescein ( Sigma Aldrich , PA ) . After 10 min , mice were bled and perfused and the BS , brain and spinal cord collected and frozen on dry ice . The organs were weighed and homogenized in 10% w/vol PBS . Homogenates were treated with 15% TCA and extracted with 1 . 5 M NaOH . The supernatants were analyzed for fluorescence at 475 nm , and the amount of sodium fluorescein in sera and BS were extrapolated from a standard curve . The following formula was used to calculate the amount of sodium fluorescein in BS or brain: ( mg fluorescein in brain tissue/mg of protein ) / ( mg fluorescein in sera/ml of blood ) and the result was expressed as fold increase in fluorescence in comparison to naïve mice [54] . To prepare F ( ab ) 2 and Fc fragments , IVIG was dialyzed into PBS using a Mini Dialyzer cassette ( 10 kDa , Thermo Scientific , Rockford , IL ) prior to enzymatic cleavage with either Pepsin or papain ( Thermo Scientific ) using the manufacturer's protocol . The reaction was stopped by spinning down the enzyme-linked beads and the reaction mixture was dialyzed into PBS prior to partitioning the Fc and F ( ab ) 2 fragments using HPLC . The F ( ab ) 2 fragments retained antigen binding and neutralizing activities . Aggregated and non-aggregated IgGs were separated using HPLC , and these comprised primarily head-to-head dimers and monomers of IgG , respectively . The non-aggregated fraction of IVIG was collected based on size and the purity determined by SDS PAGE electrophoresis . To label IVIG with radiolabeled 64Cu , IVIG was conjugated with DO3A-VS ( 1 , 4 , 7-tris ( carboxymethyl ) -10- ( vinylsulfone ) -1 , 4 , 7 , 10-tetraazacyclododecane ) , a bifunctional chelator , under Argon for 2 h at room temperature as previously described [55] . Labeling with 64Cu was performed in 0 . 1 M ammonium citrate , pH 5 . 5 for 45 min at 43°C and the reaction terminated by addition of 10 mM DTPA to achieve a final concentration of 1 mM . The radiolabeled product was purified on a size exclusion column . HSV infected or naïve mice were injected with IVIG spiked with 50 ul of 64Cu-labeled IVIG ( 50 µl ) at 24 h pi . At 4–8 h intervals over a 48 h period , mice were imaged with a small animal PET scanner ( microPET R4 , Siemens/CTIMI , Knoxville , TN ) . At 44–48 h pi , animals were euthanized and organs such as spleen , liver , brain , BS , trigeminal ganglia , etc . were weighed and assayed for radioactivity using a gamma counter . All image processing and analysis was performed using standard microPET software [55] . Graph Pad Prizm Software was used to analyze mortality data by log rank ( Mantel Cox ) test , taking into account both time of death and mortality . | We show that fatal HSV encephalitis ( HSE ) is caused by excessive brainstem inflammation . Once brainstem inflammation is initiated , antiviral drugs that inhibit only viral replication are ineffective in protecting against fatal HSE . Infusion of high doses of pooled human IgG ( IVIG ) is an effective anti-inflammatory treatment for various autoimmune diseases . One anti-inflammatory mechanism depends on sialylated IgGs ( sIgG ) present in limiting amounts ( 1–3% ) in IVIG , hence the need for high doses of IVIG . We discovered a novel anti-inflammatory pathway mediated by low doses of IVIG independent of sIgG that prevented fatal HSE by suppressing CNS inflammation . The non-sIgG fraction of IVIG induced regulatory CD4+ T cells that produced the immunosuppressive cytokine IL-10 in the brainstem . Importantly , we show that IL-10 is critical for suppressing the generation of pathogenic inflammatory macrophages . Thus , IVIG has a remarkable ability to balance the host inflammatory responses to virus infection and thereby promotes virus clearance without bystander damage to the CNS , accounting for survival of all infected mice . Overall , our results provide important new insights in understanding IVIG's anti-inflammatory activity and further reveal its potential for use in treatment of viral inflammatory diseases . | [
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| 2011 | Passively Administered Pooled Human Immunoglobulins Exert IL-10 Dependent Anti-Inflammatory Effects that Protect against Fatal HSV Encephalitis |
Complement Receptor 3 ( CR3 ) and Toll-like Receptor 2 ( TLR2 ) are pattern recognition receptors expressed on the surface of human macrophages . Although these receptors are essential components for recognition by the innate immune system , pathogen coordinated crosstalk between them can suppress the production of protective cytokines and promote infection . Recognition of the virulent Schu S4 strain of the intracellular pathogen Francisella tularensis by host macrophages involves CR3/TLR2 crosstalk . Although experimental data provide evidence that Lyn kinase and PI3K are essential components of the CR3 pathway that influences TLR2 activity , additional responsible upstream signaling components remain unknown . In this paper we construct a mathematical model of CR3 and TLR2 signaling in response to F . tularensis . After demonstrating that the model is consistent with experimental results we perform numerical simulations to evaluate the contributions that Akt and Ras-GAP make to ERK inhibition . The model confirms that phagocytosis-associated changes in the composition of the cell membrane can inhibit ERK activity and predicts that Akt and Ras-GAP synergize to inhibit ERK .
Receptor-mediated engagement followed by phagocytosis by professional phagocytes is the first critical step in microbial clearance or , in the case of intracellular pathogens , entry to a safe niche . The molecular mechanisms underlying phagocytosis are complex , usually involving more than one receptor and rapidly culminating in the combinatorial generation of a variety of biochemical signals along with rearrangement of the actin cytoskeleton to engulf the microbe [1] . There are substantial differences in cellular responses for almost every phagocytic receptor used , and complex interactions between receptors can be expected since a variety of ligands usually coat microbes . In this context , computational modeling becomes an essential tool through which experimentalists can enhance their understanding . Complement Receptor 3 ( CR3; CD11b/CD18 ) , the major integrin of phagocytic cells ( monocytes , macrophages and neutrophils ) , provides a highly effective mode of entry for many microbes and has long been postulated to provide the microbe safe passage into macrophages in particular , since ligation of CR3 by complement-opsonized microbes does not uniformly trigger toxic host cell responses [2] . Many intracellular pathogens use CR3 to evade intracellular killing [3]–[9] . Still , CR3 is a notoriously enigmatic receptor , capable of conveying diverse and even opposing signals in response to distinct combinations of ligands [10]–[13] and often in concert with pattern recognition receptors ( PRRs ) such as Toll-like Receptors ( TLRs ) . A mounting body of research suggests that integrins are important regulators of TLR signaling [14]–[17] . The mechanisms by which CR3 regulates TLR signaling are an area of active research , in part because CR3/TLR crosstalk is implicated in the pathogenesis of several diseases . Francisella tularensis is an extremely virulent intracellular pathogen of macrophages and potential bioweapon . Indeed , the bacteria may be aerosolized and inhalation of as few as ten bacteria can result in the fatal disease pneumonic tularemia [18]–[20] . In the lung , F . tularensis is rapidly phagocytosed by alveolar macrophages while suppressing their cytokine production . One mechanism the bacterium uses to accomplish this feat is to selectively engage only a few choice receptors . Although multiple types of receptors can mediate phagocytosis of Francisella , appreciable phagocytosis of the most virulent strains requires CR3 engagement by complement C3-opsonized bacteria [19] , [21] , [22] . In fact , CR3 is thought to be critical to the success of F . tularensis as an intracellular pathogen [18] , [19] , [21] , [23]–[26] . Cytokine production in response to Francisella comes almost exclusively from its stimulation of TLR2 [18] , [19] , [27] . As noted above , although TLR2 signaling is inflammatory , it is also subject to regulation by CR3 [15] , [17] . In what follows we construct a model of immediate membrane proximal signaling in response to F . tularensis . The model , which serves as a formal hypothesis , is shown to be consistent with the experimental results of S . Dai et al ( unpublished data ) . Its implications are explored via numerical simulations .
The response of macrophages to F . tularensis depends heavily on the presence of complement . Dai et al found that complement opsonization substantially decreases cytokine production in response to F . tularensis , and identified key players in this immuno suppressive pathway ( unpublished data ) . Their results are summarized as follows: ERK activation in response to F . tularensis is suppressed by complement-mediated signaling through CR3 . Furthermore , ERK inhibition is rapid , being evident just 5 minutes post infection . In addition to suppressing ERK activation , CR3 ligation induces the rapid activation of Lyn kinase , which functions to inhibit cytokine production in response to F . tularensis . Finally , TLR2 and CR3 signaling intersect at the PI3K/Akt pathway , and the two receptors cooperate to support a complement dependent enhancement of Akt activity in response to F . tularensis . The observations of Dai et al were supplemented with existing literature to construct a model of the very earliest signaling events that occur in response to F . tularensis infection . In this model TLR2-induced ERK activation occurs through a previously characterized MyD88 independent pathway in which Rac and Ras associate with the cytoplasmic domain of TLR2 and undergo rapid activation in response to bacterial stimuli [28] , [29] . The pair then cooperate to activate Raf which leads to ERK activation [30] . Activation of the PI3K/Akt pathway by TLR2 , meanwhile , is mediated by Rac [29] . In our model of complement-mediated signaling , CR3 ligation leads to the rapid activation of Lyn which subsequently activates PI3K [31] , [32] . PI3K activation leads to a buildup of PtdIns ( 3 , 4 ) P ( abbreviated here as PI ( 34 ) P ) and PtdIns ( 3 , 4 , 5 ) P ( PI ( 345 ) P ) at the phagosomal cup ( consistent with Clemens et al [21] ) , which antagonizes ERK . Specifically , Akt , which is activated after binding to these lipids , phosphorylates Raf at Ser 259 thereby inhibiting its association with Ras [33] , and these lipids recruit GAPs , which deactivate both Rac and Ras [34]–[37] . The model also includes additional interactions which may detract from its ability to explain complement-mediated ERK inhibition . In particular , PI ( 34 ) P and PI ( 345 ) P can also recruit the Rac-GEF , Vav , [38] , which initiates Rac activation , and Lyn can enhance Raf signaling [39] . Figures 1 and 2 provide a schematic description of membrane proximal TLR2 and CR3 signaling in response to Francisella tularensis . Figure 3 synthesizes and simplifies the CR3 and TLR2 signaling networks . In particular , in the interest of simplicity , our model uses the concentration of active Raf as a proxy for the concentration of active ERK . Although significant feedback from ERK to Raf could alter the model's dynamics , this simplification seems reasonable in view of the following facts: ERK mediated feedback is not significant until later time points [40] , and the proposed mechanisms of CR3-mediated ERK inhibition target molecules that lie upstream of ERK itself . Because the network is complex , we resort to mathematical modeling in order to deduce function from structure , that is , in order to check that the model is in fact consistent with experimental observations . The model equations , based on Figure 3 , give the local concentrations of various signaling molecules in the vicinity of the immunological synapse . In these equations represents ligand bound TLR2 heterodimers; represents ligand bound CR3; represents 3 phosphoinositides ( i . e . both PI ( 345 ) P and PI ( 34 ) P ) ; represents active Ras; represents active Akt; represents active Lyn; represents active Rac; and represents active Raf . The model equations are given in the section Materials and Methods where they are supplemented by Tables 1 and 2 of parameter values . We next present the results of numerical simulations on the mathematical model . In order to test the model's consistency we compare the results of simulations in the presence and absence of complement . Figures 4–7 show how the concentrations of signaling molecules change through time , when the bacteria are not opsonized , but carry a low density TLR2 ligand . In the absence of complement the model predicts that Francisella will elicit a slight increase in 3 phosphoinositides and a substantial increase in active Ras , Rac and Raf . As Raf is a proxy for ERK , we see that in the absence of complement the model is in agreement with the experimental results of S . Dai et al ( unpublished data ) . In particular , TLR2 signaling stimulates both the ERK and PI3K pathways . Figures 8–11 show how the concentrations of the above signaling molecules change through time in response to opsonized Francisella . A comparison of Figures 7 and 11 shows that the model proposed in Figure 3 is consistent with the experimental data , and in particular , is capable of explaining CR3-mediated ERK inhibition . Specifically , Figure 11 shows that F . tularensis induced Raf stimulation is markedly inhibited in the presence of complement . Having confirmed that , as parameterized , the mathematical model is consistent with complement-mediated ERK inhibition we performed an uncertainty and sensitivity analysis in order to assess how uncertainty in the model's parameters impacts its consistency with experimental data . In particular , as some of the model's parameters are uncertain , we wished to know if complement-mediated ERK inhibition is robust to variations in the model's parameters , i . e . is the model consistent with experimental result over a wide range of parameter values . We ran 10 , 000 numerical simulations in which the model's parameters were varied according to a Latin hypercube sampling scheme . The sensitivity of the model's output ( as measured by the concentration of active Raf at 5 minutes post infection ) to uncertainty in the parameters was then quantified through a partial rank correlation coefficient [41] that is , we calculated the partial correlation coefficients of the rank transformed data . This provides a robust sensitivity measure of nonlinear but monotonic relations between the parameters and the output [41] . A detailed description of the process is presented in [41] . The results of this analysis are presented in Table 3 . Figures 12–15 show scatter plots of rank transformed Raf concentration at 5 minutes versus rank transformed parameter values for a few of the most important parameters . Importantly , , the concentration of complement on the surface of the bacteria shows a significant negative correlation with the concentration of active Raf , i . e . the model is consistent with experimental data over a wide range of parameter values . In addition to showing that the model's consistency is robust to parameter variations , the uncertainty and sensitivity analysis can be used to identify molecules and parameters that are important for complement-mediated ERK inhibition . Although many of the parameters show a small but significant correlation with the concentration of Raf at 5 minutes post infection , the strongest negative correlations are associated with the parameters for GAP-mediated Ras deactivation , namely and , and , the rate of Akt catalyzed Raf phosphorylation . This suggests that both Ras-GAPs and Akt are important regulators of Raf . To analyze the relative importance of Ras-GAPs and Akt in CR3-mediated Raf inhibition , we ran numerical experiments in which the and were reduced to 10% of their baseline values , and the activity of Raf 5 minutes post infection with opsonized F . tularensis was compared to that when the parameters were set to their baseline values . A ten percent reduction in resulted in a 8 . 5 fold increase in the concentration of Raf at five minutes post infection , whereas a ten percent reduction in resulted in a 5 . 5 fold increase in Raf activation 5 minutes post infection . When both parameters were reduced to 10% of their baseline values , the model predicts a 31 . 5 fold increase in Raf activation at 5 minutes post infection , i . e . Akt and Ras-GAPs synergize to inhibit Raf signaling . The uncertainty and sensitivity analysis also indicates that CR3-mediated ERK inhibition is sensitive to concentrations of key cellular proteins . In particular , it indicates that over expression of TLR2 or Ras , or reduced expression of Ras-GAP will dampen CR3-mediated ERK inhibition .
Crosstalk between the complement and TLR systems is an essential determinant of the early immune response to pathogens [42] . In this paper we have presented a mathematical model of TLR2/CR3 crosstalk to test the hypothesis that CR3 ligation fosters the robust production of PI ( 34 ) P and PI ( 345 ) P which is incompatible with TLR2-mediated Raf signaling . Our own experimental data , in addition to the observation that less virulent strains of Francisella undergo PI3K-independent phagocytosis [43] , lends support to the hypothesis that the membrane's phosphoinositide content during phagocytosis is a critical determinant of cytokine production in response to infection . In defining crosstalk between CR3 and TLR2 within the context of Francisella infection , we also defined crosstalk between the ERK and PI3K signaling cascades within this context . Although several computational analyses of differential ERK signaling and ERK/PI3K crosstalk have already been performed [44]–[46] , to the best of our knowledge this is the first mathematical model to investigate crosstalk between these two pathways within the context of infection . Although our model is by necessity a simplification it contains information about all of the major and immediate components in the CR3/phagocytic and TLR2/cytokine signaling pathways . In the case of CR3 signaling these components are Src family kinases , Rho family GTPases and lipids . In the case of TLR2 these components are Ras GTPase and Raf and Rac GTPase , Akt and lipids . By tracking the dynamics of these important and well characterized signaling molecules we confirm that the model reproduces experimental results , characterizes mechanisms of inhibition , and identifies targets for experimental manipulation . In particular , the model confirms that phagocytosis-associated changes in the composition of the cell membrane can inhibit ERK activity , predicts that Akt and Ras-GAP signaling synergize to inhibit ERK , and identifies Ras-GAP and Akt as a future target for experimental manipulation . According to our model , CR3-mediated inhibition of TLR2 signaling initiated by Francisella is different from CR3-mediated inhibition of TLR2 signaling initiated by Porphyromonas gingivalis , wherein IL-12 production is inhibited by ERK [42] . This difference may stem from the fact that P . gingivalis binds to CR3 via its natural fimbriae , while Francisella cannot efficiently bind CR3 unless opsonized with C3bi . Or , it could also be that P . gingivalis engages other receptors that augment or interfere with CR3/TLR2 crosstalk . However , complement receptor-mediated PI3K activation has been observed to inhibit TLR-induced IL-12 production in response to Hepatitis C virus [47] . Furthermore , as C3bi ligation of CR3 is known to inhibit IL-12 production in response to a variety of stimuli [48] , and support the pathogenesis of a variety of diseases [3]–[9] , it seems likely that the model proposed here is applicable to a variety of pathogens .
TLRs are pattern recognition receptors ( PRRs ) that detect and respond to a broad range of pathogen products including bacterial lipoproteins [27] , [51] . The molecular mechanisms that enable TLRs to respond to bacterial ligands are extremely complex , and can involve crosslinking of TLR heterodimers as well as multiple accessory proteins [52] . In particular , the recognition of pathogen associated molecular patterns ( PAMPs ) by TLR2 , the most promiscuous of all the TLRs , is an involved process [53] . TLR2 is expressed on the cell surface where it constitutively associates with either TLR1 or TLR6 [54] , [55] . TLR1 mediates the recognition of triacylated lipoproteins while TLR6 mediates the recognition of diacylated lipoproteins . We assume that the primary source of TLR1/2 stimulation is through direct contact with the bacterial membrane . Indeed TLR2 is recruited to developing phagosomes [56] . Ligand binding to TLR2 and subsequently TLR1 or TLR6 then induces a crosslinking of these receptors that initiates signal transduction [55] , [57] . The diagram is as follows:We denote by the total concentration of TLR1 ligand in the immunological synapse , and by the concentration of the active signaling complex in the immunological synapse . Our quantitative description of activation is as follows: ( 1 ) ( 2 ) ( 3 ) where denotes the membrane proximal concentration of TLR1/2 heterodimers , which we assume is a constant . In [52] it was shown that TLR2 binds lipoproteins directly with a of . Assuming that the rate at which TLR2-lipoprotein complexes form , , is approximately equal to the rate at which CR3-C3bi complexes form , i . e . , we estimate . According to [58] human monocytes have an average of 2100 molecules of TLR1 per cell . Assuming that monocytes are spherical , have a radius of , and that all of the TLR1 is concentrated in a space around the membrane we estimate the membrane proximal concentration of TLR1/2 heterodimers is approximately . Under normal conditions , macrophages likely express lower levels of TLR1/2 than do monocytes [59] . CR3 has an active conformation that binds to and mediates the phagocytosis of C3bi-opsonized particles and an inactive conformation that does not [32] , [60] . As a result , inside-out signaling through receptors such as TLR2 is often viewed as important for CR3-mediated phagocytosis [15] . In macrophages , however , CR3 readily binds to C3bi-opsonized particles [61] . As a result , the following equations will be used to determine the concentration of CR3-C3bi complexes in the immunological synapse ( ) . ( 4 ) ( 5 ) ( 6 ) where denotes the concentration of C3bi in the synapse and denotes the membrane proximal concentration of CR3 , which we assume is a constant . Cai et al [60] used a soluble monomeric probe , C3bi-AP , in order to estimate the on rate , , and the equilibrium dissociation constant , , of C3bi for active CR3 . We used and to calculate . In vivo C3bi-CR3 complexes likely dissociate more quickly due to uncharacterized active cellular processes . Ross et al [62] used labeled CR3 specific mAbs to determine the molecules of CR3 per alveolar macrophage . Assuming the previously described dimensions , we determined the membrane proximal concentration of CR3 , . Src family kinases , of which Lyn is a member , are rapidly activated in response to integrin ligation [32] . Although the precise mechanisms of integrin-mediated src kinase activation are unknown , some studies support a model in which inactive integrins associate constitutively with Src kinases which are then activated through trans-phosphorylation as a result of integrin clustering [32] . For simplicity we will assume that each molecule of CR3 is associated with a single molecule of Lyn . Under this assumption ( 7 ) – ( 8 ) determines the concentration of active Lyn ( ) . ( 7 ) ( 8 ) where the first term is the rate of Lyn activation through the juxtaposition of two inactive CR3-associated Lyn molecules , and the second term is the rate of Lyn activation due to the juxtaposition of one active CR3-associated Lyn molecule and one inactive CR3-associated Lyn molecule . Although we observe rapid activation of Lyn in response to Francisella , the precise rates of Lyn activation and deactivation within this context are not known . Several in vitro studies , however , enable us to estimate , the rate of trans-phosphorylation [63] , [64] . The rate of dephosphorylation is then chosen to ensure that in resting cells the concentration of phospho-Lyn is low [65] . Since the basal concentration of phospho-Lyn is inversely related to the ratio , we assume that is large so that the basal concentration of phosph-Lyn is small . The activity of Akt is regulated by multiple kinases , phosphatases and lipids . In resting cells Akt is sequestered in the cytoplasm . Upon stimulation Akt translocates to the membrane where it achieves full activation through phosphorylation at Ser and Thr [66] . In particular , and coordinate AKT activation by recruiting Akt and its kinase PDK-1 to the plasma membrane [67] . and , however , are not equivalent in this respect , as Akt binds with slightly greater affinity than , and Ser phosphorylation of Akt requires [67] . Although the majority of Akt targets are cytosolic , our focus is on the negative regulation of membrane proximal Raf by Akt . For this reason our model tracks the concentration of membrane bound Akt which we denote by . Because Akt activation is a complex process involving multiple steps of undetermined significance we resort to a simplified model , in which PI ( 345 ) P and PI ( 34 ) P are treated as equivalent and the activity of Akt at the membrane is approximated by the concentration of membrane bound Akt . Assuming that the concentration of unbound PI ( 345 ) P and PI ( 34 ) P is approximately equal to the total concentration of PI ( 345 ) P and PI ( 34 ) P , we derive the following equation for at steady state:where is the total concentration of Akt in the macrophage and , is the concentration of PI ( 345 ) P and PI ( 34 ) P at the immunological synapse , and is the equilibrium dissociation constant of the Akt-PI ( 345 ) P complex . Given the rapid translocation of Akt to the membrane in response to 3PI production we approximate asWe set the parameter to be equal to the equilibrium dissociation constant for the Akt PH domain-PI ( 345 ) P complex which was measured in [68] through surface plasmon resonance . As we were unable to find a quantitative estimate of the cellular concentration of Akt in macrophages we take the cellular concentration of Akt , , to be equal to that of PC12 cells , which was measured in [69] . Rac and Ras are examples of GTPases which are proteins that cycle between an inactive GDP bound form and an active GTP bound form . GTPase activity is tightly regulated by guanosine nucleotide exchange factors ( GEFs ) , GTPase activating proteins ( GAPs ) , and in some cases guanosine dissociation inhibitors ( GDIs ) [70] . In resting cells GTPases are maintained in their inactive GDP bound form by their slow intrinsic rate of guanosine nucleotide dissociation . Upon stimulation , GEFs activate GTPases by accelerating the dissociation of GDP [70] . Furthermore , because the intrinsic rate of GTPases hydrolysis is also extremely slow , GTPase deactivation is mediated by GAPs that catalyze the reaction [70] . Rac is a GTPase of the Rho family that is activated by both CR3 and TLR2 regulates a variety of cellular processes including cytoskeleton rearrangements and cytokine production [30] , [70] , [71] . Multiple GAPs and GEFs contextualize Rac's response to stimuli . In PC12 cells , a positive feedback loop , involving Vav , Rac , and PI3K maintains Rac activity in response to NGF [72] . Indeed , studies indicate that PI ( 345 ) P enhances Vav's GEF activity by disrupting inhibitory intramolecular interactions [38] . Since PI ( 345 ) P production is required for CR3 mediated phagocytosis of Francisella [21] , and Vav is responsible for Rho GTPase activation downstream of CR3 [71] , it seems likely that a similar feedback loop is operative in this context . In addition to phosphoinositide binding , Vav is regulated through phosphorylation [73] . As the CR3 effector Lyn , can both phosphorylate Vav [74] , and assist in the activation of PI3K [75] we propose a model in which Lyn induces a positive Vav/Rac/PI3K feedback loop . Meanwhile , in response to bacteria , TLR2 activates both Ras and Rac [28] , [29] , [76] . Although the precise mechanisms of activation are unknown Ras can be activated through direct association with TLR2 [29] , and so we propose a model in which TLR2 activates Ras , which then activates Rac through the Rac GEF TIAM-1 [77] . These two modes of Rac activation are distinguished by their relation to PI3K . CR3-mediated Rac activation is PI3K-dependent , while TLR2-mediated Rac activation is PI3K-independent . In either case , however , both Rac and Ras are subject to PI3K-dependent deactivation , since both molecules are also regulated by PI3K sensitive GAPs [34] , [35] , [78] . Our model of Rac activity assumes that the concentrations of active Vav and GAP depend on the concentration of Lyn and PI ( 345 ) P as follows: ( 9 ) ( 10 ) As PI ( 45 ) P has been shown to inhibit the GEF activity of Vav , we consider fully active Vav to be that which is both bound to PI ( 354 ) P and phosphorylated by Lyn . The fraction of Vav bound to PI ( 345 ) P is determined by the equilibrium dissociation constant of the PI ( 345 ) P-Vav complex . Although precise measurements of the quantity were not available the equilibrium dissociation constant for the PI ( 45 ) P-Vav complex was measured in ( ) , and the affinity of Vav for PI ( 345 ) P is known to be greater than the affinity of Vav for PI ( 45 ) P [79] . Hence we estimate . We were unable to determine the cellular concentration of Vav . We assume it is somewhat less than the concentration of Rac . We were unable to determine the rates of Vav phosphorylation and dephosphorylation . As a result the fraction of phosphorylated Vav is determined by the unknown parameter which is varied in the course of our numerical simulations . The concentration of Rac GAP at the membrane is determined by the equilibrium dissociation constant of the Gap-PI ( 345 ) P complex , . As we were unable to find a measurement of we choose this parameter so that the simulated time course of Rac activation during CR3-mediated phagocytosis would resemble the experimentally determined time course of Rac activation during Fc-Receptor-mediated phagocytosis [80] . Although PI ( 345 ) P stimulated GAPs are responsible for regulating the activity of Rac in macrophages , the cellular concentration of these GAPs was not available [81] . For this reason we assume that the concentration of PI ( 345 ) P responsive Rac-GAP is somewhat less than the concentration of Rac . For simplicity we treat TIAM-1 and Vav as equivalent Rac GEFs , and assume that the concentration of TIAM-1 is equal to the concentration of active Ras . This leads to the following model of Rac activation ( 11 ) ( 12 ) where the constant denotes the cellular concentration of Rac and is the membrane proximal concentration of Rac under the assumption that all of the cellular Rac is concentrated at the membrane . The cellular concentration of Rac in neutrophils was measured in [82] . This is likely a good approximation to the actual concentration in macrophages . The rate parameters and characterize TIAM-1 catalyzed nucleotide dissociation from Rac2 as determined in [83] . The intrinsic rate of Rac2 hydrolysis and the rate of spontaneous GDP dissociation were measured in the same work . We were unable to find measurements for the parameters and that characterize the GAP-catalyzed hydrolysis of GTP by Rac , and so , we estimate these parameters using the parameters from the -catalyzed hydrolysis of GTP by Cdc42 [84] . This is a reasonable approximation since Cdc42 is closely related to Rac . Ras is a GTPase that mediates ERK activation by recruiting Raf to the plasma membrane [30] . Since the mechanism through which TLR2 activates Ras is unknown we treat TLR2 as a Ras GEF . We model the GAP induced deactivation of Ras as an enzymatic process in which GAP catalyzes the hydrolysis of GTP to GDP . Combing these two processes , i . e . the exchange of GDP for GTP and the hydrolysis of GTP to GDP , we obtain the following equation for Ras . ( 13 ) ( 14 ) ( 15 ) where the constant denotes the total cellular concentration of Ras , and is the membrane proximal concentration of Ras under the assumption that all of the cellular Ras is concentrated at the membrane . We take the cellular concentration of Ras in macrophages to be equal to that in NIH3T3 fibroblasts [85] . The parameter represents spontaneous dissociation of Ras-GDP . This parameter was determined in vitro [86] . The parameters and which determine the kinetics of Ras activation by TLR2 are not known . We take these parameters from a study on the kinetics of Ras activation by the exchange factor Cdc25 [87] . The parameters , , and , which catalyze GAP catalyzed hydrolysis were measured in [88] . The equilibrium dissociation constant , , between the Ras GAP and PI ( 345 ) P was reported in [37] . Although PI ( 345 ) P sensitive Ras-GAPs are expressed by macrophages [89] , [90] , we were unable to find estimates of their levels of expression in macrophages . Hence , we assume that the concentration of Ras-GAP is somewhat less than the concentration of Ras . Phosphatidylinositol , or PtdIns , is a membrane lipid that mediates signal transduction between cell surface receptors and the cytosol . Its inositol head group contains several free hydroxyls which can be phosphorylated to generate a variety of distinct derivatives termed phosphoinositides [91] . For brevity we abbreviate PtdIns by PI and the phosphoinositides by PI ( ) P , where the terms in the parentheses correspond to the phosphates' positions . For example PtdIns ( 4 , 5 ) P is abbreviated as PI ( 45 ) P . Distinct phosphoinositides transduce distinct signals depending on the location and number of phosphates they contain . In particular , several pivotal proteins bind to their partner phophsoinositides with high specificity [92] , [93] . This allows phosphoinositides to determine the activity of these proteins , by localizing them to cell membranes . In several instances , phosphoinositides can also serve as allosteric activators . In vivo the inositol head group of PtdIns can be phosphorylated at positions 3 , 4 , and 5 [91] . The membranes of resting cells contain minute quantities of PI ( 4 ) P and PI ( 45 ) P , while PI ( 345 ) P andPI ( 34 ) P are virtually undetectable [92] , [93] . Upon stimulation , however , a variety of cell surface receptors , including CR3 , induce a rapid increase in the concentration of the 3 phosphoinositides which are important mediators of cytokine production , phagocytosis and chemotaxis [91] , [94] . In particular , the generation of 3 phosphoinositides is an essential step in the phagocytosis of bacteria [95] , [96] . Experiments conducted in vivo suggest that phosphoinositide production proceeds according to the following diagram [97]:in particular , hydrolysis of PI ( 345 ) P is the primary mode of PI ( 34 ) P production . Stimulation of TLR2 and CR3 is known to induce phosphoinositide 3 and phosphoinositide 5 kinases as well as phosphoinositide 5 phosphatases . The Src kinase Lyn and the small GTPase Rac both contribute to the activation of PI3K . Rac-GTP contributes to PI3K activation by binding to its regulatory subunit [29] , while Lyn activates PI3K through Cbl [31] , [32] . As the parameters with which Lyn and Rac activate PI3K are not known , we take the concentration of active PI3K to be a function of the concentration of active Lyn and Rac , ( 16 ) where is the cellular concentration of PI3K , which we assume to be a constant , and , are the factors by which the levels of active Rac and Lyn must be elevated over their basal values , and respectively , in order to support half maximal activation of . For simplicity we will assume that the two species of 3 phosphoinositides PI ( 34 ) P and PI ( 345 ) P are equivalent and we will neglect PI , PI ( 4 ) P , and PI ( 3 ) P so that in the model PI ( 34 ) P and PI ( 345 ) P are synthesized directly from PI ( 45 ) P and PI ( 45 ) P is the direct product of PI ( 345 ) P and PI ( 34 ) P degradation . As this part of our model is largely conceptual the values of the parameters and are unknown and are varied in the course of the numerical simulations . The cellular concentration of PI3K , , is taken from a study NIH3T3 fibroblasts [69] . The basal activity of Rac and Lyn , and is determined by the model . With these assumptions , the production and degradation of the PI ( 34 ) P and PI ( 345 ) P are described by the following equation , ( 17 ) ( 18 ) ( 19 ) ( 20 ) where denotes the concentration of PI ( 34 ) P and PI ( 345 ) P and denotes the concentration of PI ( 45 ) P , and the total concentration of PI ( 45 ) P , PI ( 34 ) P , and PI ( 345 ) P is assumed to be a constant . The concentration of PI ( 45 ) P , PI ( 345 ) P and PI ( 34 ) P in resting neutrophils [97] was used to determine the initial conditions and . We were unable to find estimates of the parameters and which determine the kinetics of CR3-stimulated PI ( 345 ) P formation in macrophages . A study of PI ( 345 ) P formation during Fc Receptor-mediated phagocytosis in macrophages reported rapid and substantial accumulation of PI ( 345 ) P , with maximal levels reached 30–90 seconds after stimulation [96] . Furthermore the rate of PI ( 345 ) P degradation during this process , , was estimated to be somewhat greater than . Initial conditions ( 18 ) – ( 19 ) along with ( 20 ) and enable us to estimate . In its inactive state Raf is sequestered by 14-3-3 binding proteins , the association of which is supported by phosphorylation at Ser 259 [98] , [99] . Dephosphorylation of Raf at Ser 259 precedes Raf activation . Similarly , phosphorylation of Raf at Ser 259 precedes Raf deactivation [98] , [99] . Although the kinase responsible for phosphorylating Ser 259 in this context is unknown , Akt has been demonstrated to regulate Raf through phosphorylation at Ser 259 in several systems [33] , [98] , and so , we assume that this is the case . Activation of Raf is a multistep process . Raf is recruited to the membrane by Ras-GTP , to which it binds with high affinity . This recruitment places Raf in close proximity to kinases that activate Raf through phosphorylation [100] . One such kinase , Pak , is activated after binding to active Rac [30] The Src family kinase Lyn can also activate Raf [39] . Because many of the parameters that describe the transition between these many states of Raf are unknown we consider a simple model in which Raf exists in three states: active membrane proximal Raf , , inactive and free Raf , , and inactive Raf that is bound to Ras . For simplicity we assume that recruitment of Raf by Ras-GTP is rapid , so that a the fraction of inactive Ras-bound Raf is ( 21 ) The equilibrium dissociation constant for the Raf RBD-Ras complex , , was measured in [100] . In standard Michaelis-Menton kinetics , the substrate concentration is generally assumed to be in excess of the enzyme concentration . Because cellular concentrations of Raf are extremely low , this assumption is unlikely to hold for Raf . Hence in our model of Raf activation and deactivation we employ a modified Michaelis-Menton type model in which Raf is limiting so that the reaction rates depend linearly on the concentration of Raf . Furthermore , since the differences between Lyn- and Pak-catalyzed phosphorylation of Raf are unknown we assume that the concentration of active Pak is proportional to the concentration of active Rac , and that Pak and Lyn are equivalent . With these assumptions , the concentration of active Raf is determined by the following equation . ( 22 ) where denotes the cellular concentration of Raf , which we assume is a constant , and determines the fraction of Pak that is bound to Rac and therefore active . We were unable to find the concentration of Raf in macrophages or closely related cells and have estimated from data on COS cells [69] . The parameter is unknown . We set its baseline value to one and vary it in the course of numerical experiments . We were unable to find the specific parameters and that determine the kinetics with which Akt phosphorylates Raf in macrophages . Instead we estimate these parameters to be equal to the parameters that determine the kinetics of the reaction between Akt and a small peptide substrate [101] under the assumptions that the concentration of ATP in a resting pig alveolar macrophage is equal to that of resting human alveolar macrophage [102] , and that the volume of a pig alveolar macrophage is [103] . The parameters which determine the rate of Raf activation by Lyn and Pak are also unknown . We estimate them from knowledge of the kinetics with which closely related Src family kinases catalyze the phosphorylation of their substrates [104] , [105] , and the kinetics with which Raf is phosphorylated on the plasma membrane of living cells [106] . We should note that Raf is also subject to ERK-mediated negative feedback . Indeed ERK was shown to inhibit Raf kinase activity by phosphorylating Raf at several sites [40] . Although this feedback plays an important role in determining the duration of Raf signaling , it does not significantly influence the activity of Raf at early time points [40] . Since our intent is only to describe the very earliest of signaling events , our model neglects this feedback . | In the current work we construct a highly contextual model of membrane-proximal crosstalk between the ERK and PI3K cascades that is initiated through contact with F . tularensis . The model is used to test the hypothesis that phagocytic signaling downstream from CR3 is responsible for an early inhibition of ERK activity , which is seen subsequent to contact with the complement C3-opsonized Schu S4 strain of F . tularensis . In addition , the model predicts that Akt and Ras-GAP synergize to inhibit ERK . To the best of our knowledge this is the first mathematical model to investigate crosstalk between these pathways within the context of infection . By providing a comprehensive picture of the initial host-pathogen interaction , and pathogen-induced crosstalk between cell surface receptors in particular , this model is important in the context of microbial immunopathogenesis . | [
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| 2012 | A Mathematical Model of CR3/TLR2 Crosstalk in the Context of Francisella tularensis Infection |
Crimean-Congo hemorrhagic fever ( CCHF ) activity has recently been detected in the Kordufan region of Sudan . Since 2008 , several sporadic cases and nosocomial outbreaks associated with high case-fatality have been reported in villages and rural hospitals in the region . In the present study , we describe a cluster of cases occurring in June 2009 in Dunkop village , Abyei District , South Kordufan , Sudan . Seven CCHF cases were involved in the outbreak; however , clinical specimens could be collected from only two patients , both of whom were confirmed as acute CCHF cases using CCHF-specific reverse transcriptase polymerase chain reaction ( RT-PCR ) . Phylogenetic analysis of the complete S , M , and L segment sequences places the Abyei strain of CCHF virus in Group III , a virus group containing strains from various countries across Africa , including Sudan , South Africa , Mauritania , and Nigeria . The Abyei strain detected in 2009 is genetically distinct from the recently described 2008 Sudanese CCHF virus strains ( Al-fulah 3 and 4 ) , and the Abyei strain S and L segments closely match those of CCHF virus strain ArD39554 from Mauritania . The present investigation illustrates that multiple CCHF virus lineages are circulating in the Kordufan region of Sudan and are associated with recent outbreaks of the disease occurring during 2008–2009 .
Crimean-Congo hemorrhagic fever ( CCHF ) , caused by Crimean-Congo hemorrhagic fever virus ( CCHFV ) , is a tick-borne virus with a tripartite RNA genome ( segments designated S , M , and L ) . CCHFV can be transmitted to humans by the bite of an Ixodid tick and by contact with blood or tissue from infected livestock and humans [1]–[3] . CCHFV has been detected in a broad geographic zone including much of Africa , southern Europe and Asia , extending from western China to the Middle East and Southern Russia , where focal endemic areas have been identified [1] , [4]–[13] . Disease outbreaks and sporadic cases of CCHF have been recorded throughout these endemic areas , with several recent outbreaks occurring in the Sudan [14] . In 2008 , a nosocomial outbreak of CCHF occurred in a rural hospital in the Al-fulah District , Western Kordufan , Sudan [14]; two minor genetic variants , designated Al-fulah 3 and 4 ( Genbank Q862371-2 ) , were identified during that outbreak and were thought to be responsible for the emergence of the disease in the region . We conducted the present study to analyze a June 2009 disease cluster involving approximately seven suspect cases of CCHF in Dunkop village , Abyei District , South Kordufan , Sudan . This represents the second CCHF outbreak reported in Sudan's Kordufan region last year . Although clinical samples could only be collected for two of the cases involved in the June outbreak , both cases were laboratory confirmed as acute CCHF by reverse transcription polymerase chain reaction ( RT-PCR ) targeting the viral nucleoprotein ( NP ) of the S segment , which is one of the least variable areas of the genome [15] . Genetic analysis of virus from one case-patient demonstrated that the virus strain belonged to Group III – a virus lineage associated with strains found in Sudan , South Africa , Mauritania , and Nigeria . However , additional analysis revealed that the virus strain implicated in the June 2009 cluster was genetically distinct from that involved in the 2008 Al-fulah outbreak .
The case definition used for identification of suspect hemorrhagic fever patients included onset of fever and one or more of the following symptoms: hemorrhagic manifestations ( ecchymosis , petechia , epistaxis , hematemesis ) , bloody diarrhea , severe headache , joint pain , chills , headache , and nausea . Blood samples were collected from two acute hemorrhagic fever ( HF ) patients in clean , sterile vacutainers . Samples were collected as part of routine diagnostic testing during outbreak response . Ethical clearance was obtained from the Ministry of Health of Sudan and informed consent from all patients was provided through an ethical clearance form which permitted use of the samples for diagnostic and research purposes . In addition to being used for routine malaria screening , blood was allowed to clot , and sera were separated and sent to the Division of Virology , National Medical Health Laboratory , Khartoum , Sudan , for diagnostic screening . Aliquots of serum samples were shipped frozen in Trizol buffer at a ratio of 1∶5 to the Viral Special Pathogens Branch , U . S . Centers for Disease Control and Prevention , Atlanta , GA for viral RNA extraction and subsequent sequencing of the whole virus genome . Due to the hazards associated with cultivation of CCHFV ( the presumptive cause of disease ) , virus isolation attempts were not conducted in Sudan , and regulations prevented shipment of the infectious clinical samples to CDC's high-containment facility in Atlanta . Therefore , identification of the virus was solely dependent on serology and RT-PCR amplification , using primers targeting the S segment of the Al-fulah strain of CCHFV ( Genbank accession number GQ862371 ) , followed by direct sequencing . Serologic tests were conducted in Sudan to screen the sera for antibodies to yellow fever and dengue fever using kits provided by NAMRU3 , Cairo , Egypt . Rift Valley fever and CCHF virus antibody testing in Sudan was done using ELISA kits from Biological Diagnostic Supplies Limited ( South Africa ) . Screening was performed using either IgM enzyme-linked immunosorbent assays or indirect immunofluorescence assays ( IIFT ) . Viral RNA was extracted from the patient's serum samples either using QIAamp viral RNA kit ( QIAamp , GmHb , Germany ) or Qiagen's RNeasy kit as per manufacturer's instructions . The nucleoprotein gene was targeted , using a pair of primers designed based on alignments of nucleotide sequences of Al-fulah strain ( Genbank accession number GQ862371 ) with several CCHFV strains using BioEidit software ( Carlsbad , CA , USA ) . The forward primer CCHF1 ( 5′-ATA CGA GTG TGC ATG GGT CA-3′ ) included bases 286–305 of the positive sense strand of the virus S segment , whereas the reverse primer CCHF2 ( 5′-TTT GCA ATG TGC TTG AGG AG -3′ ) included bases 892-912 of the negative sense strand . Use of primers CCHF1 and CCHF2 in an RT-PCR produced a 627-bp primary PCR product . All primers were synthesized on a DNA synthesizer ( Milliigen/Biosearch , a division of MilliporeBurlington , MA , USA ) and purified using oligo-pak oligonucleotide purification columns ( Glen Research Corporation , Sterling , VA , USA . ) as per manufacturer's instructions . Five microliters of extracted RNAs were employed in RT-PCR assays to detect the CCHF viral genome using primers either described in this study or by Schwartz et al [16] . The RNA specimens were also submitted to the Molecular Biology Laboratory of the Faculty of Veterinary Medicine , University of Khartoum , and the Faculty of Medical Laboratory Sciences , the National Ribat University , Sudan , for further confirmation of the results by RT-PCR as described by Schwartz et al [16] . Additional suspected hemorrhagic fever viruses including Rift Valley fever and yellow fever were ruled out by RT-PCR using previously described assays [17] . Detection of dengue virus serotypes was carried out using Geno-Sen's DENGUE 1–4 Real Time PCR Kit following the manufacturer's instructions ( Genome Diagnostic Pvt . Ltd , India ) . Sequences of the virus S , M , and L genome RNA segments were generated as previously described [15] . Viral RNA extracted from serum was used for RT-PCR amplification of all three segments for one patient from the Abyei outbreak ( Genbank HQ378179 , HQ378183 , HQ378187 ) . Additionally , sequences of two complete and one partial M segments ( Genbank HQ378184-6 ) and three complete L segments ( Genbank HQ378180-2 ) were generated from viruses detected in patient sera obtained from the previously reported Sudanese Al-fulah outbreak in 2008 [14] . SeqView was used to align complete S , M , and L segment sequences for all CCHFV strains available in GenBank , as well as the sequences obtained from Abyei 1–2009 and Al-fulah 1 , 2 , 3 , 4 , 6 , 7 , 8 , and 9–2008 virus strains . Maximum likelihood analysis was conducted for each segment using default settings in GARLI ( v0 . 96b8 ) [18] . Bootstrap support values were created from 1 , 000 replicates , and 50% majority rule trees were generated for each of the segments . CCHFV strains are represented by their country of origin , strain name , and date of collection when available .
During June 2009 , seven patients meeting the hemorrhagic fever ( HF ) case definition were identified from Dunkop village . Dunkop village is located 15 kilometers to the east of Abyei in the former state of Western Kordufan , Sudan , an area that is shared in part by the government of South Sudan and the government of National Unity , Sudan . The population in Abyei District includes the nomadic Arab tribes of Misariya from the north and the Nilotic tribes of Dinka from the south . Transport from Dunkop village to the hospital in Abyei town is extremely difficult , if not impossible , during the rainy season , which includes the month of June . When passable , the distance between the primary-care centers and the surrounding villages is usually crossed by walking; therefore , evacuation of suspect cases from Dunkop village to Abyei hospital was not possible for all patients due to poor road conditions and prevailing insecurity at the time . Of the five patients able to travel to Abyei hospital , two died and one was discharged before samples could be collected for laboratory testing . Given the proximity of approximately 250 kilometers from Al-fulah ( Figure 1 ) , the location of a nosocomial outbreak of CCHF in October 2008 , CCHF was considered as a possible cause of the HF illness [14] . Blood samples were available from two patients with signs of acute HF illness . The extracted RNA samples were used in RT-PCR as targets for CCHF definitive diagnosis and subsequent sequencing . Limited epidemiologic and clinical details were obtained where possible from interviews with patients or close relatives , or from hospital records ( Table 1 ) . The age range of the seven patients suspected of having CCHF was 21–65 years . Five of the suspect patients were female ( all housewives ) , and the last two were male ( a student and a day laborer ) . Traditionally , the activities of women in these rural villages include shepherding livestock and milking of cows , and both the index case and one of the confirmed cases specifically reported animal contact . The potential for exposure to viremic livestock or virus infected ticks would be considered high . No evidence of nosocomial transmission was apparent based on the lack of any reports of infections with compatible illness among health care workers potentially exposed to these cases . All cases were from the same small village , and although there were reports of contact between several of the cases , no strong evidence of a clear chain of transmission could be discerned based on the limited data available . Contact transmission cannot be ruled out however , as the dates for onset of symptoms ranged over a 6 day period from June 12th through 18th 2009 , allowing for the possibility of later cases having acquired the infection from the earliest cases identified . Symptoms and clinical signs of the disease among the seven suspected patients , included rapid onset of fever ( common to all suspected patients ) , and some or all of the following symptoms: headache; nausea; vomiting ( with or without blood ) ; severe muscle , joint , and/or chest pain; agitation; diarrhea; bleeding from the nostrils , mouth , gum , and upper and lower gastrointestinal tracts . Four of the seven cases progressed to coma and fatal outcome . Serologic tests on sera from two suspected HF patients were negative for yellow fever , dengue fever , West Nile fever , and Rift Valley fever , as determined by either IgM enzyme-linked immunosorbent assay ( ELISA ) or indirect immunofluorescence assay ( IIFT ) . Sera from these patients also tested negative for the presence of CCHF antibodies , as is common in fatal CCHF cases when sera is drawn 1–3 days post onset of illness . A second blood sample was not available from the surviving patient for further serological testing despite having been hospitalized for 2 days before discharge . RT-PCR assays of RNAs extracted from sera of suspected HF patients were negative for Rift Valley fever , yellow fever , and dengue viruses; however , conventional RT-PCR resulted in amplification of a 627-bp PCR product specific for the CCHFV S segment ( data not shown ) . The two RNA specimens were submitted to the Molecular Biology Laboratories at the University of Khartoum and the National Ribat University , and were confirmed as CCHF cases by RT-PCR using the same primers described in this study and CCHF primers described by Schwartz et al [16] ( data not shown ) . In addition , blood smears were obtained for malaria testing at Abyei hospital , and both were positive . The finding of positive malaria smears is not uncommon in this area of rural Sudan and the clinical significance of the finding in these cases was difficult to evaluate . Using the method reported earlier [15] , whole CCHF virus genome ( S , M and L RNA segments ) sequencing was attempted for each of the CCHF virus PCR positive specimens collected from the June 2009 Abyei outbreak and those from the earlier Al-fulah CCHF outbreak [14] . The complete sequence of the virus S , M and L genomic segments was successfully obtained for one sample from the Abyei outbreak , and two complete and one partial M segments , and two full L segments were generated from the earlier Al-fulah specimens ( Table 2 ) . Comparison of the virus sequences detected in the 2008 Al-fulah outbreak revealed that few mutations occurred in any of the three segments . Only one nucleotide difference was found among the virus S segments; both the partial Al-fulah 4-2008 and complete Al-fulah 9-2008 M segment sequences had one base change when compared to the reference Al-fulah 3–2008 sequence . The complete L segment sequences for viruses Al-fulah 4–2008 and 9–2008 contained two and one nucleotide differences , respectively , when compared to Al-fulah 3–2008 . These results indicated circulation of identical or near-identical virus variants within the outbreak , consistent with the report of most of these cases being linked by nosocomial transmission [14] . Phylogenetic analysis of the virus sequences derived from the Abyei outbreak samples demonstrated that this outbreak was also caused by a virus belonging to Group III [15] . While viruses from the Al-fulah 2008 and Abyei 2009 outbreaks are both located in Group III , the Abyei strain was genetically distinct from the Al-fulah strain ( Figures 2 , 3 , and 4 ) . The two Sudanese strains both displayed closer genetic relationships with other Group III viruses from elsewhere in Africa than with one another . The S and L segments of the Abyei strain matched most closely with those of the Group III Mauritanian virus strain ArD39554 ( Figures 2 & 4 ) . This virus had been shown previously to be an M segment reassortant virus with the virus M segment being the sole representative of a highly unique Group VII [15] , [19] . The M segment of the Abyei strain matched another Group III virus , namely the Nigerian IbAr10200 strain , consistent with the Abyei virus not being a reassortant ( Figure 3 ) . The Al-fulah strain genome segments matched most closely to other Group III viruses from South Africa . No evidence of reassortment or recombination was found for either of the Sudanese CCHFV strains [9] , [13] , [20]–[21] .
In Sudan , sero-epidemiological surveys conducted over the past few decades have suggested the presence of various arboviruses in human populations [22]–[23] . In addition , CCHFV specific antibodies have been detected in camels in Egypt , and in sheep and goats in Jeddah , Saudi Arabia , all of which had been imported to these countries from Sudan [24]–[25] . Recently , CCHFV activities have been reported in the Kordufan region of Sudan . Several suspected sporadic cases and small outbreaks of CCHF associated with high case-fatalities were reported in rural hospitals from October 2008 through June 2009 . CCHF was first confirmed to be present in Sudan during the 2008 nosocomial outbreak in a rural hospital in Al-fulah , Western Kordufan [14] . During the Al-fulah outbreak , the initial treatment in the hospital focused primarily on malaria , as blood smears from some of the CCHF suspect patients were positive for malaria . Both Plasmodium falciparum and P . vivax are found in Sudan , and malaria is endemic in Kordufan region , with the transmission season lasting from June/July to October/November [26]–[27] . Given how common positive smears are in this locale , it is difficult to assess the clinical significance of the positive smears reported in these two lab confirmed CCHF cases , particularly in the absence of identification of Plasmodium species involved . While speculative , it is possible that the immunosuppression associated with CCHFV infections [28] might facilitate malaria recurrence and invasion of malaria parasites from the liver to the blood stream . Our point is that the finding of a malaria positive smear should not be basis of cessation of testing for other etiologies which pose a risk of nosocomial transmission to the medical staff , as fever of unknown origin can also indicate viral hemorrhagic fever ( VHF ) in endemic countries like Sudan [14] . In this outbreak , serum samples from the patients were also tested for CCHF which led to the identification of etiology of the disease outbreak . Transmission of CCHFV in the recent Sudanese outbreaks likely involved either tick-bites or person-to-person spread . Tick bite transmission or exposure to blood or tissues of infected livestock is more likely to occur during rainy months , when roads become very muddy and transport between villages or cities , becomes extremely difficult . In these conditions , the local residents typically live in close contact with their cattle within their home villages , increasing the risk of acquiring infection from infected livestock or by tick bite . Once CCHF cases are admitted to medical facilities , nosocomial chains of transmission can occur within hospitals and clinics lacking resources or training to fully implement universal barrier nursing conditions and effective infection control practices [29]–[30] . This was clearly the case in the Al-fulah 2008 outbreak [14] . The genetic diversity of CCHFV , its virulence , and its potential as a bioterrorism agent underscore the importance of genetic characterization of virus strains from all geographically distinct endemic areas . This type of analysis improves the design and development of rapid diagnostics and potential candidate vaccines for this devastating disease . This investigation expands on existing data indicating that Group III CCHFV lineage , known to be endemic in Sudan , South Africa , Mauritania , and Nigeria , is broadly distributed within Africa . Furthermore , our study illustrates that multiple strains belonging to Group III virus lineage from the African continent are currently present in the Sudan and are responsible for the recent outbreaks of the disease in the Kordufan region . The genetic linkage of the recent Sudan outbreak viruses would be compatible with viruses having been introduced to Sudan from South Africa , Senegal , Mauritania or Nigeria as a result of the international trade of livestock or by bird migration . However , as the Sudan is the largest country in Africa and the richest in animal resources , it seems unlikely to import livestock and associated products from these distant regions of Africa . Phylogenetic study of the sequence diversity of all the characterized CCHFV strains clearly indicates that virus genetic lineages are not precisely restricted to defined geographic locations , but considerable movement of viruses is occurring over long distances . These data would suggest that the role of bird migration in disease transmission should strongly be considered in the dissemination of CCHFV . The occurrence of multiple CCHF outbreaks in Sudan in the past few years , and the risks these cases pose for medical staff in resource poor health care facilities indicate the importance of improved surveillance in Sudan for this important disease . The attending physicians , working in those areas of endemicity , should consider this virus in their efforts to diagnose the disease in patients presented to hospitals or clinical centers with symptoms indicative of HF . | The tick-borne virus which causes the disease Crimean-Congo hemorrhagic fever ( CCHF ) is known to be widely distributed throughout much of Africa , Southern Europe , the Middle East , Central Asia , and Southern Russia . Humans contract the virus from contact with infected people , infected animals ( which do not show symptoms ) , and from the bite of infected ticks . CCHF was recently recognized in the Sudan when several hospital staff and patients died from the disease in a rural hospital . The genetic analysis of viruses associated with the 2008 and 2009 outbreaks shows that several CCHF viral strains currently circulate and cause human outbreaks in the Sudan , highlighting CCHF virus as an emerging pathogen . The Sudanese strains are similar to others circulating in Africa , indicating movement of virus over large distances with introduction and disease outbreaks in rural areas possible . Understanding the epidemiology of zoonotic diseases such as CCHF is especially important in the Sudan given the large numbers of livestock in the country , and their importance to the economy and rural communities . It is imperative that hospital staff consider CCHF as a possible disease agent , since they are at a high risk of contracting the disease , especially in hospitals with limited medical supplies . | [
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| 2011 | Multiple Crimean-Congo Hemorrhagic Fever Virus Strains Are Associated with Disease Outbreaks in Sudan, 2008–2009 |
Huntington's disease ( HD ) is a fatal neurodegenerative condition caused by expansion of the polyglutamine tract in the huntingtin ( Htt ) protein . Neuronal toxicity in HD is thought to be , at least in part , a consequence of protein interactions involving mutant Htt . We therefore hypothesized that genetic modifiers of HD neurodegeneration should be enriched among Htt protein interactors . To test this idea , we identified a comprehensive set of Htt interactors using two complementary approaches: high-throughput yeast two-hybrid screening and affinity pull down followed by mass spectrometry . This effort led to the identification of 234 high-confidence Htt-associated proteins , 104 of which were found with the yeast method and 130 with the pull downs . We then tested an arbitrary set of 60 genes encoding interacting proteins for their ability to behave as genetic modifiers of neurodegeneration in a Drosophila model of HD . This high-content validation assay showed that 27 of 60 orthologs tested were high-confidence genetic modifiers , as modification was observed with more than one allele . The 45% hit rate for genetic modifiers seen among the interactors is an order of magnitude higher than the 1%–4% typically observed in unbiased genetic screens . Genetic modifiers were similarly represented among proteins discovered using yeast two-hybrid and pull-down/mass spectrometry methods , supporting the notion that these complementary technologies are equally useful in identifying biologically relevant proteins . Interacting proteins confirmed as modifiers of the neurodegeneration phenotype represent a diverse array of biological functions , including synaptic transmission , cytoskeletal organization , signal transduction , and transcription . Among the modifiers were 17 loss-of-function suppressors of neurodegeneration , which can be considered potential targets for therapeutic intervention . Finally , we show that seven interacting proteins from among 11 tested were able to co-immunoprecipitate with full-length Htt from mouse brain . These studies demonstrate that high-throughput screening for protein interactions combined with genetic validation in a model organism is a powerful approach for identifying novel candidate modifiers of polyglutamine toxicity .
Huntington's Disease ( HD ) is a member of a family of dominantly inherited neurodegenerative diseases caused by expansion in a glutamine-encoding CAG tract . HD occurs when the polyglutamine ( polyQ ) tract in huntingtin ( Htt ) expands beyond ~35 glutamine ( Q ) repeats and manifests with movement disorder , psychological disturbances , and cognitive dysfunction progressing over a period of about ten to 15 years until death . Currently there is no effective treatment or cure for HD . Mutant Htt is thought to cause cellular dysfunction , neurodegeneration , and associated clinical features primarily through a toxic gain of function [1] . Indeed , proteins containing expanded polyQ tracts are toxic when expressed in a wide range of experimental transgenic systems including yeast , cultured mammalian cells , Caenorhabditis elegans , Drosophila , and mouse [2–4] . Determining the precise mechanism of polyQ-mediated toxicity is a subject of intense inquiry , and there is evidence supporting a role for aberrant protein-protein interactions in pathogenesis . In HD , expanded Htt is processed to N-terminal fragments that form inclusions found both in the cytoplasm and nucleus [5 , 6] . A number of proteins localize to expanded polyQ inclusions , including ubiquitin/proteasome components , heat shock proteins , and transcription factors [7–12] . These findings support the idea that mutant Htt may interfere with the functions of diverse cellular proteins directly , through protein interactions . Some interacting proteins have been shown to be functionally compromised when bound to mutant Htt [11 , 13 , 14] . In addition , some of these proteins localize to insoluble Htt-fragment-containing inclusions present in affected tissues [15 , 16] . Recent work , however , has suggested that inclusions may be benign or even protective and that other misfolded forms of Htt may be the primary toxic species [17–19] . Since interactions between cellular proteins and soluble or aggregated Htt may have a general role in HD pathogenesis , identification of Htt-interacting proteins will further elucidate toxic mechanisms and therapeutic targets for the disease . Htt is a large , ubiquitously expressed protein comprised nearly entirely of HEAT repeats , a characteristic protein-protein interaction motif [20 , 21] . Nearly 50 proteins capable of interacting directly with Htt or Htt fragments have been described . Most proteins have been found to interact with N-terminal polyQ containing Htt fragments and , in some cases , the strength of these interactions has been shown to be sensitive to the length of the polyQ tract [22 , 23] . Htt-interacting proteins represent diverse cellular roles including intracellular transport , transcription , and ubiquitin-mediated proteolysis . These observations suggest that the normal function of Htt involves multiple protein-protein interactions in the context of diverse multiprotein cellular complexes . Indeed , loss of normal Htt function is a component of HD pathology [24] . Identifying Htt protein-protein interactions may help to elucidate the functions of wild-type Htt as well as the novel gain of function of mutant Htt . In this study we report a large set of novel Htt-fragment-interacting proteins using yeast two-hybrid ( Y2H ) and affinity pull-down/mass spectrometry ( MS ) protein interaction screens . We used both approaches in parallel in an effort to define a comprehensive set of interactors , as there is evidence that each method explores different groups of interacting proteins [25–28] . We reasoned that if expanded Htt can influence the functions of its interacting proteins ( and vice versa ) , genes encoding interacting proteins should be enriched for genetic modifiers of neurotoxicity mediated by expression of a mutant Htt fragment . We used a Drosophila model of polyQ toxicity to test this idea and found that 45% of the interactors behave as high-confidence genetic modifiers ( i . e . , interaction confirmed with more than one allele ) . Importantly , protein interactions validated as genetic modifiers in Drosophila were equally represented in the Y2H and MS derived datasets , demonstrating the complementary nature of these independent methods . A standard method for validation of large-scale interaction datasets relies on co-affinity precipitation of samples of the protein interaction pairs [29 , 30] . Whereas co-affinity precipitation does confirm a physical interaction , it does not establish the biological relevance of that interaction . The high-content validation method used in this study ( genetic interaction in a whole organism ) strongly supports the conclusion that this dataset is highly enriched for interacting proteins with functional roles in polyQ-mediated neurodegeneration . Using co-immunoprecipitation , we show further that a number of these modifier proteins physically associate with Htt in brain tissue of transgenic mice expressing full length Htt protein . An ultimate result of this study is to provide insight into potential therapeutic targets for HD . The 17 loss-of-function suppressors of Drosophila HD reported here constitute a significant collection of novel targets ( and pathways ) to be considered as targets for therapeutic intervention .
In a comprehensive search for novel Htt-fragment-interacting proteins , we performed two large-scale screens for interactions using MS and Y2H methods ( Figure 1A ) . Multiple fragments of Htt , including both wild-type and mutant N-terminal fragments , were cloned and expressed for pull-down experiments and Y2H screens ( Figures 1B and S1 ) . We purified five recombinant Htt-fragment baits ( corresponding to amino acid residues 1-90-23 Q , 1-90-48 Q , 1-90-75 Q , 443–1 , 100 , and 2 , 758–3 , 114 ) from Escherichia coli in sufficient quantities for pull-down experiments . A total of 97 pull-down experiments were performed with these Htt-fragment baits and mammalian tissue or cell protein lysates ( Figure 1 ) . Tandem affinity purification ( TAP ) -tagged Htt-fragment containing protein complexes were allowed to form in protein extracts prepared from mouse or human brain tissue or mouse muscle tissue . Complexes were copurified with the affinity tagged Htt-fragment proteins and analyzed by MS . Of the five Htt-fragment bait proteins , only the 1–90 amino terminal fragments yielded specific and reproducible protein complexes . The wild-type and mutant Htt-fragment baits ( corresponding to exon 1 of the HD gene ) ( Figure 1B ) were also used to probe protein extracts prepared from cultured cells ( HEK293 , HeLa , and M17 neuroblastoma ) . Using the database-searching tool MASCOT , we generated a primary dataset of 1 , 107 unique high-scoring peptides present in the Htt-fragment pull downs ( Figure 1A; Table S1 ) . To generate a high-confidence interaction list , we subjected these peptides to a statistical test for specific association with Htt fragments by comparison to a database of 15 , 131 high-scoring peptides identified in pull downs performed with 88 different protein baits ( unpublished data ) . This analysis was used to generate a p-value for the association of a particular peptide with Htt-fragment pull downs . A total of 410 unique peptides from Htt-fragment pull downs met a p-value limit of ≤0 . 05 , and each of these peptides was manually validated by inspection of the MS spectra ( see detailed methods in Supporting Information ) . The data were filtered further by excluding any proteins identified by peptides observed in control pull downs with TAP-tag alone or proteins containing any peptides not meeting the p-value cut-off ( i . e . , peptides not specific to Htt-fragment pull downs ) . These methods identified 145 mouse and human proteins specific to Htt-fragment pull downs and eliminated many proteins considered to be false positives in other studies ( Tables 1 and S1 ) [25] . Genes encoding orthologs of 28 of these proteins were tested for genetic interaction with a truncated mutant N-terminal human HD gene in a Drosophila model of polyQ toxicity . In addition to solution-based MS protein interaction studies , we performed Y2H searches with Htt-fragment baits using a high-throughput automated screening platform [31] . A total of 3 , 749 individual Y2H searches of HD fragment baits were performed against prey libraries prepared from 17 different human tissue cDNA sources ( Figure 1 ) . Multiple overlapping baits were searched extensively but only baits located near the Htt N terminus ( including polyQ containing fragments ) gave reproducible interactions ( Figure 1B , solid lines ) . PolyQ containing Htt-fragment baits of amino acids 1–90 , 1–450 or 1–740 were screened in both wild-type ( 23 Q ) and mutant ( >45 Q ) forms . Screens were performed under stringent selection conditions requiring simultaneous activation of two independent auxotrophic reporter genes , HIS3 and ADE2 [31 , 32] . Initial results identified a total of 562 unique interacting prey proteins ( Figure 1A; Tables S2 and S3 ) . Because Y2H screens have been estimated to contain up to 50% false positives among the primary positives [33 , 34] , the data were filtered using stringent criteria to eliminate false positives and generate a high-confidence dataset . First , only interactions that had been independently observed at least three times in Y2H screens were included . Next , proteins were excluded if they were observed to interact with more than 174 unique partners in a database of 110 , 000 interacting protein pairs generated from approximately 290 , 000 Y2H screens . These searches were performed in a large random screen for human protein interactions ( unpublished data ) . This cut-off , designed to eliminate promiscuous interactors , was calculated by k-means clustering analysis of the random dataset [31] . Finally , genes encoding interacting preys were recovered from positive yeast colonies , sequenced twice , and reintroduced with Htt-fragment bait into naive Y2H assay cells . Genes were excluded if the interaction with Htt fragments could not be reproduced in the Y2H assay ( as measured by activation of two reporter genes ) . Previously published Htt and Htt-fragment interactors were included in the final list regardless of the exclusion criteria . A total of 104 unique interacting proteins ( 18% of the primary dataset ) met these conditions and were included in the final high-confidence dataset ( Table 2 ) . A complete list of all Htt-fragment interactions found in our Y2H screens is shown in Table S2 . Sequences derived from all positive colonies used to identify the interacting proteins are presented in Table S3 . Orthologs of 35 of these genes were tested for genetic interactions with a mutant human N-terminal portion of the HD gene in a Drosophila model of polyQ toxicity . While more than 3 , 500 searches were performed with Htt fragments , after 800 searches the rate of discovery for novel interacting proteins approached zero , indicating that these screens were close to saturation ( Figure S2 ) . This result demonstrates that the total number of Htt-fragment-interacting proteins discovered in our Y2H screens represents a finite set and is not simply a function of the number of searches . Examining the gene ontology annotations associated with interacting proteins reveals that the two methods differ to some degree in the type of proteins identified ( Figure 1C ) . Y2H clearly identified more proteins involved in protein turnover , signal transduction , and transcription , while MS identified more proteins involved in metabolic processes . However , proteins involved in cytoskeletal or protein-trafficking processes were similarly represented among the Y2H and MS data . Overall , there was little overlap of specific interacting proteins between the two datasets . Only four high-confidence proteins were found using both methods: clathrin , pyruvate kinase , GAPDH , and YWHAB ( Tables S1 and S2 ) . Two of these , clathrin and GAPDH have been previously reported to associate with Htt fragments [35 , 36] . To directly address the biological relevance of the Htt-fragment protein-interaction dataset and to assess the relative validity of results generated using the Y2H and MS methods , we tested a sample of interacting proteins in a high-content independent method , a genetic modifier assay in a fly model of polyQ toxicity . An arbitrary sample of 60 proteins in the dataset was tested for the ability to modify an Htt-fragment-induced neurodegeneration phenotype in Drosophila . This polyQ toxicity model was generated using an N-terminal fragment of the human HD cDNA , encoding the first 336 amino acids of the protein , including a 128 Q expansion in exon 1 ( see Materials and Methods ) . Directed expression of this expanded human HD transgene fragment in the Drosophila eye causes a neurodegenerative phenotype evident by external examination and retinal histology . Of the 234 nonredundant mammalian protein interactors found in the MS and Y2H screens , 213 had apparent orthologs in Drosophila ( unidirectional top hit with BLAST score less than 10−3 ) , and 127 of these had available Drosophila stocks suitable for screening . We tested 60 of these , divided roughly equally between genes discovered using Y2H ( 35 ) and MS ( 28 ) methods ( including three genes found in common ) , for possible genetic interactions in the fly model of polyQ toxicity ( Table S4 ) . A total of 48 of the 60 genes in the sample ( 80% ) either enhanced or suppressed the expanded Htt-fragment-induced neurodegeneration in the Drosophila eye when tested in either over-expressing or in partial loss-of-function strains ( Tables 3 and S5 ) . In some cases a modifier effect was observed , but only one background strain could be tested ( Table S5 ) . However , for 27 of these genes , modification either by more than one allele or in more than one genetic background was observed . These genes comprise a high-confidence set of genetic modifiers of mutant Htt-fragment toxicity ( Figures S3 and S4; Table 3 ) . The 27 high-confidence modifiers represent a 45% validation rate among those interactors tested . Since the collection of genes tested in the fly assay represented an arbitrary sample of the protein interaction collection , this result indicates that as much as half of the proteins in our dataset may be modifiers of mutant Htt toxicity . The hit rate for genetic modifiers seen among our interactors is an order of magnitude higher than the expected 1%–4% typically observed in unbiased genetic screens [37–39] , including a comparable modifier screen using a Drosophila model of the polyQ disease spinocerebellar ataxia type 1 [40] . Validation rates for proteins discovered by either Y2H ( 27/35 or 77% ) or MS ( 21/28 or 75% ) methods were similar , indicating that these methods are comparable in their ability to uncover biologically relevant interactions ( Table 3 ) . These relative validation rates demonstrate further that the MS and Y2H datasets are complementary in nature and that each dataset is similarly enriched for genes and proteins that modify mutant Htt toxicity in vivo . Furthermore , the majority of these modifiers were discovered in interaction screens performed with human brain protein extracts or brain-derived cDNA libraries indicating that they are expressed in tissues relevant to HD ( Tables 1 and 2 ) . Among the 27 high-confidence modifiers , partial loss-of-function mutations were tested for 27 of them and over-expression mutations for nine . A total of 18 of the modifiers behaved as suppressors of neurodegeneration , ( 14 by partial loss-of-function and four by over-expression ) ( Figure S3 ) , whereas 18 behaved as enhancers ( 13 by partial loss-of-function and five by over-expression ) ( Figure S4 ) . In all 13 cases where both over-expression and loss-of-function alleles were tested , suppression was observed in one condition and enhancement in the other . These modifiers cluster into several functional groups including proteins involved in cytoskeletal organization and biogenesis , signal transduction , synaptic transmission , proteolysis , and regulation of transcription or translation ( Table 3 ) . Histological analysis of eye phenotypes from representative enhancers and suppressors from each of these groups is shown ( Figure 2 ) . One interesting subset of modifiers is a group of proteins involved in SNARE-mediated vesicle fusion [41 , 42] . This includes STX1A , NAPA , and the voltage-gated calcium channel delta subunit CACNA2D1 . Interestingly , alleles encoding all of these proteins act both as loss-of-function suppressors and gain-of-function enhancers in the fly assay . Collectively , these modifier results point toward a model of Htt toxicity involving dysregulation in synaptic function at the level of SNARE-mediated vesicle fusion . Additional experiments were performed to further validate a role for a SNARE component in modifying mutant Htt toxicity ( Figure 3 ) . In contrast to expression in the eye , pan-neural expression of N-terminal expanded Htt leads to a shortened lifespan in the fly model of polyQ toxicity . Pan-neural expression also results in late-onset progressive motor dysfunction that can be quantified in terms of climbing performance as a function of age . These behavioral assays confirm the results obtained in the eye assay: partial loss-of-function of STX1A ameliorates both the disorganization and fusion of ommatidia seen in flies expressing the gene that encodes N-terminal expanded Htt as well as the retinal degeneration . The shortened life-span and the late-onset progressive motor dysfunction phenotypes were also improved by a partial loss-of-function of STX1A , confirming that the modifier effects seen in the eye were not limited to a particular phenotypic assay ( Figure 3B and 3C ) . Htt is known to interact with proteins involved in endocytosis and vesicle trafficking such as PACSIN1 , HAP1 , HIP1 , and HIP14 [22] , however , this is the first report showing that Htt interacts directly with the SNARE complex and that partial loss-of-function can suppress mutant Htt toxicity . A network summarizing interactions relevant to Htt and proteins with gene ontology annotations ( http://www . geneontology . org ) related to vesicle traffic and/or neurotransmission is shown in Figure 4 . Included here are Y2H interactions ( rectangles and thick lines ) and proteins identified by MS ( ovals ) in pull downs using lysates prepared from mouse and/or human brain tissue ( set included in dotted circle ) . A total of 11 proteins in this interaction subnetwork ( shown in red ) are encoded by human orthologs of genes shown to act as modifiers in the Drosophila model of polyQ toxicity ( Tables 3 and S3 ) . Notably , several modifiers are present in a highly connected cluster of Htt-fragment-interacting proteins known to function in receptor-mediated endocytosis: CLTC , AP2A2 , AP2B1 , PACSIN1 , and DNM1 . The observations that Htt is localized to endosomal vesicles and associated with clathrin in fibroblasts derived from HD patients [5 , 43] and that vesicle associated proteins are found in Htt-fragment inclusions [44] makes this interconnected cluster of modifiers particularly striking . Curated Htt-fragment-interacting proteins obtained from BioGRID ( http://www . thebiogrid . org ) [45] and/or the Human Protein Reference Database ( http://www . hprd . org ) [46 , 47] are included in the network . These bridging proteins ( blue triangles ) represent all curated interactions contained in these databases that connect HD to at least one other protein in the subnetwork though a single protein node and link some of our novel Y2H interactions and MS associations to known Htt-interacting proteins ( e . g . , HIP1 , GIT1 ) . Together , this interaction network provides additional proof that our dataset is enriched for proteins that are important in HD pathogenesis and underscores the role of proteins involved in vesicle traffic as being relevant to HD function and pathology . For further in vivo validation of Htt-fragment protein interactions in mammalian tissue , we performed co-immunoprecipitation experiments from brains of wild-type mice and mice expressing a 128 Q full length YAC transgene [48] . Figure 5 shows the results of co-immunoprecipitation experiments using antibodies raised against Htt-fragment-interacting proteins . In all , we observed co-immunoprecipitation with seven of 11 interacting proteins tested . These included the SNARE-associated proteins STX1A and CACNA2D1 , both of which are modifiers in the Drosophila assay . We also observed co-immunoprecipitation with SNAP25 ( another SNARE component ) . Other modifiers observed to associate with Htt in mouse brain were the ubiquitin hydrolase USP9X and the proteasome component PSMC2 . None of these interacting proteins appeared to show a strong preference for wild-type versus CAG expanded Htt in this assay . Immunoprecipitation using antibodies directed against GAPDH and PARP are included as positive and negative controls . We observed a polyQ length-dependent association of GAPDH with Htt . The GAPDH protein has been reported to bind Htt and act as a modifier of mutant Htt toxicity [36 , 49] . Overall , in this sample , we observed a 60% validation rate in this assay ( seven of 11 proteins tested ) . Of the seven proteins observed to co-immunoprecipitate with Htt from mouse brain , three were discovered using Y2H ( CUL2 , PSMC2 , and USP9X ) , two were discovered using MS ( STX1A and SNAP25 ) , and one by both methods ( PKM2 ) . This further underscores a specific utility of both methods for discovery of interacting proteins .
Although the gene encoding Htt was identified over a decade ago , the normal function of this protein and the precise mechanisms by which expanded polyQ exerts its toxic effects remain the subjects of intense inquiry . In this study we identified 234 potential new Htt-associated proteins using high-throughput proteomic screens . The diverse functions of Htt and Htt-fragment protein partners and modifiers reported here are consistent with the functional diversity of pathogenic processes and targets in HD . Htt is localized to a number of different cellular compartments , and there is a large body of evidence showing that mutant Htt fragments can interfere with a diverse range of proteins and pathways including , transcriptional activation and co-activation [12 , 13 , 15] , ubiquitin-mediated proteolysis [50] , mitochondrial energy metabolism [51 , 52] , receptor-mediated signal transduction [53] , axonal transport [54] , and vesicle trafficking [43 , 44] . These observations suggest models of Htt-mediated pathology that involve interference in multiple cellular pathways . Furthermore , we have identified a novel association between Htt fragment and components of the vesicle secretion apparatus ( Table 1 ) . Stx1A , NAPA , and CACNA2D1 were confirmed as modifiers in the fly polyQ toxicity model ( Table 3 ) , and SNAP25 , STX1a , and CACNA2D1 proteins were observed to co-immunoprecipitate with full length Htt from mouse brain ( Figure 5 ) . Protein interactions and localization experiments have placed Htt primarily at postsynaptic sites ( reviewed in [55] ) , but Htt has also been shown to be associated with N-type calcium channels in presynaptic cells [56] . These results suggest that modulation of SNARE-mediated neurotransmitter secretion may be a normal function for Htt and/or may be perturbed by mutant Htt . In addition to the general large-scale protein interaction screens reported for human proteins , two screens have been reported that focus specifically on proteins related to polyQ disease . A large-scale Y2H screen for Htt-fragment binding proteins uncovered 15 novel interacting proteins , including GIT1 , an enhancer of polyQ aggregation [57] . A more recent screen for protein interactions relevant to inherited ataxias reported a large network of interaction involving 54 proteins implicated in human ataxia [29] . Interestingly , there was more overlap between high-confidence interactions in our dataset and the previously published Htt dataset [57] than the ataxia dataset [29] , suggesting that protein-protein interactions may contribute to pathogenic specificity found among the polyQ diseases . Validation of interactions in the ataxin network study relied on demonstration of co-affinity precipitation of tagged expressed protein pairs . Here we tested the ability of a genetic model to validate protein interactions . 48 of 60 genes tested in a polyQ-induced fly eye degeneration model of HD modified the polyQ-induced toxicity , suggesting that this list contains protein interactors that also genetically interact with Htt . Our validation rate using the Drosophila genetic model ( 80% ) is similar to that found using co-affinity purification in the ataxia and Htt studies ( 80% and 65% , respectively ) [29 , 57] . Moreover , whereas co-affinity purification gives validation of the physical interaction of proteins , the genetic modification screen provides additional information suggesting a biological role in genetic pathways relevant to HD . Overall , these observations demonstrate the utility of combining protein-interaction screening with genetic-interaction screening to provide insight into disease mechanisms and identify potential targets for therapeutic intervention . Whereas our datasets more than quadruple the potential number of interactions attributed to Htt or Htt fragments , the in vitro derived interactor datasets do contain nonrelevant interactions ( false positives ) and do not represent all binding proteins ( false negatives ) , an issue common to high-throughput screens . For example , despite the saturation of the screens we identified some , but not all , of the known Htt-fragment-interacting proteins . Our protein interaction screens revealed 14 of the 40 interactions previously discovered using Y2H methods [22 , 23] . Using different Y2H methods , a recent high-throughput screen isolated 19 Htt-fragment-interacting proteins , four of which had been previously described [57] . Together , these data suggest that different Y2H methods yield overlapping but not identical datasets , likely due to differences in selection stringency as well as other technical differences . Surprisingly , only Htt fragments near the N terminus of the protein were able to generate reproducible protein interaction in our Y2H screens ( Table 2 ) . This finding is consistent with a previous report in which Y2H methods failed to detect interactions from Htt-fragment baits outside the amino terminus [58] and may be in part due to technical limitations of the Y2H method . For example , C-terminal Htt fragments may not fold properly in yeast , may require post-translational modifications not found in yeast for interaction with protein partners , or may be localized away from the nucleus . Even fewer known Htt-interacting proteins were found by pull-down/MS methods . Interestingly , the cytosolic chaperonin-containing t-complex ( CCT or TriC ) was recently shown to physically interact with Htt and modify the course of polyQ-induced toxicity in mammalian cells [59 , 60] . We found that two components of the CCT complex , CCT6 and CCT8 , were associated with Htt exon1 in pull downs . Together , these data suggest that many potential Htt-interacting or Htt-associated proteins remain to be discovered by other methods . Overall , there was little overlap between interactions found by the Y2H and pull-down methods ( 4/234 ) . This low degree of overlap is consistent with results seen in other systems-scale protein interaction datasets generated using Y2H and MS methods . For example , interaction screens of the yeast proteome using Y2H ( 4 , 476 and 915 binary protein interactions ) [27 , 28] and MS-based screens ( 3 , 767 and 3 , 727 interactions in proteins complexes ) [25 , 26] yielded a 2%–5% overlap . It has been suggested that this low overlap between interaction screening methods may arise from several factors including method-specific biases [34] . Ultimately , the value of protein interaction data generated by any method needs to be evaluated through experimental validation . We clearly demonstrate here that both methods are similarly capable of identifying Htt-fragment-interacting proteins that can be validated by assays based upon genetic interaction and physical association in mammalian tissues relevant to HD pathology . Most specific molecular mechanisms proposed for Htt-mediated pathogenesis can , in principle , be attributed to a direct interaction between Htt and a protein component ( or components ) of a given pathway . Consistent with this assertion , we demonstrate here that a large set of Htt-interacting proteins is highly enriched for genetic modifiers of Htt-mediated neurodegeneration . Currently , there are efforts directed toward discovering genetic modifiers of human HD . Since the modifiers reported here were first discovered in screens performed with mammalian genes and proteins and subsequently validated in Drosophila , it would be of interest to determine whether human gene variants encoding similar proteins and pathway act can act as modifiers in human neurodegeneration .
Automated screens were done as described in LaCount et al . [31] . Briefly , haploid yeast expressing Htt-bait fusion proteins were grown in liquid medium in 96-well plates . Aliquots of yeast of the opposite mating type expressing prey libraries were added to each well and allowed to mate overnight . Matings were plated on medium selecting for diploids , the expression of the auxotrophic markers fused to the cDNA inserts and to the activity of the metabolic reporter genes ADE2 and HIS3 [32 , 61] . cDNA prey inserts from yeast that grew under selection were PCR-amplified and sequenced . Identities of prey inserts were determined by BLAST comparison against the National Center for Biotechnology ( NCBI ) RefSeq database ( http://www . ncbi . nlm . nih . gov ) . All reported interactions were verified by recovering prey plasmids from positive colonies , transforming these into yeast strains expressing Htt baits and reconfirming the ADE+ , HIS+ phenotype . Beta-galactosidase measurements were performed according to manufacturer's directions ( Pierce , http://www . piercenet . com ) . Control yeast strains carrying Htt bait and prey plasmids without an insert were used as baseline . The Htt 55 Q bait had slightly higher background levels than the corresponding Htt 23 Q bait . Y2H interactor lists were filtered to remove promiscuous proteins . Additional yeast methods can be found in Supporting Information . Htt-fragment-interacting proteins underwent TAP and were identified by MS [62] . Affinity-tagged Htt N-terminal fragments fused to GST and 6 × His were incubated with protein lysates prepared from mouse and human tissues and cultured cells . After TAP , proteins were digested with trypsin , desalted , and subjected to strong cation exchange ( CEX ) . CEX fractions were further separated by reverse-phase HPLC and subjected to MS analysis by matrix-assisted laser desorption/ionisation-time of flight ( MALDI ) MS/MS and electrospray ionization MS/MS . MS/MS data were used for protein sequence database searches by Mascot ( Matrix Sciences , http://www . matrixscience . com ) [63 , 64] . All searches were performed against the subset of either human or mouse proteins in the NCBInr protein sequence database ( HumanNR or MouseNR ) . The minimum peptide score was set at 10 , and the minimum peptide length was set to 5; otherwise the default instrument-specific Mascot settings were used . A variable cut-off was applied to proteins , which was dependent upon the number of peptides identified for a given protein . For any protein from which only one peptide was identified , a minimal peptide score threshold of 60 was required . If two peptides were identified , a threshold ion score of 50 was required , and for three peptides an ion score of 40 was required . Any peptides observed in control pull downs done with beads bound to TAP-tag alone were excluded . A statistical method , based on comparison of a wide variety of pull downs , was used to identify nonspecific interactors , which were also excluded . To validate protein identification subsequent to the automated thresholding and initial filtering , each remaining MS/MS spectrum was manually inspected to ensure that there were no spurious results matched by Mascot . Detailed MS and statistical methods can be found online with Supporting Information . A Drosophila polyQ toxicity model was generated using an N-terminal fragment of the human HD cDNA that encodes the first 336 amino acids of the protein including a 128-Q expansion in exon 1 . The construct was cloned into the pUAST vector for generating transgenic lines [65] . This HD Drosophila model is most similar to the expanded version ( 82 Q ) of the N171 mouse model , which shows abundant intranuclear inclusions [66] and neuronal degeneration [67] . Expression of the 128-Q N-terminal Htt fragment in Drosophila leads to neurodegenerative phenotypes . In the eye , these phenotypes are evident both externally and in the retina following expression using the glass multimer reporter ( GMR ) -GAL4 driver ( Figures 2 and 3 ) . In the nervous system , Elav-GAL4-directed expression of the transgene leads to progressive impaired motor ability and reduced life span ( Figure 3C ) . Also as in the N171-82Q mouse , intranuclear inclusions are observed in Drosophila neurons expressing the 128-Q N-terminal Htt fragment ( unpublished data ) . For the modifier screen , females of the genotype y1w118; GMR-GAL4/CyO; UAS:128QHtt[M64] were crossed to males from the mutant strains . In cases where the mutation was on the X chromosome , the cross was reversed . Crosses were incubated at 27 °C and 29 °C to provide two different phenotypic readouts . Strains modifying the eye phenotype were recrossed to verify the modification . Only genes that consistently showed modification at different temperatures or using different alleles were further analyzed . Potential modifiers behaving as enhancers were tested for possible nonspecific eye phenotypes by crossing them to control females of the genotype y1w118; GMR-GAL4/CyO . For scanning electron microscopy ( SEM ) images , flies were crossed at 29 °C and newly eclosed adults were aged for five days . Whole flies were dehydrated in ethanol , critical-point dried , and analyzed with a JEOL JSM 6100 microscope . For paraffin sections of enhancers , flies were crossed at 25 °C and adults were aged for five days ( for suppressors , the crosses were done at 27 °C and the flies were aged for one day ) . Adult heads and torsos were fixed in 4% formaldehyde/85% ethanol/5% acetic acid , dehydrated , embedded in paraffin for vertical semi-thin sections , and then stained with Hemathox . For the climbing and survival assays , females of the genotype Elav-GAL4; UAS:128QHtt[F27B] were crossed to males of the mutant strains . Climbing assays were performed on 30 age-matched adult virgin female flies raised at 27 °C as described [68] . The flies , placed in a plastic vial , were tapped to the bottom of the vial , and the number of flies above a 5-cm line was counted after 18 seconds . A total of ten trials were performed every 48 hours . Each climbing and survival experiment was repeated three times . Whole brains from wild-type or YAC128 mice were lysed in T-PER ( Pierce ) with protease inhibitors ( Complete Mini , Roche Applied Science , http://www . roche . com ) . Protein determination was carried out with the BCA method ( Bio-Rad , http://www . bio-rad . com ) . Lysate ( 500 μg , 0 . 7 ml T-PER with protease inhibitors ) were precleared with mouse IgG beads ( Sigma A6531 , http://www . sigmaaldrich . com ) and immunoprecipitated with monoclonal Htt antibody ( 5 μl , Chemicon 2166 , http://www . chemicon . com ) by incubating overnight at 4 °C and then with protein G ( 40 μl , Amersham 17-0618-01 , http://www . amersham . com ) . Beads were washed 5× with TBS/0 . 05% Tween , sample was eluted with 1× sample buffer ( Invitrogen , http://www . invitrogen . com ) and then resolved using 4%–12% Bis-Tris precast gels ( Invitrogen ) . Western blot was preformed , and blots were probed with rabbit antibody to USP9X ( 1:200 , Abcam 19879 , http://www . abcam . com ) , Cullin 2 ( 1:500 , Abcam 1870 ) , CACNA2D1 ( 1:200 , Sigma CS105 ) , Htt BKP1 ( 1:500 ) , PARP ( 1:300 , BioMol SA253 , http://www . biomol . com ) , mouse monoclonal GAPDH ( 1:100 , Chemicon MAB374 ) , STX1A ( 1:1000 , Synaptic Systems , 11001 , http://www . sysy . com ) , SNAP25 ( 1:1000 , Santa Cruz Biotechnology SC-7539 , http://www . scbt . com/ ) , goat antibody PKM2 ( 1:500 , Abcam 6191 ) , and PSMC2 ( 1:1000 , GeneTex 23322 , http://www . genetex . com ) .
The National Center for Biotechnology Information ( NCBI ) ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=Protein ) accession numbers for MS studies ( RefSeq ) are: NP_000302 . 1 , NP_000382 . 3 , NP_000524 . 3 , NP_000602 . 1 , NP_000708 . 1 , NP_001019645 , NP_001367 . 2 , NP_001377 . 1 , NP_001419 . 1 , NP_001753 . 1 , NP_001779 . 2 , NP_001834 . 2 , NP_001853 . 2 , NP_001854 . 1 , NP_001907 . 2 , NP_001914 . 2 , NP_001951 . 2 , NP_001990 . 1 , NP_002046 . 1 , NP_002064 . 1 , NP_002065 . 1 , NP_002102 . 4 , NP_002329 . 2 , NP_002536 . 1 , NP_003033 . 2 , NP_003124 . 1 , NP_003156 . 1 , NP_003170 . 1 , NP_003356 . 2 , NP_003357 . 2 , NP_003365 . 1 , NP_003366 . 2 , NP_003696 . 2 , NP_004246 . 1 , NP_004309 . 2 , NP_004364 . 2 , NP_004365 . 1 , NP_004484 . 1 , NP_004491 . 1 , NP_004539 . 1 , NP_004542 . 1 , NP_004543 . 1 , NP_004594 . 1 , NP_004850 . 1 , NP_004993 . 1 , NP_004996 . 1 , NP_004997 . 4 , NP_005156 . 1 , NP_005264 . 2 , NP_005268 . 1 , NP_005653 . 3 , NP_005736 . 3 , NP_005995 . 1 , NP_006046 . 1 , NP_006279 . 2 , NP_006308 . 3 , NP_006576 . 2 , NP_006810 . 1 , NP_006830 . 1 , NP_008839 . 2 , NP_009034 . 2 , NP_009204 . 1 , NP_031407 . 2 , NP_031457 . 1 , NP_031464 . 1 , NP_031669 . 2 , NP_031736 . 1 , NP_031773 . 1 , NP_031887 . 2 , NP_031959 . 1 , NP_032246 . 2 , NP_032518 . 1 , NP_032644 . 2 , NP_033012 . 1 , NP_033033 . 1 , NP_033321 . 1 , NP_033332 . 1 , NP_033333 . 2 , NP_033441 . 1 , NP_033805 . 1 , NP_033851 . 1 , NP_033914 . 1 , NP_034053 . 1 , NP_034078 . 1 , NP_034438 . 1 , NP_034442 . 1 , NP_034715 . 1 , NP_034829 . 1 , NP_034944 . 1 , NP_035229 . 2 , NP_035253 . 1 , NP_035523 . 1 , NP_035558 . 1 , NP_035824 . 1 , NP_035825 . 1 , NP_036288 . 2 , NP_036560 . 1 , NP_036611 . 2 , NP_038709 . 1 , NP_057049 . 3 , NP_057223 . 1 , NP_057606 . 1 , NP_058084 . 2 , NP_060064 . 2 , NP_061359 . 2 , NP_062681 . 1 , NP_065593 . 1 , NP_066268 . 1 , NP_067541 . 1 , NP_075553 . 1 , NP_077128 . 2 , NP_077173 . 1 , NP_077725 . 1 , NP_077745 . 2 , NP_079589 . 1 , NP_079612 . 1 , NP_079634 . 1 , NP_079683 . 2 , NP_080175 . 1 , NP_080720 . 1 , NP_080971 . 2 , NP_080979 . 1 , NP_084501 . 1 , NP_114080 . 2 , NP_149124 . 2 , NP_443106 . 1 , NP_444427 . 1 , NP_536846 . 1 , NP_536849 . 1 , NP_542970 . 1 , NP_570824 . 1 , NP_598429 . 1 , NP_613063 . 1 , NP_619621 . 1 , NP_659409 . 2 , NP_663493 . 1 , NP_663589 . 2 , NP_766024 . 1 , NP_776169 . 2 , NP_796376 . 2 , NP_849209 . 1 , NP_976218 . 1 , XP_128725 . 4 , XP_131103 . 3 , XP_203393 . 2 , and XP_622887 . 1 . The NCBI ( GeneID ) ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=gene ) accession numbers for Y2H studies are: 120 , 161 , 323 , 1315 , 1387 , 1499 , 1759 , 1778 , 1785 , 2597 , 3064 , 3092 , 3093 , 3275 , 3329 , 3338 , 3839 , 4209 , 4361 , 4790 , 5033 , 5295 , 5296 , 5315 , 5468 , 5493 , 5710 , 5753 , 6670 , 6721 , 6829 , 6867 , 7430 , 7529 , 7644 , 7692 , 7704 , 7802 , 8065 , 8239 , 8453 , 8462 , 8503 , 8539 , 9093 , 9330 , 9611 , 9638 , 9810 , 9818 , 9901 , 9938 , 10010 , 10133 , 10422 , 10456 , 10458 , 10464 , 10540 , 10580 , 10906 , 10915 , 11177 , 11193 , 23116 , 23328 , 23332 , 23348 , 23360 , 23380 , 23609 , 23613 , 23641 , 25764 , 26578 , 27068 , 28969 , 29062 , 29072 , 29993 , 51061 , 51322 , 51586 , 51593 , 51667 , 55219 , 55660 , 55704 , 55735 , 56254 , 57489 , 57509 , 57522 , 57616 , 63908 , 79027 , 79813 , 80254 , 83478 , 84936 , 128866 , 134218 , 139818 , 152789 , and 171392 . | Huntington's Disease ( HD ) is a fatal inherited neurodegenerative disease , which typically begins in middle age and progresses with symptoms of severe uncontrolled movements and cognitive dysfunction . HD is uniformly fatal with death occurring ten to 15 years after onset of symptoms . There is currently no effective treatment for HD . The genetic mutation underlying HD causes a protein called huntingtin ( Htt ) to contain an abnormally long tract of the amino acid glutamine . This extended span of glutamines changes the shape of the Htt protein , which can cause it to interact in abnormal ways with other cellular proteins . In this study , we have identified a large number of new proteins that bind to normal and mutant forms of the Htt protein . To establish a potential role for these interacting proteins in HD , we show that changing the expression of many of these proteins can modulate the pathological effects of mutant Htt on fly neurons that deteriorate when they express mutant Htt . Identifying cellular proteins that bind to Htt and modulate its pathological activity may facilitate the discovery of an effective treatment for HD . | [
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| 2007 | Huntingtin Interacting Proteins Are Genetic Modifiers of Neurodegeneration |
Taenia solium is an important zoonosis in many developing countries . Cysticercosis poses a serious public health risk and incurs sizeable economic losses to pig production . Because data on the epidemiology of porcine cysticercosis in Mozambique are scarce , the present study was conducted to determine the prevalence and risk factors for porcine cysticercosis . A cross-sectional survey was carried out in 11 villages in Angónia district , Tete province in northwestern Mozambique . Between September and November , 2007 , a total of 661 pigs were tested serologically and examined by tongue inspection . Serum samples were tested for the presence of circulating parasite antigen using a monoclonal antibody-based sandwich enzyme-linked immunosorbent assay ( Ag-ELISA ) . In addition , a questionnaire survey to collect information on pig production , occurrence and transmission of porcine cysticercosis , risk factors and awareness of porcine cysticercosis was conducted in the selected households from which pigs were sampled . Two hundred thirty-one samples ( 34 . 9% ) were found positive by the Ag-ELISA , while by tongue inspection on the same animals cysticerci were detected in 84 pigs ( 12 . 7% ) . Increasing age ( OR = 1 . 63; 95% CI = 1 . 13–2 . 37 ) and free-range pig husbandry system ( OR = 3 . 81; 95% CI = 2 . 08–7 . 06 ) were important risk factors for porcine cysticercosis in the district . The present findings indicate that porcine cysticercosis is endemic in the region , and that increasing pig age and pig husbandry practices contribute significantly to porcine cysticercosis transmission . Further epidemiological studies on the prevalence and transmission of porcine cysticercosis in rural communities in Mozambique are needed to enable collection of more baseline data and implementation of effective control strategies within the country .
Porcine cysticercosis is an infection caused by the larval stage of Taenia solium , a zoonotic tapeworm that is transmitted among humans and between humans and pigs . The life cycle of this parasite includes pigs as the normal intermediate hosts , harbouring the larval cysticerci , and humans as definitive hosts , harbouring the adult tapeworm causing taeniosis . Humans can also serve as intermediate hosts and develop the cystic form by accidental ingestion of T . solium eggs [1] , [2] . Human cysticercosis causes a variety of neurological symptoms , most commonly seizures due to cysts in the brain , a condition known as neurocysticercosis [3] , [4] . Taenia solium cysticercosis is prevalent in humans and pigs in many developing countries of Latin America , Asia , and Africa , where its life cycle is sustained because of the coexistence of poor sanitary conditions , free range management of pigs , and absence or inadequate meat inspection [1] , [5] , [6] . In Latin America [7] , parts of Asia [8] and Africa [9] , cysticercosis has been reported as endemic . This disease constitutes a serious but under-recognised public health problem [10] and causes important economic losses due to condemnation of infected pork [4] . In Mozambique , pig production is mainly practiced by smallholders under extensive conditions where food is largely obtained through scavenging . In this system primitive housing is normally provided to protect the animals only during the night . Additionally , there is a lack of slaughterhouse facilities for pigs and inspection and control of pork is poor . These conditions are favourable for the maintenance and spread of T . solium . The presence of T . solium cysticercosis in Mozambique has been confirmed in a few studies carried out in humans with neurological problems [11]–[14] and in a serological survey of pigs that found a prevalence range of 6 . 5–33 . 3% in 11 districts of Tete province using an antibody ELISA test [15] . Additionally , based on old abattoir records , cysticercosis in pigs has been reported from all provinces of the country [16] , [17] . However , very few data exist on the epidemiology of porcine cysticercosis in the country . Therefore , the current study was conducted to determine the prevalence and associated risk factors for porcine cysticercosis in Angónia district , Mozambique .
The survey was conducted in Angónia district located in north-western Mozambique ( 14°47′S , 34°29′E ) and with an altitude that varies from 700 to 1655 meters above sea level . The district was selected following an initial survey conducted by Afonso and others [15] , which indicated the presence of free-roaming pigs in the area . Agriculture is the most important economic activity . Pig production , based on an extensive system , dominated by free-ranging pigs , is also an important economic activity in the district . A cross-sectional study was conducted between September and November 2007 . The sample size to estimate prevalence of porcine cysticercosis , using the formula n = Z2PQ/L2 [18] and estimating a 30% prevalence , was 322 pigs but to adjust for the multi-stage sampling design used , at least 644 pigs were to be sampled [19] . Households with pigs were identified using the snowballing technique and all pigs in those households were included in the survey . Snowballing is a technique for developing a research sample where existing study subjects recruit future subjects from among their acquaintances . Piglets younger than 2 months , pregnant sows and nursing sows with litters less than 2 months old were excluded from the survey . All pigs that met our selection criteria were examined for the presence of T . solium cysticerci by tongue inspection . Briefly , the pig was firmly restrained in lateral recumbence , a pig snare was used to stabilize the head and a hard wooden stick was used to open the mouth . Using a piece of cotton cloth for grip , the tongue was pulled out , examined and palpated all along its ventral side for the presence of cysticerci . Subsequently , 5 ml of blood were obtained from the cranial vena cava using plain vacutainers . The blood was transported on ice to the laboratory of the Estação Zootécnica de Angónia ( EZA ) and allowed to clot at 4°C . To obtain serum , the clotted blood was separated by centrifugation , and serum was dispensed into 2 ml labelled aliquots and stored at −20°C until use . A questionnaire survey to collect information on pig production , occurrence and transmission of T . solium cysticercosis , risk factors and awareness of cysticercosis in pigs was carried out in households from the selected villages , in which pigs were sampled as described above . Hygienic and sanitary conditions were inquired about and responses confirmed by direct observation . The respondent in each household was the person taking care of the pigs or the head of the household . It was administered by a field assistant in charge of the agricultural rural extension services in the district using the native language . The Ag-ELISA was performed as described by Brandt and others [20] and modified by Dorny and others [21] . Briefly , the serum samples were pre-treated using trichloroacetic acid ( TCA ) and used in ELISA at a final dilution of 1/4 . Two monoclonal antibodies ( MoAb ) were used in a sandwich ELISA . MoAb B158C11A10 was diluted at 5 µg/ml in carbonate buffer ( 0 . 06M/pH 9 . 6 ) for coating and a biotinylated MoAb B60H8A4 ( 1 . 25 µg/ml in PBS-Tween20 + 1%NBCS ) was included as detector antibody . The incubation was carried out at 37°C on a shaker for 30 min for the coating of the first MoAb and for 15 min for all subsequent steps . The substrate solution consisting of ortho phenylenediamine ( OPD ) and H2O2 was added and incubated without shaking at 30°C for 15 min . To stop the reaction , 50 µl of H2SO4 ( 4N ) was added to each well . The plates were read using an ELISA reader at 492 nm . Sera from two known positive pigs ( confirmed at slaughter ) were used as positive control . To determine the cut-off , the optical density ( OD ) of each serum sample was compared with a series of 8 reference negative serum samples at a probability level of 0 . 1% using a modified Student's t-test [22] . Data on pig seroprevalence and risk factors were entered and analysed using STATA version 9 . 1 ( Stata Corp . , College Station , TX , 2006 ) , and a descriptive analysis was made . A univariate analysis was first performed by calculating odds ratios ( OR ) for various potential risk factors at the individual level . To investigate whether any presence or lack of association was due to confounding , a multivariate logistic regression analysis was then performed , calculating OR and 95% confidence intervals for risk factors for seropositivity to cysticercosis in pigs , taking into account possible clustering by household . The study protocol was approved by the scientific board at Veterinary Faculty , Eduardo Mondlane University , and the study permissions obtained from the Livestock National Directorate , Mozambique , from village leaders and from the pig owners . Lingual examination and blood sampling on pigs were conducted by a professional veterinary , according to Mozambican guidelines for animal husbandry . Due to high level of illiteracy among villagers , the scientific board at the Veterinary Faculty approved the use of oral consent , and before the commencement of the study it was obtained from pig owners in the presence of a witness , who signed on their behalf .
A total of 661 pigs were examined from 306 households in the district of which 383 pigs were from 170 households in Dómuè ward and 278 pigs were from 136 households in Ulóngue ward . In all , 11 villages were visited , 6 in Dómuè and 5 in Ulóngue . All sampled pigs were of the indigenous breed ( black pigs ) , predominantly females ( 59% ) and about 75% were less than 12 months old . They were mainly left to scavenge during the day and during the dry season and kept in corrals at night and during the rainy season ( crop growing season ) . Relatively few pig farmers ( 18% ) practiced total confinement of their pigs . The pigs were mainly fed maize bran and watermelons , cabbage , and sweet potatoes leaves . Most of the pig farmers ( 92 , 5% ) kept pigs for both sale and consumption whereas 21 . 9% of farmers kept pigs for sale and 5 . 9% for consumption only . A fair proportion of farmers ( 18 . 6% ) had slaughtered pigs at home , and almost all ( 99% ) did it without inspection . The overall prevalence of porcine cysticercosis was 12 . 2% ( 7 . 8%–23 . 8% ) by tongue examination and 34 . 9% ( 22 . 1%–66 . 7% ) by Ag-ELISA ( Table 1 ) . From a total of 306 households visited , 66% had a female respondent that was taking care of the pigs . Most households ( 56% ) consumed water from wells , and the rest drank water from bore-holes . Few households ( 5 . 2% ) had no latrines , but most of the latrines ( 57 . 9% ) in those households with latrines were open and easily accessible for free roaming pigs . Most households ( 79 . 1% ) reported that had seen cysts in pigs and about 43 . 5% had sometimes cysticercosis infected pigs . However , few households ( 17 . 4% ) knew how pigs get the infection and 83% ate pork at least once a month . Of the total number of households visited , 23 . 5% ( 11 . 1%–42 . 9% ) and 56 . 2% ( 35 . 7%–71 . 4% ) had at least one pig positive for porcine cysticercosis by tongue examination and Ag-ELISA , respectively . The factors that were considered in the analysis as risks associated with porcine cysticercosis at animal and household level are presented in Table 2 . Considering other factors in the regression model , only age-group and husbandry system were significantly associated with porcine cysticercosis . Age-group of pigs was strongly associated with porcine cysticercosis . While fitted as a linear term to the model , increasing age was significantly associated with porcine cysticercosis . Adult pigs were 63% more likely to be infected with T . solium cysticercosis than younger pigs ( OR = 1 . 63; 95% CI = 1 . 13–2 . 37 ) . Free-range husbandry system was also significantly associated with porcine cysticercosis compared to confinement system ( OR = 3 . 81; 95% CI = 2 . 08–7 . 06 ) .
This study has investigated the prevalence and the potential risk factors associated to T . solium cysticercosis in pigs in Angónia district . The overall prevalence based on detection of circulating antigens ( 34 . 9% ) in this study indicates that porcine cysticercosis is highly prevalent in the district . Similar results were reported by Afonso and others [15] in a study conducted in Tete province using an antibody detection ( Ab-ELISA ) test in animals . This high value suggests that pigs in Angónia district are exposed to T . solium eggs . Most households ( 94 . 8% ) had latrines but yet had infected pigs . However , the prevalence of porcine cysticercosis did not differ between households with and without latrines . This agrees with a study conducted in Cameroon [23] , which found no statistical difference in cysticercosis prevalence in pigs raised in households with or without latrines . Similarly , a study in Mexico [24] found no association between the absence of latrines and the prevalence of porcine cysticercosis . Impressively , indoor latrines have been shown in a Mexican study to be positively associated with porcine cysticercosis in a setting where the outlets of the latrines were deliberately put in the pig pens [24] . In contrast surveys conducted in Tanzania [25] and Zambia [26] showed that the prevalence of porcine cysticercosis was considerably higher in pigs reared in households lacking latrines than in those reared in households that had latrines . This finding could suggest that , either farmers in Angónia district were not using the latrines , or pigs had access to the latrines since most of them were open and easily accessible for roaming pigs . Additionally , farmers spent most of their time in the fields , and might have been practicing indiscriminate defecation . The lack of association between latrines and porcine cysticercosis might have been due to the fact that pigs were allowed to roam freely in both households with and without latrines . It has been shown in this study that pigs from households that practiced free-range system were more likely to be positive for cysticercosis than corralled pigs . Therefore , free range pig management system represented by far the most important risk factor for porcine cysticercosis in Angónia . Free ranging has been previously associated with porcine cysticercosis in Mexico [24] and Africa [23] , [25] , [26] , as it allowed pigs easy access to human faeces . The present study demonstrated that the prevalence of porcine cysticercosis increased with age of the pigs . These results are in agreement with those reported in Mexico [24] , Cameroon [23] and Peru [27] . This may indicate either that older animals might have had longer exposure than the younger ones or that younger pigs are protected through the initial exposure period , perhaps via maternal transfer of antibodies , but become susceptible later . Maternal antibodies are protective for other larval cestode infections [28] and have been shown to slowly decrease in piglets born to cysticercosis infected sows [29] . However , other studies did not report positive association [25] , [30] . Although many households ( 79 . 1% ) reported that have observed cysts in pigs , few ( 17 . 4% ) were aware of how pigs got the infection . However , the prevalence of porcine cysticercosis among households with knowledge of the causative agent of cysticercosis was similar to that without such knowledge . According to Sarti and others [24] the lack of knowledge about the parasite life-cycle and the way socio-economic conditions ( sanitation , pig husbandry , contact of pigs with human faeces ) affect transmission helps to promote transmission of T . solium in rural communities . On the other hand , although education campaigns studies have demonstrated that villagers understand the role of T . solium infections in pigs and humans , this knowledge does not appear to result in dramatic changes in risk behaviour [2] . However , health education campaigns have been effective in reducing the transmission rates of T . solium infections in humans and pigs [31] . Recently , a study conducted in Tanzania demonstrated that health education intervention led to an important reduction in the incidence rate of porcine cysticercosis and reported cases of household consumption of infected pork , despite minimal improvement in behaviour and practices related to its transmission [32] . Our findings give clear evidence that porcine cysticercosis is endemic in Angónia district , and that increasing age and free ranging of the pigs increase the likelihood of exposure to T . solium eggs and thereby the transmission of the disease within the study community . Given the importance of this disease , educational programmes should be initiated to build awareness on the transmission of T . solium infections , and further epidemiological studies in rural communities in Mozambique are required to allow collection of more baseline data and effective implementation of control strategies . | Porcine cysticercosis is an infection of pigs caused by the larval stage of Taenia solium , a tapeworm that causes taeniosis in humans . The disease is very common in pigs from many developing countries around the world and poses a serious public health risk and causes significant economic losses in pig production . In Mozambique there is scanty information regarding the occurrence of this disease . Our work conducted in Angónia district , northwestern Mozambique , allowed us to collect important information to evaluate the magnitude and identify the risk factors associated with porcine cysticercosis . This study found that cysticercosis is highly prevalent in pigs and that the free-range husbandry system was by far the main risk factor for the transmission of the disease in the district . These findings should help in the design of control strategies to prevent continuous transmission of this disease in Angónia and in Mozambique . | [
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| 2010 | Prevalence and Risk Factors of Porcine Cysticercosis in Angónia District, Mozambique |
Calcium is vital to the normal functioning of multiple organ systems and its serum concentration is tightly regulated . Apart from CASR , the genes associated with serum calcium are largely unknown . We conducted a genome-wide association meta-analysis of 39 , 400 individuals from 17 population-based cohorts and investigated the 14 most strongly associated loci in ≤21 , 679 additional individuals . Seven loci ( six new regions ) in association with serum calcium were identified and replicated . Rs1570669 near CYP24A1 ( P = 9 . 1E-12 ) , rs10491003 upstream of GATA3 ( P = 4 . 8E-09 ) and rs7481584 in CARS ( P = 1 . 2E-10 ) implicate regions involved in Mendelian calcemic disorders: Rs1550532 in DGKD ( P = 8 . 2E-11 ) , also associated with bone density , and rs7336933 near DGKH/KIAA0564 ( P = 9 . 1E-10 ) are near genes that encode distinct isoforms of diacylglycerol kinase . Rs780094 is in GCKR . We characterized the expression of these genes in gut , kidney , and bone , and demonstrate modulation of gene expression in bone in response to dietary calcium in mice . Our results shed new light on the genetics of calcium homeostasis .
Normal calcium homeostasis is regulated by three major hormones acting on their corresponding receptors in gut , kidney , and bone: parathyroid hormone ( PTH ) release governed by the calcium-sensing receptor ( CASR ) , calcitonin , and the active metabolite of vitamin D , 1 , 25 ( OH ) 2-D . Despite heritability estimates of 33–78% , the genetic determinants of serum calcium are poorly understood [1] , [2] , [3] . We have previously reported a variant in CASR associated with calcium concentrations in European-ancestry individuals [4] , [5] . To detect additional loci , we conducted a two-stage genome-wide association meta-analysis of serum calcium and studied expression of identified genes in key calcium homeostatic organs in the mouse under various calcium diets .
The discovery analysis consisted of 39 , 400 individuals from 17 population-based cohorts of European descent ( Table 1 and Table S1 ) . There was little evidence for population stratification at study level ( median genomic inflation factor , λ = 1 . 006 ) or meta-analysis level ( λ = 1 . 03 ) , and we detected an excess of association signals beyond those expected by chance ( Figure S1 ) . The CASR locus , previously identified in Europeans , was confirmed in our meta-analysis ( P = 6 . 5E-59 , Figure S2 ) [4] , [5] . In addition , SNPs from five independent regions reached genome-wide significance ( P<5E-08 ) in the overall discovery meta-analysis ( Figure 1 , Table 1 , Table S2 ) : rs1550532 ( in DGKD , P = 4 . 60E-08 ) , rs780094 ( in GCKR; P = 3 . 69E-11 ) , rs17711722 ( near VKORC1L1 , P = 2 . 78E-11 ) , rs7481584 ( in CARS , P = 9 . 21E-10 ) and rs1570669 ( near CYP24A1; P = 3 . 98E-08 ) . Fourteen SNPs from Stage 1 were sent for Stage 2 validation in ≤21 , 679 additional Europeans: the twelve independent ( ≥1 Mb apart ) SNPs with lowest P values ( 6 . 5E-59 to 8 . 1E-06 ) in Europeans and two additional genome-wide significant loci ( rs9447004 and rs10491003 ) from a combined sample including 8318 Indian-Asians ( Table 1 ) . Of the fourteen SNPs , seven were considered successfully replicated ( i . e . were in the same direction of effect as the discovery meta-analysis , had a one-side replication P<0 . 05 and were genome-wide significant ( P<5E-8 ) in combined meta-analysis of discovery and replication sets ) . These were rs1801725 in CASR , rs1550532 in DGKD , rs780094 in GCKR , rs7336933 near KIAA0564 and DGKH , rs10491003 ( closest gene GATA3 ) , rs7481584 in CARS and rs1570669 near CYP24A1 ( Table 1 ) . Regional association plots are presented in Figure S3 . Details on the seven SNPs that did not replicate are presented in Table S2 . Association results for serum calcium in Caucasians for all SNPs with P value<5*E-5 are listed in Table S3 . In a secondary analysis , all SNPs identified in the primary analysis showed consistent and significant association with serum calcium adjusted for serum albumin ( Table S4 , Figure S4 ) , as well as an excess of association signals beyond those expected by chance ( Figure S5 ) ; no additional locus was identified using albumin-corrected serum calcium ( Table S5 ) . We found no significant association of the 7 replicated SNPs known to provide reliable tags for copy number variations ( CNVs ) in people of European-descent from the Hypergene dataset . For all the SNPs , the calculated correlation was below 0 . 002 . We also explored a list of SNPs tagging CNVs from the GIANT consortium . Out the 7 SNPs tested , only the rs1570669 was in slight linkage disequilibrium ( r2 = 0 . 54 ) with one SNP of the WTCCC2 list ( rs927651 ) . The corresponding SNP tags the CNVR7875 . 1 CNV located 455b from the SNP of interest . For each of the 7 replicated SNPs , we identified all proxy SNPs with r2>0 . 8 in HapMap CEU ( releases 21 , 22 , and HapMap 3 version 2 ) using the online SNAP database ( http://www . broadinstitute . org/mpg/snap/ ) . This led to the identification of 40 SNPs . We then queried each of these SNPs in the eQTL database of the University of Chicago ( http://eqtl . uchicago . edu/cgi-bin/gbrowse/eqtl/ ) . Three of the seven SNPs are in strong linkage disequilibrium with an eQTL , as illustrated in Table S6 . Proposed functions of the genes mapping into the associated intervals ( ±250 kb ) are in Box 1 and in Table S7 for the gene-rich GCKR region . We report in Table S8 the mechanism and/or location of all available biological processes , cellular components and molecular functions related to the genes mapping into the associated intervals from the AmiGo 1 . 8 gene ontology database . We also queried the OMIM database for each genes located within ±250 kb of the replicated loci ( Table S9 ) In Indian-Asians , all 7 replicated SNPs had beta-coefficients that were direction-consistent with the primary analysis and 3 were statistically significant ( P<0 . 05 ) : rs1801725 ( CASR , P = 1 . 4E-31 ) , rs1550532 ( DGKD , P = 0 . 002 ) and rs10491003 ( GATA3 , P = 0 . 009 ) ( Table S10 ) . In Japanese , 3 SNPs had betas that were direction-consistent with the primary analysis , but only rs1801725 ( CASR ) was associated with serum calcium ( P = 0 . 001 ) ( Table S10 ) . We conducted analyses of related bone mineral and endocrine phenotypic traits for the 7 replicated loci ( Table 2 ) . Several SNPs were associated ( P<0 . 05 ) with bone mineral density ( BMD ) in the GEFOS consortium [6]: rs1801725 at CASR ( P = 0 . 025; previously reported [4] , [5] ) and rs780094 ( GCKR ) at the lumbar spine ( P = 0 . 006 ) , rs1570669 at CYP24A1 at the femoral neck ( P = 0 . 04 ) , and rs1550532 at DGKD at both the lumbar spine ( P = 0 . 003 ) and the femoral neck ( P = 0 . 003 ) . For endocrine phenotypes , rs1570669 at CYP24A1 was associated with higher PTH concentrations ( P = 0 . 0005 ) and rs1801725 at CASR with higher serum PTH concentrations ( P = 0 . 028 ) and lower serum phosphate concentrations , as previously reported [4] , [5] . No SNP was associated significantly with circulating 25-OH vitamin D concentrations ( all P>0 . 05 ) in the SUNLIGHT consortium [7] . We selected biologically plausible gene ( s ) at each locus for in vivo studies in a mouse model as described in Methods' section . We first analyzed gene expression in the three primary calcium-handling organs: duodenum , kidney and bone ( tibia ) . CASR for the rs1801725 locus , DGKD for the rs1550532 locus , GATA3 for the rs10491003 locus , CARS , NAP1L4 and CDKN1C for the rs7481584 locus , DGKH and KIAA0564 for the rs7336933 locus , were expressed in all organs , whereas CYP24A1 ( rs1570669 locus ) was solely , and PHLDA2 ( rs7481584 locus ) mainly , expressed in the kidney ( Figure 2 ) . No significant expression of GCKR ( rs780094 locus ) was observed in any organ tested , which is of interest considering the strong attenuation of the association of rs780094 with serum calcium after adjustment for albumin ( Table S4 ) . In micro-dissection of nephron segments [8] , [9] , DGKD , DGKH , CARS , KIAA0564 and CYP24A1 were primarily transcribed in the proximal tubule , CASR in the thick ascending limb , and GATA3 predominantly in the distal nephron and collecting duct ( Figure 3 ) . In order to determine regulation of gene expression by calcium intake , we measured gene expression levels in mice fed low and high calcium diets ( 0 . 17% vs . 1 . 69% calcium ) for one week , with normal diet as control ( 0 . 82% ) ( Figure 4 and Table S11 ) . In the kidney , both DGKD and DGKH were upregulated in response to low calcium diet ( P≤0 . 05; Figure 4 ) . In the tibia , CASR was markedly upregulated in response to low calcium diet ( 2 . 5-fold increased expression ) , as were GATA3 , KIAA0564 and CARS ( P≤0 . 05 for all; Figure 4 ) , findings that suggest regulation by 1 , 25 ( OH ) 2-D . DGKD and DGKH were upregulated in the tibia in response to high and low calcium diet ( P≤0 . 05 for all; Figure 4 ) . The expression in duodenum of the majority of genes was not modified by dietary calcium , with the exception of NAP1L4 and CDKN1C .
We have identified and replicated one known and six new loci for serum calcium near genes linked to bone metabolism and endocrine control of calcium . Of these , 4 loci ( DGKD , GCKR , CASR , and CYP24A1 ) were nominally associated with BMD in the general population . In supporting mouse studies , we demonstrate expression of several of these genes in tibia , and show regulation of gene expression in response to dietary calcium intake . We also demonstrate expression in nephron segments known to regulate calcium homeostasis . Taken together , these results shed new light on the genetics of calcium balance . The vast majority of total body calcium is bound in the skeleton as hydroxyapatite and other calcium-phosphate complexes [10] . Apart from providing skeletal strength , bone serves as a calcium reservoir to maintain tightly controlled circulating concentrations vital to cellular signaling , muscle contraction and coagulation [10] . However , the genetic basis of the dynamic cross talk that occurs between these compartments is poorly understood . Our results advance our understanding in this area . Eight genes identified in the GWAS are constitutively expressed in bone and are regulated in response to dietary calcium , in particular low calcium diet , whereas no clear change was observed in kidney or duodenum . This bone reactivity in response to dietary calcium intake is consistent with what was recently reported for CASR [11] . Further , of the eight genes expressed in bone and regulated in response to dietary calcium , we show that rs1550532 ( DGKD ) and rs1801725 ( CASR ) are associated with BMD in humans , the primary determinant of fracture risk . The A allele of rs1570669 ( CYP24A1 locus ) was associated with reduced BMD at the femoral neck although CYP24A1 was not found to be expressed in bone in mice experiment , which suggests an indirect role in bone mineralization . This may occur via its documented role in vitamin D metabolism , discussed below , and/or its association with higher PTH concentrations identified in the present analysis . We observed specific expression patterns of several genes in the mouse nephron: DGKD , DGKH , CARS , KIAA0564 and CYP24A1 were primarily transcribed in the proximal tubule , CASR expression was mostly localized to the thick ascending limb , whereas GATA3 was predominantly found in the distal part of the nephron and the collecting duct . This pattern of expression in segments known to be involved in calcium reabsorption suggests a role in renal calcium handling and is consistent with previous exploratory transcriptome analyses in humans and mice [12] , [13] . Both DGKD and DGKH were significantly upregulated in the kidney in response to low calcium diet , suggesting specific involvement of these genes in renal calcium handling . Several of the newly identified loci harbor genes linked to the hormonal control of serum calcium . First , the association of CASR with PTH concentrations is consistent with its known role in PTH signaling . Second , several lines of evidence implicate rs1570669 ( CYP24A1 ) in the vitamin D pathway: its association with serum calcium and PTH concentrations , its selective expression in the proximal tubule where 1 , 25 ( OH ) 2-D metabolism occurs , and that loss-of-function CYP24A1 mutations cause vitamin D-induced hypercalcemia in children ( idiopathic infantile hypercalcemia ) . Third , we identified variants linked to 2 chromosomally distinct isoforms of diacylglycerol kinase , part of the phosphoinositol second messenger system , that may interact with each other at the protein level [14] , [15] . Strengths of this study are the large sample size and consistent mouse studies to support the statistical associations and advance our knowledge of the biology at these loci . Human and mice largely share physiological processes linked to calcium metabolism , including tissue-specific gene expression . Limitations include the lack of a direct marker of bone remodeling and the potential for bias in gene selection for experimental follow-up . Mice may display subtle differences in the regulation of the genes tested compared to humans . We have identified and replicated one known and six new loci for serum calcium near genes linked to bone metabolism and endocrine control of serum calcium . Supporting experimental mouse studies suggest a role for dietary calcium in bone-specific gene expression . Further work is needed to identify the causal variants and to understand how they influence calcium homeostasis .
In each human study , the local institutional review board approved the study and participants signed written informed consent , including for DNA analyses . The experimental protocol in mice was approved by the local veterinarian authorities and fulfilled Swiss federal regulations for experiences with animals . Detailed information on the genotyping plateforms and data cleaning procedures for each discovery and replication cohort can be found in Table S13 . De novo replication genotyping was perfomed in 4670 participants to the Bus Santé Study using KASPar v4 . 0 after whole genome amplification by primer extension pre-amplification ( PEP ) using thermostable DNA polymerases . In each discovery study , genotyping was performed using a genome-wide chip and nearly 2 . 5 million SNPs were genotyped or imputed using the HapMap CEU panels release 22 or 21 as the reference . Each study applied quality control before imputation . Detailed imputation information is provided in Table S13 . Each SNP was modeled using an additive genetic effect ( allele dosage for imputed SNPs ) , including age and sex as covariates in the model as well as study-specific covariates if needed ( e . g . principal components , study center ) . The primary dependent variable in each discovery study was untransformed and uncorrected serum calcium expressed in mg/dL . Beta regression coefficients and standard errors were used with at least 5 decimal places . For secondary analyses , albumin-corrected serum calcium was computed using the following formula: ( [4-plasma albumin in g/dL]×0 . 8+serum calcium in mg/dL ) and the same model as for the primary analyses was used . Each file of genome-wide summary statistics underwent extensive quality control prior to meta-analysis both for primary and secondary analyses , including ( 1 ) boxplots of all beta coefficients , as well as all standard errors multiplied by the square-root of the sample size , for each study separately; ( 2 ) the range of P values , MAF , imputation qualities , call rates and Hardy-Weinberg equilibrium P values and ( 3 ) QQ plots . In addition , we checked the direction and magnitude of effect at the previously reported rs1801725 CASR variant . Genome-wide meta-analyses were conducted in duplicate by two independent analysts . For each SNP , we used a fixed effect meta-analysis using inverse-variance weights as implemented in the meta-analysis utility Metal [16] . Results were confirmed by a z-score based meta-analysis . Data were available for 2 , 612 , 817 genotyped or imputed autosomal SNPs for the primary and secondary analyses . After the meta-analysis , genomic control correction was applied ( λGC was 1 . 03 for both uncorrected and corrected serum calcium ) . Our pre-specified criterion to declare genome-wide significance was P value<5E-8 to account for 1 million independent tests according to the Bonferroni correction . We choose to move forward for replication all SNPs with discovery P value<1E-7 in the European sample or genome-wide significant SNP in the overall sample that included Indian Asians . To choose a single SNP per genome-wide associated region for replication , we merged all SNPs within 1 Mb region and selected the lowest P value for each region . Altogether , fourteen SNPs were moved forward for replication . Up to 17 , 205 participants contributed information to the replication analyses in silico and 4 , 670 participants provided data for de novo genotyping . We used fixed-effects inverse-variance weighted meta-analysis to combine discovery and replication meta-analysis results . Replication was considered as present whenever a combined P value<5E-8 together with an effect-concordant one-sided replication P value<0 . 05 were obtained . We conducted look-ups for femoral and lumbar bone density in the GEnetic Factors of OSteoporosis ( GEFOS ) dataset [17] . Bone mineral density ( BMD ) is used in clinical practice for the diagnosis of osteoporosis and bone density at different skeletal sites is predictive of fracture risk . BMD was measured in all cohorts at the lumbar spine ( either at L1–L4 or L2–L4 ) and femoral neck using dual-energy X-ray absorptiometry following standard manufacturer protocols [17] . Serum phosphorus was looked up from a previously published GWAS meta-analysis , including 16 , 264 participants of European ancestry [18] . Serum phosphorus concentrations were quantified using an automated platform in which inorganic phosphorus reacts with ammonium molybdate in an acidic solution to form a colored phosphomolybdate complex [18] . The 25-hydroxyvitamin D was looked-up in the SUNLIGHT consortium [7] , which includes data from 33 , 996 individuals of European descent from 15 cohorts . 25-hydroxyvitamin D concentrations were measured by radioimmunoassay , chemiluminescent assay , ELISA , or mass spectrometry [7] . PTH was looked-up in the SHIP and SHIP-Trend studies . The serum parathyroid hormone concentration was measured on the IDS-iSYS Multi-Discipline Automated Analyser with the IDS-iSYS Intact PTH assay ( Immunodiagnostic Systems Limited , Frankfurt am Main , Germany ) according to the instructions for use . This chemiluminescence immunoassay detects the full-length parathyroid hormone ( amino acids 1–84 ) and the large parathyroid hormone fragment ( amino acids 7–84 ) . The measurement range of the assay was 5–5000 pg/mL . The limits of blank , detection and quantitation were 1 . 3 pg/mL , 1 . 4 pg/mL , and 3 . 6 pg/mL , respectively . As recommended by the manufacturer , three levels of control material were measured in order to verify a decent working mode . During the course of the study , the coefficients of variation were 14 . 02% at low , 6 . 64% at medium , and 6 . 84% at high serum parathyroid hormone concentrations in the control material in SHIP and the corresponding percentages were 16 . 8% at low , 10 . 7% at medium , and 9 . 0% at high serum parathyroid hormone concentrations in the control material in SHIP-Trend . The Hypergene dataset ( a 4206 samples case-control study concerning hypertension genotyped using the Illumina 1M chip ) has been used to call CNVs and to check their correlation with the SNPs of interest . The CNVs calls have been done using pennCNV software [19] . A SNP by sample matrix with the copy number status was created . Then the square correlation ( Pearson correlation ) between value of each SNP of interest and the SNPs copy number status in a +/−2 Mb region was calculated . The SNPs of interest for which no correspondence has been found in the Hypergene dataset have been replaced by the closest SNPs in high linkage disequilibrium ( LD ) and present in the Hypergene dataset . LD between the SNPs of interest and a list of SNPs tagging CNVs from the GIANT consortium has also been calculated . The SNPs from the GIANT list are in LD higher than 0 . 8 with their corresponding CNV . We queried the AmiGo 1 . 8 gene ontology database for each gene located within ±250 kb of the seven replicated SNPs , including rs1801725 ( CASR ) . ( http://amigo . geneontology . org/cgi-bin/amigo/go . cgi , last accessed November 6 , 2012 ) . We used Homo sapiens as a filter for species . For each of the 7 replicated SNPs , we identified all proxy SNPs with r2>0 . 8 in HapMap CEU ( releases 21 , 22 , and HapMap 3 vers . 2 ) using the online SNAP database ( http://www . broadinstitute . org/mpg/snap/ ) . We then queried each of these 40 SNPs in the eQTL database of the University of Chicago ( http://eqtl . uchicago . edu/cgi-bin/gbrowse/eqtl/ ) . The rs1801725 SNP encodes a missense variant in exon 7 of the CASR gene leading to an alanine to serine substitution ( A986S ) . Given the key physiological role of CASR in calcium homeostasis ( monogenic disorders of calcium balance ) , this gene was the logical candidate for analysis in mouse at this previously identified locus . For the 6 newly identified loci , the precise rationale for gene selection varied from one locus to the other , but the main criteria was to focus on the most biologically relevant gene . Rs1550532 on chromosome 2 is an intronic SNP of DGKD , which was the most likely biological candidate for this locus and was therefore selected for analysis in mouse . None of the other genes located in this region ( ±250 Kb ) has a known link with calcium homeostasis ( Box 1 ) and rs1550532 is not in strong linkage disequilibrium with an eQTL ( Table S6 ) . We also took into account the fact that another member of the DGK family , namely DGKH was located near one of the other replicated loci , on chromosome 13 . Rs780094 , on chromosome 2 , is located in intro 16 of GCKR and is in strong linkage disequilibrium ( r2 = 0 . 93 ) in Caucasians [20] , with a common non-synonymous SNP ( P446L , rs1260326 ) associated with glucokinase activity in vitro [20] , [21] . This SNP has been associated with multiple other phenotypes in previous GWAS and it is in strong linkage disequilibrium with an eQTL ( Table S6 ) . Previous fine mapping analysis of this locus has attributed the signal from rs780094 to the functional rs1260326 variant [20] . The GCKR locus may indirectly influence calcium concentrations via its association with albumin levels [22] . In line with this , we observed an attenuation of the association of rs780094 with albumin-corrected serum calcium compared to the association with uncorrected serum calcium and we found GCKR not to be expressed in any of the key organs involved in calcium homeostasis that we tested in mice . We selected GCKR for analysis in mouse at this locus . Rs10491003 on chromosome 10 is located within a long non-coding RNA . For this locus , we selected GATA3 , the nearest and only gene located within this region , for analysis in mouse . GATA3 is implicated in monogenic disorders of calcium balance . Rs7481584 is located within CARS ( intronic SNP ) in an imprinted region known to play a role in multiple cancers , which makes this locus a plausible candidate for malignancy-related hypercalcemia . Other plausible biological candidates in this locus are NAP1L4 , PHLDA2 and CKDN1C ( Box 1 ) . Rs7481584 is in strong LD with 2 eQTLs , one associated with the expression of NAP1L4 ( rs2583435 ) and the other one associated with the expressions of SLC22A18 and SLC22A18AS . We selected CARS , NAP1L4 , PHLDA2 and CKDN1C for analyses in mouse . For rs7336933 , we selected the two only genes ( DGKH and KIAA0564 ) located under this association peak on chromosome 13 for analyses in mouse . Finally , rs1570669 is an intronic SNP of CYP24A1 , a strong biological candidate implicated in monogenic disorders of calcium balance . The two other genes of this region ( BCAS1 and PFDN4 ) have no known link with calcium homeostasis . Furthermore , rs1570669 and PFDN4 are separated by a recombination hot spot . We selected CYP24A1 for analysis in mouse . As animal experiments started while the replication process was underway , we had also initially selected the following genes for analysis in mouse: RSG14 and SLC34A1 at locus rs4074995 ( discovery P value = 2 . 4E-07 ) , VKORC1L1 at locus rs17711722 ( discovery P value = 2 . 8E-11 ) , PYGB at locus rs2281558 ( discovery P value = 6 . 4E-07 ) , CD109 at locus rs9447004 ( discovery P value = 8 . 1E-06 ) . No gene was selected for the rs2885836 and rs11967485 and rs12150338 loci in the absence of obvious candidate . Results for these unreplicated loci can be found in Figures S6 , S7 and S8 . We present these results for quality control purposes: SLC34A1 ( also known as NAPI-3 or NPT2 ) , which encodes solute carrier family 34 ( sodium phosphate ) , member 1 , was expressed in the kidney , but neither in duodenum nor in bone , as expected based on current knowledge on this phosphate transporter . In the kidney SLC34A1 was mainly expressed proximally and SLC34A1 expression was upregulated under low calcium diet , which is in line with the known function of this gene . Five C57bl/6 mice ( Janvier ) per group were fed , for one week , three different diets in which the percentage of calcium were 0 . 17% ( low calcium diet ) , 0 . 82% ( normal calcium diet ) and 1 . 69% ( high calcium diet ) and had free access to water . 12∶12 hours light/dark alternance was imposed . At the end of the week of the specific diet , spot urine were collected and mice were anesthetized . Blood was collected by retro-orbital puncture . Organs were immediately harvested and snap frozen . RNA was extracted using Trizol ( Invitrogen ) and reversed transcribed with PrimeScriptTM RT reagent Kit ( Takara Bio Inc ) . Calcium , sodium , phosphate and creatinine in plasma and urine were analyzed at the central lab of the Lausanne University hospital using a Cobas-Mira analyzer ( Roche ) . | Calcium is vital to many biological processes and its serum concentration is tightly regulated . Family studies have shown that serum calcium is under strong genetic control . Apart from CASR , the genes associated with serum calcium are largely unknown . We conducted a genome-wide association meta-analysis of 39 , 400 individuals from 17 population-based cohorts and investigated the 14 most strongly associated loci in ≤21 , 679 additional individuals . We identified seven loci ( six new regions ) as being robustly associated with serum calcium . Three loci implicate regions involved in rare monogenic diseases including disturbances of serum calcium levels . Several of the newly identified loci harbor genes linked to the hormonal control of serum calcium . In mice experiments , we characterized the expression of these genes in gut , kidney , and bone , and explored the influence of dietary calcium intake on the expression of these genes in these organs . Our results shed new light on the genetics of calcium homeostasis and suggest a role for dietary calcium intake in bone-specific gene expression . | [
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| 2013 | Meta-Analysis of Genome-Wide Association Studies Identifies Six New Loci for Serum Calcium Concentrations |
Buruli ulcer ( BU ) is an infection of the subcutaneous tissue leading to chronic necrotizing skin ulcers . The causative pathogen , Mycobacterium ulcerans , grows best at 30°C–33°C and not above 37°C . We explored the safety , tolerability and efficacy of phase change material ( PCM ) , a novel heat application system for thermotherapy of BU . In a prospective observational single centre proof-of-principle trial in Ayos/Cameroon , six laboratory reconfirmed patients with ulcerative Buruli lesions received 28–31 ( ulcers ≤2 cm ) or 50–55 ( ulcers >2 cm ) days of thermotherapy with the PCM sodium acetate trihydrate as heat application system . This PCM is widely used in commercial pocket heat pads , it is easy to apply , rechargeable in hot water , non-toxic and non-hazardous to the environment . All patients enrolled in the trial completed treatment . Being completely mobile during the well-tolerated heat application , acceptability of the PCM bandages was very high . In patients with smaller ulcers , wounds healed completely without further intervention . Patients with large defects had skin grafting after successful heat treatment . Heat treatment was not associated with marked increases in local inflammation or the development of ectopic lymphoid tissue . One and a half years after completion of treatment , all patients are relapse-free . Our reusable PCM-based heat application device appears perfectly suited to treat BU in endemic countries with limited resources and infrastructure . Controlled-Trials . com ISRCTN88392614
Buruli ulcer ( BU ) is a chronic necrotizing disease of skin and soft tissue caused by Mycobacterium ulcerans [1] . The disease starts as a subcutaneous nodule , papule or plaque that eventually ulcerates and progresses over months to years . In BU lesions , clumps of extra-cellular acid-fast organisms surrounded by areas of necrosis are found primarily in subcutaneous fat tissue [2] . M . ulcerans produces a macrolide toxin , mycolactone , which is associated with tissue destruction and local immunosuppression [3] . BU has been reported in >30 countries , but the major burden lies on children living in remote areas of West Africa associated with swamps and stagnant water bodies . Traditionally wide excision of the infected tissue alone was the standard treatment for BU . This is hampered by traumatic interventions , high cost and very high recurrence rates [4] . Chemotherapy with streptomycin and rifampicin is currently re-evaluated as an adjunct treatment to surgery and as a therapy in its own right [5] , [6] , [7] , [8] . M . ulcerans differs from most other pathogenic mycobacteria in that it grows best at 30–33°C and not above 37°C [9] . This characteristic feature of the pathogen was first used for therapeutic purposes in the early 1970s . Meyers et al . treated 8 patients from Zaire maintaining a temperature of approximately 40°C in the ulcerated area for a mean duration of 68 days [10] . There was no evidence of local recurrence during follow-up periods of up to 22 months . Based on this impressive success rate , WHO guidelines listed the application of heat as a treatment option for BU [11] . However , the heat application devices employed so far were impractical in most endemic countries . Here we describe the use of a cheap and easy to apply phase change material ( PCM ) device suitable for thermotherapy of BU in countries with limited resources .
In the current study we tested the hypotheses that Histopathological responses in week 4 of thermotherapy compared to reference samples at day 0 .
Seven patients with ulcers suggestive for BU on clinical grounds were recruited by active and passive case detection . In six of the seven patients enrolled the diagnosis was laboratory confirmed . We extended the total duration of heat application of large ulcers ( >2 cm ) and ulcers with prominent surrounding oedema from 4 week to 50–55 days and did not , as originally planned , treat small and large ulcers equally for 4 weeks only . This was done even though all ulcers appeared clinically healed after 4 weeks of heat treatment , independent of size and surrounding oedema . This decision was taken on the basis of the results of the punch biopsies in week 4 of thermotherapy showing residual AFB with intact rod-shaped appearance . Eligible patients were recruited between February 28 , 2007 and March 3 , 2007 . Patients stayed in the hospital during the course of heat treatment and thereafter until the wound was closed ( patients with small ulcers; patients 1 , 2 , 3 , 4 , ) or skin grafted ( patients with large ulcers; patient 5 and 6 ) . All patients were followed up until 18 months after completion of heat treatment . The age range of the seven patients enrolled was six to 21 years . Three patients had single ulcers on the upper and four had single ulcers on the lower extremities . Medical history and physical examination revealed no significant health problem other than BU . In six out of seven patients enrolled in the study on clinical grounds , diagnosis was laboratory confirmed . The unconfirmed patient was excluded from the analysis ( Fig . 2 ) . All patients enrolled into the trial completed treatment . In all patients temperatures at the lesion and over a wide margin of healthy looking skin were maintained above ≥39°C for between 8 . 4 and 13 . 2 hours and ≥40°C for between 4 . 4 and 9 . 3 hours per day ( Fig . 2 ) . Undermined margins collapsed between day 1 and day 3 . Epithelialization started in all patients between 4 and 11 days after the start and was almost completed in patients 1 , 2 , and 3 at the end of heat treatment ( Fig . 2 and Fig . 3 ) . In particular in patients with oedematous lesions ( patients 4 , 5 ) white discharge from ulcers was observed during initial treatment for various lengths of time . The two patients with large defects ( patients 5 and 6 ) had skin grafting after completion of heat treatment ( Fig . 3B ) . All six reconfirmed patients were healed and relapse-free 18 months after completion of treatment . In the punch biopsies taken prior to start of treatment , histopathological changes characteristic for BU , such as fat cell ghosts , deep dermal necrosis and/or psoriasiform epidermal hyperplasia , were found in six patients ( Fig . 2 ) . All patients yielded positive semi-quantitative IS2404 real-time PCR results . AFBs were detected in swabs or punch biopsies of 4 out of 6 patients included in the study . Analysis of serial sections of punch biopsies taken at day 0 and in week 4 of thermotherapy showed , that heat treatment was not associated with marked increases in local inflammation , the development of ectopic lymphoid tissue or haemorrhages . At both time points small numbers of both polymorphonuclear cells as members of the innate and T cells as members of the adaptive immune system were present , with polymorphonuclear cells mainly located around necrotic areas and T cells more confined to areas close to vessels in the upper dermis . Only the lesion of patient 3 contained both on day 0 and in week 4 of thermotherapy mixed cellular infiltrates , which were much more pronounced than in typical untreated BU lesions . The heat treatment procedure was very well tolerated by all patients . Patients with one ( patients 1 , 2 , 3 , 4 ) and with two PCM packs ( patient 5 ) could move around freely and did not feel unacceptably disturbed during their daily activities nor during sleep at night . Patient 6 with four PCM packs also walked with acceptable restrictions and slept largely undisturbed . None of the patients and their guardians requested termination of treatment at any time . Temperatures between 40–43°C were observed only for short intervals of time immediately after mounting of the PCM packs without causing unacceptable discomfort . Only initially a few small blisters were occasionally observed . With a simple patient-controlled method the therapeutic target temperature of 40°C at skin surface was quickly reached and maintained without further side effects .
Successful treatment of BU with heat has been reported in individual patients and small case series since 1950 [10] , [13] , [14] , [15] . This has not been carried further into clinical research and practice due to the fact that available heat application systems were cumbersome and not suited for use in developing countries . We achieved a break through by employing PCM packs as a cheap heat application system which is rechargeable in hot water , non-toxic and non-hazardous to the environment . In this proof-of-principle study we demonstrated that our heat application system is easy to use and allows the patient to move freely . Family members and the hospital community accepted the treatment very well and favoured it over other treatments currently offered ( surgery , antibiotics ) . Nurses quickly adopted the techniques of mounting the PCM packs and of recharging the packs in boiling water . The only side effects observed were sensation of excessive heat for a short period after applying the PCM packs . Lowering of the temperature at the skin surface by an elastic bandage interposed between tube gauze and PCM packs reliably prevented skin irritation and development of blisters , which may occur if the initial temperature at skin surface is less rigorously controlled . With our PCM-based heat application system we reproduced the excellent results of the thermotherapy study of Meyers' group in 1974 [10] with significantly shorter heat application times both with respect to length of heat treatment per day ( close to 24 hours [39°C–40 . 5°C] vs a mean of 10 hours , range 8 . 4–13 . 2 hours [≥39°C] ) and to total heat application time ( 28 to 115 days vs 28 to 55 days ) . Since both systems worked at the same temperature range measured at skin surface , the minimum length of heat application to achieve healing of BU appears to be in the range of our heat treatment schedule or even shorter . The initial clinical improvement of ulcerative lesions in our series was as fast as in the patient series of Meyers et al . As early as three days after initiation of heat treatment undermined ulcer margins collapsed and the skin attached to the underlying subcutaneous tissue with re-epithelialization starting at the edges . Discharge of the wound decreased over various lengths of time . Firm attachment of the affected skin was complete only after discharge stopped . By using heat treatment alone no viable tissue is lost and even the overarching margins at undercutting edges are often rescued . Lesions were clinically inactive in all of our patients with very good granulation and re-epitheliazation responses after 28 days of heat treatment . In one of our patients ( patient 6 ) non-viable tissue extended far beyond the ulcerated area , which had to be excised before skin grafting . In this patient and one other patient with a large defect ( patient 5 ) skin grafting was performed after a good granulation response had been achieved . Currently , all our patients are relapse-free 18 months after completion of heat therapy . Rifampicin/streptomycin chemotherapy of BU is associated with the development of ectopic lymphoid tissue in the lesions [16] . In some patients , effects reminiscent of the immune reconstitution syndromes observed in tuberculosis and leprosy patients after highly active antiretroviral therapy [17] are observed . In contrast , heat treatment did not lead to massive increases in local inflammation and this less vigorous response may favour rapid re-epithelialization . Also haemorrhages , which are regarded as negative indicators for uncomplicated wound healing [18] were not observed . Results of two pilot studies , the study of Meyers et al . in the 1970s [10] and our study , demonstrate that heat is a highly efficacious therapy for M . ulcerans disease . Use of PCM packs represents a break through for thermotherapy with respect to its practicality in endemic areas with poor infrastructure . Further optimization of the heat treatment schedule should make it suitable for community application . | Buruli ulcer is an infection of the subcutaneous tissue leading to chronic necrotizing skin ulcers . The causative pathogen , Mycobacterium ulcerans , grows best at 30°C–33°C and not above 37°C , and this property makes the application of heat a treatment option . We achieved a breakthrough in heat treatment of Buruli ulcer by employing the phase change material sodium acetate trihydrate as a heat application system for thermotherapy , which is widely used in commercial pocket heat pads . It is easy to apply , rechargeable in hot water , non-toxic and non-hazardous to the environment . Six laboratory reconfirmed patients with ulcerative Buruli lesions were included in the proof-of-principle study and treated for four to six weeks . In patients with small ulcers , wounds healed completely without further intervention . Patients with large defects had skin grafting after successful heat treatment . Heat treatment was not associated with marked increases in local inflammation or the development of ectopic lymphoid tissue . One and a half years after completion of treatment , all patients are relapse-free . The reusable phase change material–based heat application device appears perfectly suited for use in remote Buruli ulcer–endemic areas of countries with limited resources and infrastructure . | [
"Abstract",
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]
| 2009 | Phase Change Material for Thermotherapy of Buruli Ulcer: A Prospective Observational Single Centre Proof-of-Principle Trial |
As many neglected tropical diseases are co-endemic and have common risk factors , integrated control can efficiently reduce disease burden and relieve resource-strained public health budgets . Diarrheal diseases and dengue fever are major global health problems sharing common risk factors in water storage containers . Where provision of clean water is inadequate , water storage is crucial . Fecal contamination of stored water is a common source of diarrheal illness , but stored water also provides breeding sites for dengue vector mosquitoes . Integrating improved water management and educational strategies for both diseases in the school environment can potentially improve the health situation for students and the larger community . The objective of this trial was to investigate whether interventions targeting diarrhea and dengue risk factors would significantly reduce absence due to diarrheal disease and dengue entomological risk factors in schools . A factorial cluster randomized controlled trial was carried out in 34 rural primary schools ( 1 , 301 pupils ) in La Mesa and Anapoima municipalities , Cundinamarca , Colombia . Schools were randomized to one of four study arms: diarrhea interventions ( DIA ) , dengue interventions ( DEN ) , combined diarrhea and dengue interventions ( DIADEN ) , and control ( CON ) . Interventions had no apparent effect on pupil school absence due to diarrheal disease ( p = 0 . 45 ) or on adult female Aedes aegypti density ( p = 0 . 32 ) ( primary outcomes ) . However , the dengue interventions reduced the Breteau Index on average by 78% ( p = 0 . 029 ) , with Breteau indices of 10 . 8 and 6 . 2 in the DEN and DIADEN arms , respectively compared to 37 . 5 and 46 . 9 in the DIA and CON arms , respectively . The diarrhea interventions improved water quality as assessed by the amount of Escherichia coli colony forming units ( CFU ) ; the ratio of Williams mean E . coli CFU being 0 . 22 , or 78% reduction ( p = 0 . 008 ) . Integrated control of dengue and diarrhea has never been conducted before . This trial presents an example for application of control strategies that may affect both diseases and the first study to apply such an approach in school settings . The interventions were well received and highly appreciated by students and teachers . An apparent absence of effect in primary outcome indicators could be the result of pupils being exposed to risk factors outside the school area and mosquitoes flying in from nearby uncontrolled breeding sites . Integrated interventions targeting these diseases in a school context remain promising because of the reduced mosquito breeding and improved water quality , as well as educational benefits . However , to improve outcomes in future integrated approaches , simultaneous interventions in communities , in addition to schools , should be considered; using appropriate combinations of site-specific , effective , acceptable , and affordable interventions . ClinicalTrials . gov no . ISRCTN40195031
Integrating the control of diseases can reduce their burden while relieving resource-strained public health budgets [1–3] . Many tropical diseases are co-endemic and have overlapping risk factors and strategies for control and prevention . Recent assessments of co-occurrence and co-infection of diseases have shown the potential for the integration of control and prevention strategies [4–8] . Diarrheal diseases and dengue fever co-occur in many parts of the world and water management practices can influence the number of infections of both . Storing water is a necessity in many places due to a lack of regular safe water supply . The storage of drinking water can be a determinant of both diseases if the stored water is fecally contaminated [9] and the containers used for storage provide breeding sites for dengue vector mosquitoes [10 , 11] . Close to 90% of the global diarrheal disease burden is thought to be caused by unsafe water supply and lack of sanitation and hygiene [12 , 13] . About 748 million people , 9% of the global population , lack access to safe water sources , of which >90% live in rural areas [14] . Dengue fever , caused by a flavivirus with four different serotypes , is the most common arboviral disease in the world [11] . Dengue is mainly transmitted by Aedes aegypti , which can also transmit chikungunya , Zika and yellow fever viruses [15] . Unplanned and unregulated urban development , poor water storage , and unsatisfactory sanitary conditions are all determinants of dengue transmission [17–21] . There are no theraputic drugs for dengue and although a recent licensed vaccine , Dengvaxia by Sanofi Pasteur , has been recommended by the World Health Organization ( WHO ) and is being rolled out in several countries [16] , vector control will remain an important part of integrated control of dengue . Effective control of both of these diseases largely depends on the provision of a reliable supply of safe water , appropriate water management practices , and community participation in control efforts [13 , 17 , 18] . This functional relationship lends itself to integrated control approaches , which may be both efficient and cost-effective . Although there are currently no analyses on the co-occurrence and co-infection of dengue and diarrheal pathogens , it is clear that both diseases individually are of great public health importance globally . More than 1 . 4 million deaths from diarrheal diseases were recorded in 2010 , of which approximately 800 , 000 were children younger than 5 years [19 , 20] . The annual number of deaths from dengue has been estimated at 14 , 000–22 , 000 [19 , 21] , mainly among children [21] . Approximately 2 . 5 billion people live in risk areas for dengue and an estimated 390 million infections occur annually in approximately 100 countries [17 , 22 , 23] . In 2010 , more than 89 million disability-adjusted life years ( DALYs ) were estimated to be attributed to diarrheal disease and 825 , 000 DALYs attributed to dengue globally [24] . Both diarrhea and dengue are endemic throughout Latin America . Diarrhea is a leading cause of morbidity in Colombia and one of the ten most important causes of mortality [25] . The prevalence of diarrhea in children under five years old in 2010 was 13% [26] . Colombia has one of the highest levels of dengue transmission in the Americas . In 2014 , dengue incidence was 413 . 5 cases per 100 , 000 inhabitants ( 110 , 473 cases and 294 deaths ) [27] . About 50% of the urban population in the country is considered to be at high risk [28] . In the 2010 dengue epidemic , 85% of the cases came from municipal capitals , 8% from other population centers , and 7% from rural areas [29] . However , generally about 13–29% of dengue cases are reported from rural areas [30–32] . All four dengue virus serotypes circulate in the country and both Ae . aegypti and Ae . albopictus are present [33 , 34] . Aedes aegypti is not only an urban species , but is also abundant in rural areas where it can have relatively high infection rates [35–37] . Inadequate safe drinking water supply and waste disposal services have been identified as principal drivers of Ae . aegypti propagation in Latin America [38 , 39] . Storing water is common in Colombian households , even in areas with access to piped water . Water storage tanks , laundry basins ( albercas ) , and drums are the primary dengue vector breeding sites in much of Colombia [40–42] . In 2009–2010 , 63 . 5% of the population lacked access to water suitable for human consumption [43] . In 2012 the coverage of piped water was 97% in urban areas but only 53% in rural areas [44] . Lack of access to reliable , clean drinking water is likely a key factor in making diarrhea a leading cause of morbidity , particularly among children . Disease risk for both dengue and diarrheal illnesses is often estimated by household-level variables , e . g . presence of mosquito positive containers and adult mosquitoes for dengue [11]; and lack of access to safe drinking water , inadequate sanitation and hygiene for diarrhea [13] . Control interventions for dengue and diarrhea often target households as well; e . g . insecticide treatment of containers , household repellents , window screening for dengue , and boiling or filtering of drinking water and improving toilets for diarrhea [11 , 13] . However , children spend large portions of their days in school and could potentially be at risk of contracting illnesses while in the school environment . Only 54% of rural public schools in Colombia have access to drinking water , 57% to sewerage and 40% to a sufficient number of toilets [45] . For example , in the neighboring municipality of Apulo clean drinking water ( absence of E . coli ) was only available in 5 out of 14 schools ( 36% ) [46] , potentially exposing pupils to diarrheal pathogens from water ingested at school . Similarly , school children may be disproportionately exposed to mosquito bites , because the peak biting times of dengue vectors occur during school hours [42] . Educational interventions targeting schools to promulgate public health messages and engage students in practical control efforts are receiving increasing attention [47–49] . School children are responsive to public health education and may act as messengers of behavioral change to their households and communities [48 , 50 , 51] . The aim of this cluster-randomized controlled trial was to determine whether integrated interventions targeting determinants of diarrhea and dengue delivered to rural primary schools would reduce diarrheal disease , dengue entomological risk factors , school absenteeism due to illness , and contamination of stored water . A cluster design was used because the interventions are delivered at schools , with students nested within schools . Disease incidence and absence rates apply to both cluster and individual level , whereas entomological outcomes and water quality indicators pertain to the cluster level .
The study was undertaken in the municipalities of Anapoima and La Mesa in Cundinamarca department , Colombia . In 2011 Anapoima had a population of 12 , 539 inhabitants ( 57% in rural areas ) , a total area of 124 . 2 km2 , an average altitude of 700 meters above sea level ( m . a . s . l . ) , and an average temperature of 26°C [52] . The population for La Mesa in 2011 was 29 , 566 inhabitants ( 45% in rural areas ) , with a total area of 148 km2 , an average altitude of 1 , 200 m . a . s . l . and an average annual temperature of 22°C [53] . The average annual rainfall in the area is 1 , 300 mm . The rainfall pattern is bimodal with a rainy peak in April-May , a relatively dry period in June-September and a second rainy peak in October-November . People cultivate crops; such as sugar cane , coffee , fruit; raise livestock , as well as work in tourism . Natural vegetation consists of dry tropical forest , premontane and lower montane moist forests . We performed a 2×2 factorial cluster-randomized controlled trial in rural primary schools in the two municipalities . All rural primary schools were assessed for eligibility to participate in the trial . Inclusion and exclusion criteria for schools and pupils are described in detail in Overgaard et al . [54] . In brief , large schools ( >100 pupils and >five grades ) were excluded due to different teaching strategies , teacher-student dynamics , and number of pupils compared to primary schools . Thirty-five rural primary schools were randomized to the arms of the study ( Fig 1 ) . All pupils were eligible for inclusion . Those moving to a school outside the study area during the study were considered lost to follow up . However , those who moved to a school within the study area became part of the arm into which they moved . Each school ( cluster ) was randomized to one of four study arms: 1 ) DIA , 2 ) DEN , 3 ) DIADEN , and 4 ) untreated control ( CON ) . Interventions relating to either diarrheal diseases ( DIA ) , dengue ( DEN ) , or both ( DIADEN ) were implemented in the schools during February-April 2012 . Interventions were implemented and generally maintained by the project team for two years ( four complete school semesters ) . All materials were supplied by the project . The DIA interventions–targeting drinking water quality , sanitation , and hygiene–consisted of installing water filters , fitting lids or nets on all drinking water storage containers to prevent contamination , and cleaning of the containers once per year . Lids and nets were inspected once or twice per month and improved if needed . Container cleaning was done once per semester by municipal workers . Hygiene practices included the promotion of hand washing with soap ( before eating and after toilet visits ) and proper use and daily cleaning of toilets . The DEN interventions–targeting adult and immature mosquito control and solid waste management–consisted of installing deltamethrin-treated curtains made from LifeNet material ( Bayer CropScience ) in classrooms and computer rooms , and fitting lids or nets on all water storage containers to prevent mosquito entry . Containers that could not be fitted with lids/nets , mostly albercas ( laundry basins ) , were treated with pyriproxyfen ( Sumilarv , Sumimoto Chemical Company ) , an insect growth regulator , which prevents the emergence of adult mosquitoes [55] . New pyriproxyfen was added to containers once every two months . Larval source management was carried out by pupils during weekly solid waste clean-up and collection campaigns . In addition , each set of interventions contained educational components consisting of project-designed educational teacher’s manuals and training guides adjusted to the curricula of children’s ages . The diarrhea educational component included lessons on symptoms , transmission pathways , risk factors , role of hand washing and hygiene , water and health relationships , etc . The dengue educational component included lessons on symptoms , transmission and risks , vector biology/ecology/control , the role of solid waste as mosquito breeding sites , etc . Every two months the project team met with teachers for training and delivery of new educational material . All DIA and DEN interventions pertained to the cluster level . Details of the interventions are provided in Overgaard et al . [54] . No dengue or diarrhea interventions by governmental or other actors were carried out in the rural schools during the study period . Both the DIA and DEN interventions included lids or nets on containers . Since both target diarrhea and dengue outcomes , especially water quality and larval indices , they could have potential practical and statistical implications on the outcomes . This is discussed further in the Limitations section . The primary outcome measure for diarrheal disease ( individual level outcome ) was incidence rate of diarrhea in school children , assessed as the number of episodes ( and days ) students were absent due to diarrhea . The primary outcome measure for dengue entomological risk was the density of adult female Ae . aegypti mosquitoes per hour ( Adult index ) , pertaining to the cluster level . The reason for selecting a mosquito index , and not a child health measure , as the primary dengue outcome is that , given the dengue incidence in the area , the required sample size would have been prohibitively large . Secondary outcome measures were the Breteau index ( number of containers with Ae . aegypti immatures /100 schools ) , number of pupil absence episodes ( and days ) due to probable dengue and to any illness , and concentration of Escherichia coli in drinking water storage containers . Of the secondary outcomes , the Breteau index and level of E . coli contamination in water were cluster level outcomes , whereas pupil absence records were individual level outcomes but analyzed at the cluster level . Outcomes specific to the educational interventions will be presented in a separate publication . Sample sizes were calculated incorporating both the diarrhea and dengue primary outcome indicators . The sample size calculation based on diarrhea data considered both the number of schools and number of children per school . For dengue , the sample size was determined in terms of numbers of schools , since this outcome was only measured at the school level . Calculations were carried out using a target number of participants of 873 pupils ( data from 2006–2007 ) from 35 schools with an average of 25 pupils per cluster ( school ) ( range: 5–96 ) . The harmonic mean of 17 children per school was used in calculations to allow for school size variation . The sample size for the primary outcome of diarrhea incidence was calculated using methods for cluster-randomized trials [56] . The baseline diarrhea incidence ( 0 . 28/person-year ) and within-school clustering ( coefficient of variation k = 0 . 8 ) was calculated from existing data from the study area ( Instituto de Salud y Ambiente , El Bosque University , Bogotá ) . For 17 children per school followed up for two years , 35 schools achieved 90% power for a 75% reduction in incidence and 5% two-sided significance level , or 80% power for a 65% reduction . For the sample size calculation for the dengue endpoint , we used data on Ae . aegypti adult density from Mexican schools [57] , since no comparable data were available from Colombia . A negative binomial distribution was fitted to these data to allow for overdispersion relative to Poisson , giving a mean of 24 mosquitoes per school and a dispersion parameter of 0 . 75 . Power was estimated assuming equal numbers of mosquitoes per arm [58] . Using these parameters and 17 schools per arm , a 70% reduction in mosquito numbers was detectable with 84% power and a 75% reduction was detectable with 92% power . Schools ( clusters ) were allocated to trial arms at a public randomization event in each municipality before the 2012 school year , which started in February . At each event , a raffle was arranged by project investigators at El Bosque University where a representative of each school drew a number indicating to which arm their school would be allocated . This method maintained allocation concealment , i . e . the investigators and participants were ignorant of the upcoming assignment of each school . However , the assignment was not blind . The achieved allocation ratio was 9:9:8:9 schools and 231:187:200:210 pupils in the DIA:DEN:DIADEN:CON arms , respectively ( Fig 1 ) . All pupils in each cluster were included in the study , i . e . complete enumeration of participants . Allocation was stratified by municipality [59] , because the two municipalities differed in ways , which were likely to be associated with the trial outcomes . In particular , the La Mesa schools are located at higher altitudes ( 712–1610 m . a . s . l . ) than the ones in Anapoima ( 588–1089 m . a . s . l . ) and only Anapoima has a municipal educational board , potentially improving educational follow-up . Baseline data were collected during July-September 2011 ( dry season ) and October-November 2011 ( rainy season ) . All data collections and follow-up of interventions were done by the project teams at four time points after implementation of interventions , May-June 2012 , October-November 2012 , May-June 2013 , and October-November 2013 . Absences were recorded daily by teachers and every week absence records were collected by project staff . An absence episode was defined as the absence of a pupil for all or part of a school day . Absences for health reasons were confirmed by phone calls to parents and , if necessary , house visits . The condition of the child was verified by the project physician or project staff by confirming symptoms and disease criteria . Diarrhea was defined as the passage of three or more loose or liquid stools per day ( or more frequent passage than is normal for the individual ) [60] . A new absence episode due to diarrhea was defined as one occurring after at least three consecutive diarrhea-free days [61] . This 3-day criterion was also used for any absence reason . Probable dengue was defined according to WHO criteria [11] . Adult mosquito collections were carried out inside schools using a battery-driven Prokopack aspirator [62] for 10 minutes in each classroom and computer room . Immature mosquito collections were carried out in all artificial and natural water holding containers within the perimeter of each school property ( maximum ~80 m ) . Mosquitoes were identified to species in a field laboratory using common identification keys [63 , 64] . A 200 mL water sample was collected from each drinking water container ( storage tanks , water stored in the filter , tap water after filtration , and unfiltered water from taps ) . Escherichia coli presence was used as an indicator of fecal contamination and risk of diarrheal illness according to WHO guidelines [9] . Between 24 to 48 hours after collection , water samples were analyzed for E . coli using the analytical method 9222 B described in Eaton et al . [65] . Results were read between 24–48 hours and recorded as colony forming units ( CFU ) /100mL . All water analyses were carried out at Daphnia Laboratory , Bogotá , Colombia ( certified laboratory by IDEAM , Ministry of Environment and Sustainable Development , Res . 0347/2010 and 0710/2012 ) . The primary diarrhea outcome , diarrhea incidence in school children , was expressed , for each school , as the incidence of episodes of school absence ascribed to diarrhea per school year . Incidence was also calculated in terms of numbers of absence days . These rates , and those of other causes of absence , were calculated per year based on a school year of 185 days . Analysis of covariance ( ANCOVA ) was used to estimate the effect of the diarrhea interventions on these absence rates . The factorial design was represented by including one dichotomous explanatory variable for each of the two interventions . The stratification was represented by including a further such variable for municipality . The primary entomological outcome , adult female Ae . aegypti density ( Adult Index ) , was expressed as mean number of adult female mosquitoes collected per hour averaged over the four collection times . A negative binomial regression model was used to analyze number of mosquitoes with the logarithm of the sampling effort ( i . e . person-time spent aspirating ) as an ‘offset’ . This analysis yielded density ratios . As for the diarrhea outcome , the explanatory variables for the primary analysis were trial arm and stratum . Secondary analyses were carried out for both outcomes including another binary explanatory variable representing the interaction term between the two interventions . The Breteau index ( BI: number of containers with Ae . aegypti immatures/100 schools ) , was calculated at baseline as well as for each follow up time point . Four additional entomological variables were calculated: the School Index ( SI: number of schools with Ae . aegypti immatures/schools inspected × 100 ) , the Container index ( CI: number of containers with Ae . aegypti immatures/containers inspected × 100 ) , pupae per person ( number of Ae . aegypti pupae/person ) , and the proportion of schools with adult female Ae . aegypti ( % ) . These indices were analyzed similarly to the Adult Index . For BI and SI , the denominator for each school was the number of times it was sampled in the intervention period . For CI it was the total number of containers inspected , and for pupae per person it was the number of persons present per school—children plus teachers and other staff—at the end of 2012 , multiplied by the numbers of times the school was sampled . Although not pre-specified , the above analyses of the Adult and Breteau indices were repeated including the respective baseline values as an additional covariate . Due to the skewness of the values , this was done by categorizing the index as zero , or above or below the median of the positive values . The percentage of E . coli positive water samples taken from water containers and mean E . coli concentration per sample ( based on all samples , including negatives , expressed as colony forming units , CFU/100mL ) were compared between arms using factorial analysis of covariance as for diarrhea incidence . Due to the skewness of CFU counts , the Williams mean ( WM ) CFU was calculated , i . e . 1 was added to all counts and the geometric mean was calculated , then 1 was subtracted again . This was done for all sampled containers over all surveys in the intervention period . The logarithm of WM for each school was used as the response variable in the analysis , and the coefficients anti-logged to give results in terms of ratios of WM . The effect of duplicating interventions ( lids / nets on containers ) in both the DIA and the DEN interventions was not explicitly analyzed . Instead , the interpretation of the outcomes should be as follows: When comparing DIA against DEN , the shared interventions ( lids / nets ) are effectively not evaluated . When comparing DIADEN and DEN , only the additional DIA interventions are effectively compared with the non-DIA interventions , and the shared ones are not evaluated . A similar argument as the previous one applies for the DIADEN and DIA comparison . The scope and objectives of the project were presented to the mayors and the secretaries of education and health in the two municipalities . The project was then presented to school principals and teachers who signed consent to participate ( before randomization ) on behalf of each school . The study was approved by the Comité Institucional de Ética en Investigaciones de la Universidad El Bosque , Bogotá , Colombia ( Acta No . 146 of 30/08/2011 ) and the Ethical Review Board of London School of Hygiene and Tropical Medicine ( Ref . no . 6289 ) . The trial protocol was reviewed by the Regional Committees for Medical and Health Research Ethics ( REC ) in Norway . Pupils with written or oral assent and written or oral parental consent were to be included in the study . Written consent and assent were documented from the majority of parents and pupils . Parental consent was sought via information and consent forms which pupils were asked to take home . Some forms were mislaid and , on belatedly collating the returned ones , many were found to be illegible , or unidentifiable for other reasons such as the names being incomplete or at variance with those in our records . However , oral consent from parents or guardians were sought during telephone calls when establishing reasons for student’s school absence . Bearing in mind that the study was minimal risk in the terms of the Colombian Ministry of Health’s Resolution 8430 of 1993 , we sought and received permission from the ad-hoc ethical committee of the Universidad El Bosque ( Acta No . 009 of 27/11/2014 ) and the Ethics Committee of the London School of Hygiene and Tropical Medicine ( reference 10453/6289 , 7 March 2016 ) to publish all data collected . Both ethics committees approved the described consent procedures . The trial is registered in the Current Controlled Trials ( no . ISRCTN40195031 ) .
In December 2011 , 35 schools were randomized to four study arms with nine schools in each of the DIA , DEN , and CON arms; and eight in the DIADEN arm ( Fig 1 ) . At the start of the trial , there were 828 pupils in these schools , with a total of 941 pupils participating in 2012 and 948 pupils in 2013 . The total number of pupil observation days was 287 , 578 . One school in the DIA arm in La Mesa was closed in the end of 2011 due to unstable ground conditions and was treated as lost to follow up . Another school in the DEN arm in Anapoima was closed in 2012 ( after the first semester of interventions ) due to structural damage to the building and the pupils in this school were moved to the closest available school . Since the closest school was in the CON arm and the transferred pupils had already started receiving the DEN interventions , this school was moved to the DEN arm . After reconstruction of the first school , the pupils returned there , resulting in both schools remaining in the DEN arm for the duration of the study . There were only minor differences between schools in general baseline characteristics ( Table 1 ) . The altitudinal range of schools in the DEN and CON arms was high compared to the other arms . Entomological indices were generally higher during the rainy season . There were no major differences in entomological indices between arms , apart from slightly higher larval indices in the DIADEN schools . Schools in the DIADEN arm seemed to rely relatively more on rainwater rather than piped water and had a less frequent daily water supply compared to the other schools . A high proportion of schools across arms used boiling as a main water treatment method . Water contamination was quite similar across arms with 56–76% of samples containing E . coli and no significant differences between contamination levels ( Table 1 ) . The mean density of adult female Ae . aegypti mosquitoes varied from 1–2 per hour and there were no significant differences between arms ( Table 3 ) . Similarly , exploratory analysis showed no significant differences between arms comparing female Ae . aegypti density in classrooms ( p = 0 . 89 ) , toilets ( p = 0 . 24 ) , canteens ( p = 0 . 68 ) , kitchens ( p = 0 . 51 ) , or teacher’s bedrooms ( p = 0 . 21 ) . The overall total absence due to any causes of illness were 1 , 935 episodes and 3 , 569 days with a mean rate of 1 . 2 episodes/pupil/year ( range: 1 . 0–1 . 2 ) and 2 . 3 ( range: 1 . 9–2 . 7 ) days/pupil/year . These absence rates were similar between municipalities and there was no significant effect of either set of interventions . There were 10 cases of probable dengue in 2012 and 19 in 2013 . The mean between-arm rates of probable dengue varied between 0 . 01–0 . 23 episodes/pupil/year and 0 . 05–0 . 12 days/pupil/year . There were no significant differences between arms in absence episodes ( p = 0 . 97 ) or number of absent days ( p = 0 . 77 ) due to probable dengue . La Mesa had a significantly higher number of absence episodes and days due to probable dengue compared to Anapoima ( La Mesa: 0 . 03 episodes , Anapoima: 0 . 019 episodes , p = 0 . 03; La Mesa: 0 . 15 days , Anapoima 0 . 008 days , p = 0 . 047 ) . A total of 420 water samples were collected from water storage tanks ( n = 159 ) , taps ( n = 138 ) , water filters ( n = 86 ) , and boiled water ( n = 37 ) . The percentage of E . coli positive samples and mean E . coli concentration was significantly lower in the DIA and DIADEN arms ( 34% and 40% , respectively ) than in the DEN and CON arms ( 63% and 61% , respectively ) ( p<0 . 05 ) ( Fig 4 ) . On average the diarrhea interventions reduced the Williams mean E . coli CFU by 78% ( ratio 0 . 22 , 95% confidence interval 0 . 07–0 . 65 , p = 0 . 008 ) compared to schools which did not receive these interventions . Twelve of the 16 DIA and DIADEN schools ( 75% ) were free of E . coli contaminated water in filtered water samples . Although the study was designed to evaluate the overall effect of each of the two sets of disease-specific interventions and not specific single interventions within these sets , it is important to consider the duplication of container lids / nets on the outcomes . When interpreting these results ( see Limitations section ) one must recognize that in any DIA vs . DEN comparison the lids and covers will not count as these are shared between the two interventions . Some minor adverse reactions , possibly arising from contact to insecticide-treated curtains , were noted during the installation of curtains and first year of the study . Of the 400 pupils , 21 teachers , 11 project staff , 4 tailors , and a few others who were exposed to the nets 16 developed slight allergic reactions . Of these , 7 were project staff , 4 tailors , 2 pupils , 2 teachers , and a housewife . The most common symptoms were slight numbness , skin reactions , and itching , which usually resolved after 24–48 hours . None of the cases required medical examination . No further adverse reactions were noted during the remainder of the project .
The efficacy of the dengue interventions is clearly shown by the 78% reduction in immature Ae . aegypti infestation ( BI ) in schools that received dengue interventions , either alone ( DEN ) or in combination ( DIADEN ) compared to the DIA and CON arms . The DIADEN arm had a mean BI of 6 . 5 ( positive containers per 100 schools ) and the DEN arm 10 . 5 . The lower BIs show that targeting larval breeding sites with covers , container-cleaning , pyriproxyfen , and residual waste clean-up campaigns resulted in reduced larval breeding in these school settings . Recent trials have reached similar reductions in BI’s . For example , BI was reduced by 57% through implementation of insecticide-treated curtains and water container covers in Colombia [67] , by 65% through pesticide-free evidence-based community mobilization interventions in Mexico and Nicaragua [18] , and by 86% through community-based control including container covers , health education , and garbage clean-up campaigns in India [68] . Overall BIs were exceptionally high in the study area , reaching values close to 90 ( Fig 3 ) . This is much higher than a BI of 5 which has been considered a threshold for disease transmission [69] . However , the BI and other Stegomyia indices ( HI and CI ) have been shown not to consistently reflect dengue transmission risk [70] . For example , dengue transmission occurred frequently in several studies even though the BI was lower than the proposed threshold value of 5 ( summarized in [70] ) . Other indices such as adult or pupal indices have been proposed as better indicators of dengue transmission risk as they more accurately reflect the adult stage when mosquitoes can be infectious [71] . The observed differences in BI are also reflected in the other immature indices , but not in the adult indices ( Table 4 ) . In fact , adjusting for baseline Adult index changed the estimate of the effect of the dengue interventions from null to detrimental , with a borderline p value ( 0 . 04 ) , although this analysis was not pre-specified and omitted one school due to lack of baseline data . The number of pupae per person in this study appeared to be lower in the DEN and DIADEN arms ( 0 . 04 and 0 . 05 , respectively ) compared to the other arms ( DIA = 0 . 18 and CON = 0 . 36 ) , a reduction of about 94% , although not significant ( p = 0 . 11 ) . The pupae per person values observed in schools in the CON arm were similar to those found in households in a cluster-randomized trial in a nearby municipality [67] . One reason that no apparent benefit of interventions was detected on the adult mosquito population was that they could have flown in from nearby breeding sites that were not targeted in the study . Most schools had 1–5 households nearby , potentially providing mosquito breeding opportunities ( Table 1 ) . Few cryptic breeding sites were found in the schools . There were no storm drains in any of the schools; all rainwater gutters were inspected and were negative; septic tanks only contained Culex larvae; and all elevated tanks were treated . In a separate paper we have reported on mosquitoes collected in households near schools [37] . The indoor resting density of Ae . aegypti females was average 2 . 7–3 . 0 mosquitoes ( maximum 22 mosquitoes ) per 10 minutes collection effort . All of the above indicate that adult mosquitoes collected in schools probably did not originate from the school area . Insecticide susceptibility tests carried out before the trial confirmed that Ae . aegypti from the study area were susceptible to pyrethroid insecticides , including deltamethrin which was used in the curtains ( S1 Table ) . Assuming that the curtains were indeed effective , a difference between arms should have been observed in this situation . Thus , the absence of resistance does not explain the observed results . Furthermore , it is interesting to note that the overall female density of Cx . quinquefasciatus was 3–12 times higher than Ae . aegypti ( [36] , S1 Fig ) , but there were no significant differences between arms ( S1 Fig ) . It is likely that curtains did not provide an effective physical and chemical barrier preventing mosquito entry into school classrooms . In fact , it was not possible to cover all possible mosquito entry sites to classrooms with curtains . Another observed problem was that the polypropylene material of these curtains was very light and a slight breeze could make curtains move away from the open windows allowing mosquitoes to enter . This was partially amended by adding weights to the lower ends of curtains to keep them hanging straight . The physical integrity of the curtain material was also observed to deteriorate very quickly with exposure to wind and sun potentially allowing mosquitoes to enter . The lack of complete blockage by insecticide treated window and door curtains has been observed in other studies , notably in Thailand where open housing structures were suggested to reduce the likelihood of mosquitoes making contact with insecticide-treated curtains [72] . On the other hand , a significant reduction in BI was observed in urban households in Girardot , Colombia using insecticide-treated curtains in windows and doors , although these differences were not significant for the pupae per person index ( proxy for adult vector densities ) [67] . This was also explained by the incomplete coverage of all household points of potential mosquito ingress and egress , allowing some mosquitoes to avoid contact with treated materials . However , an additional successive intervention in the Girardot study consisting of water storage container covers made from the same insecticide-treated material as the curtains showed a significant reduction in pupae per person [67] . As in our study , this demonstrates the importance of combining multiple vector control interventions targeting different stages of the mosquito life cycle . Although there were , in general , few cases of dengue during the study period , we found a significantly higher rate of absence due to probable dengue in La Mesa than in Anapoima ( 0 . 03 vs . 0 . 019 episodes/student/year; p = 0 . 03 and 0 . 15 vs . 0 . 008 days/student/year; p = 0 . 047 ) . The reasons for these geographical differences are unclear , but other studies have shown high spatial and temporal variability in occurrence of dengue infection in schools [73 , 74] . Population density is a possible explanation that favors dengue transmission , due to the higher human-vector contact . The overall human density in La Mesa was close to double that of Anapoima ( La Mesa: 200 persons/km2; Anapoima: 102 persons/km2 ) . In addition , the number of pupils in the schools in La Mesa was higher than that of the schools in Anapoima . It is possible that children in the DIADEN arm had a higher knowledge of either disease compared to children in other arms due to the educational intervention . This could have contributed to the observed lower BIs , through more vigilant and knowledgeable pupils who were active in cleaning up garbage around schools . Similar results have been found in Ecuador [75] , where pupal indices were lower in schools which received integrated intervention strategies ( including dengue education , patio and container clean-up campaigns through community empowerment and social mobilization ) compared to the conventional dengue prevention government program ( including routine temephos larval control and reactive/targeted deltamethrin and malathion fogging ) . From the above it is clear that combinations of interventions are needed to reduce not only larval indices , but also the number of pupae and adult mosquitoes , which eventually could have an effect on dengue transmission [76] . The interventions targeting diarrhea risk factors were effective at providing cleaner water to pupils . The mean E . coli concentrations were 78% lower in schools receiving diarrhea interventions ( DIA and DIADEN arms ) compared to schools in the CON and DEN arms ( p<0 . 05 ) . Furthermore , the proportion of E . coli contaminated water samples were also significantly lower in the DIA and DIADEN intervention arms . These results mean that provision of water filters , hand washing , cleaning and covering of water storage containers can effectively reduce exposure to fecally contaminated water in school children in these settings . These interventions alone or in combination with other interventions have shown to reduce water contamination and diarrheal disease in a variety of settings . Water filters implemented in households in Bolivia provided 100% coliform-free drinking water ( based on 96 water samples ) and significantly reduced diarrheal disease in children less than 5 years of age [77] . In Cambodia , water samples taken from intervention households where two different kinds of water filters ( ceramic with or without iron enrichment ) had been installed showed that 37% and 40% , of samples , respectively were free of E . coli , and , in contrast , 85% of water samples from control households were considered higher risk ( ≥101 CFU/100 mL E . coli ) [78] . The Cambodia study showed that , although only about 40% of water samples were completely free from E . coli , households using filters had significantly less diarrheal disease than control households . In the current study in Colombia , as many as 60–66% of water containers were free of E . coli in the arms receiving DIA interventions ( Fig 4 ) . Pupils in schools in a water-scarce environment in Kenya receiving water , sanitation and hygiene ( WASH ) improvements showed a reduction in diarrhea incidence and days of illness compared to control schools [79] . Despite this reduction in disease incidence there were no significant effects on overall pupil absence [80] , indicating that other reasons for absence were common in that setting . The interventions targeting diarrhea risk factors in our study did not appear to have an effect on school absence . There were no significant differences between arms in school absence due to any reason , due to any disease , or due to diarrhea . The reason for no apparent effect on absence due to diarrhea could be that these constituted a relatively minor proportion of the absences . Of the 25% of all absences that were due to illnesses , only 8% were due to diarrhea . The overall incidence rate of diarrhea was 0 . 3 absence days per pupil per year or 0 . 1 absence episodes per pupil per year . These incidence rates could have been too low to detect significant differences . In other potentially comparable studies , there were 5 . 9 absence days due to diarrhea for children in schools in Bogotá , Colombia [81] and 0 . 6 absence episodes per child per year in state schools in Spain [82] . The first is almost 20 times higher and the second about six times higher than what we found . The incidence rate reported in the Spanish study also included respiratory diseases and influenza , so diarrhea alone would be lower and potentially closer to the figures in our study . Nonetheless , it is not clear why there is such a large difference and normally one would expect higher diarrhea incidence in rural areas than in urban areas [83] . A systematic literature review of diarrhea incidence estimates from 139 low and middle-income countries showed an average of 2 . 9 diarrhea episodes per child per year in 2010 [84] . This is 29 times higher than in our study , but this estimate is for children under 5 years old and would naturally be higher than in school-aged children . Some limitations of this study are worth mentioning . The interventions were exclusively implemented in schools , with no attempt to control exposure in households and communities . Schools-based interventions only target the time at risk when students are in school . It is possible , therefore , that the lack of an effect on the primary diarrhea endpoint was a result of external factors outside the school environment . In retrospect , it was optimistic to base the sample size calculation on a 75% reduction in diarrhea incidence by the interventions targeting water , sanitation , and hygiene factors , especially since there were no interventions outside the school . Another issue is the school hours and children’s time at risk . In this location , children were in school from 07:00–13:00 . Mosquito collections done in a nearby community in 1978–1979 found that Ae . aegypti had two biting peaks at 10:00–11:00 and at 16:00–17:00 [42] . In the current study , biting times were not studied . However , if the mosquito has not changed its behavior it is possible that children were also exposed when they were not at school , highlighting the importance of simultaneous community interventions . Disease assessment based on reporting by parents or non-clinical observations by project staff ( as done regularly in this study ) may not be sufficient to assess an impact on a disease endpoint , particularly in the case of dengue . Another confounding factor is that some children might have gone to school despite being ill . This was not measured in this study , but should be accounted for in future work , for example by observations of fever or other obvious symptoms by teachers and other school staff . Information about general school absence is lacking in Colombia and no published information was found on children going to school sick . Future studies should include better epidemiological endpoints , with laboratory confirmation where necessary , although these may be expensive . Due to the large spatial-temporal variations in dengue , large randomized controlled trials are needed to find suitable interventions and combinations of interventions for each setting . As the new Dengvaxia vaccine , which has been rolled out in several countries , is not 100% effective [85] , integrated interventions , including vector control , will remain important for future dengue control . There are potential practical and statistical implications of duplicating container lids or nets in the DIA and DEN interventions . Lids and nets on water containers target both diarrhea and dengue outcomes , especially water quality and larval indices . The implications of this is that the observed better water quality in DIA schools compared to DEN schools ( Fig 4 ) would more likely be due to water filters and container cleaning in the DIA schools rather than the joint lids and nets interventions . Similarly , the lower BI’s in the DEN and DIADEN schools compared to DIA schools ( Fig 3 ) would more likely be due to the additional pyriproxyfen and garbage clean-up campaigns in the DEN schools rather than the lids and nets . This study was conducted in rural areas because they are often neglected in terms of national health policies . Although national dengue control primarily takes place in urban areas , dengue transmission also occurs in rural areas [86 , 87] . In Colombia between 13–29% of dengue cases are reported from rural areas [30–32] . We also found a high prevalence of DENV infected Ae . aegypti in the study area; 62% of mosquito pools were positive , the estimated individual mosquito infection rate was 4% , and in 74% of the examined households DENV-positive mosquitoes were present [37] . Relatively few studies have investigated the effect of health interventions on dengue or diarrhea in schools [e . g . 80 , 88] . School children participating in school-based interventions may bring health messages back home to their parents and diffuse them through the wider community [48] . We will report on the effect of the educational components on knowledge , attitudes and practices in students and their parents , as well as teachers , in a subsequent publication . This study was not designed to evaluate the effect of specific single interventions on outcome measures . The overall effect of each of the two sets of disease-specific interventions was of interest here . Reliance on a single intervention to control vector borne diseases has often been ineffective and combinations and integration of interventions are recommended by the WHO [76] . Future research using similar integrated interventions in schools should also involve parents and the surrounding communities . The effect of school-based integrated interventions should also be implemented in urban areas to assess the effectiveness and sustainability of interventions in settings with higher human and vector densities and more complex infrastructure and human dynamics . Finally , the results of this study will hopefully encourage development of policy recommendations for this school-based approach . The appropriate combination of interventions should be location-specific , effective , acceptable , and affordable . Therefore , the selected combination of interventions must be tested first before scale-up . Research on joint school and community-based interventions should be carried out in different settings to allow clear location-specific policy recommendations .
A cluster randomized control study was carried out in rural primary schools in Colombia with the aim to reduce diarrheal disease and dengue entomological risk factors . Integrated control of these two diseases is justified because of common risk factors existing in potentially contaminated drinking water stored in containers which may also harbor immature dengue vectors . Integration of interventions could , therefore , effectively control disease outcomes in cost-efficient ways . Two sets of interventions , one targetting diarrheal risk factors and the other dengue risk factors , were implemented . The results show that schools with dengue interventions had a significantly lower Breteau index ( larval breeding ) and schools with diarrhea interventions had significantly cleaner drinking water compared to schools without these interventions . There were no significant differences in pupil school absence due to diarrhea ( absence used as proxy for incidence ) or density of adult mosquitoes . The reason for no apparent effect on absence due to diarrhea could be that pupils were exposed to risk factors at homes and elsewhere , which these school-based interventions did not target . No effect on adult mosquito populations were likely due to a failure of insecticide-treated curtains and mosquitoes flying in from nearby untreated breeding sites . The study highlights the importance of combining several vector control interventions targeting different stages of the mosquito life cycle . Overall , the study suggests that integrated approaches to disease control in school settings can be effective in reducing disease risk factors in the school environment , but that simultaneous interventions in communities must be emphasized . The appropriate combination of interventions must be location-specific , effective , acceptable , and affordable and tested before scaling up and providing policy recommendations . | Many tropical diseases co-occur in the same areas and have overlapping risk factors and strategies for control and prevention . Therefore , integrating several diseases in control activities can be both effective and cost-efficient . We evaluated sets of diarrhea and dengue interventions in rural primary schools in Colombia to reduce absence due to diarrheal disease among pupils and dengue risk factors . Integrating the control of these two diseases is justified because of the potential joint risk factor of water storage behavior . Stored drinking water may be contaminated with enteric pathogens and such containers also provide breeding opportunities for dengue vectors . Water storage is common in schools in many tropical countries . Although the study did not demonstrate clear evidence of a reduction in pupil absence due to diarrhea or reductions of adult mosquito densities in schools , significant reductions were detected in the number of mosquito breeding sites and drinking water quality was significantly improved . Integrated disease control interventions in school settings should be further explored to prevent diseases with overlapping etiologies amongst pupils and school staff . | [
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| 2016 | A Cluster-Randomized Controlled Trial to Reduce Diarrheal Disease and Dengue Entomological Risk Factors in Rural Primary Schools in Colombia |
Conditions of chronic stress are associated with genetic instability in many organisms , but the roles of stress responses in mutagenesis have so far been elucidated only in bacteria . Here , we present data demonstrating that the environmental stress response ( ESR ) in yeast functions in mutagenesis induced by proteotoxic stress . We show that the drug canavanine causes proteotoxic stress , activates the ESR , and induces mutagenesis at several loci in an ESR-dependent manner . Canavanine-induced mutagenesis also involves translesion DNA polymerases Rev1 and Polζ and non-homologous end joining factor Ku . Furthermore , under conditions of chronic sub-lethal canavanine stress , deletions of Rev1 , Polζ , and Ku-encoding genes exhibit genetic interactions with ESR mutants indicative of ESR regulating these mutagenic DNA repair processes . Analyses of mutagenesis induced by several different stresses showed that the ESR specifically modulates mutagenesis induced by proteotoxic stress . Together , these results document the first known example of an involvement of a eukaryotic stress response pathway in mutagenesis and have important implications for mechanisms of evolution , carcinogenesis , and emergence of drug-resistant pathogens and chemotherapy-resistant tumors .
Sensing and responding to environmental cues are ubiquitous cellular functions essential for survival . Budding yeast cells respond to a variety of stresses by inducing or repressing specific sets of genes in a stereotypical fashion that , to a certain degree , does not depend on the identity of the stress . This process is termed the environmental stress response ( ESR ) [1] , [2] . Paradoxically , the ESR provides little protection from the initiating stress – genes required to survive the stress do not significantly overlap those that change expression in response to the stress and mutations in ESR regulators do not significantly sensitize cells to stress [3] , [4] . This observation raises the possibility that ESR activation may have other cellular roles . One potential role of the ESR is suggested by observations that chronic stress can induce genetic instability in different organisms [5]–[10] . The phenomenon of stress-associated genetic instability impinges on medical issues , such as the role of tumor microenvironment in genetic instability of cancer cells and emergence of drug-resistant pathogens and chemotherapy-resistant tumors . While in Escherichia coli stress response can activate mutagenic DNA repair [11] , [12] , no evidence exists as yet for the involvement of the ESR in mutagenesis in a eukaryote . In this manuscript we investigate the effects of stress on mutagenesis in yeast and the role of the ESR in this process . Numerous studies have indicated that environmental stress can affect genome stability . For instance , in mammalian cells in tissue culture hypoxia and starvation can suppress error-free DNA repair pathways ( e . g . mismatch repair and homologous recombination ) and cause an increase in mutagenesis [13]–[19] . In yeast , various types of stress can affect chromosome segregation and promote aneuploidy [6] . Interestingly the most potent inducer of aneuploidy is proteotoxic stress , e . g . inhibition of HSP90 protein chaperone by radicicol [6] . One explanation of this phenomenon is that HSP90 can become “overtaxed” , such that its client proteins that function in chromosome segregation would interact with their targets in a misfolded , disfunctional state , with aberrant consequences for ploidy maintenance [6] . Other instances of genetic instability , in particular mutagenesis , were reported in response to chronic osmotic and DNA replication stresses [20] , [21] . These types of stress are thought to be mutagenic at least in part because they can directly cause DNA damage: osmotic stress induces DNA breaks [22] and replication stress stalls DNA replication forks and creates regions of ssDNA [23] . Finally , several groups reported the phenomenon of “adaptive mutation” ( alternately termed “stationary phase” or “selection-induced” mutation ) in budding yeast Saccharomyces cerevisiae ( [9] , [10]; for a comprehensive review , see [24] ) . In these experiments , starvation for an amino acid induced reversions of mutations in amino acid biosynthesis genes , enabling cells to grow on the starvation medium . Besides amino acid starvation , “adaptive” mutants were also observed after exposure of yeast cells to the drug canavanine [25] . Together these studies suggest that sensing and responding to environmental stress may have important consequences for genome stability , but mechanisms underlying this assertion and the involvement of stress responses in these phenomena remain underexplored . Cellular pathways that function in mutagenesis in eukaryotes have been extensively studied , predominantly by identifying mutants defective in spontaneous and/or DNA damage-induced mutagenesis . These analyses have identified DNA translesion synthesis ( TLS ) as a key mutagenic pathway in both yeast and higher eukaryotes [26] . In yeast , TLS is largely carried out by two specialized DNA polymerases Rev1 and polymerase ζ ( Polζ ) that , unlike replicative polymerases δ and ε , can polymerize DNA using damaged or distorted DNA templates and thus function in DNA damage bypass pathways [27] . While Rev1 and Polζ can interact and function together in vivo , they do not have identical phenotypes in all mutation assays , suggesting that they have some independent roles [26] . In contrast to spontaneous and DNA damage-induced mutagenesis , genetic requirements for stress-associated mutagenesis are less well characterized . However , Heidenreich et al . reported that starvation-associated frameshift reversion was independent of both Rev1 and Polζ [28] , instead requiring proteins that function in non-homologous end joining ( NHEJ ) , such as Ku [29] . NHEJ is a DNA double-strand break ( DSB ) repair pathway that directly ligates broken ends together without relying on a homologous template [30] . Whether these mutagenic repair pathways are important for other types of stress-associated mutagenesis and are influenced by cellular stress responses has not yet been examined . The ESR in S . cerevisiae is activated in response to any one of a large number of environmental stresses [1] , [2] , including DNA damaging agents , such as the DNA alkylating drug methylmethane sulfonate and inhibitor of ribonucleotide reductase hydroxyurea [31] . The genes that are repressed by the ESR largely function in translation and other growth-promoting pathways . The genes that are induced by the ESR function in several molecular processes , such as protein folding and repair of oxidative damage , but the functions of many of them are not known . Stress-driven induction of most ESR-activated genes is largely regulated by partially redundant transcription factors , Msn2 and Msn4 [1] , [2] , [32] , [33] . Of the two proteins , Msn2 plays a greater role in transcriptional activation and its behavior has been relatively well examined . In unstressed cells , Msn2 is localized almost exclusively to the cytoplasm . Upon a sudden stress , such as a drop in glucose concentration or osmotic shock , Msn2 moves into the nucleus in the majority of cells where it binds to stress response elements in its targets' promoters to activate transcription [34]–[36] . In this manuscript , we examine the effect of the ESR on mutagenesis in S . cerevisiae by analyzing spontaneous and stress-associated mutagenesis in mutants lacking MSN2 and MSN4 . We report that the drug canavanine causes proteotoxic stress and activates the ESR , and that under conditions of severe canavanine stress MSN2 and MSN4 promote certain types of mutation events , most notably single nucleotide deletions in simple repeats . Furthermore , while MSN2 and MSN4 are dispensable for mutagenesis induced by osmotic and DNA replication stresses , they can promote or suppress mutagenesis induced by different types of proteotoxic stress . Furthermore , TLS polymerases and Ku also function in proteotoxic stress-induced mutagenesis and exhibit unanticipated genetic interactions with MSN2 and MSN4 . Together , these results implicate the yeast ESR in regulation of mutagenic DNA repair pathways activated by proteotoxic stress .
Canavanine is a toxic analog of arginine and can be imported into yeast cells via an arginine transporter , Can1 . The CAN1 gene is a commonly used mutation reporter in yeast as can1 mutations can be selected on plates containing canavanine . In a typical experiment , CAN1 cultures grown in the absence of canavanine are plated on canavanine-containing plates , so that only can1 mutants can form colonies . The mutation rates are then calculated from the can1 colony distribution data [25] , [37] . If can1 mutants form spontaneously during cell division in culture , the frequency of mutants should follow a Luria-Delbrück distribution [38] . An earlier study by Lang and Murray , designed to accurately calculate CAN1 mutation rate in culture using a large-scale fluctuation assay , detected a significant deviation of the data from a Luria-Delbrück distribution , suggesting that some can1 mutants were forming after plating the cultures on canavanine plates [25] . Raising the concentration of canavanine ten-fold ( to 600 µg/ml ) decreased , but did not eliminate , post-plating mutation . Using this high canavanine concentration and excluding small colonies from the calculation led to better fit of the data to a Luria-Delbrück distribution , suggesting that small colonies were largely can1 mutations that occurred after plating . We used the same large-scale fluctuation assay and also obtained evidence for post-plating can1 mutation . Similar to Lang and Murray , we found that eliminating small colonies ( Figure 1A ) from the dataset improved the fit of the data to a Luria-Delbrück distribution . The frequency of large colonies exhibited a better fit to a Luria-Delbrück distribution than to a Poisson distribution , while small colonies fit a Poisson distribution better than a Luria-Delbrück distribution ( Figure 1B ) , consistent with the conclusion that small colonies were more likely to have arisen after selection had been imposed . We performed a reconstruction experiment to investigate the possibility that small colonies were simply inherently slower growing than the large colonies ( Figure 1C ) . Six large and six small independent can1 mutants were picked , purified and seeded into a culture of a carrier strain that harbored two copies of the CAN1 gene , and whose rate of canavanine resistance was accordingly negligible relative to the experimental strains . The carrier cultures were then plated on canavanine-containing medium . We observed that all seeded cells , whether they came from original large or small can1 colonies , formed large colonies in the reconstruction experiment ( Figure 1C ) , ruling out any general or context-specific growth defects for cells in small colonies . In sum , the statistical modeling and the reconstruction experiment strongly supported the conclusion that the majority of small colonies were due to post-plating can1 mutations . The only pathway known to promote “adaptive” mutagenesis in previously reported reversion assays is non-homologous end joining ( NHEJ ) [29] . To investigate the role of NHEJ in post-plating can1 mutation , we deleted YKU80 and observed that our yku80Δ strain exhibited an approximately two-fold reduction in the frequency of post-plating can1 mutation without any change in CAN1 mutation rate during mitotic growth ( Figure 1D ) . We also tested the involvement of a TLS DNA polymerase , pol ζ , by deleting its subunit REV3 . The rev3Δ mutant exhibited reduced CAN1 mutation frequency both in culture ( two-fold ) and after plating ( 2 . 75-fold ) . Overall , these results demonstrated that NHEJ and TLS help promote CAN1 mutagenesis during acute canavanine exposure . We speculated that if small can1 colonies arose under different conditions than large colonies ( i . e . during acute exposure to canavanine on plates ) , they might have been generated by different mutagenesis mechanisms and thus might have different mutation spectra . Therefore , we sequenced the can1 ORF from a large number of pre-plating ( large ) and post-plating ( small ) can1 colonies . All of the sequenced alleles contained at least one mutation in the CAN1 ORF ( Table S1 ) . The predominant classes of mutations were deletions and base pair substitutions , and the overall distribution of these broadly defined mutation classes was not significantly different between large and small colonies ( Figure 2A ) . However , finer grained analysis of the sequence data yielded several significant results . First , a significant proportion of mutations in large can1 colonies occurred at sites previously identified as hot-spots of transcription-associated mutation ( TAM ) in CAN1 [39] . For example , ATΔ at position 1127 was previously identified as the most frequent mutation in actively transcribed CAN1 [39] and was , in this study , the single most frequent mutation in the large can1 colonies ( Figure 2B; Table S1 ) . This result showed that can1 mutations in large colonies were generated in cells actively transcribing CAN1 , consistent with our earlier conclusion that these mutations occurred in actively growing cells in culture . In contrast , can1 mutations in small colonies did not exhibit a TAM signature , indicating that these mutations occurred under conditions of low transcriptional activity and consistent with the hypothesis that they arose after plating . Second , small colonies differed significantly from large ones ( Fisher exact test P<0 . 002 ) with respect to the types of deletions in CAN1 . In particular , the majority of deletions in large colonies were those of 2–5 nucleotides , while over 70% of deletions in small WT colonies were those of a single nucleotide ( Figure 2C; Table S1 ) , with 13 out of 17 such −1 deletions occurring in simple repeats ( i . e . mononucleotide runs ) . Finally , we observed a statistically significant difference in the types of base pair substitutions in large versus small colonies ( P = 0 . 028; Figure 2D ) . Together , these data confirmed our earlier conclusions that can1 mutations in small colonies arose under different conditions than those in large colonies and were generated by distinct mutagenesis mechanisms . Our results revealed that we had recovered two classes of can1 mutants – those that arose in culture in absence of exogenous stress and those that arose in cells experiencing acute canavanine toxicity on plates . While other examples of mutagenesis in yeast during stressful conditions ( including post-plating mutagenesis on canavanine [25] ) have been reported , the roles of stress responses in this mutagenesis have not been examined . We addressed the role of the ESR in mutagenesis on canavanine plates by , first , asking whether canavanine activated the ESR and , second , whether attenuation of the ESR affected post-plating mutagenesis . Re-localization of Msn2 from the cytoplasm to the nucleus is a key marker of ESR activation [35] . Thus , to examine the effect of canavanine on the ESR , we examined the subcellular localization of Msn2-GFP in both CAN1 and can1 cells after plating them on canavanine medium . While Msn2-GFP was cytoplasmic in the majority of unstressed cells , it relocated to the nucleus in CAN1 cells after plating on canavanine , with about 80% of the cells showing nuclear Msn2-GFP by 12 hours after plating ( Figure 3A and 3B ) . In contrast , Msn2-GFP in can1 cells remained cytoplasmic . This difference was not due to a general defect of CAN1 cells in responding to stress , as CAN1 and can1 cells mount a similar response to glucose starvation ( Figure S1 ) . Thus , canavanine treatment activated the ESR in yeast cells . We next asked whether a functional ESR was required for post-plating mutation on canavanine . MSN2 and MSN4 function in a partially redundant manner , so we deleted both genes and measured the effect of the msn2Δ msn4Δ mutant ( hereafter referred to as msnΔ ) on CAN1 mutation in culture and on canavanine plates . Strikingly , and as predicted if ESR is necessary for post-plating mutations , the frequency of post-plating can1 mutants was reduced over 3-fold in the msnΔ strain relative to the MSN strain ( Figure 3C ) . In contrast , CAN1 mutation rate of msnΔ cells in culture was only slightly decreased relative to that of MSN cells . To test whether reduced frequency of post-plating mutants in the msnΔ strain might be simply explained by its reduced proliferation or viability on canavanine , we measured proliferation and viability of both WT and msnΔ cells after plating them on canavanine . As expected , acute exposure to the high concentration of canavanine in the plates ( 600 µg/ml ) was lethal to cells , but the onset of lethality was gradual , with about a third of the cells still viable after 24 hours . The msnΔ mutants were only slightly more sensitive to canavanine than WT cells ( Figure 3D ) , consistent with previous reports that the MSN genes support acquired , rather than primary , stress resistance [3] . Also , for both WT and msnΔ strains , cell number increased by approximately 50% between 10 and 24 hrs after plating on canavanine , indicating that at least half of the cells divided during that time ( Figure 3D ) . We also ruled out the possibility that post-plating mutant formation was simply delayed in the msnΔ strain by monitoring emergence of new can1 colonies in 72 WT and 72 msnΔ cultures for 15 days after plating . We detected no evidence that post-plating mutations simply arise later in the msnΔ strain ( Figure S2 ) . Together , these results demonstrated that the difference in post-plating can1 mutation between WT and msnΔ strains could not be attributed to differences in survival , proliferation , or a delay in mutant emergence . We conclude therefore that in the msnΔ strain the reduction in post-plating can1 mutants is due to a defect in mutagenesis . If Msn2-Msn4 were important for generating can1 mutations on canavanine plates but not in culture , we might expect deletion of MNS2 and MSN4 to affect specifically the post-plating can1 mutation spectrum . To address this possibility , we sequenced the CAN1 ORF in pre-plating ( large ) and post-plating ( small ) can1 mutants generated in the msnΔ strain ( Figure 4 ) . The resulting mutation spectra had two similarities and two important differences compared to the WT can1 spectra . First , as in the WT strain , the overall distribution of broadly defined mutation types was not significantly different between large and small msnΔ colonies ( Figure 4A ) . Second , as in the WT strain , only large msnΔ colonies were significantly enriched for mutations at CAN1 TAM hot-spots , such as ATΔ at position 1127 ( Figure 4B ) . The observation of TAM hot-spots in large but not small msnΔ colonies indicated that , as in the WT strain , the majority of large msnΔ colonies were due to mutations that arose in culture , while the majority of small msnΔ colonies were due to mutations that arose after plating on canavanine . This conclusion was especially significant given that with regard to deletion and base pair substitution spectra msnΔ post-plating mutants were strikingly different from WT post-plating mutants . With respect to deletion types , in contrast to the WT strain , msnΔ small colonies showed no shift toward −1 deletions relative to large colonies ( Figure 4C ) . Also unlike the WT strain , where small colonies showed a statistically significantly different base pair substitution spectrum from large colonies , large and small msnΔ colonies showed no difference in base pair substitution spectra ( Figure 4D ) . Intriguingly , the unstressed base substitution spectra looked somewhat different for WT and msnΔ strains ( compare large WT spectra in 2D to msnΔ spectra in Figure 4D ) . This difference was not statistically significant ( P = 0 . 12 ) but nevertheless suggested that even in the absence of exogenous stress ESR activity may also exert subtle effects on mutagenesis . In sum , we observed that msnΔ affected specifically post-plating can1 deletion and base pair substitution spectra , indicating that the ESR controls specific mutagenesis mechanisms operating in cells exposed to canavanine . If canavanine exerted general mutagenic effects , we should be able to detect these effects at other reporter loci , as well as test whether mutagenesis at these loci is affected by the ESR . However , the lethality of CAN1 cells in the high concentration of canavanine in plates makes it difficult to ask whether mutation at loci other than CAN1 is also induced after plating . Accordingly , we identified a low concentration of canavanine ( 2 . 5 µg/ml ) that elicited a stress response , as judged by increased Msn2 nuclear localization ( Figure S3A ) and slower growth ( Figure S3B ) but did not affect cell viability ( Figure 5A ) . Then , under conditions of chronic sub-lethal canavanine stress in culture , we measured the rate of forward mutation resulting in resistance to 5-fluoroorotic acid ( FOA ) . In yeast , FOA resistance ( FOAR ) is associated with mutations in the URA3 gene; however , mutations at other loci can also cause FOA resistance in URA3 cells [25] . Indeed , we observed that mutations in URA3 accounted for only a fraction of spontaneous or canavanine-induced FOAR mutants , the rest being comprised of mutations in other , as yet unidentified loci ( Figure S4 ) . As with CAN1 , rates of generation of FOAR mutations were similar in WT and msnΔ cells growing in culture in the absence of stress ( Figure 5B ) . The low concentration of canavanine was mutagenic in culture , inducing formation of FOAR mutations by five-fold in WT cells ( Figure 5B ) . Importantly , and as predicted by results obtained with can1 , this mutagenesis depended in part on Msn2-Msn4 , as canavanine induced FOAR in msnΔ by only 2 . 6-fold ( Figure 5B ) . These results showed that the mutagenic effect of canavanine was not limited to can1 and could be detected at other loci , where it also depended on the function of Msn2-Msn4 . To ask whether mutagenesis by chronic , low-level canavanine exposure also depended on NHEJ and TLS , we analyzed the involvement of Ku , Rev1 , and Polζ in this process by deleting YKU80 , REV1 , and REV3 and measuring spontaneous and canavanine-induced rates of FOAR in the mutant strains . We observed that deletion of REV1 partially reduced canavanine-induced FOAR , while deletion of REV3 or YKU80 completely abolished it ( Figure 5B ) . Thus , in an otherwise WT background , the functions of Polζ and Ku were required for canavanine-induced mutagenesis . To begin to understand the relationships between the ESR and the DNA repair and DNA damage bypass pathways implicated in canavanine-induced mutagenesis ( i . e . TLS and NHEJ ) , we combined deletions of MSN2 and MSN4 with rev1Δ or rev3Δ or yku80Δ and measured the rates of emergence of FOAR in the triple mutants in culture in the absence and presence of canavanine ( Figure 5B ) . Canavanine induced FOAR mutations in the msnΔ rev1Δ mutant by about two-fold , showing an attenuation of mutagenesis relative to the msnΔ or the rev1Δ mutants ( Figure 5B ) . This result indicated that the msnΔ and rev1Δ mutations had at least partially independent effects on canavanine-induced mutagenesis ( Figure 5C ) . When we measured canavanine-induced mutagenesis in msnΔ rev3Δ and msnΔ yku80Δ strains , we were surprised to find that , unlike the rev3Δ or yku80Δ single mutants , the triple mutants were not defective for canavanine-induced mutagenesis , but instead were able to induce mutagenesis in canavanine at least as well as the msnΔ mutants did ( Figure 5B ) . Thus , msnΔ was epistatic to rev3Δ and yku80Δ for canavanine-induced mutagenesis . This result suggested that in the presence of low level , chronic canavanine stress the ESR functioned upstream of Rev3 and Yku80 and also suppressed another mutagenic pathway , potentially one involving Rev1 ( Figure 5C ) . Together , these results showed that the ESR could either promote or suppress pro-mutagenic pathways and linked the ESR and error-prone DNA repair and DNA damage bypass pathways . Our results indicated that under conditions of canavanine stress MSN2 and MSN4 could either promote or suppress mutagenesis , depending on the genetic context ( e . g . presence or absence of Polζ or Ku ) . Several other types of environmental stress are mutagenic in yeast , in particular osmotic and DNA replication stresses [20] , [21] . Both types of stress also activate the ESR , as evidenced by characteristic gene expression signatures and/or localization of Msn2 to the nucleus [1] , [2] , [31] , [35] . To investigate whether these types of stress-induced mutagenesis also required the function of Msn2 and Msn4 , we measured the rates of emergence of resistance to canavanine or 5-FOA in cells growing in the presence of osmotic stress ( 1M NaCl ) or replication stress ( 100 mM hudroxyurea [HU] ) . These drug concentrations retarded cellular growth and reduced viability slightly ( NaCl ) or moderately ( HU ) , and their effects were similar in WT and msnΔ cells ( Figure 6A ) . Consistent with published reports , we observed that both types of stress were mutagenic ( Figure 6B ) . Interestingly , while 100 mM HU induced CAN1 mutagenesis very strongly ( consistent with results in [21] ) , it had a much weaker effect on promoting FOAR mutations ( Figure 6B ) , suggesting that replication stress has different effects on mutagenesis at different genomic loci . Importantly , we observed that neither osmotic stress-induced mutagenesis nor replication stress-induced mutagenesis depended on MSN2 and MSN4 ( Figure 6B ) . This result showed that although various stresses can activate the ESR , its role in mutagenesis is specific to certain types of stress . Although canavanine toxicity is well documented , the nature of canavanine-induced stress has not been examined . We hypothesized that canavanine induces proteotoxic stress due to accumulation of unfolded and nonfunctional proteins in which canavanines had replaced arginines . Indeed , we observed that Kar2p , an ER chaperone whose protein level is sensitive to levels of unfolded proteins [40] , was increased in abundance in the presence of canavanine ( Figure 7 ) . This observation raised the possibility that Msn2-Msn4 function impinged specifically on mutagenesis caused by proteotoxic stress . There have been several other reports of proteotoxic stress promoting genome instability in yeast . For example , cells growing in the presence of another amino acid analog , p-Fluorophenylalanine ( PFPA ) , showed increased rates of forward mutation at the CAN1 locus [41] . More recently , Chen et al . demonstrated that various types of stress could induce aneuploidy in yeast , but that aneuploidy was induced most strongly by proteotoxic stress ( specifically by an inhibitor of HSP90 , radicicol ) [6] . In both of these cases , the investigators hypothesized that under conditions of proteotoxic stress , increased mutagenesis and aneuploidy were due to the action of misfolded DNA repair and chromosome segregation proteins , respectively [6] , [41] . To further analyze the relationship between proteotoxic stress , the ESR , and mutagenesis , we measured forward mutation rates to canavanine or FOA resistance in WT and msnΔ strains growing in the presence of radicicol , PFPA , or tunicamycin ( a drug that inhibits protein glycosylation in the ER ) . We chose concentrations of the drugs that retarded cell growth and only slightly ( or not at all ) reduced cell viability ( Figure 8A ) . Interestingly , we observed that each of the proteotoxic agents had a distinct effect on mutagenesis , both in terms of affected loci and in terms of Msn2-Msn4 involvement . For example , treatment with radicicol did not induce mutagenesis of CAN1 ( and may have even slightly reduced it ) but did induce FOAR in a manner independent of MSN2 and MSN4 ( Figure 8B ) . These results showed that although radicicol is a potent inducer of aneuploidy [6] , its effects on mutagenesis were mild and locus-specific . Treatment with PFPA induced mutation of CAN1 by two-fold ( consistent with data reported in [41] ) in both WT and msnΔ strains ( Figure 8C ) . Interestingly , PFPA induced formation of FOAR mutations only in the msnΔ strain , showing that under these conditions , as in the rev3Δ and yku80Δ mutants , Msn2 and Msn4 must suppress a pathway that promotes mutagenesis . Finally , tunicamycin treatment had no effect on mutation of CAN1 but induced formation of FOAR mutations by over two-fold in the WT strain but not in the msnΔ mutant ( Figure 8D ) . This result showed that tunicamycin-caused ER stress was mutagenic and that this mutagenesis was promoted by Msn2 and Msn4 . Together , these results showed that proteotoxic stress could induce genetic instability via multiple pathways and that proteotoxic stress-induced mutagenesis was regulated by the ESR .
In this study , we present evidence that transcriptional activation of the ESR in the yeast S . cerevisiae can regulate mutagenesis elicited by several types of proteotoxic stress , including two amino acid analogs , canavanine and PFPA , and a drug that interferes with protein glycosylation , tunicamycin . The effect of the ESR was specific to proteotoxic stresses , as osmotic and DNA replication stresses elicited mutagenesis that was not affected by deletion of ESR activators , MSN2 and MSN4 . Moreover , Msn2 and Msn4 promoted specific types of mutation events at the CAN1 locus , including −1 deletions in simple repeats and altered types of base pair substitutions . Two TLS polymerases , Rev1 and Rev3/Polζ , and NHEJ factor Ku promoted canavanine-induced mutagenesis in culture , and deletion of MSN2 and MSN4 was epistatic to rev3Δ and yku80Δ mutants . Together these results establish a previously unknown connection between a stress response pathway and specific mutagenic DNA repair and DNA damage bypass processes and provide the first example in eukaryotes of the involvement of the general stress response in mutagenesis . Proteotoxic stress is associated with various forms of genetic instability: radicicol is a potent inducer of aneuploidy and PFPA enhances mutagenesis of CAN1 [6] , [41] . In both cases , the effect of proteotoxic stress was explained by invoking a direct role of misfolded , defective chromosome maintenance and DNA repair proteins in creating the genetic instability , without the participation of any intermediate signaling pathways [6] , [41] . If this were the sole source of mutagenesis in cells experiencing proteotoxicity , then different proteotoxic agents would be expected to have similar effects . However , in this study , we observed that different types of proteotoxic stress affected different loci differently: for example , two amino acid analogs , canavanine and PFPA , have different effects on forward mutagenesis leading to FOA resistance . Furthermore , radicicol , while being a very potent inducer of aneuploidy , had relatively minor effects on mutagenesis . Even more strikingly , we observed that proteotoxic stress-induced mutagenesis was either promoted or suppressed by the ESR , arguing that proteotoxic stress induces specific signaling pathways that are regulated by Msn2-Msn4 and that culminate in the activation of mutagenic DNA repair pathways . The locus specificity may be due to the fact that mutation rates are not uniform across the genome and are influenced by local parameters such as replication timing and chromatin structure [42]–[45]; thus , proteotoxic stress and the ESR could affect at least one of these parameters . This alternative model of proteotoxic stress-induced mutagenesis is further supported by the dependency of this mutagenesis on specific DNA repair pathways , TLS and NHEJ , and by genetic interactions of the msnΔ mutant with DNA repair mutants , indicating that Msn2 and Msn4 functions affect DNA repair . While our results implicate the ESR in mutagenesis , ESR-dependent mutation is not a universal consequence of every type of environmental stress . This result is consistent with recent evidence showing that both type and degree of stress affect the dynamics of Msn2 cytoplasmic-nuclear shuttling [46] . Precisely how different Msn2 nuclear dynamics correlate with downstream transcriptional changes is not yet well understood , but it is highly likely that different patterns of Msn2 nuclear entrance and exit may result in activation of different target genes . In a related fashion , different dynamics of p53 induction lead to different types of downstream responses: oscillating p53 levels activate cell cycle and DNA repair genes while constant p53 induction activates pro-apoptotic and pro-senescence genes [47] . The ESR target gene set contains several genes with known roles in chromatin structure and DNA repair that could potentially regulate mutagenesis , as well as over 100 genes with unknown functions . Undoubtedly , ongoing studies of Msn2 and Msn4 behavior in response to various stresses and of downstream effects on global transcript and protein levels will reveal the relevant targets of the ESR that regulate mutagenesis during specific types of stress . Our results implicate two processes with known roles in mutagenesis – TLS and NHEJ . Deletion of REV1 resulted in a partial reduction in canavanine-induced mutagenesis that was further decreased by deletion of MSN2 and MSN4 . In contrast , rev3Δ and yku80Δ were fully defective for canavanine-induced mutagenesis but msnΔ , which partially suppressed canavanine-induced mutation , was fully epistatic to these mutations . Although biochemical evidence indicates that Rev1 and Polζ can function together in TLS , with Rev1 creating a substrate for Polζ , genetic evidence has shown that their functions in vivo can be separable [48] . The different phenotypes of rev1Δ and rev3Δ mutants in canavanine-induced mutagenesis suggest that in this case Rev1 and Rev3 function in different branches of mutagenesis . A simple model consistent with our data is shown in Figure 5C . Canavanine induces mutagenesis through two branches , one of which is mediated by Msn2-Msn4 through Ku and Polζ while the other is inhibited by Msn and promoted by Rev1 . Thus , mutation of REV3 or YKU80 results in inactivation of both mutagenic pathways while elimination of Msn2-Msn4 eliminates only one , even if Rev3 or Yku80 are concomitantly inactivated . No evidence currently exists for transcriptional regulation of REV1 , REV3 , or genes encoding the Ku complex by Msn2 and Msn4 . However , our genetic data strongly suggest that the ESR regulates aspects of TLS and NHEJ . Future research will determine whether other factors in these pathways are subject to ESR regulation and/or whether this regulation may be indirect or occur at the post-translational level . We observed that rev3Δ and yku80Δ exhibited identical phenotypes in these assays: both deletions were fully defective for canavanine-induced mutagenesis in the MSN strain but this defect was significantly rescued by msnΔ . Both Polζ and Ku have been associated with repair of DNA DSBs [30] , [49] , suggesting that DSBs may be an important intermediate in proteotoxic stress-induced mutagenesis . In yeast DSBs are predominantly repaired by one of two repair pathways: homologous recombination ( HR ) or by NHEJ [30] . HR , unlike NHEJ , uses an intact homologous sequence as template for repair and thus has been traditionally considered as the error-free DSB repair pathway . However , recent results indicate that DSB repair by HR is associated with increased mutagenesis around the DSB and that this mutagenesis is partially dependent on Polζ [50] , [51] . Interestingly , DSB repair-associated mutagenesis is characterized by a distinct mutation spectrum that includes an increase in deletions in mononucleotide runs [50] . Furthermore , Lehner et al . recently reported that defects in NHEJ can also result in mononucleotide run instability [52] . Thus , increased deletions in mononucleotide runs observed during canavanine-induced mutagenesis of CAN1 are consistent with DSB involvement in this mutagenic process . Interestingly , mutagenic repair of DNA DSBs also underlies stress-induced mutagenesis in E . coli [53] , [54] and may thus represent a universal mechanism of producing genetic change during environmentally unfavorable conditions . Our study implicates the ESR in regulating DNA repair pathways in response to proteotoxic stress in a model eukaryote and as such touches on several issues with important implications for human health . First , several lines of evidence have suggested that proteotoxic stress is an important driver of emergence of drug resistance in fungal pathogens [6] , [55] . Second , tumor microenvironments are characterized by a variety of stresses , such as nutrient deprivation and hypoxia , that activate the unfolded protein response in tumor cells [56] , raising the possibility that unfolded protein responses are implicated in genetic instability of cancer cells . Third , proteotoxic stresses have been recently implicated in the process of aging in worms [57] , although a connection between stress and increased genetic instability of aging cells has not yet been established . To develop therapeutic approaches against stress-induced genetic instability it is essential to identify cellular pathways that promote this process . In this study we have identified several factors that promote proteotoxic stress-induced mutagenesis , including Polζ , Ku , and Msn2-Msn4 . Further research into this phenomenon will reveal fundamental biological principles that underlie the roles of stress responses and DNA repair pathways in stress-induced mutagenesis , and thereby enhance the development of therapeutic approaches to combat emergence of drug resistance and genetic instability during carcinogenesis .
Strains were constructed and cultured using standard yeast methods . All strains ( Table S2 ) were derived from W303 ( leu2-3 , 112 trp1-1 ade2-1 his3-11 , 15 URA3 CAN1 RAD5 ) . The carrier strain for the reconstruction experiment ( Figure 1C ) contained two CAN1 copies: one at the endogenous locus and one at the rDNA locus . The rDNA::CAN1 gene was partially silenced but provided sufficient canavanine sensitivity to easily distinguish it from a fully canavanine-resistant strain that carried no wild type copies of CAN1 . The strains used to analyze Msn2 loclization during canavanine stress carried endogenously expressed MSN2-GFP . We performed the CAN1 mutation assay almost exactly as described in [25] with some minor differences: 100 µl cultures of CAN1 cells were grown at 23°C in synthetic complete medium containing 2% glucose and lacking arginine ( SC-arg ) in 96-well plates for 2 to 3 days ( usually to a final concentration of approximately 107 cells/culture ) , then spotted onto SC-arg plates containing 600 µg/mL canavanine and incubated at 23°C for 5 days . 23°C was used because originally some of the experiments included a temperature-sensitive ( t . s . ) mutant and to allow future comparisons to other t . s . mutants . Furthermore , incubating cells at 23°C allowed us to avoid temperature fluctuations and any potential accompanying transcriptional responses that may occur when cells are transferred from room temperature to the incubator . For analyses of pre-plating and post-plating can1 mutants , after 5 days of growth the plates were scanned and colony sizes were analyzed . To categorize can1 colonies as “large” or “small” , we used Image J software ( National Institutes of Health ) on scanned plate images . Image J assigned a numerical value to the area of each colony and we applied a uniform threshold to categorize them as “large” or “small” . Several different threshold values were tried and the results consistently indicated that larger colonies were more likely to have arisen in culture ( showed a better fit to Luria-Delbrück ) while smaller colonies were more likely to have arisen after plating ( showed a poorer fit to Luria-Delbrück and a better fit to Poisson ) . To find the best-fitting L-D distribution for a given set of data we used the MATLAB code of Lang and Murray [25] which is based on the maximum likelihood estimation method . The code was modified appropriately to find the best fitting Poisson distributions . To calculate mutation rates in culture , large colony data were analyzed using the FALCOR tool ( http://www . mitochondria . org/protocols/FALCOR . html ) to calculate mutation rates and 95% confidence intervals [37] . To calculate post-plating can1 mutant frequencies , for every culture , the number of small colonies was divided by the total number of cells in the culture and these ratios were then averaged over a single experiment ( 72–80 cultures ) . For each genotype , averages and standard deviations were calculated for two to three independent experiments . To measure survival on plates containing 600 µg/ml canavanine , at different times after plating cells were micromanipulated onto canavanine-free SC plates so that viable cells could form colonies . Three to nine biological replicates were examined for each genotype at each time point . To measure the amount of post-plating proliferation , at different times after plating agar plugs containing the entire 100 µl cultures were pulled from canavanine plates , the cells were resuspended in sterile water , sonicated , and counted using a Beckman Coulter Z2 Particle Counter . Three to six biological replicates were examined for each genotype at each time point . The FOAR mutation assay was performed similarly to that for CAN1 with a few differences . Cells were cultured in 200 µl or 250 µl of SC-arg medium +/− indicated drug concentrations at 23°C . Because the stresses retarded cell growth , the cultures were incubated for 5 to 6 days to reach 106 to 107 cells/well , at which point they were plated on 5-FOA plates . For canavanine-containing cultures , the wells that contained pre-existing can1 mutations or can1 mutations that occurred during the growth of the culture were easily identifiable because those cultures proliferated much faster and reached saturation within two or three days . Accordingly , such cultures were deemed not to be experiencing canavanine stress and were excluded from the analyses . To confirm that the cells in the slow-growing cultures had not accumulated can1 mutations , several of these cultures were spotted onto SC-arg+600 µg/ml canavanine plates and confirmed to have only a few can1 mutants per culture . 5-FOA-resistant colony distribution data were analyzed using the FALCOR tool ( http://www . mitochondria . org/protocols/FALCOR . html ) to calculate mutation rates and 95% confidence intervals [37] . The URA3 locus was amplified from FOAR colonies using primers URA3-F3 ( TGCCCAGTATTCTTAACCCAAC ) and URA3-R1 ( TGTTACTTGGTTCTGGCGAGG ) . Primer URA3-F3 was then used for sequencing by Macrogen USA . Analysis of the sequencing data revealed that in many cases the FOAR colonies did not carry mutations at the URA3 locus , suggesting that the FOA resistance was due to mutation of another gene ( FigureS4 ) . To verify that these colonies were indeed wild type for URA3 , we performed the following phenotypic and genetic tests . We streaked 20 FOAR colonies on SC-ura medium – four containing ura3 mutations ( as determined by DNA sequencing ) , and sixteen without detectable mutations at URA3 . Consistent with the sequencing results , the four ura3 mutants were unable to grow in the absence of uracil , while the sixteen URA3 colonies were uracil prototrophs . Also , we crossed four independent URA3 FOAR colonies to a ura3-1 strain and found each of the four FOAR mutations complemented ura3-1 for FOA resistance ( the diploids were FOAS ) and segregated independently from ura3-1 in the cross . Thus , we concluded that in many cases FOA resistance was not due to mutation of URA3 . We are currently investigating the identity of the non-ura3 FOAR mutations . We examined Msn2-GFP localization using a wide-field inverted microscope ( Deltavision; Applied Precision , LLC ) with a charge-coupled device camera ( CoolSNAP HQ; Roper Scientific ) , using a 100× oil-immersion objective , at 25°C and a FITC filter set to detect GFP fluorescence ( Chroma , Brattleboro , VT ) . The transmittance was set at 10% , and the exposure time for Msn2-GFP was 200 ms , except when analyzing low fluorescence conditions ( e . g . no GFP and the estradiol-inducible Msn2-GFP in the absence of estradiol ) when exposure time was increased to 250 ms . To analyze whether canavanine activated the ESR , cells were taken from canavanine plates at different times after plating , resuspended in water on microscope slides , and immediately analyzed by fluorescent microscopy . To subject cells to glucose starvation , cells from the SC-arg cultures were briefly centrifuged and resuspended in SC-arg medium without glucose , incubated for one hour , then analyzed by fluorescent microscopy . Four to six z-stacks of every field were taken and projected into one image using the average pixel intensity method . To measure the viability of cells in growing culture in the absence or presence of stress agents , the corresponding cultures were briefly sonicated , appropriately diluted in SC-arg medium , and plated onto YPD plates . Cell concentrations in the original cultures were obtained by using a Beckman Coulter Z2 Particle Counter . Yeast cells were grown to mid-log phase in SC-arg medium at 23°C , then canavanine was added to a concentration of 100 µg/ml or cells were collected by filtration and transferred to SC-arg medium lacking glucose . After 6 hours , approximately 6×107 cells were collected by filtration and snap-frozen at −80°C . Protein lysates were prepared form the cell pellets as described in [58] . Briefly , cells were lysed in 20% TCA using glass beads and the beads were washed twice in 5% TCA . The lysates were centrifuged , pellets resuspended in Laemmli buffer , and their pH neutralized by 2M unbuffered Tris . The resulting protein lysates were separated using 12% SDS-PAGE and probed using antibodies against Kar2 [40] and an anti-MYC antibody ( Clontech ) to detect Msh2-13xMyc . DNA sequencing of the CAN1 ORF was performed by Macrogen USA using primers CAN1-R1 ( TGAGAATGCGAAATGGCGTG ) and CAN1-R2 ( TTTTGATGGCTCTTGGAACG ) . Statistical analyses of mutational spectra were performed and all the Fisher Exact Test ( FET ) p-values calculated using R open software ( www . r-project . org ) . | Cellular capability to mutate its DNA plays an important role in evolution and impinges on medical issues , including acquisition of mutator phenotypes by cancer cells and emergence of drug-resistant pathogens . Whether and how the environment affects rates of mutation has been studied predominantly in the context of environmental agents that damage DNA ( e . g . UV and γ-rays ) . However , it has been observed that conditions of chronic non-DNA-damaging stress ( e . g . starvation or heat shock ) also increase mutagenesis . It has been shown that in bacteria , activation of the general stress response activates a pro-mutagenic pathway and thus promotes mutagenesis during periods of stress . However , in eukaryotes , so far there has been no evidence of a stress response regulating mutagenesis . In this manuscript we demonstrate that in budding yeast , a model eukaryote , the general environmental stress response ( ESR ) regulates mutagenesis induced by proteotoxic stress ( accumulation of unfolded proteins ) at several loci . We also identify two pro-mutagenic DNA metabolic pathways that contribute to this mutagenesis and present genetic data showing that the ESR regulates these pathways . Together , these data advance our understanding of how cellular sensing and responding to environmental cues affect cellular capability for mutagenesis . | [
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| 2013 | The Yeast Environmental Stress Response Regulates Mutagenesis Induced by Proteotoxic Stress |
Upon telomerase inactivation , telomeres gradually shorten with each cell division until cells enter replicative senescence . In Saccharomyces cerevisiae , the kinases Mec1/ATR and Tel1/ATM protect the genome during pre-senescence by preventing telomere-telomere fusions ( T-TFs ) and the subsequent genetic instability associated with fusion-bridge-breakage cycles . Here we report that T-TFs in mec1Δ tel1Δ cells can be suppressed by reducing the pool of available histones . This protection associates neither with changes in bulk telomere length nor with major changes in the structure of subtelomeric chromatin . We show that the absence of Mec1 and Tel1 strongly augments double-strand break ( DSB ) repair by non-homologous end joining ( NHEJ ) , which might contribute to the high frequency of T-TFs in mec1Δ tel1Δ cells . However , histone depletion does not prevent telomere fusions by inhibiting NHEJ , which is actually increased in histone-depleted cells . Rather , histone depletion protects telomeres from fusions by homologous recombination ( HR ) , even though HR is proficient in maintaining the proliferative state of pre-senescent mec1Δ tel1Δ cells . Therefore , HR during pre-senescence not only helps stalled replication forks but also prevents T-TFs by a mechanism that , in contrast to the previous one , is promoted by a reduction in the histone pool and can occur in the absence of Rad51 . Our results further suggest that the Mec1-dependent depletion of histones that occurs during pre-senescence in cells without telomerase ( tlc1Δ ) prevents T-TFs by favoring the processing of unprotected telomeres by Rad51-independent HR .
Telomeres are highly specialized nucleoprotein structures that hide the ends of chromosomes from double-strand break ( DSB ) repair and DNA damage checkpoint activities . In this way , telomeres protect the chromosome ends from fusions and degradations and from eliciting an erroneous DNA damage response . Accordingly , defects in telomere maintenance are linked to cancer and aging [1] . Telomere DNA consists of repeated DNA sequences that end in a 3′ single-stranded G-rich tail . To compensate for the natural shortening that telomeres undergo every cell cycle during DNA replication , many cells express a reverse transcriptase , the telomerase , which adds telomere repeats . However , telomerase expression is repressed in many tissues of multicellular organisms , leading to continuous telomere erosion that eventually activates replicative senescence [2] . Premature senescence entry can affect tissue homeostasis [3] and accordingly , cells are endowed with mechanisms to maintain the proliferative state of cells with short telomeres . As a major risk for genome integrity during this time is the instability of critically short telomeres , an essential task during pre-senescence is to protect these telomeres . In sharp contrast to dysfunctional telomeres caused by mutations in the telomeric shelterin proteins , eroded telomeres by physiological shortening are able to repress non-homologous end joining ( NHEJ ) , thus preventing telomere-telomere fusions ( T-TFs ) and the subsequent genetic instability associated with fusion-bridge-breakage cycles [4 , 5] . In yeast , telomeres consist of ~300 bp of TG1-3 repeats that are covered by 15–20 molecules of Rap1 ( repressor activator protein 1 ) and its partners Rif1 and Rif2 , and ~12–15 bases of a G tail that are covered by the Cdc13/Stn1/Ten1 complex . Similar to most organisms , yeast also contains two classes of subtelomeric elements: X elements , which are present at virtually all telomeres , and Y′-elements , which are present in zero to four tandem copies immediately internal to the telomere repeats [6] . In yeast cells that lack telomerase , telomeres progressively shorten with each cell cycle until cells enter replicative senescence with critically short but protected telomeres [7–10] . Mec1 and Tel1 ( yeast homologs of the tumor suppressor genes ATR and ATM , respectively ) are master checkpoint kinases with specific and redundant roles in many processes related to genome integrity , such as DSB signaling [11 , 12] . Specifically , Mec1 transduces the signal that activates senescence in cells lacking telomerase when telomeres reach a critical length [13 , 14] . Telomeres regulate the binding and activity of many DNA repair and checkpoint factors that are essential for telomere maintenance but prevent that these factors process the chromosome ends as DSBs . Thus , Mec1/ATR and Tel1/ATM binding to telomeres is regulated in order to facilitate their role in promoting the recruitment of the telomerase to short telomeres [15–19] without leading to an inadvertent activation of the DNA damage checkpoint signaling [20–22] . The telomerase recruitment function is carried out mainly by Tel1 in wild-type cells , although Mec1 can partially complement this function in the absence of Tel1 [17] . Consistently , telomeres are barely affected in mec1Δ cells , are very short but stable in tel1Δ cells , and only the lack of both Mec1 and Tel1 leads to short and unstable telomeres and to the activation of replicative senescence [7 , 8] . However , and in contrast to cells lacking telomerase , mec1Δ tel1Δ cells accumulate T-TFs , indicating that Mec1 and Tel1 have additional functions in protecting telomeres [9 , 23 , 24] . These functions seem to be conserved as inferred from the accumulation of telomere fusions observed in yeast , fly and mammalian cells lacking ATM and ATR [25–28] . Yeast and mammalian cells also share a reduction in the synthesis of histones during pre-senescence [29 , 30] . The mechanism of histone reduction has been elucidated in yeast cells lacking the telomerase RNA coding gene ( TLC1 ) [30] . In particular , it has been reported that telomere shortening is accompanied by a relocalization of Rap1 from eroded telomeres to the promoter of hundreds of new genes . A subset of these genes includes the core histone–encoding genes , which are repressed by Rap1 leading to a reduction in the pool of available histones and a loss of histones at Rap1-targeted promoters . Importantly , Rap1 relocalization and histone depletion require Mec1 [30] . In this study , we asked what defects in mec1Δ tel1Δ cells are due to their inability to reduce the level of histones as compared to tlc1Δ cells . We show that an induced reduction in the pool of available histones in mec1Δ tel1Δ does not affect the length of telomeres or the entry into senescence , but prevents T-TFs . This histone depletion–induced protection require a HR mechanism that , in contrast to the one that maintains the proliferative state of mec1Δ tel1Δ cells during pre-senescence , can occur in the absence of Rad51 . Likewise , cells lacking Tlc1 requires Rad51-independent HR to prevent telomere fusions , opening the possibility that the Mec1-dependent depletion of histones that occurs during pre-senescence protects telomeres from fusions by favoring the repair of unprotected ends by HR rather than NHEJ .
To address the genetic consequences of the inability to reduce the amount of histones during pre-senescence of cells lacking Mec1 and Tel1 , we induced a partial depletion of histones in mec1Δ tel1Δ cells using a previously reported genetic system , in which the sole source of histone H4 is under the control of the doxycycline-inducible tet promoter ( t::HHF2 ) [31] . For this , MEC1/mec1Δ TEL1/tel1Δ HHF1/hhf1Δ HHF2/hhf2Δ diploids transformed with the plasmid p413TARtetH4 were dissected on plates containing rich medium with 5 μg/mL doxycycline and the colonies were streaked on the same medium; a smear of cells from this streak was then restreaked after 3 days , and this step was repeated for several times ( each streak involved ~20 generations ) . Mec1 is essential to maintain the levels of dNTPs during replication and DNA damage , but can be eliminated without affecting viability in cells lacking Sml1 , a Mec1-regulated inhibitor of the dNTPs synthesis [32] . Since the intracellular pool of dNTPs has a direct impact on telomere biology [33–35] , all analyses were performed in sml1Δ strains except for tlc1Δ , which was compared with its isogenic wild-type strain . Wild-type and mec1Δ tel1Δ cells from streak 1 displayed similar levels of histone H4 , which dropped two-fold in mec1Δ tel1Δ t::HHF2 cells ( Fig 1A ) . However , histone levels in mec1Δ tel1Δ t::HHF2 cells were still higher than those displayed by cells lacking telomerase . This difference was confirmed by chromatin fractionation and western blot ( Fig 1B and S1A Fig ) . To follow senescence , we analyzed cell growth during several streaks from different spores of each genotype . As previously reported , cells lacking Mec1 and Tel1 senesced after 3 to 5 streaks but a small fraction of them survived senescence ( S1B and S1C Fig ) [7] . This loss of cell viability was less clear in some clones , likely due to the high cell-to-cell heterogeneity in the entry into senescence [36] . Moreover , viability was much lower in mec1Δ tel1Δ cells than in tlc1Δ cells ( S1C Fig ) , consistent with the major functions of Mec1 and Tel1 in genome integrity . Parallel cultures from mec1Δ tel1Δ t::HHF2 spores from the same diploids also displayed high growth clonal variation with a similar pattern of senescence ( S1B Fig ) and viability ( S1C Fig ) . However , a more detailed analysis showed that histone depletion slightly rescued the growth defects of mec1Δ tel1Δ pre-senescent cells ( S1D Fig ) , and this suppression was not associated with an increase in cell viability but with a slight reduction in the doubling time ( S1E and S1F Fig ) . To address whether histone depletion prevents T-TFs in cells lacking Mec1 and Tel1 , DNA from liquid cultures from streak 1 was isolated and amplified by semiquantitative PCR to specifically detect T-TFs between chromosomes V and XV as previously reported [9] . Histone depletion largely suppressed T-TFs in mec1Δ tel1Δ t::HHF2 pre-senescent cells ( Fig 1C ) . To calculate more precisely this effect , the frequency of T-TFs was determined by quantitative PCR ( Fig 1D ) . The frequency of telomere fusions between the right arm of chromosome V and the left arm of chromosome XV was 3 . 2 × 10−3/genome in mec1Δ tel1Δ cells . Histone depletion suppressed T-TFs in mec1Δ tel1Δ t::HHF2 an average of ~73 times . Finally , a detailed analysis in pre-senescent ( streak S1 ) , senescent ( streak S4 ) and surviving cells ( streaks S7 and S10 ) showed that T-TFs accumulated in mec1Δ tel1Δ during pre-senescence but disappeared in the surviving cells , and that histone depletion prevented T-TFs during the whole process ( Fig 1E ) . Indeed , T-TF accumulation in mec1Δ tel1Δ and protection by histone depletion was observed as early as in cultures inoculated directly from the spore ( ~25–30 generations ) ( S2A Fig ) . Many of the phenotypes associated with partial depletion of histone H4 can be mimicked in cac1Δ rtt106Δ replication-coupled chromatin assembly mutants: loss of chromatin integrity and negative supercoiling , replication fork instability , hyper-recombination and chromosome missegregation [37–41] . Thus , we analyzed T-TFs in mec1Δ tel1Δ cac1Δ rtt106Δ cells from streak S1 to test whether telomere protection in mec1Δ tel1Δ t::HHF2 was due to global defects in chromatin assembly . Notably , the absence of Cac1 and Rtt106 did not reduce the amount of T-TFs induced by mec1Δ tel1Δ ( Fig 1D and 1F ) , suggesting that the effects of histone depletion in preventing T-TFs are not due to global defects in replication-coupled chromatin assembly . In Drosophila melanogaster , the accumulation of T-TFs in the absence of ATR and ATM can be suppressed by depleting the histone variant H2A . Z , which restores the loading of the HOAP capping protein [42] . Given that histone depletion is likely to reduce the amount of H2A . Z ( Htz1 in yeast ) -containing nucleosomes , we addressed the effect of the lack of Htz1 in the formation of T-TFs in mec1Δ tel1Δ cells . The absence of Htz1 did not prevent the formation of T-TFs in mec1Δ tel1Δ htz1Δ ( Fig 1D and S2B Fig ) . Interestingly , both mec1Δ tel1Δ cac1Δ rtt106Δ and mec1Δ tel1Δ htz1Δ cells senesced much earlier ( streaks S2-S3 ) than mec1Δ tel1Δ cells , and surviving cells took longer to appear ( S2C and S2D Fig ) , suggesting that Cac1 and Rtt106 , as well as Htz1 , are required to delay the entry into senescence . T-TFs in mec1Δ tel1Δ cells can be suppressed if telomeres are artificially elongated by expressing a Cdc13-Est2 fusion protein [9] . This might explain the disappearance of T-TFs in post-senescent cells ( Fig 1E ) , in which survival is associated with telomere lengthening [7] . Thus , we asked whether histone depletion increases telomere length in mec1Δ tel1Δ cells . For a direct comparison , telomere length was analyzed with the same DNA samples used for T-TF analyses . Total DNA was digested with XhoI , run into a gel and hybridized to a Y′-specific probe . This assay generates a broad band ( ~1 . 25 kb in wild-type cells ) encompassing the telomere fragments from Y′-containing telomeres , and two upper bands ( ~6 . 7 and ~5 . 2 kb ) that represent the two sizes of tandemly arranged Y′ subtelomeric repeats ( Fig 2A ) [7 , 43 , 44] . Telomere length in t::HHF2 cells was not apparently affected ( Fig 2B , top , and S3A Fig ) , although the analysis of individual chromosomes showed shorter telomeres than those in wild-type cells in some cases ( e . g . , Fig 3A ) . As reported , tel1Δ but not mec1Δ cells displayed short telomeres , whereas mec1Δ tel1Δ cells rapidly shortened their telomeres; the mec1Δ tel1Δ mutant displayed short telomeres as early as ~25–30 generations after dissection of heterozygous diploids ( S3B Fig ) [7] , which showed wild-type telomeres ( S3B Fig ) and did not accumulate T-TFs ( S2A Fig ) . Telomere length analysis during pre-senescence , senescence and post-senescence showed a subpopulation of cells in some clones with longer Y′-containing telomeres , which were reduced and disappeared as cells entered into senescence; we do not have an explanation for these events , although they do not seem to be related to the accumulation of T-TFs as they appeared both in mec1Δ tel1Δ and mec1Δ tel1Δ t::HHF2 cells ( see asterisks in Fig 2B , and S3A and S3B Fig ) . Indeed , this subpopulation was also detected in tel1Δ ( S3B Fig ) . Importantly , histone depletion did not affect bulk telomere length in mec1Δ tel1Δ t::HHF2 cells as compared to mec1Δ tel1Δ cells , which were similar from S1 to S10 ( Fig 2B , top gel , and S3A and S3B Fig ) . A similar result was obtained using a TG1–3 telomere-specific probe , which also detected telomeres that only contain X subtelomeric elements ( Fig 2A and 2B , bottom gel , and S3C Fig ) . These results suggest that telomere protection against fusions by histone depletion in mec1Δ tel1Δ t::HHF2 cells is not due to bulk telomere elongation . The telomere-specific probe also showed that the amount of X-only telomeres is reduced in mec1Δ tel1Δ cells ( see arrows in Fig 2B , bottom gel , and S3C Fig ) . This is a characteristic of type I survivors , which extend telomeres by Y′-element acquisition through Rad51-dependent HR mechanisms [45] , although Rad51-independent , Rad59-dependent Y′-acquisition events can also be detected during pre-senescence in cells lacking telomerase [44] . Accordingly , surviving mec1Δ tel1Δ cells amplified the Y′ elements ( 5 . 2 and 6 . 7 kb bands ) , whereas telomere length remained as short as in S1 ( Fig 2B , top gel ) [7] . The acquisition of Y′ subtelomeric elements might explain the reduction in T-TFs observed in mec1Δ tel1Δ survivors ( Fig 1E ) because the T-TF assay is based on a X-only telomere . Next , we wondered whether the accumulation of T-TFs in mec1Δ tel1Δ cells , and its suppression by histone depletion are associated with specific changes in the structure of the subtelomeric chromatin . To address this , we analyzed nucleosome positioning at the subtelomeric X element of the left telomere of chromosome III ( TEL03L ) by indirect–end labeling of MNaseI–treated cells from streak 2–derived cultures ( Fig 3A ) . We also analyzed the chromatin structure of the subtelomeric Y′ elements because X elements are characterized by low nucleosome density [46] . For this , and due to the lack of specific DNA sequences to follow nucleosome positioning at a single Y′ element , global chromatin accessibility was followed with a probe that hybridizes with the long Y′ subtelomeric repeats ( Fig 3B ) . Signal profiles from lanes displaying similar MNase digestion were plotted to compare the patterns of MNaseI accessibility . The absence of Mec1 and Tel1 did not lead to major changes in the structure of either the subtelomeric elements or the Ty5-1 proximal transposon ( Fig 3A and 3B; compare mec1Δ tel1Δ with the wild type ) . Remarkably , chromatin structure was also basically unaltered in tlc1Δ cells as compared to the wild type ( Fig 3A and 3B; only a subtle modification in the Y′ element that was shared by all mutants , and therefore was not associated with T-TFs ) , despite having reduced histone levels [30] . In line with this result , histone loss seems to be specific for Rap1-targeted promoters in tlc1Δ cells [30] , suggesting that chromatin assembly is properly regulated under conditions of programmed histone depletion during pre-senescence . In contrast to mec1Δ tel1Δ and tlc1Δ cells , histone depletion caused major changes in the chromatin structure of the Ty5-1 transposon ( gain or loss of DNA accessibility sites; marked with arrows ) and subtle changes in the Y′ and X elements ( high background signal and small changes in DNA accessibility sites; marked with asterisks ) in both t::HHF2 and mec1Δ tel1Δ t::HHF2 cells ( Fig 3A and 3B; compare t::HHF2 and mec1Δ tel1Δ t::HHF2 with the wild type ) . Finally , we analyzed the same regions in cac1Δ rtt106Δ and mec1Δ tel1Δ cac1Δ rtt106Δ cells . These mutants displayed similar chromatin changes at the Ty5-1 transposon as t::HHF2 and mec1Δ tel1Δ t::HHF2 cells ( Fig 3A; compare the changes marked with arrows in cac1Δ rtt106Δ and mec1Δ tel1Δ cac1Δ rtt106Δ with those in t::HHF2 and mec1Δ tel1Δ t::HHF2 ) , as expected for mutants affected in replication-coupled chromatin assembly . However , subtelomeric Y′ chromatin was much less affected in cac1Δ rtt106Δ cells than in t::HHF2 cells ( Fig 3B; compare the Y′ element MNaseI profiles in t::HHF2 and mec1Δ tel1Δ t::HHF2 with those in cac1Δ rtt106Δ and mec1Δ tel1Δ cac1Δ rtt106Δ ) . Therefore , subtelomeric chromatin changes seem to occur specifically in response to induced histone depletion . T-TFs in mec1Δ tel1Δ cells are NHEJ events [9] . To explore the possibility that histone depletion impairs NHEJ , we used an in vivo plasmid-recircularization assay in which the repair of an induced DSB can only occur by NHEJ [47] . For this , S2 pre-senescent cells were transformed with a plasmid linearized at a region with no homology in the yeast genome . Accordingly , cells lacking the NHEJ protein Ku70 , but not the recombination protein Rad52 were defective in plasmid recircularization ( Fig 4A ) . Interestingly , whereas the absence of telomerase activity in tlc1Δ cells did not affect NHEJ efficiency as compared to wild-type cells , the lack of Mec1 and Tel1 –but not of Mec1 or Tel1 –increased the efficiency of NHEJ repair ~7-fold ( Fig 4A ) , which might explain the high levels of T-TFs in mec1Δ tel1Δ cells . Remarkably , histone depletion also increased NHEJ efficiency in t::HHF2 ( ~9-fold ) , suggesting that not only the absence of Mec1 and Tel1 but also histone depletion inhibit DNA resection , thus increasing NHEJ frequency . The triple mutant mec1Δ tel1Δ t::HHF2 led to an additive increase ( ~20-fold ) ( Fig 4A ) , suggesting that mec1Δ tel1Δ and histone-depleted cells affect DNA resection by different mechanisms . Importantly , the fact that mec1Δ tel1Δ t::HHF2 cells , which have protected telomeres , displayed even higher NHEJ levels than mec1Δ tel1Δ cells rules out the possibility that histone depletion prevents T-TFs in mec1Δ tel1Δ t::HHF2 cells by impairing NHEJ . A putative protection mechanism against T-TFs might be HR , which competes with NHEJ for DSB processing [48] . In yeast , HR is required to delay senescence early after telomerase inactivation , likely through template switching mechanisms that seem to facilitate the restart of stalled replication forks with the sister chromatid [43–45 , 49–55] . Thus , a regulated activity of HR at eroded telomeres might be a protective mechanism against telomere fusions . To address this , we first tested the effect of HR on the accumulation of T-TFs in mec1Δ tel1Δ by deleting Rad52 , which is essential for all types of recombination events in yeast [56] . We detected a significant increase ( ~5-fold ) in T-TFs in mec1Δ tel1Δ rad52Δ cells as compared to mec1Δ tel1Δ cells ( Fig 1D and S4A Fig ) . We then dissected diploids heterozygous for mec1Δ , tel1Δ , hhf1Δ , hhf2Δ and rad52Δ , but we failed to obtain mec1Δ tel1Δ t::HHF2 rad52Δ cells , likely due to a combination of mutations affecting genome integrity . To overcome this problem , diploids containing HA-RAD52 under control of the GAL1 promoter were dissected in galactose-containing plates and streaked in glucose-containing plates . Although this tagged form of Rad52 was hardly functional ( S4B Fig ) , it allowed us to obtain some mec1Δ tel1Δ t::HHF2 GAL1::HA-RAD52 spores that , similar to mec1Δ tel1Δ GAL1::HA-RAD52 cells , senesced in the first streak ( Fig 4B ) , in line with HR playing a major role in the replication of telomeres during pre-senescence [55] . This residual activity of HA-Rad52 might explain why the frequency of T-TFs was lower in mec1Δ tel1Δ GAL1::HA-RAD52 cells than in mec1Δ tel1Δ rad52Δ cells ( Fig 1D ) . Importantly , the amount of T-TFs in mec1Δ tel1Δ cells was not reduced by inducing histone depletion in the absence of Rad52 activity ( Figs 1D and 4C ) , indicating that suppression of T-TFs in mec1Δ tel1Δ t::HHF2 cells completely depends on HR . Again , T-TF accumulation in the absence of Rad52 was not associated with changes in bulk telomere length , which was similar in S1 pre-senescent mec1Δ tel1Δ and mec1Δ tel1Δ t::HHF2 cells regardless of the presence or absence of Rad52 ( S4C Fig ) . Therefore , HR is necessary during pre-senescence not only to help stalled replication forks but also to prevent T-TFs by a mechanism that further requires a reduction in the pool of available histones . In order to gain a deeper insight into the mechanism of HR that protects telomeres from fusions in mec1Δ tel1Δ t::HHF2 cells , we analyzed the role of the strand exchange protein Rad51 . As for rad52Δ strains , we failed to obtain mec1Δ tel1Δ t::HHF2 rad51Δ spores and thus decided to dissect diploids heterozygous for RAD51/ GAL1::RAD51; in this case , some mec1Δ tel1Δ t::HHF2 GAL1::RAD51 spores germinated even in glucose-containing medium . The absence of Rad51 accelerated the entry into senescence of mec1Δ tel1Δ cells ( Fig 4D ) [53 , 57] , although the effect was less pronounced than the one observed in the absence of Rad52 ( compare Fig 4B and 4D ) , as previously reported for telomerase defective cells [53] . In addition , the absence of Rad51 prevented the appearance of mec1Δ tel1Δ survivors after streak S2 ( Fig 4D ) , as expected for type I survivors [45] . Importantly , the absence of Rad51 did not increase the levels of T-TFs in mec1Δ tel1Δ t::HHF2 cells ( Figs 1D and 4E ) , indicating that Rad51 is dispensable for protecting telomeres by histone depletion . Our results suggest that the inability of mec1Δ tel1Δ cells to induce histone depletion during pre-senescence leads to the formation of telomere fusions . This raises the possibility that the Mec1-mediated histone depletion that occurs in cells lacking telomerase during pre-senescence prevents T-TFs . Indeed , the absence of Mec1 increases the frequency of T-TFs in tlc1Δ cells [9] . To investigate this possibility , tlc1Δ cells were transformed with a multicopy plasmid expressing the four core histones . However , this genetic strategy hardly increased the levels of histones and did not lead to T-TFs in tlc1Δ background ( S5 Fig ) . This is not unexpected , considering the number of mechanisms that prevent histone overexpression [58] . Since the mechanism by which histone depletion prevents T-TFs in mec1Δ tel1Δ t::HHF2 cells depends on HR , we asked if Rad52 is required to prevent T-TFs in tlc1Δ cells . The absence of Rad52 shortened dramatically the pre-senescent state of tlc1Δ ( Fig 5A ) [57] , as observed for mec1Δ tel1Δ cells ( Fig 4B ) . We thus analyzed T-TFs from the streak S1 biomass and found that the absence of Rad52 in tlc1Δ rad52Δ cells increased ~10-fold the frequency of T-TFs as compared to tlc1Δ cells ( Fig 5B and 5C ) . This indicates that HR also prevents T-TFs in the absence of telomerase activity . However , this increase in T-TFs was variable and in most cases small as compared to that observed by the lack of Rad52 activity in mec1Δ tel1Δ t::HHF2 GAL1::HA-RAD52 cells ( Fig 1D; compare Figs 4C and 5B ) , suggesting that additional HR-independent mechanisms that protect the telomeres in tlc1Δ cells are lost in mec1Δ tel1Δ cells . To further compare the protective role of HR in tlc1Δ and mec1Δ tel1Δ cells , we analyzed the effects of rad51Δ . As reported , Rad51 was also essential to maintain the pre-senescence state of cells lacking telomerase ( Fig 5D ) [57] . Moreover , most tlc1Δ rad51Δ clones did no accumulate T-TFs ( Fig 5C and 5E ) , except for 2 out of 16 strains in which the absence of Rad51 led to an accumulation of telomere fusions ( 2 × 10−4/genome ) . Indeed , we cannot discard that these events also occurred in a low number of mec1Δ tel1Δ t::HHF2 clones lacking Rad51 activity , as only three clones could be analyzed ( Fig 4E ) . To address whether the variability in T-TFs was associated with telomere length , DNA samples from the streak S1 biomass were used to analyze T-TFs and telomere length ( S6A and S6B Fig ) . In general , telomeres were slightly longer in tlc1Δ rad52Δ and tlc1Δ rad51Δ cells than in tlc1Δ cells . However , there was no apparent correlation between bulk telomere length and T-TFs in tlc1Δ cells lacking HR activity .
One of the means by which ATR/Mec1 and ATM/Tel1 preserve genome integrity is preventing T-TFs . Despite the importance of understanding how genetic instability accumulates in the absence of these tumor suppressor genes , the mechanisms by which they carry out this protective role remain unknown . We show that T-TFs in mec1Δ tel1Δ cells can be suppressed by inducing a partial reduction in the pool of available histones . This suppression , together with the fact that Mec1 is required for histone depletion in pre-senescent tlc1Δ cells [30] , which have protected telomeres [9] , suggest that T-TFs accumulate in mec1Δ tel1Δ cells due in part to their inability to induce histone depletion . We show that the absence of Mec1 and Tel1 strongly augments DSB repair by NHEJ , which might also contribute to the high frequency of T-TFs in mec1Δ tel1Δ cells . However , histone depletion does not prevent telomere fusions by inhibiting NHEJ . Rather , histone depletion prevents telomere fusions by facilitating the recombinational processing of unprotected telomeres through a Rad51-independent mechanism . This recombination mechanism is different from the main mechanisms that facilitate the elongation of critically short telomeres during pre-senescence or that amplify Y′ subtelomeric elements in surviving cells , which do not require reduced levels of histones and are highly dependent on Rad51 . Mec1 and Tel1 regulate DNA resection [59] . Using a plasmid recircularization assay we observed that the absence of Mec1 and Tel1 –but not of Mec1 or Tel1 –strongly increases DSB repair by NHEJ , suggesting that they have essential and redundant functions in DNA resection that are revealed only after eliminating both factors . According to this result , T-TF accumulation in mec1Δ tel1Δ cells might result as a consequence of defective DNA resection at telomeres , which would shift the balance between NHEJ and HR toward NHEJ . In addition , this result raises the possibility that histone depletion prevents telomere fusions by inhibiting NHEJ . However , histone depletion also increases NHEJ , not only in t::HHF2 ( ~8-fold ) cells but also in mec1Δ tel1Δ t::HHF2 cells ( ~20-fold ) , making it unlikely that histone depletion prevents T-TFs by directly inhibiting NHEJ . Instead , our results show that histone depletion prevents T-TFs in pre-senescent mec1Δ tel1Δ t::HHF2 cells by a Rad51-independent mechanism of HR . This is in apparent contradiction with the observed increase in NHEJ in histone-depleted cells . However , histone-depleted cells are proficient in HR [31 , 39] , suggesting that histone depletion facilitates HR under conditions of impaired DNA resection . Histone depletion in yeast impairs the stability of advancing replication forks , leading to fork breakage and rescue by a Rad51-independent HR mechanism [39] . This phenotype is shared with the replication coupled–chromatin assembly asf1Δ and cac1Δ rtt106Δ mutants [37 , 40 , 60] . Therefore , histone depletion in mec1Δ tel1Δ t::HHF2 might increase the amount of recombinogenic lesions at telomeres , leading to a local high concentration of recombination factors that would compete with the NHEJ machinery thus preventing T-TFs . We have discarded this possibility by showing that cac1Δ rtt106Δ did not prevent the accumulation of T-TFs in mec1Δ tel1Δ background . Further , this possibility assumes that HR is limiting at telomeres in the absence of Mec1 and Tel1 , which seems not to be the case for two reasons: first , HR is proficient in maintaining the pre-senescence state in mec1Δ tel1Δ cells ( Fig 4B ) ; and second , post-senescence mec1Δ tel1Δ surviving cells require HR ( Fig 4B and 4D ) [9] . Therefore , HR is efficient at telomeres in the absence of Mec1 and Tel1 , and histone depletion hardly affects this efficiency , as mec1Δ tel1Δ and mec1Δ tel1Δ t::HHF2 cells display similar profiles of senescence entry and survivor formation . We observed only a slight increase in the doubling time of pre-senescent cells that might be associated with the suppression of telomere fusions . HR maintains the proliferative pre-senescent state of cells lacking telomerase by facilitating the recombinational restart of stalled replication forks at telomeres [55] . This is achieved through different recombination mechanisms . The accumulation of the non-coding RNA TERRA as R-loops at short telomeres has been shown to promote the HR-dependent restart of stalled replication forks [61] . Replication fork restart at telomeres can occur both by break-induced replication ( BIR ) , which deals with one-ended DSBs [36 , 44 , 54] , and sister-chromatid recombination ( SCR ) [62] . These mechanisms are highly dependent on Rad51 , and accordingly the lack of Rad51 accelerates the entry into senescence of cells lacking telomerase [57] . Rad51-independent , Rad59-dependent BIR events can also be detected , but its relevance in maintaining the proliferative pre-senescent state of cells lacking telomerase is reduced in comparison with Rad51-dependent events as inferred from the slight effect on senescence entry induced by the absence of Rad59 [44] . The fact that HR is able to maintain the proliferative pre-senescent state but not to prevent T-TFs in mec1Δ tel1Δ cells suggests that the recombination events that protect telomeres from fusions differ from those that allow telomere DNA replication before senescence . We think that HR acts upon different substrates in these two processes: stalled replication forks , to help replication at telomeres during pre-senescence , and DSBs , to compete with NHEJ and prevent T-TFs ( Fig 6 ) . They differ in that the latter further requires Mec1 and histone depletion and can operate in the absence of Rad51 . Although less efficiently , BIR can operate in the absence of Rad51 [63] . Therefore , we suggest that histone depletion promotes the processing of unprotected telomeres by BIR . Actually , although telomere protection was not associated with changes in bulk telomere length , we cannot discard that fusions affect a subpopulation of critically short telomeres , and that HR promotes their protection by BIR-induced lengthening . How histone depletion facilitates the recombinational processing of unprotected telomeres is currently unknown . One possibility is that the more accessible chromatin structure of the unprotected telomeres in mec1Δ tel1Δ t::HHF2 cells favors its processing by HR , thus reducing the potential substrate for NHEJ and consequently , the frequency of T-TFs . A more accessible chromatin might facilitate recombination-limiting steps . In line with this , the HR proteins Rad51 and Rad52 , but not the NHEJ protein Ku80 , bind with higher efficiency to DSBs under conditions of reduced levels of histones in yeast [64] , and histone depletion by knock down of human SLBP shifts the balance between NHEJ and HR during DSB repair toward HR [65] . In addition , chromatin disruption by either histone depletion or mutations in Spt6 and Spt4 stimulates Rad51-independent HR by BIR [39 , 66] . Finally , defective chromatin assembly in mec1Δ tel1Δ cac1Δ rtt106Δ cells hardly affects the subtelomeric chromatin as compared to histone depletion in mec1Δ tel1Δ t::HHF2 cells , and it does not protect telomeres . However , programmed histone depletion in pre-senescent cells is not associated with changes in the subtelomeric chromatin , arguing against the idea that histone depletion facilitates the recombinational processing of telomeres by making subtelomeric chromatin more accessible to the recombination machinery . Indeed , most T-TFs in mec1Δ tel1Δ involve the joining of the telomere repetitive tracts [9] . Alternatively , histone depletion might affect telomere anchoring to the nuclear envelope and/or the folding back of the telomere as a first step to facilitate the access of the recombination machinery , as both processes have been shown to repress recombination at telomeres [67 , 68] . Further studies will be required to elucidate how histone depletion promotes the recombinational processing of unprotected telomeres , which should include the genes regulated by Rap1 relocalization and histone depletion in response to telomere shortening in tlc1Δ cells during pre-senescence [30] . Nevertheless , it must be stressed that the fact that induced histone depletion prevents T-TFs in mec1Δ tel1Δ cells does not necessarily mean that this is the mechanism that operates in tlc1Δ cells , although the observation that Rad51-independent HR is also required for telomere protection against fusions in tlc1Δ cells supports this possibility . Furthermore , the lack of Rad52 leads to higher levels of T-TFs in mec1Δ tel1Δ t::HHF2 cells than in tlc1Δ cells . Again , this observation might reflect not different protection mechanisms but a higher frequency of telomere breakage in mec1Δ tel1Δ than in tlc1Δ cells , with the subsequent accumulation of DSBs to be processed by NHEJ . Accordingly , Mec1 is required for replication fork stability [69] , specifically in hard-to-replicate sites [70] . Alternatively , this difference could be associated with the accumulation of NHEJ observed in mec1Δ tel1Δ t::HHF2 cells as compared to tlc1Δ cells . In response to telomere shortening human fibroblasts reduce the levels of histones and chromatin assembly factors , which in turn disrupts chromatin integrity to reinforce the activation of the ATM and ATR pathways that accompany the senescence process [29] . Likewise , histone depletion is a hallmark of replicative senescence and aging in yeast [30 , 71] , suggesting that it has been evolutionarily conserved in these processes [72] . Induced histone depletion in yeast impairs chromatin integrity , DNA replication , chromosome segregation and DNA topology , leading to genetic instability and checkpoint activation [38 , 39 , 73] . Hence , we suggest that histone depletion has a dual role in genome integrity during pre-senescence in yeast . Histone depletion leads to DNA damage helping to activate senescence . On the other hand , histone depletion prevents the deleterious consequences of telomere fusions and subsequent fusion-bridge-breakage cycles during the temporal window in which telomeres are unprotected .
Yeast strains used to generate the spores analyzed in this study are listed in S1 Table . Deletion mutants were constructed by a PCR-based strategy [74] . All analyses were performed with haploid strains derived from diploids heterozygous for mec1Δ and tel1Δ and grown at 30°C in 2% glucose-rich medium ( YPAD ) –unless 2% galactose-rich medium is indicated–and containing 5 μg/ml doxycycline ( except for the tlc1Δ strain and its isogenic wild-type strain ) . Plasmid p413TARtetH4 is a centromeric plasmid in which the expression of HHF2 is under control of the doxycycline-inducible tet promoter [31] . pRS426 is a URA3-based multicopy plasmid [75] . p314N795 is a TRP1-based centromeric plasmid for rat glucocorticoid receptor ( rGR ) [76] . p426-H342A2B is a multicopy plasmid expressing the four core histones . For its construction the HHT1-HHF1 and HTA1-HTB1 genomic loci were PCR amplified , cut with XhoI/EcoRI or EcoRI/NotI , respectively , and inserted into the XhoI-NotI site of pRS426 by a triple ligation . Oligonucleotides for PCR amplification are listed in S2 Table . The repair of an induced DSB by NHEJ was performed as described [47] . Cells were transformed with 600 ng of plasmid p314N795 that had either been linearized at the rGR ORF with NcoI or was uncut , and the efficiency of plasmid recircularization was determined as the number of transformants obtained with the linear plasmid relative to that obtained with the uncut plasmid . Telomere-telomere fusions ( T-TFs ) were analyzed by semiquantitative and quantitative PCR analyses as reported [9] . Briefly , ~100 ng of Sau3A-treated genomic DNA extracted by standard protocols from asynchronous cultures was PCR-amplified for semiquantitative analyses using a primer from the X element of chromosome XV-L and a primer from the Y′ element of chromosome V-R ( coordinates 183–207 and 576759–576783 , respectively; Stanford Genome Database ) . A DNA fragment from HIS4 was PCR-amplified as input control . T-TFs and HIS4 were PCR-amplified using 35 or 20 cycles , respectively , under the conditions previously reported . Oligonucleotides for PCR amplification are listed in S2 Table . Quantitative analyses were performed by real-time PCR by using the same amount of Sau3A-treated genomic DNA , the oligonucleotides described above for semiquantitative analyses and the PCR conditions described previously [9] . The frequency of T-TFs per genome was calculated with the formula: T-TFs/genome = 2–N / N = Ct ( T-TFs ) –Ct ( HIS4 ) . Prior to applying this formula , the curves representing the increasing amounts of DNA for the two products as a function of the number of PCR cycles were confirmed to be parallel ( i . e . , the slope of the curve representing the log of the input amount versus ΔCt was < 0 . 1 ) . Total DNA from asynchronous cultures was extracted by standard protocols . DNA samples were digested with XhoI and run in 1 . 2% TBE 1× agarose gels for 15 hours at 2 V/cm . Gels were blotted onto Hybond-XL membranes and hybridized either at 65°C with a 32P-labeled PCR fragment containing 600 bp from XhoI to the centromere-distal end of the subtelomeric Y’ element ( probe Y′ ) or at 37°C with a TG1–3 oligo labeled at the 5′ terminus with ATP ( γ-32P ) and T4 polynucleotide kinase ( TG1–3 probe ) . Oligonucleotides are listed in S2 Table . All signals were quantified in a Fuji FLA5100 with the ImageGauge analysis program . Nucleosome positioning at TEL03L was determined by micrococcal nuclease ( MNaseI ) digestion and indirect end-labelling [31] . MNaseI–treated DNA was digested with BamHI , resolved in a 1 . 5% agarose gel , blotted onto a HybondTM-XL membrane and probed with a 230-bp 32P-labeled PCR fragment located at 60 bp from the BamHI site that reveals nucleosome positioning from this restriction site to TEL03L . Chromatin structure of the long subtelomeric Y′ elements was determined by the same method , except that MNaseI–treated DNA was digested with ClaI , and probed with a 517-bp 32P-labeled fragment located at 412 bp from the centromere-proximal ClaI site that reveals bulk chromatin accessibility ( mono- , di- , tri- … nucleosomes ) at and around the probed region . Oligonucleotides for PCR amplification are listed in S2 Table . MNaseI profiles were generated with the ImageGauge analysis program . Yeast protein extracts were prepared using the TCA protocol as described [31] and run on a 15% sodium dodecyl sulfate-polyacrylamide gel . Histone H4 and Pgk1 were detected with the rabbit polyclonal ab10158 ( Abcam ) and the mouse polyclonal 22C5D8 ( Invitrogen ) primary antibodies , respectively , and either fluorophore-conjugate or peroxidase-conjugate secondary antibodies . Total protein was determined by using the TFX Stain-Free FastCast Acrylamide kit ( Biorad ) [77] . Bands were visualized and quantified using either the Odyssey infrared Imaging System ( Licor ) or the ChemiDoc MP image system ( Biorad ) . Chromatin fractionation was performed as described for young yeast cells [71] with some modifications . Samples ( 30 ml ) from mid-log phase cultures were collected by centrifugation , washed with cold 0 . 1mM Tris pH 9 . 4 , 10mM DTT , and incubated for 15 min in 1 ml of the same buffer on ice . Cells were then washed with cold spheroplasting buffer ( 20mM Hepes pH 7 . 4 , 1 . 2mM sorbitol , Roche Complete EDTA free protease inhibitor cocktail ) and incubated with 1 ml of the same buffer with 210 μg zymoliase 20T for 1 h at 30°C . The spheroplasts were collected , washed twice with cold washing buffer ( 20mM Tris pH 7 . 4 , 20mM KCl , 1M sorbitol , 0 . 1μM spermine , 0 . 25μM spermidine , protease inhibitors ) , and resuspended in 1 ml lysis buffer ( 20mM Tris pH 7 . 4 , 20mM KCl , 1M sorbitol , 0 . 1μM spermine , 0 . 25μM spermidine , 1% Triton X-100 , protease inhibitors ) for 5 min on ice . An aliquot ( 80 μl ) was removed for the total sample , and the remaining sample was centrifuged for 15 min at 13000 g at 4°C to separate soluble ( supernatant ) and chromatin-enriched ( pellet ) fractions . Each pellet was washed with 0 . 5 ml cold lysis buffer and resuspended in 80 μl of water , and chromatin , soluble and total samples were mixed with SDS buffer for western blot analyses . Similar volumes were loaded for each sample , and similar cell equivalents of the chromatin and soluble fractions were loaded for the fractionation controls . Statistical analyses were performed using the Prism software ( Graphpad ) . Numerical data that underlies graphs and statistical analyses are provided in S3 Table . | Telomere shortening upon telomerase inactivation leads to an irreversible cell division arrest known as replicative senescence , which is considered as a tumor suppressor mechanism . Since pre-senescence is critical for tissue homeostasis , cells are endowed with recombination mechanisms that facilitate the replication of short telomeres and prevent premature entry into senescence . Consequently , pre-senescent cells divide with critically short telomeres , which have lost most of their shelterin proteins . The tumor suppressor genes ATR and ATM , as well as their yeast homologs Mec1 and Tel1 , prevent telomere fusions during pre-senescence by unknown mechanisms . Here we show that the absence of Mec1 and Tel1 strongly augments DSB repair by non-homologous end joining , which might explain the high rate of telomere fusions in mec1Δ tel1Δ cells . Moreover , we show that a reduction in the pool of available histones prevents telomere fusions in mec1Δ tel1Δ cells by stimulating Rad51-independent homologous recombination . Our results suggest that the Mec1-dependent process of histone depletion that accompanies pre-senescence in cells lacking telomerase activity is required to prevent telomere fusions by promoting the processing of unprotected telomeres by recombination instead of non-homologous end joining . | [
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| 2018 | Histone depletion prevents telomere fusions in pre-senescent cells |
A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity . While selection is certainly responsible for the spread and maintenance of complex phenotypes , this does not automatically imply that strong selection enhances the chance for the emergence of novel traits , that is , the origination of complexity . Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases , selection weakens and genetic drift grows in importance . Because of this relationship , many theories invoke a role for population size in the evolution of complexity . Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations . Here , we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity . We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes , which provides the opportunity for subsequent increases in phenotypic complexity . However , small and large populations followed different evolutionary paths towards these novel traits . Small populations evolved larger genomes by fixing slightly deleterious insertions , while large populations fixed rare beneficial insertions that increased genome size . These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations .
The relative importance of adaptive ( i . e . , selection ) versus non-adaptive ( i . e . , drift ) mechanisms in shaping the evolution of complexity is still a matter of contention among evolutionary biologists [1–6] . In molecular evolution , the role of non-adaptive evolutionary processes such as genetic drift and genetic draft are well-established [7–9] . Theoretical population-genetic principles argue that neutral evolution , not natural selection , drove the evolution of large , primarily non-functional , genomes [10–12] . Meanwhile , there exists abundant experimental evidence that natural selection is the main cause of evolutionary change [13–15] , including the spread of novel adaptive phenotypes [16 , 17] in experimental populations . However , it is still possible that non-adaptive processes play a significant role in the evolution of complexity . For instance , genetic drift ( or relaxed selection ) may allow for the accumulation of mutations that can later lead to the evolution of novel complexity [4 , 18] . Much of the work demonstrating the role of selection in driving the evolution of novel complex traits is based on experiments with large populations and strong selection [19] . In much smaller populations ( i . e . , those with fewer than 104 individuals ) , selection is weaker , and genetic drift begins to alter evolutionary dynamics [15 , 20] . Therefore , to explain the role of adaptive vs . non-adaptive process in the evolution of complexity , one must explore the role of population size in the evolution of complexity . Both theoretical modeling and experiments suggest many possibilities for the relationship between population size and the evolution of complexity . There are two classes of evolutionary trajectories that would favor large populations in the evolution of complexity . First , populations could perform an adaptive walk ( the fixation of a sequence of beneficial mutations ) towards the evolution of a novel complex trait [21] . If this was the case , then larger populations would follow this trajectory faster than small populations due to their larger mutation supply . Experiments with microorganisms support the possible existence of adaptive trajectories towards complexity , as there is strong evidence that the mutations leading up to a phenotypic innovation in both Escherichia coli [22] and phage λ [23] were under positive selection . However , it is unclear whether adaptive mutations generally precede the evolution of complex traits or whether these large microbial populations can only take adaptive walks due to the intensity of selection in large populations . The second type of trajectory that favors large populations is the neutral walk ( the fixation of a sequence of neutral mutations ) . While any individual neutral mutation has a low probability of fixation , a large population would be able to accumulate many neutral mutations at any given time allowing for the exploration of its fitness landscape . Work by Wagner and colleagues suggests that many phenotypic traits are connected to each other by sequences of phenotypically neutral mutations [18 , 24] . If the evolution of complexity requires the fixation of deleterious mutations ( for example , via valley-crossing ) , then the elimination of deleterious mutations by purifying selection may limit the evolutionary advantage large populations may have . Wright was the first to propose an evolutionary advantage of small populations due to valley-crossing [25] . More recently , scientists have explored under which conditions small populations have an evolutionary advantage over large populations [26 , 27] . A prominent theory that predicts that small ( but not large ) populations should evolve the greatest genomic complexity ( and subsequently organismal complexity ) is the Mutational Burden ( or Mutational Hazard ) hypothesis , proposed by Lynch and colleagues [4 , 28 , 29] . This hypothesis argues that genome size should be inversely correlated with the product of the effective population size and the mutation rate [3 , 28] . Strong purifying selection against excessive genome size streamlines the genomes in large populations [30–32] . Meanwhile , weakened purifying selection and increased genetic drift in small populations results in the accumulation of slightly deleterious excess genome content [3 , 29] . At a later time , this slightly deleterious genome content may be mutated into novel beneficial traits [4 , 33] . However , recent work on valley-crossing in asexual populations ( and sexual populations with a low recombination rate ) showed that both small and large populations cross valleys more than intermediate-sized populations [34–36] . Therefore , it is not clear whether large or small populations are expected to evolve the greatest complexity when deleterious mutations are required . The long timescales required to observe the emergence of novelty and evolution of complexity make biological experiments to distinguish between these theories difficult to perform . To overcome this difficulty , we used digital experimental evolution [37] to test the role of population size on the evolution of genome size and phenotypic complexity in asexual organisms . Digital evolution has a long history of addressing macroevolutionary questions ( such as the evolution of novel traits ) experimentally [38 , 39] . Digital populations can be manipulated in ways that biochemical organisms can not , making it possible to study aspects of the evolutionary process that are ordinarily too difficult to test [40] . In this regard , digital experimental evolution has the same goals as microbial experimental evolution: to use a well-controlled model system that is as simple as possible , to study “evolution in action” [41] . And while digital evolution studies cannot test hypotheses that depend on particular biochemical processes involved in cellular life , digital populations do undergo selection , drift , and mutation , allowing for their use in testing hypotheses derived from theoretical population genetics . Thus , digital experimental evolution represents a well-suited model system to test the population genetics-based theories concerning the role of population size in the evolution of complexity . Here , we evolved populations ranging in size from 10 to 104 individuals , starting with a minimal-genome ancestor . We found that small populations do evolve greater genome sizes and phenotypic complexity ( number of phenotypic traits ) than intermediate-sized populations . These small populations evolve larger genomes primarily through increased fixation of slightly deleterious insertions . However , the small population sizes that enhance the evolution of phenotypic complexity also enhance the likelihood of population extinction . We also found that the largest populations evolved similar complexity to the smallest populations . Large populations evolved longer genomes and greater phenotypic complexity through the fixation of rare beneficial insertions instead . Large populations were able to discover these rare beneficial mutations due to an increased mutation supply . Finally , we found that a strong deletion bias can prevent the evolution of greater complexity in small , but not in large , populations .
Of the surviving populations , we first examined how genome size changes from the ancestral value of fifteen instructions . The size of the genome from every population size increased , on average ( see Fig 1 and panel A in S1 Fig ) . However , both the smallest and the largest populations evolved the largest genomes . Populations with ten individuals evolved a median genome size of 35 instructions , while populations with ten thousand individuals evolved a median genome size of 36 instructions . The median final genome size decreased as population size increased for populations with between ten and fifty individuals . However , from populations with fifty individuals to populations with ten thousand individuals , the median final genome size increased as population size increased . Next , we examined the dynamics of fixation of insertion mutations ( insertions , for short ) to explain why both the smallest and the largest populations evolved the largest genomes . For each experimental population , we counted every insertion that occurred on the fittest genotype’s ancestral lineage that went back to the ancestral genotype ( the “line of descent” , see Methods ) . The median number of insertions fixed follows the same trend as the evolution of genome size ( S2 Fig ) . A large fraction of these fixed insertions are slightly deleterious in populations with fewer than one hundred individuals ( see Fig 2 and panel B in S1 Fig ) . However , no insertions are slightly deleterious , on average , in large populations with more than one hundred individuals . The opposite trend holds for beneficial insertions . The fraction of insertions that are under positive selection increases with increasing population size , with the largest populations usually fixing only beneficial insertions ( Fig 3 and panel C in S1 Fig ) . These data demonstrate that small populations evolve larger genomes through the fixation of slightly deleterious insertions . However , large populations can evolve similarly large genomes through the fixation of rare beneficial insertions . Next , we focus on the role of population size in the evolution of phenotypic complexity ( defined as the number of phenotypic traits ) . In Avida , a phenotypic trait is a program’s ability to perform a certain mathematical operation on binary numbers ( see Methods ) . The evolution of phenotypic complexity follows the same trend as the evolution of genome size ( see Fig 4 and panel D in S1 Fig ) . Populations with ten individuals evolved a median of four traits , while populations with one thousand and ten thousand individuals evolved a median of one trait . The rest of the population sizes evolved a median of zero traits . As an avidian’s fitness is primarily determined by its phenotypic traits in the Avida environment used here , the evolution of fitness showed a similar trend to the evolution of phenotypic complexity ( S3 Fig ) . That the trend in genome size evolution and in phenotypic complexity evolution are mirrored suggests that the evolution of larger genomes enables the evolution of increased phenotypic complexity . To establish a link between the two , we performed two tests . First , we examined the correlation between genome size and phenotypic complexity across all populations . Phenotypic complexity is positively correlated with genome size ( Fig 5 , Spearman’s ρ ≈0 . 72; p < 2 . 3 x 10−57 ) , suggesting that it was the increased genome size that allowed for the evolution of increased phenotypic complexity . However , there are two potential mechanisms that could cause an increased genome size to result in increased phenotypic complexity . On the one hand an increased genome size could simply allow more “room” for novel functional content . On the other hand , it could be that the increased genome size leads to a faster rate of evolution due to the increased genomic mutation rate . To examine the role of an increased mutation rate in driving the evolution of phenotypic complexity , we evolved a further one hundred populations of ten individuals with a fixed genomic mutation rate of 1 . 5 × 10−1 ( i . e . , the ancestral genomic mutation rate ) . Under this condition , no population went extinct ( as opposed to forty-seven in the variable mutation rate treatment ) . The fixed genomic mutation rate populations evolved a median of 2 phenotypic traits compared to the variable genomic mutation rate populations that had evolved a median of 4 phenotypic traits ( S4 Fig ) . These data demonstrate that the increased genomic mutation rate that follows from larger genomes does increase the evolution of phenotypic complexity . However , even with a fixed genomic mutation rate , the smallest populations still evolved a greater median number of traits ( on average 2 traits ) than every other population size . Thus , while an increased genomic mutation rate ( due to increased sequence length ) indeed enhances the evolution of phenotypic complexity , small populations still possess an evolutionary advantage due to drift-driven increases in genome size only . In the previous experiments , large populations evolved larger genomes and greater phenotypic complexity because they fixed rare beneficial insertions . Next , we more closely examine the finding that beneficial insertions are necessary for the evolution of complexity in large populations . We repeated the experiments with the same population sizes and mutation rates , except we changed how insertions worked . Instead of inserting one of the twenty-six instructions that compose the Avida instruction set , we inserted “blank” instructions into the genome ( see Methods for details ) . These blank instructions cannot be beneficial ( on their own or in combination with existing instructions ) and would have to be further mutated to lead to the evolution of phenotypic complexity . In this treatment , greater phenotypic complexity in large populations would require a two-step mutational process , as opposed to the single step in a beneficial insertion . We saw no qualitative difference in the trend between these experiments and the original experiments ( S5 Fig ) . Very small and large populations still both evolved the largest genomes and the greatest phenotypic complexity . Populations of all sizes evolved longer genomes and more phenotypic traits in this treatment ( S5 Fig ) than in the original treatment ( Figs 1 and 4 ) . The fraction of fixed insertions that were under positive selection decreased for every population size compared to the original experiments , as expected from the insertion of non-functional instructions ( S6 Fig ) . We observed an increased rate of extinction in the very small populations , with only 2 populations with ten individuals and 25 populations with twenty individuals surviving the experiment . Population extinction was likely enhanced by the increased growth in genome size in these experiments as compared to the original experiments . Finally , we performed experiments to test whether the effect of a deletion bias ( a higher fraction of deletions among all indels ) alters the relationship between population size and the evolution of complexity . A biased ratio of deletion to insertion mutations is found in biological organisms across the tree of life , especially in bacteria [45 , 46] . In these experiments we set the ratio of deletions to insertions as 9:1 , but kept the total indel mutation rate as in the original experiments . In this treatment , only one population with ten individuals went extinct , as opposed to 47 populations in the original treatment . However , the advantage towards evolving complexity previously enjoyed by small populations vanished ( S7 Fig ) . The median genome size increased as the population size increased for all populations sizes . Only the largest populations evolved a median number of novel phenotypic traits greater than zero . These results suggest that it is not only the role of genetic drift , but the equal frequency of insertions and deletions that results in the increased genome size and phenotypic complexity in small populations .
The idea that small populations could have an evolutionary advantage over large populations dates back to Wright and his Shifting Balance theory [25] . More recently , a potential small-population advantage has been demonstrated both theoretically [27] and experimentally [26] , but only in regard to short-term increases in fitness . The Mutational Burden hypothesis provides an evolutionary mechanism that gives small populations an advantage towards increased phenotypic complexity [4 , 33] . However , an experimental demonstration of this advantage is lacking . Our study provides further insight into the conditions that give small populations such an evolutionary advantage . We confirmed that small populations do evolve larger genomes due to the increased fixation of slightly deleterious mutations , as predicted [28] . We also showed how small populations have an increased potential to later evolve increased phenotypic complexity in small populations through the larger genomes generated by increased genetic drift [3 , 4] . As phenotypic traits are strongly beneficial in the Avida environment used here , these small populations used slightly deleterious genome expansions to cross fitness valleys and eventually reach novel fitness peaks . Our work also shows that this evolutionary advantage of small populations is limited by an increased rate of population extinction . Such a trend between the evolution of large genomes and an increased rate of extinction is seen in some multicellular eukaryote clades [47 , 48] . These small populations are still likely to have a larger risk of extinction beyond that caused by population-genetic risks such as Muller’s ratchet [49] and mutational meltdowns [50 , 51] . Ecological stressors increase extinction risk [52] and small populations are less able to adapt to detrimental environmental changes [53] . Our results concerning extinction , combined with the risk of other factors not examined here , suggest that the likelihood of a small population using genetic drift to evolve greater complexity without an increased risk of extinction may be limited . However , it is possible that multiple small populations could reduce the risk of extinction without reducing the evolution of complexity; future work should consider the interplay between population size and the evolution of complexity within a metapopulation of small populations . Large populations also evolved greater genome sizes and phenotypic complexity . In our original experiments , genome evolution in large populations was driven by the fixation of rare beneficial insertions ( Fig 4 ) . While it is likely that many gene duplications are not under positive selection and lost due to genetic drift and mutation accumulation [54] , some , especially those resulting in the amplification of gene expression , can be immediately beneficial and later lead to increased phenotypic complexity [55–58] . Due to the increased mutation supply , these events would occur at a greater frequency in large populations [59] and possibly lead to an increased probability of the evolution of complexity there . However , we also found that large populations did not require this large supply of beneficial insertions . Even when insertion mutations added non-functional instructions and further point mutations were required to evolve functional traits , large populations still evolved complexity similar to that evolved in small populations . These results suggest that purifying selection may not limit the evolution of complexity in large populations . Finally , we found that when deletions occur at a much greater frequency than insertions , only large populations have an evolutionary advantage towards complexity . As many bacteria do have a bias towards deletions [60 , 61] , this result suggests that large microbial populations can have an evolutionary advantage over small microbial populations for evolving novel traits after all . Such a trend where both large and small , but not intermediate-sized populations have an evolutionary advantage has already been theoretically proposed elsewhere . Weissman et al . showed that both small and large populations cross fitness valleys more easily than intermediate-sized populations [34] . Small populations valley-crossed due to genetic drift and large populations did so due to an increased supply of double mutants . Ochs and Desai also showed that intermediate-sized populations evolved to a lower fitness peak compared to small or large populations when valley-crossing was required for reaching a higher peak [36] . We found similar results , but from different evolutionary mechanisms . Here , populations needed to increase in genome size in order to evolve phenotypic complexity . Additionally , our populations evolved in a complex fitness landscape with many different possible paths to phenotypic complexity . While small populations did fix deleterious insertions to increase genome size , large populations evolved on a different path , either through beneficial insertions ( Fig 3 ) or neutral insertions ( S4 Fig ) . It is possible that even larger populations than those evolved here would fix more deleterious insertions , as the likelihood of a further , beneficial mutation arising on the background of a segregating deleterious mutation increases as population size increases . However , our results emphasize that large populations may not be dependent on valley-crossing in some fitness landscapes if alternative evolutionary trajectories exist , even if these trajectories are rare . While the first maps of fitness landscapes suggested mutational paths are small in number [62] , more recent work suggests that many indirect evolutionary trajectories exist in larger fitness landscapes [63] . The population sizes that led to the evolution of greater phenotypic complexity via drift are very small ( 10 individuals ) . As biological populations of that size are unrealistic , we may wonder whether such populations can actually evolve greater complexity due to increased genetic drift . However , there are reasons to believe that these results would generally hold for biological systems . The limited range of small population sizes that led to complexity is an Avida-specific result due to the severe fitness effect of insertion mutations in avidians with small sequence length . We found that for those sequences , most insertions are lethal ( about 80% ) , and the rest are significantly detrimental , of the order 10% to 90% . To overcome a detrimental effect of 20% via drift , populations must be as small as N = 10 . Insertion mutations in biological genomes are not nearly as detrimental , and therefore the critical population size to see evolution of complexity via drift is much larger . In E . coli , for example , the deleterious effect of insertions is between 1% and 3% [64] . We can therefore expect to see the effect of increased complexity due to drift in biological populations that are small , but not unreasonably small . Another possible avenue for future work suggested by this study is to use a simpler population genetics model to explore the same questions we attempted to answer here . Many previous theoretical studies have examined the relevance of valley-crossing to the evolution of complex traits in simple fitness landscapes [34–36] . One benefit of a simpler model is that it allows for a broader exploration of the relevant parameters involved in the interplay between population size , genome size , and the evolution of phenotypic complexity . While we were not able to perform large parameter searches using the Avida system , our work here establishes a possible relationship between the factors that influence the evolution of complexity in a fitness landscape with many possible mutational trajectories to novel traits [65] . These results should drive future theoretical studies on the evolution of genome size and phenotypic complexity using population genetics models with simpler fitness landscapes . Here we studied the evolution of complexity in haploid asexual digital organisms with an ancestral minimal genome on a frequency-independent fitness landscape . While beyond the scope of this work , it is worth considering how adjusting these genotype characteristics would alter our results . It is likely that the ancestral minimal genomes are a requirement for small populations to evolve the same number of novel traits as large populations . If the ancestor organism had a significant amount of non-functional genome content , the mutation supply advantage that large populations have should result in an accelerated rate of phenotypic evolution in large populations [66] . The organisms used here , as in all Avida experiments , are haploid . It is possible that polyploidy would alter the results found here . However , the implementation of a ploidy cycle in Avida is non-trivial due to the mechanistic style of replication , and so presently other experimental systems would have to be used to explore the role of ploidy in the evolution of phenotypic complexity . It is unclear how sexual , instead of asexual , reproduction would change the results . While sexual reproduction can enhance adaptation by combining beneficial mutations that arise in different background , it can also break up beneficial combinations of mutations [67] . One result that may be altered by sexual reproduction is the rate of extinction in small populations , as sex has been found to reduce the rate of mutational meltdowns [68] . Weissman et al . also demonstrate that the large population advantage towards valley-crossing does not exist under high recombination rates [35] . Sexual reproduction has previously been studied using Avida , but it is more akin to homologous recombination in bacteria [69] ( as there is no ploidy cycle ) . Future work should address the role of sexual recombination on the results shown here . Finally , the experiments performed here had no frequency-dependent fitness effects . Previous Avida studies showed that frequency-dependent interactions enhanced the evolution of complexity for a given population size [70 , 71] . It is worth exploring how the presence of frequency-dependent selection alters the evolution of complexity , especially in small populations . The benefits of the diversity seen in frequency-dependent fitness landscapes may be reduced in small populations . The extensions to the experiments performed here would provide a more complete understanding of the role of adaptive and non-adaptive evolutionary processes in the origins of complexity .
In order to experimentally test the role of population size and genetic drift in the evolution of complexity , we used the digital evolution system Avida version 2 . 14 [42] . In Avida , self-replicating computer programs ( avidians ) compete in a population for a limited supply of CPU ( Central Processing Unit ) time needed to successfully reproduce . Each avidian consists of a circular haploid genome of computer instructions . During its lifespan , an avidian executes the instructions that compose its genome . After executing certain instructions , it begins to copy its genome . This new copy will eventually be divided off from its mother ( reproduction in most Avida experiments is asexual ) . Because an avidian passes on its genome to its descendants , there is heredity in Avida . As an avidian copies its genome , mutations may occur , resulting in imperfect transmission of hereditary information . This error-prone replication introduces variation into Avida populations . Finally , avidians that differ in instructions ( their genetic code ) also likely differ in their ability to self-replicate; this results in differential fitness . Therefore , because there is differential fitness , variation , and heredity , an Avida population undergoes evolution by natural selection [72] . This allows researchers to perform experimental evolution in Avida as in microbial systems [19 , 73] . Avida has been successfully used as a model system to explore many topics concerning the evolution of complexity [2 , 65 , 71 , 74 , 75] . Twenty-six different instructions compose the Avida instruction set ( see [42] for a more complete overview ) . These include instructions for genome replication , such as an instruction to allocate memory for a new daughter genome , an instruction to copy instructions from the mother genome into the daughter genome , and an instruction to divide off the new avidian . There are instructions that allow for the input , output , and manipulation of random numbers that are used in the performance of certain Boolean logic calculations ( see below ) . There are also instructions for altering instruction execution , including conditional instructions and instructions for changing the next instruction location in the genome to be executed . It is important to note that the Avida instruction set was not designed to mimic any biological organism . Instead , it was created in order to have an organism with mechanistic reproduction in a non-specified fitness landscape that allows for studies of evolutionary dynamics . The Avida world consists of a toroidal grid of N cells , where N is the ( maximum ) population size . When an avidian successfully divides , its offspring is placed into a cell in the population . While the default setting places the offspring into one of nine neighboring cells of the parent , here the offspring is placed into any cell in the entire population . This simulates a well-mixed environment without spatial structure . When there are empty cells in the population , new offspring are preferentially placed in an empty cell . However , if the population is at its carrying capacity , the individual who is currently occupying the selected cell is replaced by the new offspring ( a new individual can also eliminate its parent if that cell is selected ) . This adds an element of genetic drift into the population as the individual to be removed is selected without regard to fitness . A population can also decrease in size by the death of individuals . An avidian will die without producing offspring if it executes 20L instructions without successfully undergoing division , where L is the avidian’s genome size . This can lead to population extinction in very small populations . Time in Avida is divided into updates , not generations . This method of keeping time was implemented in order to allow individuals to execute their genomes in parallel . During one update , a fixed number of instructions is executed across the entire population . The resource that is necessary to execute instructions ( the CPU “energy” ) is measured in SIPs ( single instruction processing ) units . By default , there are 30N SIPs available to the entire population per update , where N is the population size . SIPs are distributed among the individual genotypes within a population in proportion to the trait or traits displayed by an individual . The total amount of SIPs garnered by an individual from traits is called the “merit” . In a homogeneous population of one genotype ( clones ) where each individual has the same merit , each individual will obtain approximately 30 SIPs per update . However , in a heterogeneous population where merit differs between individuals , SIPs will be distributed in an uneven manner . That way , individuals with a greater merit will execute and/or replicate a larger proportion of their genome per update and replicate faster , thus having a greater fitness . This places a strong selection pressure on evolving a greater merit . One generation has passed when the population has produced N offspring . Typically ( depending on the complexity of an avidian ) between 5 and 10 updates pass in one generation . A genotype’s merit is increased through the evolution of certain phenotypic traits that form a “digital metabolism” [37] . These phenotypic traits are the ability ( or lack there-of ) to perform certain Boolean logic calculations on random binary numbers that the environment provides . To do this , an avidian must have the right “genes”–in this case , the right sequence of instructions . First , during an avidian’s lifespan , instructions that allow for the input and output of these random binary numbers must be executed . Further instructions should manipulate those numbers so as to perform the rewarded computations . When a number is then written to the output , the Avida program checks to see whether a logic operation was successfully performed . If so , the the individual that performed the computation consumes a resource tied to the performance of that trait ( there are many different codes , that is , combinations of instructions , that will trigger the reward ) . Resource consumption causes the offspring of that individual to have their merit modified by a factor set by the experimenter . Here , we use the “Logic-9” environment to reward the performance of nine one- and two-input logic functions [65]; see S1 Table for the names and specific rewards of each function . Each individual only gains a benefit from performing each function once per generation . There is an infinite supply of resources for the performance of each logic function in the present experiments , making fitness frequency-independent . Because the performance of these logic functions increases merit , they also increase fitness and are under strong positive selection . While increases in an individual’s merit increase replication speed and thus the individual’s fitness , fitness in Avida is implicit and not directly calculated . Unlike simulations of evolutionary dynamics , a genotype’s fitness is thus not set a priori by the experimenter . The only way to measure the fitness of an avidian is to run it through its lifecycle and examine its phenotype . This is similar in principle to how bacterial fitness cannot be calculated by examining an individual bacterium’s genome , but must be measured through a number of different experiments , such as competition assays [76] . A genotype’s fitness is determined by how many offspring it can produce per unit time . Genotypes that can reproduce faster will out-compete other genotypes , all else being equal . Therefore , evolution will increase a population’s fitness through two means . The first is that the population will evolve individuals with a greater number of phenotypic traits and thus with a greater merit , as explained above . The second way to increase replication speed is by optimizing ( shortening ) the replication time . This occurs either by shrinking the genome , which results in fewer instructions that need to be copied and replicated , or by optimizing genome architecture for faster replication . Fitness w in Avida can be estimated by the following equation: w ≈ merit replication time ( 1 ) For an avidian to be able to successfully reproduce , it must first allocate memory for the new individual , copy its genome into the allocated memory space , and then divide off the daughter organism . As instructions are copied , the avidian may inaccurately copy some instructions into the newly allocated memory at a rate set by the experimenter . Additionally , upon division , insertions and deletions of a single instructions occur at ( possibly different ) rates set by the experimenter . Finally , larger insertions or deletions ( indels ) can occur when an avidian divides into two daughter genomes if the division occurs unevenly . In most cases , this results in the creation of one larger and one smaller genome and both of these are non-viable . However , in rare cases , one of these new genotypes is able to reproduce , resulting in a large change in genome size in that individual’s descendants . Because this mutation through inaccurate division is a characteristic of a genome and thus emergent , the rate at which it occurs is not set by the experimenter . We used four experimental designs ( treatments ) to explore how population size determines the evolution of complexity: the original experiments , the non-functional insertion experiments , the fixed genomic mutation rate experiments , and the deletion bias experiments . For all experiments , we evolved populations of size N = {10 , 100 , 1000 , 10000} for 2 . 5 × 105 generations under 100-fold replication . For the original treatment , we also performed experiments with population sizes of N = {20 , 30 , 40 , 50 , 60 , 70 , 80 , 90} . All populations were initiated at full size N with an altered version of the standard length-100 Avida start organism [42] . The alteration was the removal of all non-essential genome content ( 85 nop-c instructions ) . This reduced the genome size of the ancestor organisms from 100 instructions to only 15 instructions . For the original experiments , point mutations occurred at a rate of 0 . 01 mutations per instruction copied , and insertions and deletions at 0 . 005 events per division . Insertions and deletions occur at most once per division . The ancestor thus started with a genomic mutation rate of 0 . 15 mutations per generation ( 0 . 01 mutations/instruction copied × fifteen instructions copied per generation ) , but this changes over the course of the experiment as genome size evolves . These experiments are similar to most standard Avida experiments , with the exception of a smaller genome size ( fifteen instructions ) for the ancestral organism . For the remainder of the experimental settings , one of the above settings was changed to examine a specific effect . For the experiments where the genomic mutation rate was fixed , point mutations occurred at a rate of 0 . 15 mutations per division , independently of genome size , which fixes the mutation rate at 0 . 15 mutations/genome/generation . For the non-functional insertion experiments , the mutation rates were the same as in the original experiments . However , instead of inserting one of the twenty-six instructions from the Avida instruction set ( see [42] for the Avida instruction set ) , “blank” instructions called nop-x were inserted . These instructions have no function on their own or in combination with any other instruction . Finally , for the deletion bias experiments , point mutations occurred at the same rate as in the standard experiments . However , insertions and deletions did not occur at the same rate . Insertions occurred at a rate of 0 . 001 per division and deletions occurred at a rate of 0 . 009 per division . This kept the total mutation rate equal to the other experimental treatments , while altering the ratio of insertions to deletions . In order to analyze the evolution of complexity in each population , we extracted the individual with the greatest fitness at the end of each experiment ( the “dominant” type ) . We then calculated relevant statistics for each of these genotypes by running them through Avida’s analyze mode . This mode allows us to run each genotype through its lifecycle in isolation , and calculate its fitness , its genome size , whether it performs any logic functions , and whether it produces viable offspring , among other characteristics . To measure the evolution of phenotypic complexity , we determined how many unique logic calculations each genotype could perform . Such a measure of complexity is similar to a measure of phenotypic complexity used previously [5] in population genetics . The relative fitness was calculated by dividing the analyzed fitness value by the ancestor’s fitness ( 0 . 244898 ) . To examine why certain population sizes evolved larger genomes , we examined the “line of descent” ( LOD ) of the fittest type [65] . An LOD contains every intermediate genotype between the final individual with the greatest fitness and the ancestral genotype that initialized each population . This line provides a perfect “fossil record” to examine all of the mutations , insertions , and deletions that led to the final fittest genotype for each population . We also calculated the selection coefficient s for each mutation , defined as the ratio of the offspring’s fitness to the parent’s fitness minus one . We defined beneficial mutations as those with s > 0 and deleterious mutations as those with s < 0 ( this ignores classifying slightly beneficial and slightly deleterious mutations as neutral . ) We determined the number of beneficial insertion mutations by counting those insertions on the LOD with s > 1 N , where N is the population size . These are beneficial mutations that are not nearly-neutral and hence should be under positive selection . We note that using s > 1 N is only an approximation , as the equation for a nearly neutral mutation is | s | ≪ 1 N e , where Ne is the effective population size [77] . We also examined those mutations that had a slightly deleterious effect on fitness , i . e . , those whose selection coefficient was - 1 N < s < 0 . | Since the early days of theoretical population genetics . scientists have debated the role of population size in shaping evolutionary dynamics . Do large populations possess an evolutionary advantage towards complexity due to the strength of natural selection in these populations ? Or do small populations have the advantage , as genetic drift allows small populations to cross fitness valleys that large populations are unlikely to traverse ? There are many theories that predict whether large or small populations–those with strong selection or those with strong drift–should evolve the greatest complexity . Here , we use digital experimental evolution to examine the interplay between population size and the evolution of complexity . We found that genetic drift could lead to increased genome size and phenotypic complexity in very small populations . However , large populations also evolved large genomes and phenotypic complexity . Small populations evolved larger genomes through the fixation of slightly deleterious insertions , while large populations used rare beneficial insertions . Our results suggest that both strong drift and strong selection can allow populations to evolve similar complexity , but through different evolutionary trajectories . | [
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| 2016 | Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms |
In many applications , one is interested in determining which of the properties of a network module change across conditions . For example , to validate the existence of a module , it is desirable to show that it is reproducible ( or preserved ) in an independent test network . Here we study several types of network preservation statistics that do not require a module assignment in the test network . We distinguish network preservation statistics by the type of the underlying network . Some preservation statistics are defined for a general network ( defined by an adjacency matrix ) while others are only defined for a correlation network ( constructed on the basis of pairwise correlations between numeric variables ) . Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics . We illustrate that evaluating module preservation is in general different from evaluating cluster preservation . We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics . We illustrate the use of these methods in six gene co-expression network applications including 1 ) preservation of cholesterol biosynthesis pathway in mouse tissues , 2 ) comparison of human and chimpanzee brain networks , 3 ) preservation of selected KEGG pathways between human and chimpanzee brain networks , 4 ) sex differences in human cortical networks , 5 ) sex differences in mouse liver networks . While we find no evidence for sex specific modules in human cortical networks , we find that several human cortical modules are less preserved in chimpanzees . In particular , apoptosis genes are differentially co-expressed between humans and chimpanzees . Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks . Data , R software and accompanying tutorials can be downloaded from the following webpage: http://www . genetics . ucla . edu/labs/horvath/CoexpressionNetwork/ModulePreservation .
Network methods are frequently used in genomic and systems biologic studies , but also in general data mining applications , to describe the pairwise relationships of a large number of variables [1] , [2] . For example , gene co-expression networks can be constructed on the basis of gene expression data [3]–[10] . In many network applications , one is interested in studying the properties of network modules and their change across conditions [11]–[16] . For example , [17]–[19] studied modules across multiple mouse tissues , [20] studied module preservation between human brain and blood tissue , and [21] studied module preservation between human and mouse brains . This article describes several module preservation statistics for determining which properties of a network module are preserved in a second ( test ) network . The module preservation statistics allow one to quantify which aspects of within-module topology are preserved between a reference network and a test networks . For brevity , we will refer to these aspects as connectivity patterns , but we note that our statistics are not based on network motifs . We use the term “module” in a broad sense: a network module is a subset of nodes that forms a sub-network inside a larger network . Any subset of nodes inside a larger network can be considered a module . This subset may or may not correspond to a cluster of nodes . Many cluster validation statistics proposed in the literature can be turned into module preservation statistics . In the following , we briefly review cluster validation statistics . Traditional cluster validation ( or quality ) statistics can be split into four broad categories: cross-tabulation , density , separability , and stability statistics [22]–[24] . Since cross-tabulation statistics compare cluster assignments in the reference and test clusterings , they require that a clustering procedure is also applied to the test data . On the other hand , density and density/separability statistics do not require a clustering in the test data set . These statistics typically evaluate clusters by how similar objects are within each cluster and/or how dis-similar objects are between different clusters [25] . Stability statistics typically study cluster stability when a controlled amount of artificial noise is added to the data . Although stability statistics also evaluate clusters , they are more relevant to comparing clustering procedures rather than quantifying cluster preservation and hence we do not consider them here . While many cluster validation statistics are based on within- and/or between cluster variance , several recent articles used prediction error to evaluate the reproducibility ( or validity ) of clusters in gene expression data [24] , [26] , [27] . These papers argued that the use of a measure of test set clusters defined by a classifier made from the reference data is an appropriate approach to cluster validation when the aim is to identify reproducible clusters of genes or microarrays with similar expression profiles . For example , the in-group proportion ( IGP ) , which is similar to the cluster cohesion statistic [28] , is defined as the proportion of observations classified to a cluster whose nearest neighbor is also classified to the same cluster [24] . One can also calculate a significance level ( p-value ) for the IGP statistic . A comparison of the IGP statistic to alternative cluster quality statistics found that the IGP performs well [24] . Thus , we use the IGP statistic as benchmark statistic for assessing the use of module preservation statistics in case that modules are defined as clusters . Our simulation studies and applications show that one of our module preservation statistics is sometimes closely correlated with the IGP statistic if the modules are defined as clusters . But cluster validation statistics ( such as the IGP ) may not be appropriate when modules are not defined as clusters . In general , assessing module preservation is a different task from assessing cluster preservation . In our simulations , we demonstrate that module preservation statistics can detect aspects of module preservation that are missed by existing cluster validation statistics .
Table 1 presents an overview of the module preservation statistics studied in this article . We distinguish between cross-tabulation based and network based preservation statistics . Cross-tabulation based preservation statistics require independent module detection in the test network and take the module assignments in both reference and test networks as input . Several cross-tabulation based statistics are described in the first section of Supplementary Text S1 . While cross-tabulation approaches are intuitive , they have several disadvantages . To begin with , they are only applicable if the module assignment in the test data results from applying a module detection procedure to the test data . For example , a cross-tabulation based module preservation statistic would be meaningless when modules are defined as gene ontology categories since both reference and test networks contain the same sets of genes . But a non-trivial question is whether the network connections of a module ( gene ontology category ) in the reference network resemble those of the same module in the test network . To measure the resemblance of network connectivity , we propose several measures based on network statistics . Network terminology is reviewed in Table 2 and in Methods . Even when modules are defined using a module detection procedure , cross-tabulation based approaches face potential pitfalls . A module found in the reference data set will be deemed non-reproducible in the test data set if no matching module can be identified by the module detection approach in the test data set . Such non-preservation may be called the weak non-preservation: “the module cannot be found using the current parameter settings of the module detection procedure” . On the other hand , one is often interested in strong non-preservation: “the module cannot be found irrespective of the parameter settings of the module detection procedure” . Strong non-preservation is difficult to establish using cross-tabulation approaches that rely on module assignment in the test data set . A second disadvantage of a cross-tabulation based approach is that it requires that for each reference module one finds a matching test module . This may be difficult when a reference module overlaps with several test modules or when the overlaps are small . A third disadvantage is that cross-tabulating module membership between two networks may miss that the fact that the patterns of connectivity between module nodes are highly preserved between the two networks . Network based statistics do not require the module assignment in the test network but require the user to input network adjacency matrices ( described in Methods ) . We distinguish the following 3 types of network based module preservation statistics: 1 ) density based , 2 ) separability based , and 3 ) connectivity based preservation statistics . Density based preservation statistics can be used to determine whether module nodes remain highly connected in the test network . Separability based statistics can be used to determine whether network modules remain distinct ( separated ) from one another in the test network . While numerous measures proposed in the literature combine aspects of density and separability , we keep them separate and provide evidence that density based approaches can be more useful than separability based approaches in determining whether a module is preserved . Connectivity based preservation statistics can be used to determine whether the connectivity pattern between nodes in the reference network is similar to that in the test network . As detailed in Methods , several module preservation statistics are similar to previously proposed cluster quality and preservation statistics , while others ( e . g . connectivity based statistics ) are novel . Table 1 reports the required input for each preservation statistic . Since each preservation statistic is used to evaluate the preservation of modules defined in a reference network , it is clear that each statistic requires the module assignment from the reference data . But the statistics differ with regard to the module assignment in the test data . Only cross-tabulation based statistics require a module assignment in the test data . Network based preservation statistics do not require a test set module assignment . Instead , they require the test set network adjacency matrix ( for a general network ) or the test data set of numeric variables ( for a correlation network ) . We distinguish network statistics by the underlying network . Some preservation statistics are defined for a general network ( defined by an adjacency matrix ) while others are only defined for a correlation network ( constructed on the basis of pairwise correlations between numeric variables ) . Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics . Preservation statistics 4–11 ( Table 1 ) can be used for general networks while statistics 12–19 assume correlation networks . Network density and module separability statistics only need the test set adjacency matrix while the connectivity preservation statistics also require the adjacency matrix in the reference data . It is often not clear whether an observed value of a preservation statistic is higher than expected by chance . As detailed in Methods , we attach a significance level ( permutation test p-value ) to observed preservation statistics , by using a permutation test procedure which randomly permutes the module assignment in the test data . Based on the permutation test we are also able to estimate the mean and variance of the preservation statistic under the null hypothesis of no relationship between the module assignments in reference and test data . By standardizing each observed preservation with regard to the mean and variance , we define a statistic for each preservation statistic . Under certain assumptions , each statistic ( approximately ) follows the standard normal distribution if the module is not preserved . The higher the value of a Z statistic , the stronger the evidence that the observed value of the preservation statistic is significantly higher than expected by chance . Several studies have explored how co-expression modules change between mouse tissues [19] and/or sexes [18] . Here we re-analyze gene expression data from the liver , adipose , muscle , and brain tissues of an F2 mouse intercross described in [13] , [17] . The expression data contain measurements of 17104 genes across the following numbers of microarray samples: 137 ( female ( F ) adipose ) , 146 ( male ( M ) adipose ) , 146 ( F liver ) , 145 ( M liver ) , 125 ( F muscle ) , 115 ( M muscle ) , 148 ( F brain ) , and 141 ( M brain ) . We consider a single module defined by the genes of the gene ontology ( GO ) term “Cholesterol biosynthetic process” ( CBP , GO id GO:0006695 and its GO offspring ) . Of the 28 genes in the CBP , 24 could be found among our 17104 genes . Cholesterol is synthesized in liver and we used the female liver network as the reference network module . As test networks we considered the CBP co-expression networks in other tissue/sex combinations . Each circle plot in Figure 1 visualizes the connection strengths ( adjacencies ) between CBP genes in different mouse tissue/sex combination . The color and width of the lines between pairs of genes reflect the correlations of their gene expression profiles across a set of microarray samples . Before delving into a quantitative analysis , we invite the reader to visually compare the patterns of connections . Clearly , the male and female liver networks look very similar . Because of the ordering of the nodes , the hubs are concentrated on the upper right section of the circle and the right side of the network is more dense . The adipose tissues also show this pattern , albeit much more weakly . On the other hand , the figures for the brain and muscle tissues do not show these patterns . Thus , the figure suggests that the CBP module is more strongly preserved between liver and adipose tissues than between liver and brain or muscle . We now turn to a quantitative assessment of this example . We start out by noting that a cross-tabulation based approach of module preservation is meaningless in this example since the module is a GO category whose genes can trivially be found in each network . However , it is a very meaningful exercise to measure the similarity of the connectivity patterns of the module genes across networks . To provide a quantitative assessment of the connectivity preservation , it is useful to adapt network concepts ( also known as network statistics or indices ) that are reviewed in Methods . Figure 2 provides a quantitative assessment of the preservation of the connectivity patterns of the cholesterol biosynthesis module between the female liver network and networks from other sex/tissue combinations . Figure 2A presents the composite summary statistic ( , Equation 1 ) in each test network . Overall , we find strong evidence of preservation ( , Equation 1 ) in the male liver network but no evidence ( ) of preservation in the female brain and muscle networks . We find that the connectivity of the female liver CBP is most strongly preserved in the male liver network . It is also weakly preserved in adipose tissue but we find no evidence for its preservation in muscle and brain tissues . The summary preservation statistic measures both aspects of density and of connectivity preservation . We now evaluate which of these aspects are preserved . Figure 2B shows that the module shows strong evidence of density preservation ( ) ( Equation 30 ) in the male liver network but negligible density preservation in the other networks . Interestingly , Figure 2C shows that the module has moderate connectivity preservation ( Equation 31 ) in the adipose networks . The measure summarizes the statistical significance of 3 connectivity based preservation statistics . Two of our connectivity measures evaluate whether highly connected intramodular hub nodes in the reference network remain hub nodes in the test network . Preservation of intramodular connectivity reflects the preservation of hub gene status between the reference and test network . One measure of intramodular connectivity is the module eigengene-based connectivity measures ( Equation 17 ) , which is also known as the module membership measure of gene [13] , [29] , [30] . Genes with high values of are highly correlated with the summary profile of the module ( module eigengene defined as the first principal component , see the fifth section in Supplementary Text S1 ) . A high correlation of between reference and test network can be visualized using a scatter plot and quantified using the correlation coefficient . For example , Figure 2I shows that in the female liver module is highly correlated with that of the male liver network ( , ) . Further , the scatter plots in Figure 2 show that the measures between liver and adipose networks show strong correlation ( preservation ) : ( ) , ( ) , ( ) , while the correlation between in female liver and the brain and muscle data sets are not significant . This example demonstrates that connectivity preservation measures can uncover a link between CBP in liver and adipose tissues that is missed by density preservation statistics . We briefly compare the performance of our network based statistics with those from the IGP method [24] . The R implementation of the IGP statistic requires that at least 2 modules are being evaluated . To get it to work for this application that involves only a single module , we defined a second module by randomly sampling half of the genes from the rest of the entire network . Figure 2D shows high , nearly constant values of the IGP statistic across networks , which indicates that the CBP module is present in all data sets . Note that the IGP statistic does not allow us to argue that the CBP module in the female liver network is more similar to the CBP module in the male liver than in other networks . This reflects the fact that the IGP statistic , which is a cluster validation statistic , does not measure connectivity preservation . Here we study the preservation of co-expression between human and chimpanzee brain gene expression data . The data set consists of 18 human brain and 18 chimpanzee brain microarray samples [31] . The samples were taken from 6 regions in the brain; each region is represented by 3 microarray samples . Since we used the same weighted gene co-expression network construction and module identification settings as in the original publication , our human modules are identical to those in [32] . Because of the relatively small sample size only few relatively large modules could be detected in the human data . The resulting modules were labeled by colors: turquoise , blue , brown , yellow , green , black , red ( see Figure 3A ) . Oldham et al ( 2006 ) determined the biological meaning of the modules by examining over-expression of module genes in individual brain regions . For example , heat maps of module expression profiles revealed that the turquoise module contains genes highly expressed in cerebellum , the yellow module contains genes highly expressed in caudate nucleus , the red module contains genes highly expressed in anterior cingulate cortex ( ACC ) and caudate nucleus , and the black module contains mainly genes expressed in white matter . The blue , brown and green modules contained genes highly expressed in cortex , which is why we refer to these modules as cortical modules . Visual inspection of the module color band below the dendrograms in Figures 3A and 3B suggests that most modules show fairly strong preservation . Oldham et al argued that modules corresponding to evolutionarily older brain regions ( turquoise , yellow , red , black ) show stronger preservation than the blue and green cortical modules [32] . Here we re-analyze these data using module preservation statistics . The most common cross-tabulation approach starts with a contingency table that reports the number of genes that fall into modules of the human network ( corresponding to rows ) versus modules of the chimpanzee network ( corresponding to columns ) . The contingency table in Figure 3C shows that there is high agreement between the human and chimpanzee module assignments . The human modules black , brown , red , turquoise , and yellow have well-defined chimpanzee counterparts ( labeled by the corresponding colors ) . On the other hand , the human green cortical module appears not to be preserved in chimpanzee since most of its genes are classified as unassigned ( grey color ) in the chimpanzee network . Further , the human blue cortical module ( 360 genes ) appears to split into several parts in the chimpanzee network: 27 genes are part of the chimpanzee blue module , 85 genes are part of the chimpanzee brown module , 52 fall in the chimpanzee turquoise module , 155 genes are grey in the chimpanzee network , etc . To arrive at a more quantitative measure of preservation , one may quantify the module overlap or use Fisher's exact test to attach a significance level ( p-value ) to each module overlap ( as detailed in the first section of Supplementary Text S1 ) . The contingency table in Figure 3C shows that every human module has significant overlap with a chimpanzee module . However , even if the resulting p-value of preservation were not significant , it would be difficult to argue that a module is truly a human-specific module since an alternative module detection strategy in chimpanzee may arrive at a module with more significant overlap . In order to quantify the preservation of human modules in chimpanzee samples more objectively , one needs to consider statistics that do not rely on a particular module assignment in the chimpanzee data . We now turn to approaches for measuring module preservation that do not require that module detection has been carried out in the test data set . Figures 4A , B show composite module preservation statistics of human modules in chimpanzee samples . The overall significance of the observed preservation statistics can be assessed using ( Equation 1 ) that combines multiple preservation statistics into a single overall measure of preservation , Figure 4A . Note that shows a strong dependence on module size , which reflects the fact that observing module preservation of a large module is statistically more significant than observing the same for a small module . However , here we want to consider all modules on an equal footing irrespective of module size . Therefore , we focus on the composite statistic which shows no dependence on module size ( Figure 4B ) . The median rank is useful for comparing relative preservation among modules: a module with lower median rank tends to exhibit stronger observed preservation statistics than a module with a higher median rank . Figure 4B shows that the median ranks of the human brain modules . The median rank of the yellow module is 1 , while the median ranks of the blue module is 6 , indicating that the yellow module is more strongly preserved than the blue module . Our quantitative results show that modules expressed mainly in evolutionarily more conserved brain areas such as cerebellum ( turquoise ) and caudate nucleus ( yellow and partly red ) are more strongly preserved than modules expressed primarily in the cortex that is very different between humans and chimpanzees ( green and blue modules ) . Thus the module preservation results of , corroborate Oldham's original finding regarding the relative lack of preservation of cortical modules . Since the modules of this application are defined as clusters , it makes sense to evaluate their preservation using cluster validation statistics . Figure 4C shows that the IGP statistic implemented in the R package clusterRepro [24] also shows a strong dependence on module size in this application . The IGP values of all modules are relatively high . However , the permutation p-values ( panels C and D ) identify the green module as less preserved than the other modules ( , Bonferroni corrected p-value 0 . 43 ) . Figures 4E , F show scatter plots between the observed IGP statistic and and , respectively . In this example , where modules are defined as clusters , the IGP statistic has a high positive correlation ( ) with and a moderately large negative correlation ( ) with . The negative correlation is expected since low median ranks indicate high preservation . While composite statistics summarize the results , it is advisable to understand which properties of a module are preserved ( or not preserved ) . For example , module density based statistics allow us to determine whether the genes of a module ( defined in the reference network ) remain densely connected in the test network . As an illustration , we will compare the module preservation statistics for the human yellow module whose genes are primarily expressed in caudate nucleus ( an evolutionarily old brain area ) , and the human blue module whose genes are expressed mostly in the cortex which underwent large evolutionary changes between humans and chimpanzees . In chimpanzees , the mean adjacency of the genes comprising the human yellow module is significantly higher than expected by chance , with a high permutation statistic , . But the corresponding permutation statistic for the human blue module is only weakly significant , , ( see Supplementary Text S2 and Supplementary Table S1 ) . Thus , the mean adjacency permutation statistic suggests that the blue module is less preserved than the yellow module . For co-expression modules , one can define an alternative density measure based on the module eigengene ( Figures 5A and E ) . The higher the proportion of variance explained by the module eigengene ( defined in the fifth section in Supplementary Text S1 ) in the test set data , the tighter is the module in the test set . The human yellow module exhibits a high proportion of variance explained , , and the corresponding permutation statistic is , . In contrast , for the human blue module we find and the corresponding permutation statistic is , . The permutation statistics again suggest that the yellow module is more preserved than the blue module . Although density based approaches are intuitive , they may fail to detect another form of module preservation , namely the preservation of connectivity patterns among module genes . For example , network module connectivity preservation can mean that , within a given module , a pair of genes with a high connection strength ( adjacency ) in the reference network also exhibits a high connection strength in the test network . This property can be quantified by correlating the pairwise adjacencies or correlations between reference and test networks . For the genes in the human yellow module , the scatter plot in Figure 5B shows pairwise correlations in the human network ( -axis ) versus the corresponding correlations in the chimpanzee network ( -axis ) . The correlation between pairwise correlations ( denoted by ) equals and is highly significant , . The analogous correlation for the blue module , Figure 5F is lower , 0 . 56 , but still highly significant , , in part because of the higher number of genes in the blue module . A related but distinct connectivity preservation statistic quantifies whether intramodular hub genes in the reference network remain intramodular hub genes in the test network . Intramodular hub genes are genes that exhibit strong connections to other genes within their module . This property can be quantified by the intramodular connectivity ( Equation 7 ) : hub genes are genes with high . Intramodular hub genes often play a central role in the module [5] , [33]–[35] . Preservation of intramodular connectivity reflects the preservation of hub gene status between the reference and test network . For example , the intramodular connectivity of the human yellow module is preserved between the human and chimpanzee samples , ( Figure 5C ) . In contrast , the human blue ( cortical ) module exhibits a lower correlation ( preservation ) ( Figure 5G ) . The value is more significant because of the higher number of genes in the blue module . Another intramodular connectivity measure is , which turns out to be highly related with [29] . Figure 5D shows that for the human yellow module is highly preserved in the chimpanzee network ( ) . The corresponding correlation in the human blue module is lower , ( Figure 5H ) . In summary , the observed preservation statistics show that the human yellow module ( related to the caudate nucleus ) is more strongly preserved in the chimpanzee samples than the human blue module ( related to the cortex ) . To further illustrate that modules do not have to be clusters , we now describe an application where modules correspond to KEGG pathways . KEGG ( Kyoto Encyclopedia of Genes and Genomes ) is a knowledge base for systematic analysis of gene functions , linking genomic information with higher order functional information [36] . KEGG also provides graphical representations of cellular processes , such as signal transduction , metabolism , and membrane transport . To illustrate the use of the module preservation approach , we studied the preservation of selected KEGG pathway networks across human and chimpanzee brain correlation networks . While pathways in the KEGG database typically describe networks of proteins , our analysis describes the correlation patterns between mRNA expression levels of the corresponding genes . As before , we define a weighted correlation network adjacency matrix between the genes ( described in the third section of Supplementary Text S1 and [5] ) . For the sake of brevity , we focused the analysis on the following 8 signaling pathways: Hedgehog signaling pathway ( 12 genes in our data sets ) , apoptosis ( 24 genes in our data sets ) , TGF-beta signaling pathway ( 26 genes ) , Phosphatidylinositol signaling system ( 39 genes ) , Wnt signaling pathway ( 55 genes ) , Endocytosis ( 59 genes ) , Calcium signaling pathway ( 78 genes ) , and MAPK signaling pathway ( 93 genes ) . All of these pathways have been shown to play critical roles in normal brain development and function [37]–[41] . We provide a brief description of the functions of these pathways in Methods; more detailed description can be found in the KEGG database and in numerous textbooks . Figures 6A , B show the composite preservation statistics and . Both statistics indicate that the apoptosis module is the least preserved module . To visualize the lack of preservation , consider the circle plots of apoptosis genes in Figures 7 L , M that show pronounced differences in the connectivity patterns among apoptosis genes . While we caution the reader that additional data are needed to replicate these differences , prior literature points to an evolutionary difference for apoptosis genes . For example , a scan for positively selected genes in the genomes of humans and chimpanzees found that a large number of genes involved in apoptosis show strong evidence for positive selection [42] . Further , it has been hypothesized that natural selection for increased cognitive ability in humans led to a reduced level of neuron apoptosis in the human brain [43] . Figure 6A shows that exhibits some dependence on module size . Since we want to compare module preservation irrespective of module size , we focus on the results for the statistic ( Figure 6B ) . A reviewer of this article hypothesized that gene sets ( modules ) known to be controlled by coexpression ( such as Wnt , TGF-beta , SRF , interferon , lineage specific differentiation markers , and NF kappa B ) would show stronger evidence of preservation than gene sets without a priori reason for suspecting such control ( calcium signaling , MAPK , apoptosis , chemotaxis , endocytosis ) . Interestingly , the results for the statistic largely validate this hypothesis . Specifically , the 4 most highly preserved pathways according to are Wnt ( controlled by coexpression ) , calcium ( not controlled ) , Hedgehog ( controlled ) , and Phosphatidylinositol ( not commented upon ) . The 4 least preserved pathways are apoptosis ( not controlled ) , TGF-beta ( controlled ) , MAPK ( not controlled ) , endocytosis ( not controlled ) . Since KEGG pathways are not defined via a clustering procedure it is not clear whether cluster preservation statistics are appropriate for analyzing this example . But to afford a comparison , we also report the findings for the IGP statistic [24] . Figures 6C and D show that IGP identifies Phosphatidilinositol and TGF-beta as the least preserved modules while apoptosis genes are highly preserved . We find no significant relationship between the IGP statistic and our module preservation statistics and ( Figures 6E and F ) . This example highlights that module preservation statistics can lead to very different results from cluster preservation statistics . To understand which aspects of the pathways are preserved , one can study the preservation of density statistics ( Figure 7B ) and of connectivity statistics ( Figure 7C ) . According to , the coexpresssion network formed by apoptosis genes is not preserved . It neither shows evidence of connectivity preservation ( ) nor evidence of density preservation ( , ) . The Hedgehog pathway also shows no evidence of density preservation ( , ) but it shows weak evidence of connectivity preservation ( , ) . The relatively low preservation Z statistics of the Hedgehog pathway may reflect a higher variability due to a small module size ( it contains only genes while the other pathways contain at least 22 genes ) . To explore this further , we studied the observed preservation statistics , which are less susceptible to network size effects than the corresponding statistics . The scatter plots in Figure 7D–H show the correlations between eigengene based connectivity measures between the two species . For the Hedgehog pathway , we find that ( ) which turns out to be higher than that of the TGF- pathway . The lack of preservation of the apoptosis pathway cannot be explained in terms of low module size . Figure 7E shows that it has the lowest observed statistic , . This application outlines how module preservation statistics can be used to study the preservation of KEGG pathway networks . The analysis presented here is but a first step towards characterizing molecular pathway preservation between human and chimpanzee brains , and should be extended through more detailed analyses with additional data sets in the future . A limitation of our microarray data is that they measured expression levels in heterogeneous mixtures of cells . KEGG and GO ( gene ontology ) pathways all essentially describe interactions that take place within cells . So when data have been generated from a heterogeneous mixture of different cell types , it is possible that these relationships are somewhat obscured . It is not obvious that all of the elements of a KEGG pathway should be co-expressed , particularly since the pathways describe protein-protein interactions . We briefly describe an application that quantifies module preservation between male and female cortical samples . The details are described in Supplementary Text S3 and in Supplementary Table S2 . We used microarray data from a recent publication [30] to construct consensus modules [44] in male samples from 2 different data sets . We then studied the preservation of these modules in the corresponding female samples . Cross-tabulation measures indicate that for 3 of the male modules there are no corresponding modules in the female data . However , our network preservation statistics show that in fact the three modules show moderate to strong evidence of preservation . Thus , in this application the network preservation statistics protect one from making erroneous claims of significant sex differences . In Supplementary Text S4 , we re-analyze the mouse liver samples of the F2 mouse intercross [13] , [17] to study whether “female” co-expression modules ( i . e . , modules found in a network based on female mice ) are preserved in the corresponding male network . This application demonstrates that module preservation statistics allow us to identify invalid , non-reproducible modules due to array outliers . A comprehensive table of module preservation statistics for this application is presented in Supplementary Table S3 . Our preservation statistics allow one to evaluate whether a given module is preserved in another network . A related but distinct data analysis task is to construct modules that are present in several networks . By construction , a consensus module can be detected in each of the underlying networks . A challenge of many real data applications is that it is difficult to obtain independent information ( a “gold standard” ) that allows one to argue that a module is truly preserved . To address this challenge , we use the consensus network application where by construction , modules are known to be preserved . This allows us to determine the range of values of preservation statistics when modules are known to be preserved . In Supplementary Text S5 and Supplementary Table S4 , we report three empirical studies of consensus modules [44] which are constructed in such a way that genes within consensus modules are highly co-expressed in all given input microarray data sets . The consensus module application provides further empirical evidence that module preservation statistics and the recommended threshold values provide sufficient statistical power to implicate preserved modules . In Table 1 , we categorize the statistics according to which aspects of module preservation they measure . For example , we present several seemingly different versions of density and connectivity based preservation statistics . But for correlation network modules , close relationships exist between them as illustrated in Figure 8 . The hierarchical clustering trees in Figure 8 show the correlations between the observed preservation statistics in our real data applications . As input of hierarchical clustering , we used a dissimilarity between the observed preservation statistics , which was defined as one minus the correlation across all studied reference and test data sets . Overall we observe that statistics within one category tend to cluster together . We also observe that separability appears to be weakly related to the density and connectivity preservation statistics . Cross-tabulation statistics correlate strongly with density and connectivity statistics in the study of human and chimpanzee brain data , but the correlation is weak in the study of sex differences in human brain data . We derive relationships between module preservations statistics in the sixth section of Supplementary Text S1 . In particular , the geometric interpretation of correlation networks [29] , [45] can be used to describe situations when close relationship exist among the density based preservation statistics ( , , , ) , among the connectivity based preservation statistics ( , , , ) , and between the separability statistics ( , ) . These relationships justify aggregating the module preservation statistics into composite preservation statistics such as ( Equation 1 ) and ( Equation 34 ) . To illustrate the utility and performance of the proposed methods , we consider 7 different simulation scenarios that were designed to reflect various correlation network applications . An overview of these simulations can be found in Figure 9 . A more detailed description of the simulation scenarios is provided below . Table 3 shows the performance grades of module preservation statistics in the different simulation scenarios . The highest grade of indicates excellent performance . We find that the proposed composite statistics ( mean grade ) and ( mean grade ) perform very well in distinguishing preserved from non-preserved modules . In contrast , cross-tabulation based statistics only obtain a mean grade of . Since several simulation scenarios test the ability to detect connectivity preservation ( as opposed to density preservation ) , it is no surprise that on average cluster validation statistics do not perform well in these simulations . For example , the IGP cluster validation statistic ( Table 4 ) obtains a mean grade of across the scenarios . But the IGP performs very well ( grade 4 ) when studying the preservation of strongly preserved clusters ( scenario 2 ) . Table 3 also shows the performance of individual preservation statistics . Note that density based preservation statistics perform well in scenarios 1 through 5 but fail in scenarios 6 and 7 . On the other hand , all connectivity based statistics perform well in scenarios 6 and 7 . The relatively poor performance of is one of the reasons why we did not include it into our composite statistics . In the following , we describe the different simulation scenarios in more detail . Additional descriptions of the simulations can be found Supplementary Text S6 and in Supplementary Table S5 . As caveat , we mention that we only considered 7 scenarios that aim to emulate selected situations encountered in co-expression networks . The performance of these preservation statistics may change in other scenarios . A comprehensive evaluation in other scenarios is needed but lies beyond our scope . R software tutorials describing the results of our simulation studies can be found on our web page and will allow the reader to compare different methods using our simulated data . Preservation statistics described in this article have been implemented in the freely available statistical language and environment R . A complete evaluation of observed preservation statistics and their permutation statistics is implemented in function modulePreservation , which is included in the updated WGCNA package originally described in [46] . For each user-defined reference network both preservation and quality statistics are calculated considering each of the remaining networks as test network . Our tutorials illustrate the use of the modulePreservation function on real and simulated data . All data , code and tutorials can be can be downloaded from http://www . genetics . ucla . edu/labs/horvath/CoexpressionNetwork/ModulePreservation .
Our applications provide a glimpse of the types of research questions that can be addressed with the module preservation statistics . In general , methods for quantifying module preservation have several uses . First and foremost they can be used to determine which properties of a network module are preserved in another network . Thus , module preservation statistics are a valuable tool for validation as well as differential network analysis . Second , they can be used to define a global measure of module structure preservation by averaging the preservation statistic across multiple modules or by determining the proportion of modules that are preserved . A third use of module preservation statistics is to define measures of module quality ( or robustness ) , which may inform the module definition . For example , to measure how robustly a module is defined in a given correlation network , one can use resampling techniques to create reference and test sets from the original data and evaluate module preservation across the resulting networks . Thus , any module preservation statistic naturally gives rise to a module quality statistic by applying it to repeated random splits ( interpreted as reference and test set ) of the data . By averaging the module preservation statistic across multiple random splits of the original data one arrives at a module quality statistic . We briefly point out situations when alternative procedures may be more appropriate . To identify modules that are present in multiple data sets it can be preferable to consider all data sets simultaneously in a consensus module detection procedure . For example , the consensus module approach described in application 6 results in modules that are present in multiple networks by construction . To identify individual genes that diverge between two data sets , one can use standard discriminative analysis techniques . For example , differentially expressed genes can be found with differential expression analysis and differentially co-expressed genes can be found using differential co-expression analysis [17] . While cluster analysis and network analysis are different approaches for studying high-dimensional data , there are some commonalities . For example , it is straightforward to turn a network adjacency matrix ( which is a similarity measure ) into a dissimilarity measure which can be used as input of a clustering procedure ( e . g . , hierarchical clustering or partitioning around medoids ) [25] . If a module is defined using a clustering procedure , one can use cluster preservation statistics as module preservation statistics . Conversely , our adjacency based module preservation statistics give rise to cluster preservation statistics since a dissimilarity measure ( used for the cluster definition ) can also be transformed into a network adjacency matrix . In some of our applications where modules are defined as clusters , we find that is highly correlated with the IGP cluster validation statistic [24] across modules . In our simulations , we observe that IGP and tend to be highly correlated when modules correspond to clusters with varying extents of preservation . This illustrates that leads to sensible results in the special case when modules are defined as clusters . When modules are not defined via a clustering procedure ( e . g . in our KEGG pathway application ) , we find pronounced differences between and the IGP statistic . The proposed composite preservation statistics and outperform ( or tie with ) the IGP statistic in all simulation scenarios ( see Table 4 ) . More comprehensive comparisons involving additional simulation scenarios and other cluster preservation statistics are needed but lie beyond our scope . Although not the focus of this work , we mention that a major application of density-based statistics is to measure module quality in the reference data ( for example , to compare various module detection procedures ) . Module quality measures can be defined using density-based and separability-based module preservation measures: the density and separability of a module in the reference network measures its homogeneity and separateness , respectively . In contrast , connectivity based measures ( which contrast the reference adjacency matrix with the test network adjacency matrix ) are not directly related to module quality measures ( unless a data splitting approach is used in the reference data ) . Module quality measures based on density and separability measures can be used to confirm that the reference modules are well defined . A section in Supplementary Text S1 describes module quality measures that are implemented in the R function modulePreservation . The proposed preservation statistics have several limitations including the following . First , our statistics only apply to undirected networks . Generalization of our statistics to directed networks is possible but outside of our scope . A second limitation concerns statistics of connectivity preservation that are based on correlating network adjacencies , intramodular connectivities , etc , between the reference and the test networks . Because Pearson correlation is sensitive to outliers , it may be advantageous to use an outlier-resistant correlation measure , e . g . , the Spearman correlation or the biweight midcorrelation [47] , [48] implemented in the WGCNA package [46] . Robust correlation options have been implemented in the R function modulePreservation . A third limitation is that a high value of a preservation statistic does not necessarily imply that the module could be found by a de novo module detection analysis in the test data set . For example , if a module is defined using cluster analysis , then the resulting test set modules may not have significant overlap with the original reference module in a cross-tabulation table . As explained before , this potential limitation is a small price to pay for making a module preservation analysis independent from the vagaries of module detection . A fourth limitation is that it is difficult to pick thresholds for preservation statistics . To address this issue , we use permutation tests to adjust preservation statistics for random chance by defining Z statistics ( Equation 29 ) . The R function modulePreservation also calculates empirical p-values for the preservation statistics . A potential disadvantage of permutation test based preservation statistics ( compared to observed statistics and ) is that they typically depend on module sizes . The choice of thresholds is discussed in the Methods section . A fifth limitation is computational speed when it comes to calculating permutation test based statistics ( e . g . ) . When only and observed preservation statistics are of interest , we recommend to avoid the computationally intensive permutation test procedure by setting nPermutations = 0 in the modulePreservation function . A sixth limitation is that the different preservation statistics may disagree with regard to the preservation of a given module . While certain aspects of a module may be preserved , others may not be . In our simulation studies , we present scenarios where connectivity statistics show high preservation but density measures do not and vice versa . Since both types of preservation statistics will be of interest in practice , our R function modulePreservation outputs all preservation statistics . Although we aggregate several preservation statistics into composite statistics , we recommend to consider all of the underlying preservation statistics to determine which aspects of a module are preserved . While we describe situations when cross-tabulation based preservation statistics are not applicable , we should point out that cross-tabulation statistics also have the following advantages . First , they are often intuitive . Second , they can be applied when no network structure is present . Third , they work well when module assignments are strongly preserved and the modules remain separate in the test network . In the first section of Supplementary Text S1 , we describe cross-tabulation based module preservation statistics which we have found to be useful . We note that the interpretation of gene co-expression relationships depends heavily on biological context . For example , in a dataset consisting of samples from multiple tissue types , co-expression modules ( that is , modules defined by co-expression similarity ) will often distinguish genes that are expressed in tissue-specific patterns ( e . g . , [32] , [49] ) . In a dataset consisting of samples from a single tissue type , co-expression modules may distinguish sets of genes that are preferentially expressed in distinct cell types that comprise that tissue ( e . g . , [30] ) . In a dataset consisting of samples from a homogeneous cellular population , co-expression modules may correspond more directly to sets of genes that work in tandem to perform various intracellular functions . In many cases , co-expression modules may not present immediate functional interpretations . However , previous work has shown that many co-expression modules are conserved across phylogeny [4] , [21] , [32] , [50] , enriched with protein-protein interactions [7] , [21] , [30] , and enriched with specific functional categories of genes , including ribosomal , mitochondrial , synaptic , immune , hypoxic , mitotic , and many others [7] , [21] , [30] , [33] . Although elucidating the functional significance of identified co-expression modules requires substantial effort from biologists and bioinformaticians , the importance of co-expression modules lies not only in their functional interpretation , but also in their reproducibility . Because transcriptome organization in a given biological system is highly reproducible [30] , co-expression modules provide a natural framework for comparisons between species , tissues , and pathophysiological states . This framework can reduce dimensionality by approximately three orders of magnitude ( e . g . , moving from say 40 , 000 transcripts to 40 modules ) [29] , [33] , while simultaneously placing identified gene expression differences within specific cellular and functional contexts ( inasmuch as the cellular and functional contexts of the modules are understood ) . The co-expression modules themselves are simply summaries of interdependencies that are already present in the data . Preservation statistics can be used to address an important question in co-expression module based analyses: how to show whether the modules are robust and reproducible across data sets . Given the above-mentioned limitations , it is reassuring that the proposed module preservation statistics perform well in 6 real data applications and in 7 simulation scenarios . Although it would be convenient to have a single statistic and a corresponding threshold value for deciding whether a module is preserved , this simplistic view fails to realize that module preservation should be judged according to multiple criteria ( e . g . , density preservation , connectivity preservation , etc ) . Individual preservation statistics provide a more nuanced and detailed view of module preservation . Before deciding on module preservation , the data analyst should decide which aspects of a module preservation are of interest .
Due to space limitations , we have moved our description of cross-tabulation based preservation statistics to the first section of Supplementary Text S1 . We briefly mention related measures reported in the literature . Our co-clustering statistic ( in the first section of Supplementary Text S1 ) is similar to the cluster robustness measure [23] , [51] and the accuracy based measures are conceptually related to a cluster discrepancy measure proposed in [23] . Cluster validation measures and approaches are reviewed in [52] . Many cross-tabulation based methods have been proposed to compare two clusterings ( module assignments ) , e . g . , the Rand index [53] or prediction based statistics [26] , [27] . Our methods are applicable to weighted or unweighted networks that are specified by an adjacency matrix , an matrix with entries in . The component encodes the network connection strength between nodes and . In an unweighted network , the nodes , can be either connected ( ) or disconnected ( ) . In a weighted network , the adjacency takes on a value in that encodes the connection strength between the nodes . Networks do not have to be defined with regard to correlations . Instead , they may reflect protein binding information , participation in molecular pathways , etc . In the following , we assume that we are dealing with an undirected network encoded by a symmetric adjacency matrix: . But several of our module preservation statistics can easily be adapted to the case of directed network represented by a non-symmetric adjacency matrix . To simplify notation , we introduce the function that takes a symmetric matrix and turns it into a vector of non-redundant components , ( 2 ) We assume that the diagonal of the matrix is fixed ( for example , if is an adjacency matrix , the diagonal is defined to be 1 ) , so we leave the diagonal elements out . Thus , the vector contains components . A network represented by its adjacency matrix can be characterized by a number of network concepts ( also known as network indices ) [29] , [45] . The network density is the mean adjacency , ( 3 ) Higher density means more ( or more strongly ) interconnected nodes . The connectivity ( also known as degree ) of node is defined asThe connectivity of node measures its connection strength with other nodes . The higher the more centrally located is the node in the network . The Maximum Adjacency Ratio ( MAR ) [29] of node is defined as ( 4 ) The is only useful for distinguishing the connectivity patterns of nodes in a weighted network since it is constant ( ) in unweighted networks . The clustering coefficient [54] of node is defined as ( 5 ) While the clustering coefficient was originally defined for unweighted networks , Equation 5 can be used to extend its definition to weighted networks [5]: one can easily show that implies . Many network analyses define modules , that is subsets of nodes that form a sub-network in the original network . Modules are labeled by integer labels , and sometimes by color labels . Color labels can be convenient for visualizing modules in network plots . For module with nodes , the dimensional adjacency matrix between the module nodes is denoted by . Denote by the set of node indices of the nodes in module . Network concepts ( such as the connectivity , clustering coefficient , MAR etc ) defined for are defined as intramodular network concepts . For example , the density of module is defined as the mean adjacency of : ( 6 ) The intramodular connectivity of node in module is defined as the sum of connection strengths to other nodes within the same module , ( 7 ) Nodes with high intramodular connectivity are referred to as intramodular hub nodes . Here we describe module preservation statistics that can be used to determine whether a module that is present in a reference network ( with adjacency ) can also be found in an independent test network ( with adjacency ) . Specifically , assume the vector encodes the module assignments in the reference network . Thus ( ) if node is assigned to module . We reserve the label ( and color grey ) for nodes that are not assigned to any module . For a given module with nodes , the module adjacency matrices are denoted and in the reference and test networks , respectively . We propose network concepts that can be useful for determining whether a module ( found in the reference network ) is preserved in the test network . Intuitively , one may call a module preserved if it has a high density in the test network . We define the mean adjacency for module as the module density in the test network , ( 8 ) Some of the density statistics such as the mean adjacency are similar to previously described methods based on within-cluster and between-cluster dissimilarities [22] . For example , the mean intramodular adjacency ( Equation 8 ) is oppositely related to the within-module scatter used in assessing the quality of clusters based on a dissimilarity [55] . The network density measure can be considered a generalization of the cluster cohesiveness measure [28] to ( possibly weighted ) networks . Other network concepts may be used to obtain a summary statistic of a module . For example , our R function modulePreservation also calculate preservation statistics based on the mean ( Equation 5 ) : ( 9 ) and mean MAR ( Equation 4 ) : ( 10 ) in the test network . Connectivity preservation statistics quantify how similar connectivity of a given module is between a reference and a test network . For example , module connectivity preservation can mean that , within a given module , nodes with a high connection strength in the reference network also exhibit a high connection strength in the test network . This property can be quantified by the correlation of intramodular adjacencies in reference and test networks . Specifically , if the entries of the first adjacency matrix are correlated with those of the second adjacency matrix then the adjacency pattern of the module is preserved in the second network . Therefore , we define the adjacency correlation of the module network as ( 11 ) High indicates that adjacencies within the module in the reference and test networks exhibit similar patterns . If module is preserved in the second network , the highly connected hub nodes in the reference network will often be highly connected hub nodes in the test network . In other words , the intramodular connectivity in the reference network should be highly correlated with the corresponding intramodular connectivity in the test network . Thus , we define the correlation of intramodular connectivities , ( 12 ) where and are the vectors of intramodular connectivities of all nodes in module in the reference and test networks , respectively . Analogously , we define the correlation of clustering coefficients and maximum adjacency ratios , ( 13 ) ( 14 ) The specific nature of correlation networks allows us to define additional module preservation statistics . The underlying information carried by the sign of the correlation can be used to further refine the statistics irrespective of whether a signed or unsigned similarity is used in network construction . To simplify notation , we define ( 18 ) We will use the notation for the correlation matrix restricted to the nodes in module . We define the mean correlation density of module as ( 19 ) Thus the correlation measure of module preservation is the mean correlation in the test network multiplied by the sign of the corresponding correlations in the reference network . We note that a correlation that has the same sign in the reference and test networks increases the mean , while a correlation that changes sign decreases the mean . Because the preservation statistic keeps track of the sign of the corresponding correlation in the reference network , we call it the mean sign-aware correlation . To measure the preservation of connectivity patterns within module between the reference and test networks , we define a correlation-based measure similar to the statistic ( Equation 11 ) : ( 20 ) In our applications we find that the correlation-based preservation statistic is preferable to its general network counterpart ; therefore , we only report . Typical values of module preservation statistics depend on many factors , for example on network size , module size , number of observations etc . Thus , instead of attempting to define thresholds for considering a preservation statistic significant , we use permutation tests . Specifically , we randomly permute the module labels in the test network and calculate corresponding preservation statistics . This procedure is repeated times . For each statistic labeled by index we then calculate the mean and the standard deviation of the permuted values . We define the corresponding statistic as ( 29 ) where is the observed value for the statistic . Under certain conditions , one can prove that under the null hypothesis of no preservation the statistic asymptotically follows the standard normal distribution . Thus , under the assumption that the number of permutations is large enough to approximate the asymptotic regime , one can convert the statistics to p-values using the standard normal distribution . Our R function modulePreservation outputs the asymptotic p-values for each statistic . But we should point out that it would be preferable to use a full permutation test to calculate permutation test p-values . We often report Z statistics ( instead of p-values ) for the following two reasons: First , permutation p-values of preserved modules are often astronomically significant ( say ) and it is more convenient to report the results on a Z scale . The second reason is computational speed . The calculation of a Z statistic only requires one to estimate the mean and variance under the null hypothesis , for which fewer permutations are needed . To estimate a permutation test p-value accurately would require computational time far beyond practical limits . In the sixth section of Supplementary Text S1 , we describe when close relationships exist between many of the preservation statistics presented above . This suggests that one can combine the individual preservation statistics into a composite preservation statistic . We propose two composite preservation statistics . The first composite statistic ( Equation 1 ) summarizes the individual Z statistic values that result from the permutation test . The second composite statistic ( Equation 34 ) summarizes the ranks of the observed preservation statistics . The relationships derived in Supplementary Text S1 suggest to summarize the density based preservation statistics as follows: ( 30 ) Similarly , the connectivity based preservation statistics can be summarized as follows: ( 31 ) When density and connectivity based preservation statistics are equally important for judging the preservation of a network module , one can consider the composite summary statistic ( Eq . 1 ) Alternatively , a weighted average between and can be formed to emphasize different aspects of module preservation . Future research could investigate alternative ways of aggregating preservation statistics . While our simulations and applications show that works well for distinguishing preserved from non-preserved modules , we do not claim that it is optimal . In practice , we recommend to consider all individual preservation statistics . Our simulated as well as empirical data show that the separability tends to have low agreement ( as measured by correlation ) with the other preservation statistics ( Figure 8 ) . Since the statistic often performs poorly , we did not include it in our composite statistics . Since is not a permutation statistic but rather the median of other statistics , we do not use it to calculate a p-value . Instead , the R function modulePreservation calculates a summary p-value ( ) as follows . For each permutation Z statistic , it calculates the corresponding p-value assuming that , under the null , the Z statistic has a normal distribution . The normal distribution can be justified using relatively weak assumptions described in statistics textbooks . As a caveat , we mention that we use preservation p-values as descriptive ( and not inferential ) measures . On the other hand , we cannot assume normality for . Hence , instead of calculating a p-value corresponding to , we calculate a summary log-p-value instead , given as the median of the log-p-values of the corresponding permutation statistics . Because of the often extremely significant p-values associated with the permutation statistics , it is desirable to use logarithms ( here base 10 ) . We emphasize that the summary log-p-value is not directly associated with ; rather , it is a separate descriptive summary statistic that summarizes the p-values of the individual permutation statistics . It seems intuitive to call a module with preserved , but our simulation studies argue for a more stringent threshold . We recommend the following threshold guidelines: if , there is strong evidence that the module is preserved . If there is weak to moderate evidence of preservation . If , there is no evidence that the module preserved . As discussed below , these threshold values should be considered rough guidelines since more ( or less ) stringent thresholds may be required depending on the application . The modulePreservation R function calculates multiple preservation statistics and corresponding asymptotic p-values . Similar to the case of statistics , a threshold that is appropriate in one context may not be appropriate in another . The choice of thresholds depends not only on the desired significance level but also on the research question . When several preservation statistics are analyzed individually for any indication of module preservation then the threshold should correct for the these multiple comparisons . Since several “tests” for preservation are considered , an obvious choice is to use one of the standard correction approaches ( e . g . , Bonferroni correction ) for determining the threshold that should be put on multiple tests . Toward this end , one can use the uncorrected , individual preservation statistics and p-values output by the modulePreservation function . A Bonferroni correction would be a conservative but probably too stringent approach in light of the fact that many of the preservation statistics are closely related ( see the 6th section in Supplementary Text S1 ) . Given the strong relationships among some preservation statistics , we have found it useful to aggregate the statistics ( and optionally the empirical p-values ) in a statistically robust fashion using the median but many alternative procedures are possible . To provide some guidance , we recommend thresholds for that we have found useful in our simulations studies ( Supplementary Text S6 ) and in our empirical studies . In some applications such as the human vs . chimpanzee comparison described above , one is interested in ranking modules by their overall preservation in the test set , i . e . , one is interested in a relative measure of module preservation . Since our simulations and applications reveal that ( Equation 1 ) strongly depends on module size , this statistic may not be appropriate when comparing modules of very different sizes . Here we define an alternative rank-based measure that relies on observed preservation statistics rather than the permutation statistics . For each statistic , we rank the modules based on the observed values . Thus , each module is assigned a rank for each observed statistic . We then define the median density and connectivity ranks ( 32 ) ( 33 ) Analogously to the definition of , we then define the statistic as the mean of and , ( 34 ) Alternatively , a weighted average of the ranks could be formed to emphasize different aspects of module preservation . It is worth repeating that a composite rank preservation statistic is only useful for studying the relative preservation of modules , e . g . , we use for studying which human brain co-expression modules are least preserved in chimpanzee brain networks . While all examples in this article relate to correlation ( in particular , co-expression ) networks , we have also implemented methods and R function that can be applied to general networks specified only by an adjacency matrix . For example , this function could be used to study module preservation in protein-protein interaction networks . We also define a composite statistic , which is defined for a general network specified by an adjacency matrix ( Eq . 35 ) . ( 35 ) where and . Note that is only computed with regard to a subset of the individual statistics . To invoke this preservation statistic , set dataIsExpr = FALSE in the modulePreservation R function . A detailed description of the methods is provided Supplementary Text S1 which contains the following sections . In the first section of Supplementary Text S1 , we describe standard cross-tabulation based module preservation statistics . Specifically , we present three basic cross-tabulation based statistics for determining whether modules in a reference data set are preserved in a test data set . These statistics do not assume that a test network is available . Instead , module assignments in both the reference and the test networks are needed . In the second section , we briefly review a hierarchical clustering procedure for module detection . Many methods exist for defining network modules . In this section , we describe the method used in our applications but it is worth repeating that our preservation statistics apply to most alternative module detection procedures . In the third section , we review the definition of signed and unsigned correlation networks . Correlation networks are a special case of general undirected networks in which the adjacency is constructed on the basis of correlations between quantitative variables . In the fourth section , we present module quality statistics , which we are implemented in the modulePreservation R function . While our main article focuses on statistics that measure preservation of modules between a reference and a test network , we briefly discuss the application of some of the preservation statistics to the related but distinct task of measuring module quality in a single ( reference ) network . More precisely , the density and separability statistics can be applied to the reference network without a reference to a test network . The results can then be interpreted as measuring module quality , that is how closely interconnected the nodes of a module are or how well a module is separated from other modules in the network . In the fifth section , we review the notation for the singular value decomposition and for defining a module eigennnode . The section describes conditions when the eigenvector is an optimal way of representing a correlation module . It also reviews the definition of ( the proportion of the variance explained by the eigennode ) . We derive a relationship between and the module membership measures , which will be useful for deriving relationships between preservation statistics . In the sixth section , we investigate relationships between preservation statistics in correlation networks . The KEGG database and many textbooks describe these fundamental pathways in more detail but the following terse descriptions may be helpful . The Wnt signaling pathway describes a network of proteins most well known for their roles in embryogenesis and cancer , but also involved in normal physiological processes in adult animals . The Hedgehog signaling pathway is one of the key regulators of animal development conserved from flies to humans . The apoptosis pathway mediates programmed cell death . Endocytosis is the process by which cells absorb molecules ( such as proteins ) from outside the cell by engulfing them with their cell membrane . The Transforming growth factor beta ( TGF- ) signaling pathway is involved in many cellular processes in both the adult organism and the developing embryo including cell growth , cell differentiation , apoptosis , cellular homeostasis and other cellular functions . The Phosphatidylinositol signaling system facilitates environmental information processing and signal transduction . The mitogen-activated protein kinase ( MAPK ) cascade is a highly conserved pathway that is involved in various cellular functions , including cell proliferation , differentiation and migration . The Calcium signaling pathway describes how calcium can act in signal transduction after influx resulting from activation of ion channels , or as a second messenger caused by indirect signal transduction pathways such as G protein-coupled receptors . | In network applications , one is often interested in studying whether modules are preserved across multiple networks . For example , to determine whether a pathway of genes is perturbed in a certain condition , one can study whether its connectivity pattern is no longer preserved . Non-preserved modules can either be biologically uninteresting ( e . g . , reflecting data outliers ) or interesting ( e . g . , reflecting sex specific modules ) . An intuitive approach for studying module preservation is to cross-tabulate module membership . But this approach often cannot address questions about the preservation of connectivity patterns between nodes . Thus , cross-tabulation based approaches often fail to recognize that important aspects of a network module are preserved . Cross-tabulation methods make it difficult to argue that a module is not preserved . The weak statement ( “the reference module does not overlap with any of the identified test set modules” ) is less relevant in practice than the strong statement ( “the module cannot be found in the test network irrespective of the parameter settings of the module detection procedure” ) . Module preservation statistics have important applications , e . g . we show that the wiring of apoptosis genes in a human cortical network differs from that in chimpanzees . | [
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| 2011 | Is My Network Module Preserved and Reproducible? |
In the pathogenic bacterium Bacillus anthracis , virulence requires induced expression of the anthrax toxin and capsule genes . Elevated CO2/bicarbonate levels , an indicator of the host environment , provide a signal ex vivo to increase expression of virulence factors , but the mechanism underlying induction and its relevance in vivo are unknown . We identified a previously uncharacterized ABC transporter ( BAS2714-12 ) similar to bicarbonate transporters in photosynthetic cyanobacteria , which is essential to the bicarbonate induction of virulence gene expression . Deletion of the genes for the transporter abolished induction of toxin gene expression and strongly decreased the rate of bicarbonate uptake ex vivo , demonstrating that the BAS2714-12 locus encodes a bicarbonate ABC transporter . The bicarbonate transporter deletion strain was avirulent in the A/J mouse model of infection . Carbonic anhydrase inhibitors , which prevent the interconversion of CO2 and bicarbonate , significantly affected toxin expression only in the absence of bicarbonate or the bicarbonate transporter , suggesting that carbonic anhydrase activity is not essential to virulence factor induction and that bicarbonate , and not CO2 , is the signal essential for virulence induction . The identification of this novel bicarbonate transporter essential to virulence of B . anthracis may be of relevance to other pathogens , such as Streptococcus pyogenes , Escherichia coli , Borrelia burgdorferi , and Vibrio cholera that regulate virulence factor expression in response to CO2/bicarbonate , and suggests it may be a target for antibacterial intervention .
Bacillus anthracis is a Gram-positive , endospore-forming bacterium that is the etiological agent of anthrax . Anthrax is primarily a disease of grazing herbivores with human infections as the result of either direct contact with infected animal products or intentional dispersion of anthrax spores as a biological weapon . Anthrax can manifest as localized , cutaneous infections or as systemic infections resulting from spore inhalation , ingestion , or spread of cutaneous infections . While localized , cutaneous infections are curable , systemic infections are almost uniformly fatal with death occurring within days of initial infection [1] . Virulence in the mammalian host requires expression of both the anthrax toxin and the antiphagocytic capsule . The tripartite anthrax toxin is encoded by three non-contiguous genes , lef , cya and pagA , carried on the virulence plasmid pXO1 [2] . lef encodes Lethal Factor ( LF ) , a zinc metalloprotease targeting host MAP-kinase signaling [3] , cya encodes Edema Factor ( EF ) , an adenylate cyclase that increases cellular cAMP levels [4] , and pagA encodes Protective Antigen ( PA ) , which forms a pore allowing entry of toxin components [5] . The antiphagocytic , poly-D-glutamic acid capsule , which is essential for bacterial dissemination in the host [6] , is encoded by genes in the cap operon carried on virulence plasmid pXO2 [7] , [8] . The regulatory protein AtxA , encoded by the atxA gene on pXO1 , is required for the transcription of both the toxin genes and the capsule operon [9] , [10] . Control of AtxA , in turn , is integrated into several metabolic regulatory circuits , including the sporulation phosphorelay through AbrB [11] and the phosphoenolpyruvate-dependent phosphotransferase system via regulated phosphorylation/dephosphorylation of histidine residues [12] . Many environmental cues influence the expression of B . anthracis virulence factors , one of the earliest identified being the effect of CO2/bicarbonate levels on capsule production and virulence [13] . Elevated CO2/bicarbonate levels are thought to serve as a signal of the mammalian host environment and a cue to induce expression of virulence factors . Incubation of B . anthracis in media supplemented with sodium bicarbonate and grown under elevated CO2 levels ( above 5% ) results in an approximately 10-fold increase in transcription of all three toxin genes [14] and a more than 20-fold increase in capsule operon transcription [15] . AtxA is required for CO2/bicarbonate induction of toxin and capsule genes , however , AtxA expression is unaffected by increased CO2/bicarbonate levels [16] , [17] . The presence of additional CO2/bicarbonate regulatory components on the main chromosome is suggested by the observation that pagA transcription is induced by CO2/bicarbonate in a pXO1− pXO2− strain when atxA and pagA only are supplied on multicopy plasmids [18] . Additionally , an uncharacterized gene carried on pXO1 may also play a role in CO2/bicarbonate regulation of toxin expression [19] . Notwithstanding these indirect suggestions of more extensive regulation , additional CO2/bicarbonate regulatory components have yet to be directly identified . Without a mechanistic basis for the CO2/bicarbonate regulation of virulence factor expression , our focus turned to identifying conserved responses to CO2/bicarbonate homeostasis and relating these pathways to B . anthracis . Study of CO2/bicarbonate metabolism is complicated by its labile nature , with CO2 , H2CO3 , HCO3− , and CO32− existing in equilibrium depending on pH , temperature , and partial pressure of CO2 . Under typical biological conditions , CO2 generally diffuses across membranes; once inside the cell , carbonic anhydrases can actively interconvert CO2 and bicarbonate . On the other hand , bicarbonate is impermeable across lipid bilayers , and many cellular systems rely on dedicated transporters to import bicarbonate [20] . One of the best-studied bacterial bicarbonate transporters is the CmpABCD ABC transport system in the cyanobacterial species Synechococcus PCC 7942 ( Figure 1 ) [21] . In this bacterium , elevated CO2 concentration around ribulose-1 , 5-bisphosphate carboxylase/oxygenase ( Rubisco ) is essential for efficient carbon fixation . Synechococcus uses this high affinity bicarbonate transporter to import and accumulate inorganic carbon ( such as HCO3− ) , which can then be converted by carbonic anhydrase to CO2 in the presence of Rubisco in a specialized compartment called the carboxysome [22] . Here we report the identification of an ABC transporter with similarity to the Synechococcus CmpABCD system that is essential to virulence in B . anthracis . Deletion of the genes for the transporter reduced bicarbonate uptake and eliminated toxin gene induction ex vivo in response to bicarbonate . More importantly , the strain lacking the transporter was avirulent in a mouse model of anthrax infection , demonstrating the importance of this pathway for recognition of the host environment and pathogenesis .
Despite the recognized role of CO2/bicarbonate in toxin synthesis , the mechanism linking CO2/bicarbonate levels to toxin regulation and virulence of B . anthracis remains to be characterized . As a reverse genetic approach to identify components of the CO2/bicarbonate regulatory pathway , we searched the B . anthracis Sterne strain genome ( GenBank: AE017225 ) for protein sequences similar to the products of the cmpABCD operon encoding the bicarbonate transporter of Synechococcus elongatus PCC 6301 ( GenBank: AP008231 ) . Unlike many ABC transporters , which are characterized largely based upon multisubunit organization including proteins with ABC-type ATP-binding domains in association with hydrophobic permease domains , identification of CmpABCD-like bicarbonate ABC transporters is aided by structural features of the substrate binding domain for bicarbonate and the highly similar nitrate transporters [23] , [24] . A BLASTP search revealed similarity between components of CmpABCD system and the products of the BAS2714-12 and BAS4675-77 genes ( Table 1 ) . Both operons had yet to be characterized but appeared to encode components of ABC transporters . BAS2714 and BAS4676 encode ATP-binding proteins , BAS2713 and BAS4675 are predicted to encode substrate binding proteins , and BAS2712 and BAS4677 are likely transmembrane permease proteins . Unlike cmpABCD , which encodes two ATP-binding proteins ( CmpC , also containing a CmpA-like substrate-binding protein , and CmpD ) ( Figure 1 ) , the two B . anthracis loci encode only one single-domain ATP-binding protein ( BAS2714 or BAS4676 ) . A role in bicarbonate transport was suggested by the presence in the BAS2713 and BAS4675 proteins of a TauA domain ( NCBI Accesion Number COG0715 ) , a conserved element associated with periplasmic substrate binding components of ABC transporters specific for nitrate , sulfonate , or bicarbonate . Furthermore , a fold-recognition bioinformatic analysis by the FFAS03 server revealed a highly significant score ( −60 . 5 to −64 . 8 ) between BAS2713 or BAS4675 and the bicarbonate ( CmpA ) and nitrate ( NrtA ) substrate binding protein , suggesting a structural and functional homology despite the limited sequence conservation . Based on similarity to the CmpABCD proteins and conserved features shared by bicarbonate transporters , the BAS2714-12 and BAS4675-77 systems appeared to be good candidates to function as a B . anthracis bicarbonate ABC transporter . To investigate the role of BAS2714-12 and BAS4675-77 in bicarbonate metabolism and virulence , B . anthracis 34F2 ( pXO1+ pXO2− ) derivative strains were generated containing a markerless deletion of the three genes annotated as BAS2714-12 or BAS4675-77 . As described in the Experimental Procedures , using plasmid pAW091 , a region from 97 nucleotides upstream of the translation start site of BAS2714 to 33 nucleotides upstream of the termination codon of BAS2712 was deleted . This completely eliminated the coding regions of BAS2714 and BAS2713 while leaving a small portion of the 3′ end of the BAS2712 coding sequence and the entire intergenic region between BAS2712 and BAS2711 intact so as to leave potential regulatory sequences controlling expression of the downstream gene , BAS2711 . Similarly , for the deletion of BAS4675-77 , the integration of plasmid pAW093 resulted in the deletion of a region from 70 nucleotides downstream of the translation start site of BAS4675 to 52 nucleotides upstream of the termination codon of BAS4677 . This completely eliminated the coding regions of BAS4676 while leaving a small portion of the 5′ end of the BAS4675 coding sequence and a small portion of the 3′ end of the BAS4677 coding sequence intact so as to leave potential regulatory sequences controlling expression of genes upstream and downstream of the operon . Under all conditions tested , deletion of BAS2714-12 or BAS4675-77 had no significant effect on growth relative to the parental strain 34F2 ( Figure 2 and data not shown ) . Expression of pagA , encoding the PA subunit of anthrax toxin , was monitored in different growth conditions , simulating host and non-host environments , known to affect virulence gene expression . A pagA-lacZ reporter on the replicative vector pTCV-lac [25] was transformed into the parental , the ΔBAS2714-12 and ΔBAS4675-77 strains and used to monitor pagA expression levels through β–galactosidase activity . To replicate non-host conditions that result in low-level expression of toxin genes , the strains were grown in LB broth in air under standard laboratory conditions ( Figure 2A ) while growth in defined R-medium in the presence of 0 . 8% NaHCO3 in a 5% atmosphere was used to mimic the host environment ( Figure 2B ) . Deletion of the BAS4675-77 genes did not affect pagA expression in either growth condition tested indicating that this transport system did not have a role in bicarbonate transport and/or regulation of toxin gene expression and therefore was not further analyzed ( data not shown ) . The deletion of the BAS2714-12 genes did not affect pagA expression when cells were grown in LB in air suggesting that this system does not contribute to toxin expression under non-host growth conditions ( Figure 2A ) . In contrast , when the strains were grown in defined R-medium under conditions known to induce toxin expression ( 0 . 8% NaHCO3 and 5% CO2 ) , induction of pagA in the BAS2714-12 deletion strain was abolished compared to the parental strain ( Figure 2B ) . These observations suggested that BAS2714-12 is required for induction of toxin expression under CO2/bicarbonate growth conditions believed to mimic the mammalian host . The primary regulatory protein of toxin gene expression in B . anthracis , AtxA , is required for the observed induction of toxin expression in response to CO2/bicarbonate [18] , [19] . Previous studies demonstrated that transcription of atxA is not directly induced in response to elevated CO2/bicarbonate [16] . To investigate the contribution of BAS2714-12 to atxA transcriptional regulation , an atxA-lacZ reporter carried on the pTCV-lac vector was electroporated in the 34F2 and 34F2ΔBAS2714-12 strains . Under the growth conditions that induced toxin expression and under which we observed a substantial difference in pagA expression , atxA expression was unchanged in 34F2ΔBAS2714-12 relative to the parental strain ( Figure 2C ) . Thus , consistent with the lack of effect on atxA by the growth in the presence of CO2/bicarbonate [16] , disruption of bicarbonate metabolism through deletion of the putative bicarbonate transporter BAS2714-12 did not affect atxA transcription . To ensure that deletion of BAS2714-12 was responsible for the observed phenotypes , the BAS2714-12 deletion strain was complemented with these genes carried on a replicative plasmid . The BAS2714-12 locus , as well as a region 640 base pairs upstream of BAS2714 that may carry potential promoter and regulatory sequences , was cloned in the multicopy vector pHT315 to generate plasmid pAW144 , and both plasmids were electroporated into strain 34F2ΔBAS2714-12 . Expression of protective antigen was monitored by Western blotting on culture supernatants ( Figure 3 ) . When grown under toxin-inducing conditions , 34F2 supernatant samples contained detectable amounts of PA while 34F2ΔBAS2714-12 supernatant samples did not contain detectable levels of PA . When carrying the empty plasmid pHT315 , PA remained undetectable in supernatant samples of the BAS2714-12 mutant strain while the presence of pAW144 restored PA expression , demonstrating that deletion of BAS2714-12 was , in fact , responsible for the elimination of toxin induction . The sequence similarity to known bicarbonate transporters and the elimination of bicarbonate-induced toxin expression following deletion suggested that BAS2714-12 may function as a bicarbonate transporter . To directly test the function of BAS2714-12 in bicarbonate transport , we compared the uptake of radiolabeled NaH14CO3 in the parental and mutant strain . Strains 34F2 and 34F2ΔBAS2714-12 were grown in R-media without added NaHCO3 in the presence of 5% CO2 to an OD600 of 0 . 4 . NaH14CO3 was added to each culture , and uptake of NaH14CO3 was measured at several time points by liquid scintillation counting ( Figure 4 ) . The uptake of 14C in the 34F2ΔBAS2714-12 strain occurred at a significantly lower rate ( 6 fold ) than in the parental 34F2 strain , indicative of disruption of bicarbonate uptake and providing further evidence that BAS2714-12 functions as a bicarbonate transporter . Bicarbonate transporters import membrane-impermeable bicarbonate while carbonic anhydrase enzymes interconvert bicarbonate and CO2 and , thus , are able to convert membrane-permeable CO2 to bicarbonate [20] . Induction of toxin expression in B . anthracis is influenced by both bicarbonate and CO2 , and , given the interconversion between the two compounds , separation of the relative influence of each compound on virulence has been difficult . The identification and deletion of the bicarbonate transporter essential to toxin induction now provided a tool to further probe the mechanism of induction . A panel of available carbonic anhydrase inhibitors was tested including acetazolamide , ethoxyzolamide , hydrochlorothiazide and topiramate . Hydrochlorothiazide was found most efficacious as measured by reduced toxin expression levels ( data not shown ) . In the presence of NaHCO3 and CO2 , expression of pagA-lacZ in the parental strain 34F2 was identical with or without hydrochlorothiazide ( Figure 5A ) . However , the residual pagA-lacZ expression in strain 34F2▵BAS2714-12 was completely inhibited by hydrochlorothiazide . Without added NaHCO3 but in the presence of atmospheric 5% CO2 , hydrochlorothiazide reduced the expression of pagA-lacZ in the parental strain 34F2 and hydrochlorothiazide further reduced the level of pagA-lacZ expression in the 34F2ΔBAS2714-12 strain ( Figure 5B ) . These data suggest that the residual pagA-lacZ expression in the absence of added NaHCO3 is due to the conversion of CO2 to −HCO3 by carbonic anhydrase . These data also reinforce the concept that it is bicarbonate , and not CO2 , that directly signals induction of virulence factor expression under host growth conditions . While the role of CO2/bicarbonate in the induction of virulence gene expression is well demonstrated in laboratory batch cultures ( ex vivo ) , no evidence has been provided yet that this role also is relevant in B . anthracis cells growing in the infected host ( in vivo ) . In order to investigate whether the inability to import bicarbonate had any effect on the virulence of B . anthracis , an animal model of infection was used that employs a mouse strain highly susceptible to the unencapsulated Sterne strains [26] . Six week old female mice of the complement deficient strain A/J ( The Jackson Laboratory ) were injected subcutaneously with 106 spores of the parental strain 34F2 or the 34F2▵BAS2714-12 mutant strain and monitored over the course of 12 days . A group of five A/J mice were infected for each strain . Within 54 hours , a mouse in the 34F2 control group infected with the parental strain showed significant swelling and reduced physical activity . Death occurred within the following 10 hours . In this group , two other mice became symptomatic and died within 72 hours , a fourth after 84 hours and the fifth mouse after 96 hours from the infection ( Figure 6 ) . In contrast , all 5 mice in the group infected with the mutant survived up to 12 days with no obvious signs of swelling or disease . While the parental strain is fully virulent in this mouse model , the ΔBAS2714-12 is avirulent , demonstrating for the first time that bicarbonate transport is essential to B . anthracis pathogenesis in vivo .
B . anthracis must integrate numerous environmental signals to effectively replicate and induce disease . B . anthracis relies on a multiphasic lifestyle: non-metabolically active spores are necessary for infection and spread between hosts but are themselves incapable of replication while the metabolically active vegetative cells replicate and cause disease in the host but are incapable of dissemination between hosts . During the course of an infectious cycle , the pathways leading either to development through sporulation or to pathogenesis through toxin and capsule production are mutually exclusive suggesting the existence of a regulatory balance between the two pathways [27] . What the bacterium recognizes in the host as signals to induce pathogenesis mechanisms and the nature of the mechanisms necessary for commitment to development or pathogenesis remain poorly understood . Herein , we have identified an essential component for the induction of virulence gene expression in response to host bicarbonate levels and have exploited this finding to understand the extracellular and intracellular signals controlling virulence . Our data demonstrated that the BAS2714-12 genes encode a previously uncharacterized bicarbonate ABC transporter . Similar ABC transporters have been identified and characterized in photosynthetic bacteria [21] , but this is the first report of an ABC transporter involved in virulence in a pathogenic bacterial species . The BAS2714-12 system was originally annotated ( Gen Bank: AE017225 ) as a putative sulfonate transporter , largely due to similarity to the characterized Ssu ABC transporter in B . subtilis . However , given the conservation between bicarbonate , nitrate , and sulfonate ABC transporters , the lack of characterized bicarbonate transporters in Gram-positive bacteria , and the difficulty in predicting the function of ABC transporters based upon nucleotide sequence , assignment of substrate specificity is ambiguous . Here we have shown that the ABC transporter encoded by BAS2714-12 is required for internalization of 14C-labeled bicarbonate . Together with the observation that the addition of taurine , a substrate of the B . subtilis Ssu system [28] and a commonly available sulfonate compound in the host , does not affect toxin gene expression and does not compete with bicarbonate induction ( unpublished data ) argues for the BAS2714-12 system as being specific for bicarbonate transport . The additional observation that the deletion of the BAS2714-12 genes eliminated the bicarbonate-dependent induction of toxin gene expression confirms a role for this transporter system in bicarbonate metabolism in B . anthracis . Deletion of BAS2714-12 eliminates bicarbonate induction of pagA expression in growth conditions that mimic the animal host environment but does not significantly alter expression under non-inducing conditions . In LB media without added bicarbonate or CO2 , conditions which mimic non-host and non-toxin inducing conditions , pagA expression is unaltered in the deletion strain . In contrast , when grown in R-media with added bicarbonate and CO2 , conditions which mimic host and toxin inducing conditions , pagA expression remains very low in the mutant strain while pagA expression is strongly induced in the parental strain . These observations suggest that basal levels of pagA expression are unaffected by BAS2714-12 deletion , but , instead , the specific induction by bicarbonate requires the presence of BAS2714-12 . Despite the strong effect of the BAS2714-12 deletion on toxin induction , the deletion strain shows no difference in growth under any condition tested relative to the parental strain , suggesting bicarbonate uptake through BAS2714-12 does not significantly contribute to non-virulence metabolic pathways under laboratory growth conditions . The deletion of BAS2714-12 can be complemented by supplying the locus in trans on a replicative plasmid . Small differences in pagA expression between the parental and deletion-complementation strains are likely due to differences in expression or gene copy number of BAS2714-12 carried on a relatively high copy number plasmid ( 15 copies/cell [29] ) . The presence of the AtxA regulator is required for high-level expression of pagA in response to CO2/bicarbonate , but transcription of atxA is not directly regulated by CO2/bicarbonate [16] . Consistent with these observations , deletion of BAS2714-12 did not affect atxA transcription ( Figure 2C ) . The effect of BAS2714-12 on virulence in an in vivo animal model is drastic: while , as expected , infection of A/J mice with spores of the parental 34F2 strain quickly resulted in extensive edema followed by death [30] , the infection with spores of the deletion strain showed no visual signs of infection and all mice survived to the end of the experiment ( Figure 6 ) . These results confirmed a correlation between the bicarbonate-dependent induction of toxin gene expression ex vivo and the virulence of B . anthracis in vivo . The A/J mouse model of infection was selected due to sensitivity to infection with the toxin-producing Sterne strain 34F2 ( pXO1+ pXO2− ) . However , virulence in human hosts requires expression of both the toxin and the capsule . Capsule expression , like toxin production , is induced by bicarbonate [15] . Given the similar dependence of bicarbonate-induced capsule induction on both AtxA and genomic sequences [10] , [15] , it is likely that a BAS2714-12 deletion would abolish also the induction of capsule production and thus the virulence of a fully virulent , toxin- and capsule-producing strain . Bicarbonate , and not CO2 , appears to be the primary molecule regulating induction of virulence gene expression . If CO2 were the primary signaling molecule , one would expect a reduction of toxin expression in the presence of the carbonic anhydrase inhibitor as bicarbonate in solution can no longer be quickly converted into CO2 inside the cell . Instead , we observed the opposite phenomenon: inhibition of carbonic anhydrase activity only affects toxin expression in the absence of added bicarbonate or when bicarbonate can no longer be imported into the cell ( Figure 5 ) , conditions under which carbonic anhydrase would convert CO2 into bicarbonate . Induction of toxin expression ex vivo by high atmospheric CO2 levels in the absence of added bicarbonate is likely the result of spontaneous or carbonic anhydrase-driven conversion of CO2 into bicarbonate which then signals an increase in toxin expression . The sensor or metabolic pathway that directly responds to bicarbonate and induces toxin gene expression remains unknown , but its identification is an ongoing focus of our work . Bicarbonate is a major element in the mammalian body . It is present in all body fluids and organs and plays a major role in acid-base homeostasis . The normal concentration of bicarbonate in the blood ranges between 22–26 mM and its presence , together with carbonic acid ( H2CO3 ) , hydrogen ions and carbon dioxide forms the buffering system required to provide resistance to drastic changes in pH values . Bicarbonate released from the pancreas also acts to regulate the pH in the small intestine to neutralize the acid entering the duodenum from the stomach [31] , [32] . Thus bicarbonate may be a virulence signaling molecule for enterobacteria pathogenesis as well as blood borne pathogens . B . anthracis is not alone among bacteria in regulating virulence gene expression in response to CO2/bicarbonate . CO2 and/or bicarbonate increases toxin production in Vibrio cholerae [33] and Staphylococcus aureus [34] , induces expression of attachment genes in Escherichia coli O157:H7 [35] , alters the antigenic profile of Borrelia burgdorferi [36] , and activates a virulence regulatory protein in Citrobacter rodentium [37] . In any of these systems , a bicarbonate transport system similar to BAS2714-12 may be directly involved in bicarbonate transport and stimulation of virulence factor expression . Most directly relevant to bicarbonate regulation in B . anthracis is the stimulation of the antiphagocytotic M protein in Streptococcus pyogenes [38] . M protein expression is controlled by the regulatory protein Mga , a transcription factor that is similar to the B . anthracis AtxA regulatory protein , because it contains two PRD domains and may be subject to regulation by phosphorylation/dephosphorylation through the PTS carbohydrate utilization system [12] , [39] . The apparent overlap of CO2/bicarbonate metabolic systems and regulation of PRD domains in regulatory proteins in response to carbohydrate utilization invites speculation of similar bicarbonate transport and regulatory systems between these pathogenic species . Interestingly , a BLAST search of the S . pyogenes M6 strain ( Accession number NC_006086 ) using the BAS2713 substrate binding protein as query , identified the product of the Spy1045 gene as the protein with the strongest similarity ( 21% ) and a TauA domain in its amino terminal half of the protein . In the C-terminal portion , Spy1045 contains an ABC-type permease domain ( Binding-Protein-dependent transport system inner membrane component superfamily cl00427 ) that , together with the ATPase domain encoded by the Spy1046 gene ( 35% identity to BAS2714 ) may constitute the bicarbonate transporter of S . pyogenes ( Figure 1 ) . The regulation of B . anthracis virulence factor requires a complex interaction between overlapping metabolic systems , but , for the first time , we have unraveled the dedicated transport components of the CO2/bicarbonate regulatory pathway . This has allowed us to directly separate the influences of multiple signaling molecules to discover that bicarbonate is directly responsible for the in vivo as well as ex vivo induction of virulence factor expression that is essential to B . anthracis pathogenesis . Notably , the availability of the 3-dimensional structure of the bicarbonate binding domain of the Synechococcus CmpA protein in the presence and absence of ligand may be exploited to uncover specific inhibitors of this domain and provide new avenues for antibacterial intervention [23] . In light of these findings , investigation of bicarbonate regulation and transport should be of much greater significance to a large number of pathogenic organisms .
B . anthracis Sterne 34F2 ( pXO1+ pXO2− ) and its derivatives were routinely grown in LB broth supplemented with the appropriate antibiotics at the following concentrations: chloramphenicol 7 . 5 µg/ml , tetracycline 5 µg/ml , erythromycin 5 µg/ml , lincomycin 25 µg/ml , or kanamycin 7 . 5 µg/ml . 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside ( X-Gal ) was added at a final concentration of 40 µg/ml to LB agar to monitor β-galactosidase activity in solid media . All carbonic anhydrase inhibitors ( Sigma ) were freshly prepared in DMSO immediately before addition to cultures . To induce high-level toxin expression , B . anthracis was grown in LB-agar plates or LB liquid media containing 0 . 8% sodium bicarbonate and 100 mM 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( HEPES ) [pH 8 . 0] or in R-Media [40] under 5% CO2 . Electrocompetent B . anthracis cells were prepared following the method of Koehler et al [18] . The E . coli TG1 strain was used for plasmid construction and propagation . E . coli strain SCS110 was used for the production of unmethylated DNA for transformation in B . anthracis . E . coli transformation was performed by electroporation using the Bio-Rad Gene Pulser according to the supplier . Transformants were selected on LB broth supplemented with ampicillin ( 100 µg/ml ) , chloramphenicol ( 7 . 5 µg/ml ) , or kanamycin ( 30 µg/ml ) . Gene deletions in B . anthracis were generated essentially by the method of Janes and Stibitz [41] . A 738 bp region upstream of BAS2714 was amplified using primers BAS2714U5'Bam and BAS2714U3'Sal ( Table S1 ) while an 828 bp region downstream of the BAS2712 was amplified using primers BAS2712D5'Sal and BAS2712D3'Pst . The sequenced products were then cloned into the temperature sensitive plasmid pORI-I-SceI [42] to generate plasmid pAW091 . For deletion of BAS4675-77 , a 624 bp region upstream of BAS4675 was amplified using primers BAS4675U5'Bam and BAS4675U3'Sal while a 750 bp region downstream of the BAS4677 was amplified using primers BAS4677D5'Sal and BAS4677D5'Pst . The sequenced products were also cloned in plasmid pORI-I-SceI to generate plasmid pAW093 . Plasmids pAW091 and pAW093 were electroporated into B . anthracis 34F2 and grown at the permissive temperature of 28°C in the presence of chloramphenicol . Bacteria were then shifted to the non-permissive temperature of 37°C in the presence of chloramphenicol to achieve targeted plasmid integration by homologous recombination . Following plasmid integration , the protocol of Janes and Stibitz [41] was followed to generate the markerless deletion . Diagnostic PCR was carried out to ensure that the entire coding sequence had been correctly deleted . Diagnostic PCR was also carried out on genomic DNA using atxA-specific primers to ensure that the pXO1 plasmid was not lost during the process ( Table S1 ) . The BAS2714-12 region , including a region 640 base pairs upstream of BAS2714 containing potential regulatory sequences , was amplified using primers BAS27145'Xba and BAS27123'Hind and introduced into the pCR4Blunt-TOPO vector ( Invitrogen ) . Following sequencing , the insert was removed by XbaI - HindIII digestion and ligated into XbaI – HindIII digested pHT315 multicopy plasmid vector [29] , generating plasmid pAW144 . pAW144 , as well as pHT315 vector plasmid , was electroporated into both parental 34F2 and 34F2ΔBAS2714-12 strains . Diagnostic PCR was carried out on genomic DNA using atxA specific primers to ensure that the pXO1 plasmid was not lost during the process . B . anthracis strains harboring the pagA-lacZ [12] or atxA-lacZ ( pAtxA12 [17] ) fusions on the replicative transcriptional fusion vector pTCV-lac [25] were grown at 37°C in LB or R medium supplemented with the appropriate antibiotics . β-galactosidase activity was assayed as described previously and specific activity was expressed in Miller units [43] , [44] . B . anthracis strains were grown in R Media to approximately OD600 1 . 0 for 8 hours in 5% CO2 atmosphere at 37°C , and supernatant samples were isolated by microcentrifugation of cell suspensions . SDS sample buffer was added to each supernatant , and samples were boiled for 5 minutes and loaded on 10% SDS-PAGE gels . The amount loaded was normalized relative to cell density . The gels were run at 30 mA for approximately 2 hr . The gels were transferred to a PVDF membrane ( BioRad ) in transfer buffer ( 25 mM Tris base , 192 mM glycine , 20% methanol ) at 20 V overnight . The membranes were incubated for 30 minutes at room temperature in blocking buffer ( 5% dried milk in TBST ( 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 1% Tween 20 ) ) followed by addition of a polyclonal protective antigen antibody diluted 1∶10 , 000 . The blots were washed 5 times and then incubated for 1 hour at room temperature with horseradish peroxidase-conjugated goat anti-rabbit antibody ( BioRad ) diluted 1∶10 , 000 in blocking buffer . Following washing of the membrane , binding of the antibodies was probed using the ECL Plus kit ( GE ) and the protein bands were visualized by PhosphorImager ( Molecular Dynamics ) . Overnight cultures of B . anthracis 34F2 and 34F2ΔBAS2714-12 strains were diluted 1∶100 in R Media without added NaHCO3 and grown in 60 ml sterile culture bottles at 37°C in 5% CO2 atmosphere . When cultures reached an OD600 of 0 . 4 , 50 µCi of NaH14CO3 ( MP Biomedical ) was added to each culture . At time intervals indicated , cells were separated from 1 ml of culture by vacuum filtration onto glass filters ( Millipore ) and immediately washed in 10 ml of cold medium . The filters were then placed in glass vials containing 5 ml of Bio-Safe II counting cocktail ( RPI corp . ) , and radioactivity retained on the filter was measured in a Packard 1600 TR Liquid Scintillation Analyzer . B . anthracis 34F2 and 34F2ΔBAS2714-12 strains were grown in Schaeffer's sporulation medium for approximately 72 hours until over 80% of spores were single and free by phase-contrast microscopy . Cells were collected by centrifugation at 10 , 000 g for 30 minutes , the medium was aspirated , and cell pellets resuspended in 20 ml of sterile distilled water . The cells were washed twice daily for 5 days by centrifugation at 12 , 000 g and resuspension in 20 ml fresh , sterile water in order to eliminate most vegetative cells . The cell pellets were then resuspended in 20% renografin ( Squibb ) and carefully layered over 50% renografin in a 30 ml Corex centrifuge tube . Tubes were then centrifuged at 13 , 000 g for 30 minutes . The supernatant containing vegetative forms was removed and the purified spore pellets were resuspended in 1 ml of sterile water . The spore pellets were washed twice daily for 3 days by microcentrifugation at 14 , 000 RPM followed by resuspension of spore pellet in 1 ml sterile water . Total spore counts were measured using a hemacytometer while live spore counts were measure by serial dilution followed by plating on LB-agar . 6-week-old female A/J mice ( The Jackson Laboratory ) were injected subcutaneously with 106 renografin-purified spores . Progression of disease was monitored visually over 12 days . All mice were housed and maintained at The Scripps Research Institute animal facility under the approval of the Institutional Animal Care and Use Committee . | Hospital-acquired bacterial infections are a growing public health concern . The bacteria responsible for these infections are often resistant to multiple antibiotics , making the problem of nosocomial infections even more dramatic and the need for new antibacterial treatment more urgent . Bacteria rely on a variety of mechanisms in order to trigger an infection , but the first step must be the recognition of the host environment . In this work , we have identified the first component of a pathway that allows a bacterial pathogen , Bacillus anthracis , to recognize the environment in which to thrive during an infection , i . e . the blood of the host . The molecule sensed is bicarbonate , a critical component in the blood for maintaining its correct pH . Bicarbonate is essential to induce the virulence factors of B . anthracis and is most likely relevant in infections by other organisms such as Streptococci , E . coli , Borrelia , Clostridium botulinum , and Vibrio cholera . Our identification of the B . anthracis transporter responsible for the internalization of bicarbonate and the activation of virulence factor production provides a new target for new antibacterial intervention that could be effective on a variety of bacterial pathogens . | [
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| 2008 | The Bicarbonate Transporter Is Essential for Bacillus anthracis Lethality |
Therapeutic response in infectious disease involves host as well as microbial determinants . Because the immune and inflammatory response to Leishmania ( Viannia ) species defines the outcome of infection and efficacy of treatment , immunomodulation is considered a promising therapeutic strategy . However , since Leishmania infection and antileishmanial drugs can themselves modulate drug transport , metabolism and/or immune responses , immunotherapeutic approaches require integrated assessment of host and parasite responses . To achieve an integrated assessment of current and innovative therapeutic strategies , we determined host and parasite responses to miltefosine and meglumine antimoniate alone and in combination with pentoxifylline or CpG 2006 in peripheral blood mononuclear cells ( PBMCs ) of cutaneous leishmaniasis patients . Parasite survival and secretion of TNF-α , IFN-γ , IL-10 and IL-13 were evaluated concomitantly in PBMCs infected with Luc-L . ( V . ) panamensis exposed to meglumine antimoniate ( 4 , 8 , 16 , 32 and 64 μg SbV/mL ) or miltefosine ( 2 , 4 , 8 , 16 and 32 μM HePC ) . Concentrations of 4 μM of miltefosine and 8 μg SbV/mL were selected for evaluation in combination with immunomodulators based on the high but partial reduction of parasite burden by these antileishmanial concentrations without affecting cytokine secretion of infected PBMCs . Intracellular parasite survival was determined by luminometry and cytokine secretion measured by ELISA and multiplex assays . Anti- and pro-inflammatory cytokines characteristic of L . ( V . ) panamensis infection were evaluable concomitantly with viability of Leishmania within monocyte-derived macrophages present in PBMC cultures . Both antileishmanial drugs reduced the parasite load of macrophages; miltefosine also suppressed IL-10 and IL-13 secretion in a dose dependent manner . Pentoxifylline did not affect parasite survival or alter antileishmanial effects of miltefosine or meglumine antimoniate . However , pentoxifylline diminished secretion of TNF-α , IFN-γ and IL-13 , cytokines associated with the outcome of infection by species of the Viannia subgenus . Exposure to CpG diminished the leishmanicidal effect of meglumine antimoniate , but not miltefosine , and significantly reduced secretion of IL -10 , alone and in combination with either antileishmanial drug . IL-13 increased in response to CpG plus miltefosine . Human PBMCs allow integrated ex vivo assessment of antileishmanial treatments , providing information on host and parasite determinants of therapeutic response that may be used to tailor therapeutic strategies to optimize clinical resolution .
The outcome of treatment of leishmaniasis and other infectious diseases is multi-factorial involving host as well as microbial determinants; yet evaluation of antimicrobial drug susceptibility is limited to assessment of drug effects on microbial pathogens and toxicity . However , the efficacy of antimicrobial treatment is linked to the immunocompetence of the host [1–3] , and to the role of host defense mechanisms in the containment of infection or pathogenesis of disease [4] . Hence in vitro assay systems of antimicrobial drug susceptibility that allow characterization of host response as well as antimicrobial effect are an important unmet need . The immune and inflammatory responses induced by infection with species of the Leishmania ( Viannia ) subgenus are pivotal in the pathogenesis of cutaneous and mucosal disease [5–7] and immune competence influences the efficacy of treatment [8] . These findings have motivated the exploration of therapeutic vaccines and immunomodulators as treatment strategies . Leishmania are obligate intracellular pathogens that modulate a diverse range of host cell functions [9 , 10] including the expression and function of drug transporters and metabolizing enzymes [11–13] and innate immune mechanisms [14 , 15] . The capacity of Leishmania to modify and to effectively subvert host cell functions in favor of their survival and persistence constitutes a further and complex challenge to therapeutic interventions . Although specific parameters of the host response have been evaluated independently of anti-parasite effects for a few individual drugs [16–18] , ex vivo surrogates of therapeutic response are critical to the development and preclinical evaluation of more effective treatments . Pentoxifylline is thought to mediate immunomodulatory effects via inhibition of the synthesis of TNF-α [19] and has been used in combination with meglumine antimoniate to achieve healing of refractory mucosal leishmaniasis , a disease presentation associated with an intense inflammatory response . Addition of pentoxifylline increased the cure rate and decreased the plasma levels of inflammatory cytokines such as IFN-γ and TNF-α [20 , 21] . Responses to CpG ODN have been found to be dose-dependent and of either Th1-type or T-regulatory-type in mouse models and humans [22–25] and have been investigated in human clinical trials for allergy , cancer , and autoimmunity [26 , 27] . The combination of CpG with miltefosine has been evaluated in the treatment of experimental visceral leishmaniasis allowing reduction of both the duration and dosage of miltefosine [28] . Infected hamsters and mice that received the combination therapy presented significantly suppressed levels of Th2 cytokines ( IL-10 and TGF-β ) and increased mRNA expression levels of pro-inflammatory cytokines ( IFN-γ , TNF-α and IL-12 ) [28] . This study reports the development of an ex vivo model of human host and parasite responses to antileishmanial drugs , immunomodulators and their combinations based on PBMCs from cutaneous leishmaniasis patients .
In order to discern the anti-parasitic and immunomodulatory effects of anti-leishmanial therapies , we have developed an ex vivo model that allows concomitant quantitative evaluation of parasite survival and the elicited immune response within the context of active human leishmaniasis . Human PBMC cultures are widely utilized as a surrogate of the cell-mediated host response . These cultures , in addition to T and B lymphocytes and NK cells , contain monocytes that can differentiate to macrophages , which are the natural host cell of Leishmania . We therefore determined the culture conditions , period of exposure and concentration of anti-leishmanial drugs and immunomodulators alone or in combination that allowed the concomitant evaluation of intracellular parasite survival and the secretion of the pro- and anti-inflammatory cytokines IFN-γ , TNF-α , IL-13 and IL-10 induced by Leishmania infection of PBMCs from patients having active cutaneous leishmaniasis . The host and parasite response to antileishmanial drugs meglumine antimoniate and miltefosine , which are widely used for the routine treatment of dermal leishmaniasis in the Americas , were evaluated individually and in combination with the immunomodulators pentoxifylline and CpG ODN 2006 . Overall twenty-two male and female patients , 18 years of age or older , with parasitologically confirmed cutaneous leishmaniasis participated in the study . The number of participants in the various analyses was based on the sample size that in previous studies of the immune response in leishmaniasis patients has allowed significant differences in cytokine production to be detected [29] . Participants were from the Pacific coast region of Colombia and consulted the CIDEIM facilities in the municipality of Tumaco or Cali . All patients were enrolled within 5 months of the onset of disease ( Mean 2 . 3 months ) and before initiating treatment . Parasites isolated from the participants were principally L . ( V ) panamensis ( 18/19 , 95% ) . All participants of the study were seronegative for HIV-1/HIV-2 by ELISA ( Abbott Laboratories ) . Parasitological confirmation of leishmaniasis was based on detection of amastigotes in smears from the lesions and/or isolation and identification of the Leishmania cultured from tissue fluid aspirated from lesions . The study was approved and monitored by the CIDEIM Institutional Review Board for research involving human subjects in accordance with national and international guidelines for Good Clinical Practice . Voluntary , informed , signed consent was provided by each participant . Stock solutions of meglumine antimoniate ( SbV ) ( Walter Reed 214975AK; lot no . BLO9186 90-278-1A1 W601; antimony analysis , 25%–26 . 5% by weight ) ( 30 mg/mL ) ; pentoxifylline ( PTX ) ( P1784; Sigma ) ( 10 mM ) , and CpG ODN 2006 ( tlrl-2006; Invitrogen ) ( 500 μM ) were prepared by dissolution in sterile water , filtered through a 0 . 22 μm membrane ( MS MCE Syringe filters; Membrane Solutions ) and stored at -20°C . Based on solubility characteristics , miltefosine ( HePC ) ( 63280; Cayman Chemical Co ) was dissolved in sterile dimethyl sulfoxide at a concentration of 1 . 96 mM and stored at -20°C until use . Dilutions of the drugs were prepared in RPMI 1640 medium ( Sigma ) containing 10% heat inactivated fetal bovine serum ( FBS ) on the day of use . Blood samples of 100 mL were collected after confirming diagnosis and before initiation of treatment . PBMCs were isolated by centrifugation over Histopaque 1077 solution ( Sigma-Aldrich ) according to the product instructions . The cells were frozen in 90% FBS plus 10% DMSO by slow cooling at approximately 1°C/minute using a freezing container ( Thermo Scientific ) and stored in liquid nitrogen until the time of experimental evaluation . Prior to each experiment , the cells were rapidly thawed at 37°C and PBMCs with ≥ 90% viability were used for the experiments [30] . To control potential confounding effects of drug cytoxicity for host PBMCs , viability was evaluated based on acid phosphatase activity after 120 h exposure to meglumine antimoniate , miltefosine and pentoxifylline at the concentrations employed in the combinations of drugs and immunomodulators [31] . No significant reduction in cell viability was observed when comparing treated cells vs non-treated controls . Previous studies have demonstrated that CpG , even at high concentrations ( 6 μM ) , does not alter the viability of human cells [32 , 33] . For all experiments PBMCs were cultured at a final concentration of 2 x 106 mL . Thawed PBMCs were resuspended at 4x106 cells/mL RPMI-1640 medium supplemented with heat inactivated 10% FBS ( complete medium ) , dispensed as 100 μL aliquots ( 4x105 cells/well ) in 96-well plates and cultured for 2 h at 37°C and 5% CO2 to initiate adherence . Infection with L . ( Viannia ) panamensis promastigotes transfected with the luciferase reporter gene ( Luc ) , MHOM/COL/03/3594/LUC001 [34 , 35] was achieved by adding 50 μL of opsonized stationary phase promastigotes in complete medium at 20:1 or 10:1 parasite to monocyte ratios and incubation for 24 hours at 34°C . Fifty μL of complete culture medium were then added to infected PBMCs . Parasite burden and Th1/Th2 cytokine secretion were evaluated after 6 , 12 , 24 , 48 and 72 h incubation at 34°C , 5% CO2 . Prior to infection , parasites were opsonized for 1 h at 34°C in RPMI 1640 containing 10% heat-inactivated AB+ human serum [36] . In order to standardize the inoculum for PBMCs , the parasite concentration to achieve the corresponding parasite to monocyte ratio was operationally defined based on the assumption that monocytes obtained from heparinized whole blood and multiple washings constituted approximately 10% of the mononuclear cells [37] . To evaluate and confirm the infection of macrophages differentiated from monocytes in PBMC cultures , parasite burden in total PBMCs was compared with that of adherent cells alone , using an infection ratio of 10:1 . For this assessment , luminometric readout of parasite burden was conducted in parallel in PBMCs and after removal of non-adherent cells and extracellular parasites , and the internalization of parasites was confirmed by microscopy” . Responses of PBMCs from individual patients were evaluated in triplicate . Dose-response and kinetics assays of miltefosine and meglumine antimoniate were conducted to determine their effect on TNF-α , IFN-γ , IL-13 and IL-10 secretion and parasite burden and the relationship between these parameters . Antileishmanial drugs were added to PBMCs infected for 24 h with Luc-L . ( V ) panamensis , at a parasite to monocyte ratio of 10:1 . Drug concentrations evaluated were 4 , 8 , 16 , 32 and 64 μg SbV/mL as meglumine antimoniate and 2 , 4 , 8 , 16 and 32 μM of miltefosine . This range was employed to determine the drug concentration that substantially reduced but did not eliminate infection ( mean approximating 80% ) compared with untreated controls , so that the effect of immunomodulators on parasite survival could be determined when combined with anti-leishmanial drugs . Kinetic assays were conducted at 24 hour intervals over 96 h after adding 4 μM of miltefosine or 8 μg SbV/mL as meglumine antimoniate . These assays were conducted at 34°C , 5% CO2 A range of concentrations of pentoxifylline and CpG 2006 were evaluated alone or in combination with 4 μM of miltefosine or 8 μg SbV/mL in infected PBMCs . Evaluation of parasite survival and Th1/Th2 cytokine response was conducted with a parasite to monocyte ratio of 10:1 because the parasite burden and kinetics of parasite survival allowed dose-response analyses and discrimination of treated and untreated cultures . Immunomodulators were added at concomitantly with promastigotes to achieve concentrations of 100 , 200 and 300 μM pentoxifylline and 2 . 5 , 5 , 10 μM CpG ODN 2006; anti-leishmanial drugs were added 24 h after infection . The concentration ranges of immunomodulators were based on prior exploratory experiments to evaluate the effect of pentoxifylline on TNF-α secretion and , previous investigations of CpG ODN 2006 in the in vitro immune response of human PBMCs [32 , 33] . Parasite burden and cytokine secretion were evaluated at 120 h of culture , and 96 h after addition of anti-leishmanial drugs . Plates were centrifuged at room temperature at 1097Xg for 10 min; supernatants were collected and stored at -80°C . Infection was quantified as luciferase activity using luminometry ( Chameleon V Multilabel Microplater Reader; Hidex , Finland ) as previously described [34] . Concordance between luminometric and conventional microscopic quantification of intracellular amastigotes has been previously reported for evaluation susceptibility to meglumine antimoniate and miltefosine [38 , 39] . In the present study the limit of detection of the strain Luc-L . ( V ) panamensis ( Luc 001 ) by luminometry was 50 promastigotes and 250 intracellular amastigotes . TNF-α , IL-10 , IFN-γ , IL-13 were measured in supernatants by ELISA [40] or Luminex Screening Assay ( R&D Systems , Minneapolis , MN , USA ) . Luminex assays were performed using 50 μL of culture supernatants in duplicate according to the manufacturers’ specifications . One-way ANOVA or the Kruskal-Wallis tests were used to establish statistical differences among groups . Dunnett or Dunn tests were performed to compare each group with the control group , according to the parametric or non-parametric distribution of data . Analyses were performed with GraphPad Prism 6 software ( GraphPad Inc . , San Diego , CA ) , and P values < 0 . 05 were considered significant .
Parasite burden was proportional to the infective dose of parasites and measurable throughout the observation period of 6 to 72 hours post-infection ( Fig 1A ) . The highest number of parasites was detected at the initial measurement 6 h post-infection and decreased thereafter . No significant differences were observed in the parasite burden in total PBMCs compared to macrophages alone ( Fig 1A ) . Pro-inflammatory , anti-inflammatory and Th1/Th2 cytokines observed across the clinical spectrum of infection by L . ( V ) panamensis were induced under these experimental conditions and measured concurrently with the burden of infection . Kinetics of production of individual cytokines varied: TNF-α was secreted early after infection while other cytokines ( IL-10 , IL-13 , IFN-γ ) became detectable 24 to 48 hours post-infection ( Fig 1B ) . Unlike parasite burden , cytokine expression was not proportional to the infective dose . Meglumine antimoniate and miltefosine reduced the parasite load in a concentration-dependent manner reaching statistical significance at the higher range of drug concentrations compared to untreated infected control PBMCs cultures ( Fig 2A ) . Parasitological response was based on % reduction of signal compared with untreated PBMCs for each donor , thereby controlling for variation in parasite burden among donors . Miltefosine suppressed the production of IL-10 and IL-13 in a dose-dependent manner , with the decrease becoming statistically significant at 32 μM of drug compared to control ( Fig 2B ) . In contrast , no significant effect on the production of IFN-γ or TNF-α was observed over the dose range evaluated for this drug . Notably meglumine antimoniate demonstrated statistically significant parasite reduction over a concentration range of 16–64 μg SbV/ml but did not alter secretion of any of the four cytokines evaluated ( Fig 2B ) . Based on the reduction of infection approximating 80% and non-inhibition of cytokine secretion of cytokines at the respective drug concentrations , 8 μg SbV/mL of meglumine antimoniate and 4 μM of miltefosine were selected for the concomitant assay of parasite survival and immune response . Parasite burden was significantly reduced by 48 hours of exposure to 4 μM of miltefosine or 8 μg SbV/mL of meglumine antimoniate . Survival continued to decline significantly reaching 9% and 37% at 96 hours at these concentrations of miltefosine and meglumine antimoniate respectively , compared with infection in the absence of drug ( Fig 3A ) . Secretion of TNF-α , IL-10 , IFN-γ and IL-13 was not affected by either drug at the concentrations evaluated ( Fig 3B–3E ) . Parasite survival and cytokine production were differentially affected by the individual drugs and immunomodulators and their combinations . Pentoxifylline did not directly affect parasite survival ( Fig 4A ) or the leishmanicidal activity of the anti-leishmanial drugs evaluated ( Fig 4C and 4E ) . CpG alone did not significantly affect parasite survival ( Fig 4B ) but its combination with meglumine antimoniate resulted in significantly lower leishmanicidal activity compared with antimonial drug alone ( Fig 4D ) . In contrast , miltefosine-induced killing of parasites was not altered by CpG ( Fig 4F ) . A significant , dose-dependent reduction of TNF-α , IL-13 and IFN-γ secretion occurred when infected PBMCs were exposed to pentoxifylline ( Fig 5A ) . The suppressive effect of pentoxifylline on IL-13 and IFN-γ production was conserved when this immunomodulator was combined with miltefosine or meglumine antimoniate ( Fig 5B and 5C ) . Neither pentoxifylline alone or in combination with anti-leishmanial drugs affected the secretion of IL-10 induced by infection ( Fig 5 ) . Pentoxifylline did not induce the secretion of cytokines in uninfected PBMCs being either undetectable or present in amounts less than or equal to that detected in the corresponding control cultures without pentoxifylline . In the case of CpG , IL-10 secretion diminished significantly in response to this immunomodulator alone and in combination with miltefosine or meglumine antimoniate , whereas secretion of IL-13 increased in cultures exposed to the combination of miltefosine with CpG , reaching significance at 10 μM CpG ( Fig 6 ) . Neither TNFα nor IFN-γ secretion was altered by CpG or combinations of this immunomodulator with miltefosine ( Fig 6A and 6C ) , nor was secretion of IL13 , IFN-γ and TNF-α significantly altered by CpG in combination with meglumine antimoniate ( Fig 6B ) . In uninfected PBMCs , CpG induced the secretion of IL-10 ( median concentration for PBMCs without stimulus was 0 vs 151 pg/mL with 10 μM CpG; Mann Whitney test p = 0 . 0023 , n = 8 ) but did not induce secretion of IL-13 ( median without stimulus , 38 pg/mL vs 42 pg/mL with 10 μM CpG , Mann Whitney test p = 0 . 2788 , n = 8 ) or TNF-α and IFN-γ , which were not detected in supernatants of uninfected PBMCs with or without CpG .
Pre-clinical assessment of alternative therapeutic approaches including drug combinations and co-adjuvant immunotherapy have been constrained by the unavailability of in vitro models that approximate the in vivo response . The results of this investigation substantiate the feasibility of an integrated evaluation of parasite viability and host response using an ex vivo model of infection based on peripheral blood mononuclear cells and Leishmania transfected with a luciferase reporter gene construct . This innovative strategy allows anti-parasitic efficacy and immunomodulatory effects of anti-leishmanial drugs and immunotherapeutic agents to be determined in systemically circulating cells of the human host , and thereby , access to the interplay of the antimicrobial agent and innate and acquired host defense . Parasites transfected with reporter genes have been successfully employed to quantify parasite survival in vitro , in vivo and ex vivo [34 , 41] . In particular , experiments that evaluated the susceptibility of intracellular amastigotes to meglumine antimoniate and miltefosine using luciferase activity as a measure of parasite viability have substantiated high correlation with conventional microscopy [38 , 39] . In this ex vivo model , the use of luciferase transfected Leishmania allowed us to quantify viable parasites in host macrophages differentiated during culture of human mononuclear cells ( lymphocytes and monocytes ) exposed to live promastigotes , and to do so without the biases inherent to visual assessment by microscopy . Importantly , assay of primary monocyte-derived macrophage infection was achieved in a 96-well plate format as utilized for assessment of cell-mediated immune responses using PBMCs . The advantages of microcultures of human cells are further extended by multiplex assays which require minimal volumes of supernatant ( 25–50 μl ) for the quantification of cytokines and other mediators . Cytometric ( FACS ) analyses of cell subsets and gene expression assay are also feasible using cells from these microcultures , thereby broadly expanding the immunological information that can be accessed using this analytic approach . The robustness of the assessment of parasite burden in PBMCs was supported by readily measurable signal for both 10:1 and 20:1 parasite to cell ratios at all time intervals evaluated over 3 days of culture post-infection and the comparable parasite burden of total PBMCs and adherent cells alone ( Fig 1A ) . Furthermore , PBMC cultures allowed the simultaneous assessment of surviving parasites and secretion of multiple cytokines post-infection . Interestingly , although the parasite burden was proportional to the inoculum of promastigotes , cytokine secretion was not ( Fig 1A and 1B ) . The independence of these parameters was also shown in the divergence of cytokine responses and parasite survival in the presence of increasing concentrations of meglumine antimoniate and miltefosine ( Fig 2 ) , in the distinct kinetics of these responses during exposure to a single concentration of antileishmanial drug ( Fig 3 ) , and is generally supported by other studies [42 , 43] . The conventional macrophage-amastigote model of drug susceptibility assessment evaluates the leishmanicidal effect of drugs that act directly on the parasite or the host macrophage . This long and widely used approach is not informative about collateral immunomodulatory effects of these drugs or for agents whose activity is mediated by other cells of the immune system . Hence , neither the effect of the immune response on the outcome of treatment nor the anti-leishmanial drug on the immune response is evaluable with macrophages alone . Importantly , in the ex vivo PBMC model , parasite viability diminished in a drug concentration-dependent manner , as occurs in the macrophage-amastigote model of drug susceptibility evaluation [34 , 44] . Notably , at the concentrations evaluated in combination with immunomodulators , and which are below their CMax in vivo [45 , 46] , neither meglumine antimoniate nor miltefosine altered cytokine expression of uninfected or infected PBMCs ( Fig 3 ) . Hence , these drug concentrations allowed assessment of immunodulation by CpG and pentoxifylline without the confounding influence of the antileishmanial drugs . Although time-dependent reduction in parasite viability occurred in the absence of treatment , parasite burden was sustained and significantly higher than in treated cultures from 48 hours onward . This attrition of infection is likely mediated by the activation of effector mechanisms in sensitized lymphocytes and host macrophages . Although miltefosine has been reported to induce IFN-γ secretion in uninfected human mononuclear cells [47] , in the corresponding study , an approximately 5-fold higher concentration of miltefosine ( 19 . 6 μM versus 4 μM ) was utilized and included co-stimulation with either IL-2 or IL-2/phytohemagglutinin [47] . We did not observe induction of IFN-γ secretion in dose response experiments with miltefosine even at comparable or higher concentrations of miltefosine ( Fig 2 ) but secretion of IL-10 and IL-13 was significantly reduced in a dose dependent manner . Miltefosine has been shown to act in multiple ways and is an AKT ( serine , threonine kinase ) inhibitor , which is known to be involved in immune activation [48] . However the specific effects of miltefosine on the immune system are not well understood . The suppression of IL-10 and IL-13 secretion by miltefosine in PBMCs infected with Leishmania is a novel finding . Attribution of suppression of these cytokines to miltefosine is supported by the disparity between the dose-response of the parasiticidal and immunomodulatory effects of this drug as well as the absence of immunomodulatory effects of meglumine antimony despite the significant dose-dependent reduction of parasite burden ( Fig 2 ) . In this study we found that compounds that modify the host immune response can affect the parasiticidal activity of anti-leishmanial drugs , as evidenced by a significant reduction in parasite killing when meglumine antimoniate was combined with CpG . The precise mechanisms , remain to be determined and will be of interest for future studies to define the interactions and “cross-talk” between drug-regulated and immunomodulatory processes . Considering the interdependent relationship between the host immune response and the efficacy of anti-leishmanial treatment , as illustrated by the high incidence of failures and relapses in immunocompromised patients [49–51] , this model approximates natural infection and is particularly useful for the study of therapeutic interventions involving immunomodulation . The contribution of immune response to the efficacy of anti-leishmanial treatments is acknowledged yet poorly understood . Several in vivo studies suggest a direct relation between the leishmanicidal effect of antimony and host immune factors like IFN-γ , TNF-α and IL-12 [52–55] , and recent evidence suggests that activation of the host immune response by miltefosine is a constituent of its mechanism of action [56–58] . The inclusion of sensitized cells of the adaptive immune response in the assessment of therapeutic agents allowed the quantification of pro-inflammatory , anti-inflammatory and Th1/Th2 cytokines that are elicited over the clinical spectrum of infection by species of the Viannia subgenus [59–61] . Further , high expression of pro-inflammatory chemokines such as CCL2 , CXCL-9 and CXCL-10 has been associated with chronicity of dermal leishmaniasis caused by L . ( Viannia ) braziliensis infection [48] . Reactive oxygen species and nitric oxide production have also been identified among effector mechanisms that may be induced by antimonial drugs and participate in the elimination of Leishmania [62] . These and other potential immune response parameters including cell phenotypes and gene expression associated with clinical outcome and therapeutic response can also be assessed using this model . Immunomodulators of potential use in the treatment of cutaneous leishmaniasis , demonstrated concentration dependent effects critical to the outcome of Leishmania infection and disease . These ex vivo results confirm the usefulness of PBMCs to determine whether interventions that modify the host immune response can affect parasite survival and to what extent . The observed suppressive effect of pentoxifylline on cytokine production was consistent with previous reports using re-stimulation of PBMCs with soluble Leishmania antigen and in vitro immune responses during other pathologies associated with exacerbated inflammatory response [63–66] . The relevance of these findings to treatment is also illustrated by the recently reported results of a pilot study in cutaneous leishmaniasis patients infected with L . ( V ) braziliensis treated with the combination of antimony and pentoxifylline [21] . In latter study , both the conventional and combination treatments were accompanied by reduced secretion of pro-inflammatory TNF-α and IFN-γ by re-stimulated PBMCs on day 15 of treatment compared to pretreatment . However , the reduction was more pronounced in the antimony plus pentoxifylline group ( 84% vs 48% ) . In the ex vivo analysis of PBMCs from patients infected with L . ( V ) panamensis , the combination of meglumine antimoniate and pentoxifylline resulted in a significant reduction in secretion of IFN-γ and the Th2 cytokine IL-13 . CpG alone significantly induced the secretion of IL-10 in uninfected PBMCs , corroborating a previous report [67] , yet interestingly , CpG in combination with antileishmanial drugs diminished the secretion of IL-10 by infected-PBMCs . Similarly the decrease of IL-10 and TGF-β by CpG2006 alone or in combination with miltefosine was reported recently by Shivahare and collaborators in the models of L . donovani infection in hamster and BALB/c mice . Therefore the use of immunomodulatory agents together with anti-leishmanial drugs may yield effects that differ from what has been previously observed for the agents alone , underscoring the importance of pre-clinical evaluation of immunotherapeutic strategies . In conclusion , the ex vivo PBMC model of Leishmania infection provides access to host as well as parasite determinants of therapeutic response . Analysis of the interplay of acquired and innate immunity and anti-leishmanial drugs and co-adjuvant immunotherapeutic agents is achievable using PBMCs from donors previously exposed to natural infection with Leishmania . Using this approach , therapeutic strategies for leishmaniasis , particularly those that seek to intervene in the immune and inflammatory response can be evaluated and adjusted to optimize host and parasite responses to achieve healing . | Host determinants of the response to infection have increasingly been recognized as therapeutically relevant targets . Despite the pathogenesis of dermal leishmaniasis being mediated by the immune and inflammatory response , in vitro anti-leishmanial drug screening has been based on antimicrobial effect without consideration of effects on the host response . The results of this study show that peripheral blood mononuclear cells from patients allow an integrated evaluation of both antimicrobial efficacy and host response to drugs , immunomodulatory agents , and their combinations . This integrated approach to defining treatment strategies based on host and parasite responses opens the way for the optimization and tailoring of treatment to different clinical circumstances . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2015 | Ex Vivo Host and Parasite Response to Antileishmanial Drugs and Immunomodulators |
Both malnutrition and undernutrition can lead to compromised immune defense in a diversity of animals , and “nutritional immunology” has been suggested as a means of understanding immunity and determining strategies for fighting infection . The genetic basis for the effects of diet on immunity , however , has been largely unknown . In the present study , we have conducted genome-wide association mapping in Drosophila melanogaster to identify the genetic basis for individual variation in resistance , and for variation in immunological sensitivity to diet ( genotype-by-environment interaction , or GxE ) . D . melanogaster were reared for several generations on either high-glucose or low-glucose diets and then infected with Providencia rettgeri , a natural bacterial pathogen of D . melanogaster . Systemic pathogen load was measured at the peak of infection intensity , and several indicators of nutritional status were taken from uninfected flies reared on each diet . We find that dietary glucose level significantly alters the quality of immune defense , with elevated dietary glucose resulting in higher pathogen loads . The quality of immune defense is genetically variable within the sampled population , and we find genetic variation for immunological sensitivity to dietary glucose ( genotype-by-diet interaction ) . Immune defense was genetically correlated with indicators of metabolic status in flies reared on the high-glucose diet , and we identified multiple genes that explain variation in immune defense , including several that have not been previously implicated in immune response but which are confirmed to alter pathogen load after RNAi knockdown . Our findings emphasize the importance of dietary composition to immune defense and reveal genes outside the conventional “immune system” that can be important in determining susceptibility to infection . Functional variation in these genes is segregating in a natural population , providing the substrate for evolutionary response to pathogen pressure in the context of nutritional environment .
There is strong intuition that dietary nutrition affects the quality of immune defense , and this intuition is well supported scientifically . Starvation increases susceptibility to infection in insects as well as humans [1 , 2] , and specific dietary components such as vitamins , carbohydrates , and proteins have been implicated in shaping immunity to bacterial infection [3–7] . Elevated dietary protein relative to sugar increases standing levels of immune activity in Drosophila melanogaster [8] , and diets deficient in protein increase susceptibility to infection by Salmonella typhimurium in mice [6] . Nutrition alters development in ways that may have immunological import [9–11] , and insects and other animals alter their feeding behavior in response to infection [12 , 13] . There is growing evidence that the ratio of protein to carbohydrates ( P:C ) in the diet may specifically influence several life history traits[11 , 14–18] , including some that may predict resistance to infection . For example , the African army worm Spodoptera exempta becomes more susceptible to infection by the bacterium Bacillus subtilis when supplied with diets high in sugar relative to protein , and infected caterpillars will actively choose to eat diets higher in protein without increasing sugar intake [13] . These and other such observations have led to the suggestion that “nutritional immunology” should be employed to identify ideal dietary compositions for the combat of infection [4] . However , despite the increasingly clear impact of diet on resistance to infection , we have remarkably little insight into how nutrition alters infection outcomes , and whether or why individuals in natural populations differ genetically in their immunological response to diet . Natural populations are rife with genetic variation for traits that determine health and evolutionary fitness , and both human and Drosophila populations are genetically variable for the ability to fight bacterial infection [19 , 20] . Such variation may occur in intuitively evident genes , such as those that make up the immune system [21 , 22] , but phenotypically important variation may also map to less obvious genes that shape host physiological context . Even traits that have strong genetic determination can be influenced by the environment , including the availability of nutrition [23 , 24] . Importantly , different genotypes can vary in their susceptibility to environmental influence , resulting in traits that are determined by the interaction between genotype and environment ( GxE ) [25] . In very few cases , however , have the genes underlying sensitivity to environment been determined , and it is indeed difficult to predict a priori what the genes for environmental sensitivity might be . The genetic variation that controls both direct trait determination as well as that that controls environmentally influenced phenotypic variation are critically important to the health and evolutionary potential of populations . We have previously used candidate-gene based approaches to map the genetic basis for variation in Drosophila melanogaster resistance to bacterial infection [26–28] . These studies were successful in identifying naturally occurring alleles that shape defense quality , but they focused exclusively on genes in the immune system . While we may expect diet to shape resistance to infection , we have no particular expectation that the effects of diet act through the canonical immune system ( i . e . Toll and IMD pathways [29 , 30] ) . Dietary composition has widespread metabolic and developmental consequences , and these consequences vary quantitatively and qualitatively among genetically diverse Drosophila [31] . There is evidence for crosstalk between metabolic signaling pathways such as insulin-like signaling and canonical immune pathways in Drosophila , both during development and in the initiation of an immune response [32–36] . Thus , it is plausible to imagine that the immunological effect of diet , and especially genetic variation in immunological response to diet ( genotype-by-diet interaction ) , could be controlled by genes outside of what is typically conceived to be the “immune system . ” In the present study , we conduct an unbiased genome-wide association study to identify genes that shape variation in resistance to bacterial infection among D . melanogaster reared on either a high glucose or low glucose diet . Specifically , we deliver experimental infections with the bacterium Providencia rettgeri and measure systemic pathogen load 24-hours post infection . This time point both provides a robust estimate of infection intensity [37] and correlates strongly with risk of mortality [38] . Throughout the manuscript we will refer to pathogen load as “resistance” or “immune defense” . We find that flies reared on a high glucose diet harbor significantly higher pathogen loads and substantially altered metabolite levels , including elevated free glucose , glycogen and triglycerides . Although there is considerable natural genetic variation for resistance to infection on both diets , resistance is generally well correlated across the two diets . Nonetheless , we find evidence of genotype-by-environment interactions determining immune defense , as well as metabolic alterations that correlate genetically with resistance in flies reared on the high glucose diet . We are able to map and validate several genes that contribute to variation in resistance in both diet-independent and diet-dependent manners . Importantly , most of these are not typically considered part of the canonical immune system .
We found considerable natural genetic variation for immune defense segregating within the Drosophila Genetic Reference Panel ( DGRP ) , where the quality of defense is defined as the ability to limit pathogen proliferation . We infected male flies from 172 of the complete genome-sequenced lines [39] with the Gram-negative bacterium Providencia rettgeri after rearing on either a high glucose or low glucose diet in a replicated block design ( see Methods ) , then measured systemic pathogen load 24 hours later . Pathogen load was significantly predicted by line genotype and diet ( Table 1; S1 Fig , p < 10-4 for both ) as well as by a genotype-by-diet interaction ( p = 0 . 0016 ) , indicating that genotypes differ in their immunological sensitivity to dietary glucose . Nonetheless , pathogen load was highly correlated across the two diets ( Pearson r = 0 . 69 , p < 10-4; Fig . 1 ) , indicating a strong main effect of genotype on immune performance . On average , flies reared on the high-glucose diet sustained systemic pathogen loads approximately 2 . 4 times higher than those of flies reared on the low-glucose diet . We measured several indices of nutritional status in each Drosophila line after rearing on the high-glucose and low-glucose diets because we predicted that specific metabolite profiles might be associated with changes in immunity . We measured free glucose , glycogen stores , total triglycerides , free glycerol , soluble protein , and wet mass , as these provide an overall picture of an individual’s nutritional status . The Nutritional Indices ( NIs ) showed predictable responses to diet . For example , levels of glucose , glycogen , and triglycerides were substantially elevated by rearing on the high-glucose diet ( Fig . 2; p < 10-4 in all cases ) , although wet weight and free glycerol were significantly reduced by rearing on high glucose ( Fig . 2; p < 10-4 in both cases ) . The lines exhibited highly significant genetic variation for all NIs after rearing on either diet ( p < 10-4 in all cases; Table 2 ) . Each NI was significantly genetically correlated across diets ( Fig . 3 ) , indicating strong genetic determination of NIs regardless of diet . Surprisingly , only wet mass , glycogen and free glucose showed strong genotype-by-diet interactions ( Table 1 ) . Since we found that increasing dietary glucose resulted in increased pathogen load as well as alteration of metabolic profile , we asked whether metabolic profile correlated with pathogen load across genotypes . The only NI that correlated with pathogen load was free glucose , which was slightly negatively correlated with P . rettgeri load on the high-glucose diet ( Pearson’s r = -0 . 18 , p = 0 . 033 ) . This is somewhat surprising given that the general effect of increased dietary glucose is both elevated blood glucose and an increase in pathogen load , and may indicate that variation in pathogen load is associated with rates of conversion between molecules . We hypothesized that genetic variation might shape the relationship between overall metabolic state and immune defense and that our nutritional indices might give more information about the overall metabolic status of the fly when considered in aggregate . We therefore performed a principal component analysis and tested whether the primary principal components ( PCs ) for each diet correlated with immune defense quality . The top five PCs summarizing the NIs on each diet each explain 8–41% of the total variance in nutritional state , with loadings of each NI given in Table 3 . None of the metabolic PCs correlated with pathogen load on the low-glucose diet . The fourth PC on the high-glucose diet was significantly correlated with pathogen load ( Pearson’s r = -0 . 27 , p = 0 . 001; Table 3 ) . This PC , which explains 11% of the total variance , is heavily positively loaded with free glucose ( 0 . 58 ) and soluble protein ( 0 . 36 ) and is negatively loaded with glycogen stores ( -0 . 71 ) , consistent with the observation that free glucose alone is negatively correlated with pathogen load . This is the only PC where free glucose and glycogen stores load in opposite directions , possibly indicating the rate of conversion between dietary glucose to glycogen . PC1 trends toward negative correlation with pathogen load on the high-glucose diet ( Pearson’s r = -0 . 15; p = 0 . 06 ) . This PC explains 37% of the variance and is positively loaded with all NIs , and presumably reflects overall fly mass although mass itself does not correlate with pathogen load ( Fig . 3 ) . When we compared our data to previously published DGRP phenotype data [39] , we found correlations between our NIs and three metabolism-related traits: starvation resistance , chill coma recovery , and startle response . Starvation resistance , as determined by Mackay et al . [20] , is positively correlated with all of our NIs except soluble protein with correlation coefficients ranging from 0 . 169 ( Table 4 , p = 0 . 044 ) to 0 . 388 ( p < 10-4 ) . Overall , genotypes with greater energy reserves were better able to withstand the stress of starvation: measures of wet mass , soluble protein , and glycogen stores were significantly negatively correlated with time to recovery from chill coma as measured by Mackay et al . ( chill coma recovery correlated with wet mass: r = -0 . 235 , p = 0 . 005; soluble protein: r = -0 . 248 , p = 0 . 003; glycogen: r = -0 . 20 , p = 0 . 016 ) . Our measures of free glucose levels and total triglycerides were weakly correlated with Mackay et al . ’s measure of startle response ( Pearson’s r < 0 . 20 and p < 0 . 05 for each ) . This consistency in related phenotypic measures and relationships across lab groups indicates the genetic robustness of the phenotypes . The bacterial endosymbiont Wolbachia pipientis has been shown to confer protection against RNA viruses in Drosophila [40 , 41] , but previous experiments have not uncovered any protective benefit of Wolbachia against secondary bacterial infection [42 , 43] . Richardson et al . [44] determined that 52% of the lines in the DGRP are infected with Wolbachia , and we find Wolbachia status to be a weakly significant predictor of P . rettgeri load on both diets ( S2 Fig , low glucose: p = 0 . 0361; high glucose: p = 0 . 0327; data from both diets combined: p = 0 . 014 ) , with lower average bacterial loads in the Wolbachia-infected lines than in the Wolbachia-uninfected lines . Because the complete genomes have been sequenced for every line in the DGRP , we were able to conduct unbiased genome-wide association mapping for each of our measured phenotypes . We used mixed effect linear models to identify genetic polymorphisms that predict systemic pathogen load . Using a significance criterion of 10-6 we identified seven single nucleotide polymorphisms ( SNPs ) in six genes that associate with variation in pathogen load on the high-glucose diet , 11 SNPs in 9 genes that associate with load on the low-glucose diet , and 19 SNPs in 12 genes that associate with pathogen load when the data from both diets is pooled ( Table 5; S3 and S4 Figs ) . This significance threshold corresponds to a false discovery rate of 5–10% ( depending on the phenotypic distribution of the particular trait being evaluated and the details of the analytical model ) and provided a reasonable number of SNPs for further characterization . Several of the mapped SNPs were common to multiple analyses . Overall , we mapped SNPs in the genes crinkled , defective proboscis extension response 6 , diptericin , elk , fruitless , kinesin heavy chain 73 , multiplexin , Scr64B , sema-1a , tout velu/CG12869 , CG42524 , CG7991 , CG4835 and CG15544 . We additionally mapped SNPs to 2 distinct regions annotated to encode small , nontranslated RNAs , potentially revealing variation for more complex regulation of the immune system [45] . Our mapped SNPs are highly enriched for lying within or adjacent to genes , with 21 of the total 23 ( 91 . 3% ) lying within 5 kb of an annotated gene ( Table 5 ) . In contrast , only 55% of SNPs genome-wide lie within 5 kb of a known gene . Of our 5 mapped SNPs within gene coding regions , 2 are synonymous and 3 are nonsynonymous , again in stark contrast to the genome average , for which there are approximately 2 . 7 synonymous polymorphisms for every nonsynonymous polymorphism [46] . One of the two synonymous variants we mapped is in perfect linkage disequilibrium with an amino-acid-altering SNP in Diptericin . The other is in perfect disequilibrium with a 3′ UTR variant of kinesin heavy chain 73 . Thus , both of our mapped synonymous SNPs can be considered to be redundant with more plausibly functional SNPs . We used RNAi to knock down 13 of the mapped genes , 9 of which resulted in significantly altered pathogen load either on a standard diet or in a diet-specific manner ( S5 Fig , S1 Table ) . In contrast , only one of five control genes chosen by virtue of physical proximity to mapped genes yielded an altered bacterial load phenotype after RNAi knockdown . To identify SNPs that have strongly diet-dependent effects on immunity , we first considered SNPs that had significant effects ( p < 10-6 ) on one diet but not on the other ( p > 10-4; Fig . 4 ) . Only a few SNPs meet this criterion . One SNP in crinkled ( 2L . 15045678 ) was significantly associated with variation in immunity on the high glucose diet ( p = 1 . 94 x 10-7 ) but not on the low glucose diet ( p = 0 . 0074 ) . A SNP in Sema-1a ( 2L . 8630728 ) was very on the brink of significance on the high glucose diet ( p = 1 . 42 x 10-6 ) and nowhere near our significance threshold on the low glucose diet ( p = 0 . 001 ) . Reciprocally , one SNP in elk ( 2R . 13779189 ) was significant on the low glucose diet ( p = 6 . 75 x 10-7 ) but not on the high glucose diet ( p = 4 . 49 x 10-4 ) . All SNPs with p <10-4 on either diet were significant at p < 10-6 when the data from both diets were pooled . Our second approach to finding genes with significant diet-dependent effects was to pool the data from both diets and evaluate the SNP-by-diet interaction in a second GWAS analysis . While this approach resulted in a somewhat liberal inflation in P-values ( S4b Fig ) , it revealed SNPs in several genes has having diet-dependent effects at a nominal threshold of p < 10-6 . Of the genes mapped with this second approach , we chose TepII , gprk2 , and similar to test by RNAi , and confirmed the importance of these genes on suppression of P . rettgeri proliferation ( see below ) . To determine whether any gene function categories were enriched in our set of significantly mapped SNPs , we performed a GO enrichment analysis using GOWINDA [47] , which corrects for gene size , on the reduced GO category list defined by GO Slim [48] . Because so few SNPs mapped significantly at our cutoff of p<10-6 , we performed the GO analysis at a significance threshold of p<10-5 . Categories related to immunity and metabolism were among the most enriched , but no functional categories were significantly enriched after multiple correction ( S2 Table ) . GO analysis of GWAS results implicitly assumes a quantitative genetic model where many genes in every relevant functional process each contribute small but significant effects on the overall phenotype . We have no evidence that this is an appropriate conceptual model for our defense phenotype , so we did not pursue the GO analysis further . We performed genome-wide association mapping of each of the nutritional indices , yielding several hits in or near genes with reasonable links to metabolic status [49] . We found no overlap between SNPs significantly associated with variation in the NIs and those significantly associated with variation in immune defense . The genetic basis for altered nutritional status in response to diet will be the subject of an independent paper [49] . Diptericin . Diptericin is a antimicrobial peptide that is produced in response to DAP-type peptidoglycan that makes up the cell walls of Gram-negative bacteria such as P . rettgeri [50 , 51] . Two SNPs in perfect linkage disequilibrium ( 2R . 14753586—synonymous , 2R . 14753589—nonsynonymous ) are significantly associated with variable suppression of P . rettgeri infection in flies reared on both diets ( p = 9 . 03 x 10-7 for each SNP on high glucose and p = 2 . 93 x 10-7 on low glucose ) as well as when data from the two diets are pooled ( p = 7 . 04 x 10-08 for each SNP ) . While it might seem intuitive that an antibacterial peptide gene would map in an immunity screen , this result was surprising as we have not identified any marked effect of Diptericin in previous association studies using other Gram-negative bacterial infections [19 , 27] . Indeed , it is generally believed that there is enough redundancy in AMPs that mutations in a single peptide would have little effect on organism-level immunity [e . g . 52] . The nonsynonymous SNP ( 2R . 14753589 ) results in a serine versus arginine polymorphism segregating in the population . In the DGRP , the more resistant serine allele is carried by 82% of lines and the more permissive arginine by 14% of lines ( 4% of lines are heterozygous at the SNP ) . Two of the DGRP lines are homozygous for a premature stop codon in Diptericin at position 2R . 14753502 . While this stop codon did reach not our minor allele frequency threshold for consideration in the study , we thought it was notable that two lines carrying the premature termination exhibited the absolute highest bacterial loads across the entire DGRP mapping panel . Both of these lines carried the higher-resistance serine variant at 2R . 14753589 , thereby slightly decreasing the statistical significance of the independent contrast between the serine and arginine variants . If these two lines are excluded from the analysis , the P-value for 2R . 14753589 is 4 . 43x10-9 . Interestingly , we found that serine and arginine are also segregating in Drosophila simulans through an independent mutation at the same codon , suggesting the possibility of convergent balancing selection ( Unckless et al . in prep . ; see Discussion ) . Multiplexin . Multiplexin ( mp ) encodes a collagen protein . Multiplexin is a huge gene ( 55 kb ) with 15 annotated transcripts . Annotated molecular functions include carbohydrate binding and motor neuron axon guidance [53] . Loss-of-function mutants have smaller larval fat bodies than wild-type flies [54] , which may be relevant since the fat body is the primary tissue that drives systemic immunity to bacterial infection . Three intronic SNPs in mp are significantly associated with variation in P . rettgeri load in flies reared on either the high glucose ( p = 2 . 25 x 10-7 ) or low glucose ( p = 4 . 12 x 10-6 ) diet , as well as when data from both diets are pooled ( p = 3 . 9 x 10-7 ) . Ubiquitous RNAi knockdown of mp resulted in significantly decreased P . rettgeri load after infection relative to controls with wild-type mp expression ( p = 0 . 017 ) . The relationship between resistance and the larval fat body phenotype in multiplexin mutants may suggest a role for the humoral immune response in this phenotype . Mutant flies may have altered antimicrobial peptide expression . Defective proboscis extension response 6 . An intronic SNP ( 3L . 10039434 ) in Defective proboscis extension response 6 ( Dpr6 ) was associated with variation in P . rettgeri load in flies reared on the low glucose diet ( p = 9 . 54 x 10-8 ) and when data from flies reared on both diets were pooled ( p = 3 . 51 x 10-7 ) . Dpr6 belongs to a family of genes thought to be involved in sensory perception of chemical stimulus , including gustatory perception of food , and contains an immunoglobulin domain that may be involved in cell-cell recognition [55] . Ubiquitous RNAi knockdown of dpr6 resulted in a significant decrease in P . rettgeri load after infection ( p = 0 . 0097 ) . Crinkled . An intronic SNP ( 2L . 15045678 ) in crinkled mapped for variable resistance specifically on the high glucose diet ( p = 1 . 94 x 10-7 ) , and less significantly when data from both diets were pooled ( p = 4 . 29 x 10-5 ) , but was not significantly associated with variation in P . rettgeri load when flies were reared on the low glucose diet ( p = 0 . 0075 ) . Crinkled encodes myosin VIIa , an actin-dependent ATPase . RNAi knockdown experiments for crinkled suggest that it does influence immunity in a diet-dependent manner . We used ubiquitous RNAi to knock down ck in flies reared on either the high glucose or low glucose diet . The knockdown had no significant effect on P . rettgeri load of flies reared on the low glucose diet ( p = 0 . 45 ) but was marginally significant when flies were reared on the high glucose diet ( p = 0 . 07; Fig . 5a ) . Further exploring the diet dependence , we found that Principle Component 4 of our nutritional indices measured on the high glucose diet correlated with P . rettgeri load in a ck allele-dependent manner . PC4 is strongly negatively correlated with bacterial load in flies homozygous for the A allele ( r = -0 . 417 , P = 0 . 0014 ) but is uncorrelated with load in flies bearing the T allele ( r = – 0 . 115 , P = 0 . 329; Fig . 5b ) . Since PC4 is loaded primarily with glucose , protein and glycogen , we also examined correlations between these NIs and P . rettgeri load within each ck allele in flies reared on the high glucose diet ( S6 Fig ) . Mirroring the overall phenotypic data , free glucose levels trended toward negative correlation with P . rettgeri load in flies bearing the A allele ( r = -0 . 23 , p = 0 . 088 ) but not in flies bearing the T allele ( r = -0 . 12 , p = 0 . 32 ) . Glycogen levels trended toward positive correlation with P . rettgeri load within the A allele ( r = 0 . 20 , p = 0 . 132 ) but not within the T allele ( r = -0 . 07 , p = 0 . 58 ) . Sema-1a . An intronic SNP ( 2L . 8630728 ) in Sema-1a , which encodes a semaphorin protein , fell just below our significance threshold on the high glucose diet ( p = 1 . 42 x 10-6 ) and was much less significant on the low glucose diet ( p = 0 . 0011 ) . The dramatic difference in effect on the different diets suggested to us that Sema-1a might have diet-dependent effects on immunity . Semaphorins tend to be highly pleiotropic and play major roles in developmental processes [56] . RNAi knockdown of Sema-1a resulted in flies with marginally significantly higher P . rettgeri loads than controls on the low glucose diet ( p = 0 . 081 ) , on the high glucose diet ( p = 0 . 088 ) , and when the data from both diets were combined ( p = 0 . 025 ) . CG12869 . A SNP ( 2R . 10477114 ) 1594 bp upstream of functionally unannotated gene CG12869 was significantly associated with P . rettgeri load in flies reared on the low glucose diet ( p = 7 . 65 x 10-7 ) and approached significance in flies reared on the high glucose diet ( p = 8 . 87 x 10-5 ) and when the data from both diets were combined ( p = 1 . 49 x 10-6 ) . While little is known about CG12869 , the encoded protein is predicted to have carboxylesterase activity . RNAi knockdown of CG12869 in flies reared on either the high-glucose or low-glucose diet resulted in modestly increased P . rettgeri loads when the data from both diets were combined ( ppooled = 0 . 047 ) , although not when either diet is considered independently ( low glucose: p = 0 . 133 , high glucose: p = 0 . 164 ) . G protein-coupled receptor kinase 2 . Gprk2 was previously associated with defense response to bacteria through interaction with cactus and is required for normal AMP production [57] . It is also involved with several biological processes that might be influenced by nutritional environment including hedgehog signaling and regulation of appetite {Cheng:2012kd , Chatterjee:2010df} . An intronic SNP in Gprk2 ( 3R . 27273757 ) yielded a significant SNP-by-diet interaction predicting P . rettgeri load ( p = 7 . 75 x 10-8 ) . RNAi knockdown of Gprk2 resulted in increased P . rettgeri load relative to control flies ( p = 0 . 016 ) , consistent with the results of Valanne et al . [57] , who found that Gprk2 disruption reduced resistance to infection by Enterococcus faecalis . We found no distinction between knockdown on high-glucose versus low-glucose diets ( pknockdown = 0 . 08 , pdiet = 0 . 04 , pinteraction = 0 . 94 ) . Thioester-containing protein 2 . Thioester-containing proteins ( TEPs ) are opsonins that promote phagocytosis and parasite killing in invertebrates , including phagocytosis of Gram-negative bacteria [58] . TEPs are homologous to vertebrate complement C3 and macroglobulins , and Drosophila TepII has previously been shown to evolve under adaptive positive selection in the presumptive pathogen-binding domain [59] . P . rettgeri load was determined by a significant diet*SNP interaction for four nonsynonymous SNPs in TepII ( p = 5 . 97 x 10-7 ) and an additional synonymous SNP in tight disequilibrium ( p = 7 . 00 x 10-7 ) . RNAi knockdown of TepII resulted in reduced immune defense ( p = 0 . 0017 ) , independent of diet ( p = 0 . 94 ) , which is consistent with the known role of TepII in insect immunity . Similar . An intronic SNP in similar ( 3R . 25909307 ) showed a significant Diet*SNP interaction ( p = 9 . 17 x 10-7 ) . Sima is involved in protein dimerization and signal transduction and has been associated with response to stress . RNAi knockdown of similar resulted in increased P . rettgeri load after infection of flies reared on the low glucose diet ( p = 0 . 005 ) but not on the high glucose diet ( p = 0 . 28 ) . Variants of similar may influence how an individual responds to a nutrient-poor diet which in turn may influence their ability to resist infection . Kinesin heavy chain 73 and Src64B . A synonymous SNP and a SNP in the 3′ UTR of Khc-73 were associated with variation in bacterial load when data from both diets are pooled ( 3 . 1 x 10-7 and 5 . 48 x 10-8 , respectively ) , and an intronic SNP ( 3L . 4603286 ) in Src64b mapped highly significantly on each diet ( low glucose: p = 1 . 41 x 10-7; high glucose: p = 2 . 50 x 10-7 ) and when the data from both diets were pooled ( p = 3 . 34 x 10-9 ) . Khc-73 is a microtubule motor protein [53] and Src64b is a tyrosine kinase with a wide range of reported phenotypes including cellular immune response [60] . RNAi knockdown of either gene did not result in any significant change in systemic P . rettgeri load after infection ( Khc-73: p = 0 . 67 , Src64b: p = 0 . 97 ) . Thus , neither of these mapped genes validated by our RNAi knockdown criteria . This could be because the two genes are false positive map results or because the RNAi failed to adequately block protein synthesis in the knockdown experiment . Other candidate genes . We mapped SNPs associated with variation in post-infection P . rettgeri load in the genes elk , fruitless , tout velu , CG42524 , CG7991 , CG4835 and CG15544 ( Table 5 ) , but we were unable to establish RNAi knockdowns for these and were thus unable to test whether disruption of these genes influences resistance to infection . Nearest-neighbor negative controls . It is unknown what proportion of genes in the genome could conceivably yield immune defense phenotypes when ubiquitous RNAi disrupts their expression . To estimate the false-positive rate on our RNAi knockdowns , we additionally knocked down several arbitrary genes that are physically adjacent to our mapped genes but are not known to have any immune function . Whereas 9 out of 13 of our mapped candidate genes yielded defense phenotypes upon RNAi knockdown , only one out of the five arbitrary neighboring genes yielded an immunity phenotype . Little is known about the function of that arbitrary gene whose knockdown resulted in a modest decrease in P . rettgeri load ( p = 0 . 029 ) ( CG34356 ) , but it has been shown to be involved in protein phosphorylation [61] . Our rate of 9 in 11 positive knockdown experiments among the mapped candidates is a significant excess over the 1 in 5 negative control genes that gave immune phenotypes ( Fisher’s Exact Test: p = 0 . 018 ) , giving us confidence that the majority of our mapped genes are true positive results . Diptericin is a classical immunity gene with a large effect in our study . We reasoned that genotype at Diptericin might mask genes with smaller effects , and that we could increase power to detect diet-dependent variants by controlling for Diptericin genotype . Furthermore , there was significant linkage disequilibrium between SNPs in Diptericin and other mapped SNPs ( S7 Fig ) . We therefore re-conducted the genome-wide association analysis with the addition of Diptericin genotype as a covariate that could take on three possible states: the arginine versus serine variants at position 2R . 14753589 and the premature stop codon ( although the two DGRP lines carrying the premature stop codon also carried the serine variant , we classified them separately because they were phenotypically so extreme ) . Lines carrying residual heterozygosity at Dpt were treated as having missing data for the Dpt genotype . All GWAS results and knockdown experiments reported to this point were mapped without Dpt genotype as a covariate . Unexpectedly , instead of revealing new genes that predict immune phenotype , inclusion of Dpt genotype in the mapping model caused the number of significant SNPs ( p < 10-6 ) to drop from 19 to only 4 when the data from both diets were pooled , from 11 to 2 on the low glucose diet only , and from 7 to 1 on the high glucose diet only . Inclusion of Dpt as a covariate greatly improved the observed fit of our q-q plots to the null expectation , eliminating experiment-wide p-value inflation ( S4 Fig ) . We observed an increase in the number of SNP-by-diet interactions from 77 to 88 when Dpt genotype is included as a covariate ( S4 Fig , S3 Table ) . Only one SNP ( 2L . 13072327; located in a small RNA ) was significant in both the original models and when Dpt genotype was used as a covariate . For the SNPs significant for the interaction effect , 66 were significant in both methods , 14 were specific to mapping without Dpt as a covariate , and 25 were specific to mapping with Dpt as a covariate . As shown in S4 Fig , there is generally good agreement between the two methods for interaction , although both are quite inflated . To assess whether mapping with Diptericin genotype as a covariate provided reliable results , we performed the same RNAi knockdown experiments as described above with two new candidates genes . Both resulted in increased pathogen load when knocked down ( S5 Fig , S1a Table ) . Briefly , these genes were CG33090 , a beta-glucosidase , and CG6495 , a gene of unknown function that was significantly induced upon infection in a previous study [62] . We additionally chose to validate CG12004 , which mapped with a P-value that missed our significance threshold ( p = 4 . 72 x 10-6 ) , but that has been previously shown to be involved in defense response to fungus [63] . Knockdown of CG12004 resulted in a marginally significant increase in pathogen load ( P = 0 . 0514 ) . We reexamined the correlations between nutritional indices and bacterial load when Dpt variant was included as a covariate in the regression . In all cases , the model with Dpt variant was a better fit than the model that did not include Dpt genotype ( S4 Table ) . On the high glucose diet , the correlation between free glucose level and bacterial load becomes slightly less significant ( p = 0 . 078 vs . p = 0 . 033 previously ) , while the correlation between soluble protein and bacterial load became more significant ( p = 0 . 036 vs . p = 0 . 061 previously ) . No principal components on the low glucose diet became significant with Dpt as a covariate . However , on the high glucose diet , PC3 became marginally significant ( p = 0 . 052 vs . 0 . 194 without considering Dpt genotype ) and PC4 remained significant ( p = 0 . 007 vs . 0 . 001 without considering Dpt genotype ) .
We found the Drosophila Genetic Reference Panel to be highly variable for resistance to P . rettgeri infection . We also determined that the severity of bacterial infection increased dramatically when flies were reared on a high-glucose diet , and the flies became hyperglycemic and hyperlipidemic . Relative quality of immune defense was highly correlated across the two diets , indicating strong genetic determination of the defense phenotype . However , we also observed a significant genotype-by-diet interaction shaping defense . Specifically , there were several lines that suffered disproportionately severe infections after rearing on the high-glucose diet , although these lines fell closer to the center of the resistance distribution when they were reared on the low-glucose diet . We did not find any lines that showed markedly higher resistance on the high-glucose diet . We were able to identify several genes that contributed to variation in resistance on both diets . Not only did severity of infection increase with elevated dietary glucose , the flies became hyperglycemic , hyperlipidemic , and had elevated glycogen stores after rearing on the high-glucose diet . Because both glucose levels and infection severity increased with rearing on the high glucose diet , we predicted that those two traits would also be genetically correlated . Unexpectedly , however , free glucose levels were negatively correlated with severity of infection across genotypes when flies were reared on the high glucose diet ( the two traits were uncorrelated on the low glucose diet ) . We observed an even stronger correlation between resistance and a principal component that was positively loaded with free glucose and negatively loaded with glycogen stores . Because metabolic measurements were taken from uninfected flies , they indicate genetic capacity to assimilate or manage the excess dietary glucose in the absence of the pathogen . The genetic correlation with infection severity indicates that resistance to P . rettgeri infection is linked in some way to glucose metabolism , uptake , and/or conversion to and from glycogen . Our map results suggest that this effect is partially mediated by the crinkled gene , which encodes a myosin VIIa cytoskeletal ATPase . We identified a polymorphism in crinkled that highly significantly predicted bacterial load when flies were reared on the high glucose diet , although not on the low glucose diet . Flies bearing the rarer allele show strongly negatively correlated glucose levels and pathogen loads . Furthermore , independently determined expression of the crinkled gene [64] correlates with resistance to P . rettgeri and our observed glucose level . Full characterization of the mechanism by which crinkled shapes immunity and glucose metabolism will require future study . We were more generally able to map several genes that contribute to phenotypic variation in immune performance , both in diet-specific and diet-independent manners . The mapped polymorphisms were highly significantly enriched for being nonsynonymous and for lying within or very near genes . RNAi knockdown confirmed roles for the mapped genes in resistance to P . rettgeri , with 82% of the knockdowns of mapped genes resulting in altered pathogen loads . In contrast , we found defense phenotypes after knockdown of only 17% of negative control genes that are chromosomally linked to mapped genes but were otherwise arbitrary . Only a small handful of the mapped genes had annotated immune function . Instead , we identified genes encoding proteins annotated in processes such as feeding behavior and cytoskeletal trafficking . This is a fully expected outcome of the experiment , and such genes are precisely what GWAS studies are designed to detect . Functional variation in dedicated immune genes is probably subject to strong natural selection in the wild , and most variation is probably quickly purged from the population . In contrast , however , populations may retain genetic variation that results in smaller effects on resistance , especially when the primary selection on the gene is for a function other than immune defense . Such genetic variants can then cause a large proportion of the observed phenotypic variance in natural populations , and in mapping panels derived from natural populations , such as the DGRP . The effect on immune defense of knocking down the mapped genes by RNAi was small relative to what might be expected from disruption of core components of the immune system . For this reason , it is unsurprising that these genes have not been discovered in previous mutation screens for susceptibility to bacterial infection . That we are able to map and confirm many of these non-conventional genes opens the possibility of whole new avenues of research and illustrates the value of unbiased genome-wide mapping relative to candidate gene based studies . This result also suggests that resistance to infection , especially in the context of dietary variation , is best viewed as a synthetic trait of the whole organism phenotype and is not determined solely by the canonical immune system . Genes that influence any number of developmental or metabolic processes may carry variation that directly or indirectly influences the ability of the organism to resist infection . At the outset of this experiment , we might have hypothesized that the genetic basis for immunological sensitivity to diet would map to stereotypical metabolic processes , either because of crosstalk between metabolic and immune signaling , varied ability to incorporate metabolites during development , or variation in the capacity to sequester nutrients from pathogens . However , our mapping did not uncover the most obvious potential metabolic processes , such as insulin-like signaling , carbohydrate metabolism , or energetic storage . Instead , we identified genes with highly diverse function , which indicates a much more nuanced and complex interaction between dietary intake and immune defense . Importantly , because the flies in our study were reared from egg-to-adult on the experimental diet of interest , we do not distinguish between defense-impacting effects that arise during development versus those that manifest during the response to infection . It is important to bear in mind that the effects of allelic variation in the mapped genes could manifest at any stage of development or in any aspect of host physiology that may ultimately influence antibacterial defense . Determining the mechanisms by which the mapped genes influence resistance will require considerable additional study . In most cases , the RNAi knockdowns of mapped genes confirmed an effect of immune phenotype , but did not necessarily recapitulate diet-specific effects on resistance . While RNAi knockdown is a useful tool for confirming the role of mapped genes in immune defense , it is expected that the effect of RNAi knockdown will be much larger than the difference in phenotype between two alleles of the gene . Thus , where the SNP variants may cause modest modification of defense phenotype—perhaps revealed only under certain dietary environments—the RNAi knockdowns are more of a sledgehammer whose effects will be seen under all dietary conditions . One of the variants that most significantly predicted pathogen load irrespective of diet was an amino acid polymorphism in the canonical antibacterial peptide Diptericin . This was surprising to us , as previous candidate gene studies had failed to detect major effect of allelic variation in Diptericin or any other antimicrobial peptide gene on resistance to Serratia marcescens , Enterococcus faecalis , Lactococcus lactis , or Providencia burhodogranariea [26–28] . Our interpretation had been that AMPs are plentiful and functionally redundant [52] , such that minor variation in any one peptide would not have major effect on organism-level resistance . However , in followup experiments we have confirmed that the Serine/Arginine variant mapped in the present study is a strong predictor of resistance to some but not all Gram-negative bacterial pathogens ( Unckless et al in prep ) . Thus , it would appear that the relative importance of Diptericin , and by extension presumably other antibacterial peptides , depends on the agent of infection . Moreover , we have found an independent mutation in natural populations of Drosophila simulans that converges on a Serine/Arginine polymorphism at the same Diptericin codon , with the same consequence for relative resistance to this suite of bacteria . Surprisingly , natural populations of both D . melanogaster and D . simulans are additionally polymorphic for apparent loss-of-function mutations at Diptericin , and flies carrying these variants are highly susceptible to infection by P . rettgeri and other bacteria [38] ( Unckless et al in prep ) . The collective data indicate a complex evolutionary history of Diptericin that includes convergent evolution of selectively balanced polymorphisms in two species , with variation in relative resistance to a subset of pathogens . We found that infection by the endosymbiont Wolbachia pipientis is associated with modest but significant resistance to infection by P . rettgeri . Previous studies have not found differences in Wolbachia-infected vs . uninfected flies in immune system activity or resistance to infection by secondary bacteria , including P . rettgeri [42 , 43 , 65] . Our present study is substantially larger than these others , and therefore may have greater power to detect small protective effects of Wolbachia infection . Unlike previous studies which have compared Wolbachia-infected flies to genetically matched lines which were cured of Wolbachia using antibiotics , our present study cannot fully distinguish between the effects of Wolbachia and host genotype . For example , Wolbachia infection status could be associated with general health of the lines and therefore resistance to P . rettgeri infection , or Wolbachia infection could be significantly associated with a genetic polymorphism that also predicts resistance to P . rettgeri . Presence of Wolbachia was weakly associated with a decrease in soluble protein in the present study ( p = 0 . 046 ) , and has been previously shown to alter fly physiology by buffering the effects of excess or deficit in dietary iron [66] and by modulating other metabolic processes including insulin signaling [67] . These physiological impacts may suggest indirect mechanisms by which Wolbachia infection could confer weak protection against infection by pathogens like P . rettgeri . In summary , we have shown that natural genetic variation for immune defense can be attributed to variation in several genes , with both diet-dependent and diet-independent effects . We also find that metabolic indices are correlated with immune defense when flies are reared on a high glucose diet . Importantly , several of the mapped genes would not be considered conventional “immune” genes , yet we confirm with RNAi knockdown that they pleiotropically contribute to immune defense . The genes mapped in this study harbor allelic variation that shapes the quality of immune defense , and thus may be instrumental in the evolution of resistance to bacterial infection in natural populations experiencing varied dietary environments .
The Drosophila Genetic Reference Panel ( DGRP; [39] ) is a collection of 192 lines that have been inbred to homozygosity and whose complete genomes have been sequenced . Each line is derived from an independent wild female captured in a fruit market in Raleigh , NC , USA in 2003 . We used 172 of the most robust lines for this study , though the exact number and composition of lines varied slightly among replicate blocks of the experiment . Bacterial infections were performed using Providencia rettgeri strain Dmel , which was isolated as an infection of a wild-caught D . melanogaster [68] . P . rettgeri are Gram-negative bacteria in the family Enterobacteriaceae , and are commonly found in association with insects and other animals . Injection of the Dmel strain of P . rettgeri into D . melanogaster under the conditions used here results in a highly reproducible initial dose of bacteria that proliferates 100–1000 fold over the first 24 hours post-infection , depending on the host fly genotype , with low to moderate host mortality [51] . Bacterial load at 24 hours post-infection correlates strongly with risk of host mortality [38] , but pathogen load as a phenotype does not confound resistance and tolerance mechanisms in the way that survivorship does . We used two experimental diets that varied in glucose content but otherwise had the same composition . The base diet was composed of 5% weight per volume Brewer’s yeast ( MP Biomedicals , Santa Ana , CA ) and 1% Drosophila agar ( Genesee Scientific , San Diego , CA ) . The high-glucose diet contained 10% glucose ( Sigma-Aldrich Corp . , St . Louis , MO ) while the low-glucose diet contained 2 . 5% glucose . All diets were supplemented with 800 mg/L methyl paraben ( Sigma-Aldrich Corp . , St . Louis , MO ) and 6 mg/L carbendazim ( Sigma-Aldrich Corp . , St . Louis , MO ) to inhibit microbial growth in the food . RNAi knockdown experiments for SNPs significant when data from both diets were pooled were performed on the “standard diet” which contained 8 . 2% glucose and 8 . 2% Brewer’s yeast . Each DGRP line was split and raised in parallel on both diets for at least three generations prior to the start of the experiment to control parental and grandparental effects within dietary treatments , and experimental flies were reared egg-to-adult in the dietary condition being assayed . We recognize that our diets differ in total caloric content as well as protein to carbohydrate ratios [4 , 13 , 14] . It is possible that Drosophila change their feeding behavior on the two diets , and that there may even be genetic variation for feeding behavioral response to diet . Our goal in this study is to determine the consequences of excess dietary glucose while remaining agnostic as to the precise cause of any altered nutritional assimilation . Providencia rettgeri strain Dmel was grown overnight to stationary phase in Luria-Bertani ( LB ) broth at 37°C prior to each infection day . On the morning of infections , stationary cultures were diluted in sterile LB broth to A600 = 1 . 0 . Male flies from each DGRP line were infected in the lateral scutum of the thorax by pricking with needles ( 0 . 10mm , Austerlitz Insect Pins , Prague , CR ) that had been dipped in the diluted bacterial suspension , delivering approximately 1000 bacteria to each infected fly . Infections were performed in three blocks for each diet with each block containing all or nearly all DGRP lines under study . Each block for each diet was performed on a different day , with replicate blocks for the two diets interspersed on alternating weeks . Three researchers performed the infections on each experimental day , with lines assigned randomly to infectors within each block . Males aged 3–6 days post-eclosion were infected from each line . All flies were maintained in an incubator at 24°C on a 12-hour light/dark cycle . Infections were delivered approximately 2–4 hours after “dawn” from the perspective of the flies . Approximately 24 hours after infection , males were homogenized in groups of 3 in 500 ul sterile LB broth . The homogenate was plated on standard LB agar plates using a robotic spiral plater ( Don Whitley Scientific ) . Plates were incubated overnight at 37°C , and the resultant colonies were counted using the ProtoCOL system associated with the plater . P . rettgeri grows readily on Luria agar at 37°C , but the endogenous microbiota of D . melanogaster does not . Thus , we were able to capture colonies derived from viable infecting P . rettgeri without interference from the Drosophila gut microbiota . Counted colonies were visually inspected for morphology consistent with P . rettgeri , and homogenates from sham-infected flies always failed to yield bacterial colonies within the assay period . We used systemic pathogen load at 24 hours post-infection as our measure of immune defense . In total , 6–9 data points representing 18–27 flies were collected from each line on each diet ( high and low glucose ) . The total experiment consists of 1429 data points representing 4287 flies on the low glucose diet , and 1396 data points representing 4188 flies on the high glucose diet . We queried a series of nutritional indices in flies reared on each diet . Each metabolite was assayed in three replicates on flies reared on each diet . Males were aged 3–6 days post-eclosion , then 10 live males were weighed using a MX5 microbalance ( Mettler-Toledo , Columbus , OG ) and homogenized in 200 μL buffer ( 10 mM Tris , 1 mM EDTA , pH 8 . 0 with 0 . 1% v/v Triton-X-100 ) using lysing matrix D ( MP Biomedicals , Santa Ana , CA ) on a FastPrep-24 homogenizer ( MP Biomedicals , Santa Ana , CA ) . An aliquot of 50 microliters were frozen immediately while 150 microliters were incubated at 72 degrees C for 20 minutes to denature host proteins . Nutrient assays were performed with minor modifications of the procedures described in [69] using the following assay kits from Sigma-Aldrich ( St . Louis , MO ) : glucose with the oxidase kit ( GAGO-20 ) ; glycogen using the glucose kit and amyloglucosidase from Aspergillus niger ( A7420 ) in 10 mM acetate buffer at pH 4 . 6; free glycerol and triglycerides using reagent kits F6428 and T2449 , respectively . Soluble protein was assayed with the DC Protein Assay ( BIO-RAD , Hercules , CA ) . Each metabolite was assayed on each pool of weighed and homogenized flies . Mixed effect linear models were used to test for genetic and other contributions to phenotypic variation in systemic pathogen load and nutritional indices . Overall genetic main effects on systemic pathogen load were tested with the model Yijklmno= μ+ Wolbi+ Dietj+ Linek ( Wolbi ) + Infectorl+ Platerm+ Blockn ( Dieto ) + Dieto*Linek ( Wolbi ) + εijklmno where Y is the natural log-transformed measure of pathogen load for each data point , Wolbi ( i = 1 , 2 ) has a fixed effect and indicates whether the line is infected with the endosymbiotic bacterium Wolbachia pipientis , Dietj ( j = 1 , 2 ) has a fixed effect and indicates which of the two diets the flies were reared on , Infectorl ( l = 1 , 3 ) has a fixed effect and is used to test whether the experimentor performing the infections influenced ultimate pathogen load , and Platerm ( m = 1 , 2 ) has a fixed effect and indicates which of two spiral platers were used to plate the sample . Blockn ( Dieto ) ( n = 1 , 3 ) has a fixed effect nested within the effect of Diet , and is used to test for differentiation among the three replicate blocks for each dietary treatment . Linek ( Wolbi ) ( k = 1 , 172 ) is assumed to have a random effect , and is used to test the influence of genetic line on pathogen load within Wolbachia-infected and Wolbachia-uninfected classes . The interaction Dieto*Linek ( Wolbi ) is considered to have a fixed effect and tests whether genetic lines differ in their responsiveness to the two diets . This model was run in SAS 9 . 3 ( Cary , NC ) using the “mixed” procedure . We determined line means for each nutritional index using abundance of metabolite per fly . The model used was analogous to that used for bacterial load: Yijklmno= μ+ Wolbi+ Dietj+ Linek ( Wolbi ) + Blockn ( Dieto ) + Dieto*Linek ( Wolbi ) + εijklmno Again , all factors were considered to be fixed except Line ( Wolb ) and Diet*Line ( Wolb ) and best linear unbiased predictors ( BLUPs ) were extracted for further analysis . For comparisons between diets , the model used was Nutrient/fly~Wolb+Line ( Wolb ) +Block . To determine whether there was a genetic signature of a “metabolic syndrome” that may influence immune defense , we performed principal component analysis using the BLUPs extracted for each nutritional index . This analysis was implemented in R with the prcomp function with tol = 0 . 1 and unit variance scaling on . The principal component values for were then tested for correlation with bacterial load . This analysis was done for each diet individually . The set of SNPs for mapping was described in Huang et al . ( in revision ) and consists of only SNPs with minor alleles present in at least four of the lines ( MAF >2%; 2415518 total SNPs ) . For bacterial load ( Ln CFU ) , we used SAS to run the following model: LnCFU = m+SNPi+Dietj+SNPi*Dietj+Blockk ( Dietj ) +Wolbl+Infectorm+Platern+Lineo ( SNPi ) +eijklmno , where all factors were fixed except Line ( SNP ) . P-values for the main effect of SNP and the SNP*Diet interaction were obtained for each SNP . We also ran the model separately on data obtained from flies reared on each of the two diets to obtain significance values for each SNP on each diet independently . These models were LnCFU = m+SNPi+Blockj+Wolbk+Infectorl+Platerm+Linen ( SNPi ) +eijklmn . We considered SNPs that mapped with significance level of p < 10-6 to be nominal positive hits and candidates for RNAi knockdown experiments . This p-value corresponds to a false discovery rate of 5–10% depending on the precise analysis being performed . To correct for gene size , we used GOWINDA [47] to test for the enrichment of particular functional groups . Here we relax our significance threshold to include all SNPs with p<10-5 . This allows for more power through the inclusion of additional SNPs . Relaxing the P-value threshold even further had little effect on GO enrichment results . Significantly associated SNPs for each treatment ( low glucose , high glucose , main effect ) were used with a background SNP set consisting of all SNPs used in the GWAS . GO slim [48] terms were used to reduce redundancy in GO categories . GOWINDA was run using gene mode , including all SNPs within 1000bp of a gene , a minimum gene number of 5 , and with 100 , 000 simulations . We report all GO terms with a nominal P-value less than 0 . 1 . For all SNPs with P-values meeting our significance threshold and falling within 1000 bp of an annotated gene , we performed the infection assay described above on RNAi knockdown lines from the Vienna Drosophila RNAi Center ( Vienna , Austria ) , if available . To test the effect of the gene on resistance to infection , we crossed each RNAi line to a line carrying the ubiquitous driver ( Act5C-Gal4/Cyo or da-Gal4 ) and infected F1 offspring of the knockdown genotype . We compared the immune defense in these F1 offspring to that of F1 progeny from the driver line crossed to the background genetic line of the RNAi transformant . Unless otherwise indicated , we performed RNAi knockdown experiments using a standard diet ( 1:1 glucose to yeast ratio , but more calorie dense than our high and low glucose diets—see methods ) . For those SNPs that showed a diet-specific effects , we performed RNAi knockdown experiments on the experimental high and low glucose diets . It is completely unknown what proportion of genes throughout the genome might yield an immune phenotype when expression is repressed . To test whether genes containing our significantly associated SNPs were more likely to have an immune phenotype than a set of arbitrary genes from the genome , we also performed RNAi knockdown experiments on the genes that were physically close to those of interest but not known to be involved in immunity and not essential for viability . We refer to these as “nearest neighbor controls” . | Previous studies have indicated that dietary nutrition influences immune defense in a variety of animals , but the mechanistic and genetic basis for that influence is largely unknown . We use the model insect Drosophila melanogaster to conduct an unbiased genome-wide mapping study to identify genes responsible for variation in resistance to bacterial infection after rearing on either high-glucose or low-glucose diets . We find the flies are universally more susceptible to infection when they are reared on the high-glucose diet than when they are reared on the low-glucose diet , and that metabolite levels genetically correlate with quality of immune defense after rearing on the high-glucose diet . We identify several genes that contribute to variation in defense quality on both diets , most of which are not traditionally thought of as part of the immune system . The genetic variation we observe can be important for evolved responses to pathogen pressure , although the effectiveness of natural selection will be partially determined by the host nutritional state . | [
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| 2015 | The Complex Contributions of Genetics and Nutrition to Immunity in Drosophila melanogaster |
The AP-5 adaptor protein complex is presumed to function in membrane traffic , but so far nothing is known about its pathway or its cargo . We have used CRISPR-Cas9 to knock out the AP-5 ζ subunit gene , AP5Z1 , in HeLa cells , and then analysed the phenotype by subcellular fractionation profiling and quantitative mass spectrometry . The retromer complex had an altered steady-state distribution in the knockout cells , and several Golgi proteins , including GOLIM4 and GOLM1 , were depleted from vesicle-enriched fractions . Immunolocalisation showed that loss of AP-5 led to impaired retrieval of the cation-independent mannose 6-phosphate receptor ( CIMPR ) , GOLIM4 , and GOLM1 from endosomes back to the Golgi region . Knocking down the retromer complex exacerbated this phenotype . Both the CIMPR and sortilin interacted with the AP-5–associated protein SPG15 in pull-down assays , and we propose that sortilin may act as a link between Golgi proteins and the AP-5/SPG11/SPG15 complex . Together , our findings suggest that AP-5 functions in a novel sorting step out of late endosomes , acting as a backup pathway for retromer . This provides a mechanistic explanation for why mutations in AP-5/SPG11/SPG15 cause cells to accumulate aberrant endolysosomes , and highlights the role of endosome/lysosome dysfunction in the pathology of hereditary spastic paraplegia and other neurodegenerative disorders .
Adaptor protein ( AP ) complexes are a family of 5 evolutionarily ancient heterotetramers [1] , which facilitate the transport of cargo from one membrane compartment of the cell to another . The most recently discovered AP complex , AP-5 [2] , had escaped detection for over 10 years , because its subunits were too divergent to be identified using sequence-based tools like BLAST . However , AP-5 is predicted to be structurally very similar to APs 1–4 , even though in its native form it exists as a heterohexamer rather than a heterotetramer . Its two additional subunits are encoded by the SPG11 and SPG15 genes , and they are essential for the stability and membrane association of the whole complex [2 , 3] . Understanding the function of AP-5 has been challenging . It is expressed at relatively low levels ( only about 10 , 000 copies in a HeLa cell , compared with about 300 , 000–1 , 000 , 000 copies for APs 1 , 2 , or 3 ) [4 , 5] , and it has been lost from several model organisms , including Drosophila melanogaster , Caenorhabditis elegans , and Saccharomyces cerevisiae [6] . However , it is clearly important in humans , because mutations in its ζ subunit , encoded by the AP5Z1 gene ( aka SPG48 ) , cause hereditary spastic paraplegia ( HSP ) [7] , as do mutations in either SPG11 or SPG15 ( SPG is an acronym for spastic paraplegia gene ) . In all three cases , the disorder is classified as a complicated form of HSP , with various neurological abnormalities in addition to the typical degeneration of long corticospinal axons , which is a defining feature of all forms of HSP [8] . The AP-5/SPG11/SPG15 complex localises to late endosomes and lysosomes [2 , 3] , and fibroblasts from HSP patients with mutations in any of the 3 genes contain aberrant endolysosomes filled with undigested material [9–11] . Some of the other forms of HSP , including those caused by mutations in the SPG4 , SPG8 , or SPG31 genes , are also characterised by lysosomal abnormalities [12] , suggesting that lysosome dysfunction may be a common feature of the disorder and may play a causative role . However , in the case of the AP-5/SPG11/SPG15 complex , the molecular mechanisms underlying this phenotype are unclear , because the function of AP-5 and its associated proteins is still unknown . The homology between AP-5 and the other APs suggests that it may be involved in cargo recognition , but this has not been demonstrated , and the highly conserved cargo binding sites found in the other AP complexes are absent in AP-5 . One possibility would be to look for AP-5–dependent cargo by carrying out conventional binding assays . However , previous studies have shown that adaptor-cargo interactions are generally low affinity , transient , and dependent upon the presence of other factors and/or the conformation state of the complex [13] . Therefore , we used an alternative approach: subcellular fractionation profiling combined with quantitative mass spectrometry to identify proteins whose subcellular distribution is affected by the presence or absence of AP-5 .
To investigate the phenotype of AP-5 deficiency , we first looked for changes in global protein expression levels using label-free mass spectrometric quantification [14] . Two independent knockouts of the AP-5 ζ subunit gene , AP5Z1 , were made in HeLa cells using CRISPR-Cas9 ( AP5Z1_KO1 and AP5Z1_KO2; Fig 1A ) and compared with wild-type cells . In addition , fibrobasts from 2 unrelated patients with loss-of-function mutations in AP5Z1 were compared with matched controls [9] ( Fig 1A ) . With the exception of AP5Z1 itself , there was little difference in the relative amounts of the >7 , 500 proteins identified in the HeLa cell lines or in the >7 , 500 proteins identified in the patient fibroblast lines ( Fig 1B ) . Because AP-5 localises to a late endocytic compartment [2 , 3] , and loss of AP-5 causes cells to accumulate aberrant endolysosomes [9] , we anticipated that there might be problems with lysosomal degradation and/or effects on proteins associated with late endosomes and lysosomes . However , levels of lysosomal proteins ( coloured salmon in Fig 1B ) were essentially unaffected in both HeLa cells and fibroblasts . Because fibroblasts are not very tractable for biochemical analyses , owing to their genetic heterogeneity , slow growth , and resistance to transfection , we focused on the HeLa knockout cell lines for our further investigations . Although there were no obvious changes in protein abundance when the knockout and mutant cells were compared with controls , AP-5 is likely to have a role in protein sorting; thus , we next investigated whether AP-5 ablation causes changes in the subcellular localisation of proteins . For this , we applied a proteomic technique developed in the Borner lab called “Dynamic Organellar Maps” [5 , 16] . This approach combines subcellular fractionation with quantitative mass spectrometry profiling to determine the compartment associations of proteins . Comparisons of organellar maps made under different conditions reveal shifts in the fractionation profile of proteins in an unbiased manner . Control HeLa cells and both of the AP5Z1 knockout lines were analysed in triplicate , and 2 , 046 proteins were profiled across all 9 maps . Principal component analysis showed highly resolved organellar clusters , with similar maps for controls and knockouts ( S1 Fig ) . To detect proteins whose subcellular localisation ( i . e . , fractionation profile ) was changed by AP-5 ablation , we performed a sensitive shift analysis , scoring each protein for the magnitude and reproducibility of its movement across maps ( see Materials and methods for details ) . This identified 26 candidate shifting proteins ( Fig 1C; S1 Data , “MR plot data” ) . Strikingly , 15 of them were known or predicted endosomal proteins ( GO-term “endosome membrane” >10-fold enriched , p < 1 . 5*109; S1 Data , “Enrichment of MR plot hits” ) . They included multiple subunits of the retromer complex and associated sorting nexins , which are involved in endosome–to–trans-Golgi network ( TGN ) recycling ( VPS29 , VPS35 , SNX2 , SNX3 , SNX5 , SNX27 ) , as well as multiple subunits of the HOPS complex , which is involved in endosome-lysosome fusion ( VPS16 , VPS18 , VPS33A , VPS39 ) . The cation-independent mannose 6-phosphate receptor ( CIMPR; gene name IGF2R ) was also identified as a marginal hit . Inspection of individual map positions revealed that these proteins were still predicted to be mostly endosomal in the AP-5 knockout cells , but they fractionated more closely with lysosomes than in control cells ( S1 Fig ) . Although the biological interpretation of the shifts is not straightforward , the movements of the proteins on the maps were indicative of an association with a larger or denser compartment in the AP-5 knockouts . The organellar mapping data are thus consistent with the previously observed AP-5 endolysosomal swelling phenotype [9] and identify HOPS and retromer plus sorting nexins as major candidate protein sorting complexes responding to AP-5 ablation . Because we had previously observed changes in retromer localisation following small interfering RNA ( siRNA ) -mediated depletion of AP-5 [2 , 3] , we decided to investigate the relationship between AP-5 and retromer further . We began by using immunofluorescence microscopy to investigate the effect of retromer knockdown on AP-5 and its partners , SPG11 and SPG15 . Because of signal-to-background problems with antibodies against endogenous subunits , which are expressed at very low levels [6] , we carried out these experiments on cells expressing GFP-tagged SPG15 under the control of its endogenous promoter [7] . We found that knocking down either VPS26 or VPS35 caused the tagged SPG15 to appear much brighter ( Fig 2A ) , even though western blotting showed that there was no increase in the total amount of the construct ( Fig 2B ) , and the construct still localised to a LAMP1-positive compartment ( Fig 2C ) . Quantification using automated microscopy , which objectively samples thousands of cells , revealed an approximately 2-fold increase in the size and intensity of SPG15-GFP “spots” ( Fig 2D ) . Although this increase could represent coalescence of smaller compartments , the number of quantified spots per object ( i . e . , per cell ) did not decrease but in fact increased , indicating that more SPG15-GFP was being recruited onto membranes . Similarly , western blotting of cell homogenates , which had been separated into membranes and cytosol by ultracentrifugation , showed that knocking down VPS35 caused an increase in membrane-associated SPG15-GFP ( Fig 2E ) . These findings suggest that , in the absence of retromer , the AP-5/SPG11/SPG15 complex is somehow harnessed more efficiently in order to compensate and thus that retromer and AP-5/SPG11/SPG15 might be functioning in the same or related pathways . To test this possibility , we adapted a retrieval assay for the CIMPR , which was originally designed to study retromer function [17] . Cells were incubated at room temperature with an antibody against endogenous CIMPR , then warmed to 37°C for an hour to allow the antibody to be internalised . The cells were then fixed and triple labelled for the endocytosed antibody , the TGN region ( using an antibody against TGN46 ) , and the cell boundary ( using a whole-cell stain ) ( Fig 3A ) . Both of the AP-5 knockout cell lines were significantly impaired in their ability to retrieve anti-CIMPR to the juxtanuclear region and showed less overlap with TGN46 , although at this resolution it is not clear whether the antibody is in the TGN46 compartment or in another compartment in the same general vicinity ( Fig 3B ) . The extent of overlap was quantified using an automated microscope ( Fig 3C and Fig 3D ) . There was also impaired antibody retrieval to the juxtanuclear region when the VPS35 subunit of retromer was knocked down using siRNA ( Fig 4A ) , in keeping with published results [17] . Importantly , this phenotype was even more pronounced when retromer was knocked down in AP5Z1 knockout cells ( Fig 4A and Fig 4B ) . These differences were not due to effects on CIMPR expression levels nor to the amount of CIMPR on the cell surface , as determined by flow cytometry ( Fig 4C ) . Together , our findings suggest that AP-5 and retromer may be working on parallel pathways to facilitate the retrieval of CIMPR from endosomes back to the TGN . To search for other proteins whose trafficking might be affected by the loss of AP-5 , we combined stable isotope labelling of amino acids in cell culture ( SILAC ) labelling with subcellular fractionation to isolate vesicle-enriched fractions from control and AP-5 knockout cells , which were then compared by quantitative mass spectrometry . These vesicle-enriched fractions are isolated from cell homogenates by differential centrifugation and contain a mixture of small membranous structures , including vesicles of many different types as well as other particles , such as ribosomes and proteasomes . We identified about 700 proteins across all 4 samples ( two knockout lines , repeated twice; Fig 5A , S2 Data ) and compiled a list of proteins that were consistently depleted more than 2-fold in the 2 knockout lines in both repeats . The top hits included 5 transmembrane proteins of varying topologies , all of which are reported to localise to the Golgi apparatus: SLC35B2 , GOLIM4 , GLG1 , GOLM1 , and GALNT2 [18–22] ( Fig 5A and Fig 5B ) . The identification of Golgi-localised proteins in this assay was surprising , because AP-5 localises to late endosomes and/or lysosomes , so we did not expect to find proteins residing in a relatively early secretory compartment . However , even though the 5 proteins are mainly resident in the Golgi at steady state , there is evidence that they can all traffic out of the Golgi to a later compartment and then recycle back again . GOLIM4 ( aka GPP130 , GIMPC , and GOLPH4 ) is probably the best characterised protein on our list . It was first described 20 years ago , when it was found to localise to the early Golgi but to have late Golgi posttranslational modifications , indicating that it cycles back and forth between early and late compartments [20] . GOLIM4 was also shown to redistribute to endosomes when cells were treated with pH-disrupting reagents , like chloroquine or monensin , but then to return to the Golgi when the drugs were removed [23 , 24] . Further studies showed that GOLIM4 acts as a receptor for Shiga toxin , facilitating its trafficking from endosomes back to the Golgi apparatus [25] , but that this pathway can be blocked by the addition of manganese ( Mn2+ ) , which causes GOLIM4 to accumulate in late endosomes and lysosomes [26] . To determine whether any of our other hits might be Mn2+ sensitive , we treated cells with 500 μM MnCl2 and found that GLG1 , but not GOLM1 , GALNT2 , or SLC35B2 , showed a similar behaviour to GOLIM4 ( Fig 5C and Fig 5D ) . Another way of determining whether Golgi membrane proteins move to a later compartment and then return is to find out whether they are packaged as cargo into intracellular clathrin-coated vesicles ( CCVs ) , which traffic back and forth between the TGN and endosomes . The major components of the coats on intracellular CCVs are clathrin and the AP-1 adaptor complex , and by knocking AP-1 sideways ( i . e . , rapidly redistributing it to mitochondria with a small molecule ) [27] and then using mass spectrometry to look for differences in a CCV-enriched fraction , we can identify intracellular CCV cargo [28] . SLC35B2 , GLG1 , GOLM1 , and GALNT2 were all depleted from the AP-1 knocksideways CCV fraction ( 2 . 3- , 1 . 9- , 2 . 2- , and 1 . 7-fold , respectively ) [28] . Because AP-1 facilitates cycling between the TGN and endosomes , this indicates that these 4 proteins frequently move to endosomes , even though at steady state they are mainly in the Golgi . In contrast , GOLIM4 was only weakly affected by the AP-1 knocksideways [28] . However , its accumulation in endosomes in Mn2+-treated cells was reported to be clathrin dependent [29] , indicating that it , too , can enter intracellular CCVs but that normally , there is relatively little of it in CCVs at steady state . Probably the simplest and most versatile way of looking for Golgi escape and retrieval is to treat cells with a pH-disrupting drug like monensin or chloroquine . For reasons that are still unclear , raising the pH of acidic organelles causes many cycling proteins to become trapped in endosomes , although most then return to the Golgi when the drug is removed [30] . Thus , we investigated the localisation of the proteins upon monensin treatment and washout in both wild-type and AP-5 knockout cells . We found that in the absence of monensin , the proteins all localised normally to the Golgi in AP-5 knockout cells ( Fig 6A ) . This is consistent with our organellar maps , in which the steady-state localisation of most proteins was unchanged by the AP-5 knockout ( Fig 1 and S1 Fig ) . Treating the cells with monensin for 90 min caused GOLIM4 , GLG1 , and GOLM1 to adopt a more punctate and peripheral pattern ( Fig 6B ) , presumably reflecting a move to endosomes [24] . This occurred without any loss in protein expression levels ( Fig 6C ) . When the drug was then washed out for 2 . 25 h , GOLIM4 and GOLM1 returned to a juxtanuclear pattern , while GLG1 labelling was difficult to discern because of protein loss , presumably reflecting degradation in lysosomes ( Fig 6C ) . Importantly , both GOLIM4 and GOLM1 were impaired in their ability to recycle back to the juxtanuclear region in the AP-5 knockout cells ( Fig 6D and Fig 6E ) , and we were able to quantify this effect by automated microscopy ( Fig 6F ) . We also investigated the effect of knocking down retromer in both wild-type cells and AP-5 knockout cells and then carried out monensin washout experiments . Once again , knocking down retromer alone impaired protein retrieval towards the Golgi , and knocking down retromer in AP-5 knockout cells had an additive effect ( Fig 7 ) . Thus , like the CIMPR , GOLIM4 can use both AP-5 and retromer to facilitate endosome-to-Golgi retrieval . Adaptor proteins recognise cargo by binding directly to sorting signals in their cytoplasmic tails . The CIMPR has a particularly long cytoplasmic tail ( 163 residues ) , with sorting signals for retromer , APs , and GGAs [31 , 32] . In contrast , the 5 Golgi proteins all have short cytoplasmic tails . For instance , the GOLIM4 tail is only 12 residues long , and deletion studies indicate that it does not contribute to the trafficking of the protein [23] . Thus , although there may be a direct interaction between AP-5/SPG11/SPG15 and the CIMPR , the sorting of the 5 Golgi proteins is likely to be indirect . To look for potential interactions with cargo proteins , we made several GST fusion proteins from SPG11 and SPG15 as well as from AP-5 subunits and used them to pull down binding partners from cell extracts , which were then identified by mass spectrometry . We found that a construct containing residues 1–709 of SPG15 brought down CIMPR ( IGF2R ) as one of its top hits from SH-SY5Y neuroblastoma cells ( Fig 8A and S3 Data ) . This interaction was confirmed by western blotting in both HeLa and SH-SY5Y cells ( Fig 8B and S2 Fig ) . We also probed our blots with an antibody against sortilin , which has a similar trafficking itinerary to the CIMPR and some of the same sorting signals in its cytoplasmic tail , even though the tail is shorter ( 52 residues ) and it is much less abundant [4 , 5] . Again , there was a robust signal in pulldowns from both HeLa and SH-SY5Y cells ( Fig 8B ) . In contrast , TGN46 ( used as a control ) was not pulled down by the SPG15 construct , nor was the AP-5 ζ subunit , indicating that this domain of SPG15 does not interact with AP-5 ( Fig 8B and S2 Fig ) . We were able to corroborate these interactions in native immunoprecipitations ( Fig 8C and S2 Fig ) . Sortilin has been shown to facilitate the trafficking of a number of different cargo proteins , including lysosomal hydrolases , neurotensin , and GLUT4 , which bind to its lumenal domain [33–35] , while the cytosolic domain of sortilin binds to different types of machinery , including GGAs , AP-1 , and retromer [32 , 36 , 37] . Interestingly , sortilin was on our list of proteins that were depleted from the vesicle-enriched fraction when AP-5 was knocked out ( number 17; the Golgi proteins were 1–5 ) ( S2 Data ) . This made it a strong candidate for a transmembrane protein that might facilitate the sorting of the AP-5–dependent Golgi proteins . We found that knocking down sortilin caused a reduction in the total amount of both GOLIM4 and GOLM1 ( Fig 9A ) , although their steady-state localisation was unaltered and their behaviour during monensin treatment and washout was indistinguishable from that of control cells ( Fig 9B ) . However , when sortilin was knocked down in AP-5 knockout cells and the cells were then subjected to a monensin washout , there was an increase in non-Golgi-associated GOLIM4 , when compared with the AP-5 knockout alone ( Fig 9B and Fig 9C ) . These results support a role for sortilin in the retrieval of GOLIM4 and possibly other Golgi proteins as well . Paradoxically , our findings also suggest that AP-5 may be able to traffic such proteins in a sortilin-independent manner , because if AP-5 acted solely via sortilin , then knocking it out would not exacerbate the phenotype of sortilin knockdown cells .
AP-5 is an ancient and ubiquitous protein complex , but humans and other organisms are able to survive without it . This indicates that even though its loss in humans causes neurological abnormalities , its null phenotype at the cellular level is likely to be subtle . Therefore , instead of trying to predict what AP-5 might be doing , we used 2 large-scale proteomic analyses as unbiased ways of identifying cargo and machinery that either depend upon AP-5 for trafficking or interface somehow with the AP-5 pathway . The first analysis , dynamic organellar mapping , revealed modest but highly reproducible changes in 2 types of endosomal machinery , the retromer complex , with associated sorting nexins , and the HOPS complex . Although we do not know precisely what these changes mean , they could reflect alterations in endolysosomal dynamics , and/or attempts by the cell to compensate for AP-5 loss . We have previously shown that knocking down AP-5 affects the localisation of retromer , causing it to take on a more clustered appearance [3] , and here we show that knocking down retromer also affects the localisation of the AP-5–associated protein SPG15 , causing an increase in its membrane association . In addition , we find that knocking out AP-5 impairs retrograde trafficking of the CIMPR towards the TGN and that combining the knockout with a retromer knockdown exacerbates this phenotype . Together , these observations suggest that the CIMPR is a cargo protein for AP-5 and that AP-5 and retromer both contribute to its retrieval . In the second analysis , we looked for changes in a vesicle-enriched fraction and found that knocking out AP-5 caused several proteins to be depleted , with Golgi membrane proteins among the top hits . Therefore , these 2 approaches are complementary: the first reveals changes in organelles , while the second reveals changes in transport intermediates . Although the steady-state localisation of the Golgi proteins looks normal in AP-5 knockout cells , consistent with our organellar maps , when we shifted their localisation to endosomes with monensin , their retrieval back towards the Golgi was impaired . Again , this phenotype was exacerbated by retromer knockdown . The Golgi proteins all have short and/or dispensable cytosolic tails , indicating that they do not bind directly to AP-5 or its partners . Thus , we speculated that sortilin , a multipurpose sorting receptor , might act as a bridge between these proteins and the AP-5/SPG11/SPG15 complex . This hypothesis is supported by the presence of sortilin in SPG15 pulldowns and its reduction in vesicle-enriched preparations from AP-5 knockout cells . In addition , while this manuscript was in preparation , Vencat and Linstedt reported that the Mn2+-induced exit of GOLIM4 from the Golgi is dependent on sortilin , and they suggested that the lumenal domains of the 2 proteins interact with each other [38] . However , the additive effects of sortilin knockdown and AP-5 knockout indicate that AP-5 can also act in a sortilin-independent manner . Candidates for other proteins that might act as sorting receptors for Golgi proteins include the CIMPR , which also comes down with SPG15 and has a similar tail to sortilin , and other members of the sortilin family , such as SORLA/SORL1 and SORSC1-3 . It will be important to uncover the molecular details of how the AP-5/SPG11/SPG15 complex binds to cargo , especially because there might be similarities to the COPI coat . Phylogenetic analyses indicate that some 2 billion years ago , the ancestor of all the APs , as well as COPI and TSET , was a heterohexamer rather than a heterotetramer , with 2 additional ‘protocoatomer’ subunits consisting of β-propellers , followed by an α-solenoid [39] . These subunits were retained by COPI and TSET but were lost in the AP lineage . However , AP-5 is thought to have been the first complex to branch off after the deep divide between the COPI/TSET family and the AP family , and SPG11 and SPG15 may be descendants of the protocoatomer subunits [39 , 40] . The ability of SPG15 to bind cargo supports this possibility , because the only known cargo-binding subunits of COPI are associated with the protocoatomers , not the core heterotetramer [41] . Although our pulldown experiments suggest that sortilin and CIMPR can bind to the AP-5/SPG11/SPG15 complex , it is well known that both of these cargo proteins can also bind to other trafficking machinery , including GGAs , AP-1 , and retromer . Each of these types of machinery has a somewhat different localisation , enabling them to form transport intermediates from different membranes . For instance , AP-1 localises to a tubular endosomal network [42] , while retromer localises to multivesicular bodies [43] . The additive effects of retromer knockdown and AP-5 knockout suggest that AP-5 may act on an alternative retrograde trafficking route , which may in fact be a backup pathway for retromer/sorting nexins and probably for other machinery as well . In other words , most of the time , Golgi-associated proteins would be retrieved before getting to the late endosome , but the few molecules that travel that far would be sent back again by the AP-5/SPG11/SPG15 complex , acting as a last-ditch effort before the terminal lysosome ( Fig 9 ) . This type of ‘belt and braces’ scenario , involving interplay between different pathways , is common in membrane traffic . For instance , AP-3 facilitates the trafficking of several lysosomal membrane proteins , but in its absence the proteins can still get to lysosomes by making use of other machinery , albeit less efficiently [44 , 45] . Using AP-5 as a backup retrograde sorting mechanism would help to explain why AP-5–deficient cells appear to be normal , in most respects , and why AP-5 becomes more important when there are extra demands on retrograde trafficking , such as when Golgi-resident proteins accumulate in endosomes . The gradual buildup of proteins ( and probably also lipids ) in terminal lysosomes may explain the abnormalities that are seen in fibroblasts from patients with mutations in AP-5 , SPG11 , or SPG15 . Interestingly , previous studies have implicated SPG11 and SPG15 in the reformation of free lysosomes from autolysosomes ( i . e . , lysosome-autophagosome hybrids ) [46 , 47] , a process that is conceptually similar to the retrieval of cargo proteins from endolysosomes during lysosome maturation uncovered by the present study . However , more work is needed to define the relationship between the AP-5 pathway and other pathways more precisely , including the identities of both the donor and the acceptor compartments . But why does the absence of AP-5 and its partners mainly affect neurons with long axons ? This question applies not only to AP-5 but also to all of the other ubiquitously expressed proteins encoded by genes that are mutated in HSP . Part of the answer must be that the exceptional length of these axons puts extra demands on cellular machinery , especially proteins involved in membrane traffic . In addition , a recent study showed that the axons of neurons from humans and mice with mutations in SPG4/spastin develop swellings that are filled with clusters of aberrant lysosomes [12] . These clusters could cause a traffic jam , impeding the progress of other organelles and vesicles up and down the axon [48] , eventually causing the axons to degenerate . It will be important to determine whether this is also the case for other HSP subtypes with lysosomal abnormalities , including the subtypes caused by mutations in AP-5 , SPG11 , and SPG15 as well as the subtype caused by mutations in the SPG8 gene , which encodes a retromer-associated protein , strumpellin [49] . Patients with mutations in AP5Z1 ( OMIM #613653 ) , SPG11 ( OMIM #610844 ) , or SPG15 ( OMIM #270700 ) have a ‘complicated’ rather than a ‘pure’ form of HSP , with other neurological problems in addition to lower limb spasticity . These include cognitive impairment , thinning of the corpus callosum , and parkinsonism , indicating that the AP-5/SPG11/SPG15 complex contributes to the health of many types of neurons , not just primary motor neurons . Interestingly , a missense mutation in the retromer VPS35 subunit ( OMIM #601501 ) causes an autosomal dominant form of Parkinson disease [50 , 51] , and although the precise molecular mechanism is still unclear , it seems likely that cargo missorting is a key contributing factor in both AP-5/SPG11/SPG15-related HSP and VPS35-related parkinsonism . The identification of manganese-sensitive proteins like GOLIM4 as cargo for both AP-5 and retromer provides a potential clue , because failure to deal with toxic levels of Mn2+ has been shown to cause neurodegeneration and parkinsonism [52] . Thus , our findings not only help to clarify the function of an ancient piece of cellular machinery , revealing a novel late-acting retrieval pathway , they also advance our understanding of how endosome/lysosome dysfunction can lead to neurodegenerative disorders , potentially opening up new avenues for the treatment of these diseases .
Antibodies used in this study include in-house antibodies against clathrin [53] and commercial antibodies against AP-5 ζ ( Atlas HPA035693 ) , CIMPR ( Abcam ab8093 ) , GALNT2 ( Abcam ab102650 ) , GOLIM4 ( Alexis Biochemicals 804-603-C100 ) , GLG1 ( Atlas HPA010815 ) , GOLM1 ( Abnova H0005 1280-MO6 ) , GM130 ( Abcam ab52649 [rabbit] and BD Transduction Labs 610822 [mouse] ) , VPS35 ( Santa Cruz sc374372 ) , SPG15 ( Pro Sci 5023 ) , AP-1 γ ( mAb100 . 3 ) , and sortilin ( Abcam ab188586 ) . The rabbit anti-GFP and anti-VPS26 were kind gifts from Matthew Seaman ( CIMR , Cambridge , UK ) . Fluorescently labelled secondary antibodies were purchased from Invitrogen , and HRP-labelled secondary antibodies were purchased from Sigma-Alldrich; western blots were developed using ECL Prime Western Blotting Detection Reagent ( GE Healthcare ) and quantified using IMAGEJ software . AP5Z1_KO clones were made using CRISPR-Cas9 . Guide RNAs targeting AP5Z1 were cloned into pX330 using the ‘simple method protocol’ based on the ‘ELAN’ method described by Cost and Cozzarelli [54] , with cotransfection of a G418 selectable plasmid . Cells were maintained in G418 for 3 days , and following cell death , clonal cell lines were established . Although 2 exons ( exon 2 and 3 ) were targeted with 4 different guides , the only one that produced a full knockout was one against exon 3 ( ze3g1: CAGAGGGGGACATCTCTCGC ) , from which 2 clonal knockout lines were established . For AP5Z1_KO1 , the sequencing of 26 colonies revealed 2 with a 17–base pair deletion , 2 with a 1–base pair deletion , 3 with a 7–base pair deletion , 3 with 1 base pair substitution plus a 1–base pair deletion , and 16 with a 1–base pair insertion , suggesting the existence of at least 5 alleles . For AP5Z1_KO2 the sequencing of 24 colonies revealed 5 with a 2–base pair deletion , 9 with a 1–base pair deletion , and 10 with a 14–base pair deletion . All mutations are predicted to be deleterious to the expression of ζ protein due to frameshifts . The first 709 amino acids of SPG15 were cloned into pGEX4T-1 ( GE Healthcare ) and sequence verified , to generate the SPG15N1-709 construct . GST alone and SPG15N1-709 were expressed in Escherichia coli . For GST pulldowns , HeLa or SH-SY5Y cells were lysed in PBS-T ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , and 1 . 76 mM KH2PO4 , pH 7 . 4 , adjusted to 0 . 5% Triton X-100 from a 10% stock ) and cleared of debris by centrifugation . Lysates were adjusted to a protein concentration of 2 . 5 mg/ml , and 50 μg of fusion protein was added as bait for every 4 ml of lysate . The baits and associated proteins were recovered with glutathione Sepharose 4B ( GE Healthcare ) and eluted with 2 . 5% ( wt/vol ) SDS/50 mM Tris , pH 8 . 0 , at 60°C . For immunoprecipitations , cells were lysed in PBS-T or N ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 1 . 76 mM KH2PO4 , and 0 . 1% [vol/vol] TX100 [Sigma-Aldrich] , pH 7 . 4 ) and clarified . All samples were precleared by the addition of protein A-Sepharose ( GE Healthcare ) and then incubated with antibody for 2 h followed by a further hour with protein A–Sepharose . The samples were then washed multiple times in PBS-T/N and immunoprecipitated complexes eluted with sample buffer . For cytosol and membrane fractions , cells were scraped in PBS and lysed by 8 passages through a 21-gauge needle/5-ml syringe . Nuclei and unbroken cells were removed by centrifugation at 1 , 000 × g for 5 min , and then membranes were recovered at 100 , 000 × g for 30 min . Knockdowns were performed using the following On-Target Plus SMARTpool siRNA reagents from Dharmacon , with a nontargeting SMARTpool siRNA ( D-001810-10 ) used as a control . The siRNAs for VPS35 were 010894–05 ( GAACAUAUUGCUACCAGUA ) , 010894–06 ( GAAAGAGCAUGAGUUGUUA ) , 010894–07 ( GUUGUAAACUGUAGGGAUG ) , 010894–08 ( GAACAAAUUUGGUGCGCCU ) ; for AP5Z1 , they were 025284–17 ( GGGACUUCGGUGCAGAUUA ) , 025284–18 ( GUUCCUGGGCAGCGUGAAU ) , 025284–19 ( GGAGGUGGCCUUCGAGUAC ) , 025284–20 ( CCACAGACUUCUUCACGGU ) ; and for SORT1 , they were 010620–05 ( GAGACUAUGUUGUGACCAA ) , 010620–06 ( GAGCUAGGUCCAUGAAUAU ) , 010620–07 ( GAAGGACUAUACCAUAUGG ) , 010620–08 ( GAAUUUGGCAUGGCUAUUG ) . The custom oligo to knock down VPS26 was a gift from Matthew Seaman [55] . All siRNAs were used at a concentration of 25–50 nM in a 2-hit , 5-day protocol according to manufacturer’s instructions ( Dharmacon ) . Knockdown efficiencies were determined by western blotting and showed >80% depletion for VPS26 and >90% for all other knockdowns ( quantified by ImageJ ) . HeLaM cells [56] and patient fibroblasts [9] were grown in Dulbecco’s Modified Eagle’s Medium ( DMEM , Sigma ) supplemented with 10% ( v/v ) foetal calf serum ( Sigma ) , 2 mM L-glutamine , 50 units/ml penicillin , and 50 μg/ml streptomycin . The BAC-transgenic HeLa cell line expressing SPG15-GFP under its own promoter had to be regularly sorted by FACS due to loss of expression . HeLa cells expressing the reporter constructs composed of the cytoplasmic tail of CIMPR ( CD8-CIMPR ) or sortilin ( CD8-sortilin ) coupled to the transmembrane and lumenal domain of CD8 were kind gifts from Matthew Seaman [43] . For proteomics , HeLa cells were grown in SILAC medium supplemented with 10% ( v/v ) dialysed foetal calf serum ( 10 , 000 MW cutoff; Invitrogen ) , penicillin/streptomycin ( Sigma ) , and either “Heavy” amino acids ( L-arginine-13C615N4:HCl [50 mg/L] and L-lysine-13C615N2:2HCl [100 mg/L]; Cambridge Isotope Laboratories ) or the equivalent “Light” amino acids . Cells were grown for at least 7 days to achieve metabolic labelling , and the average incorporation efficiency was approximately 95% , as determined by mass spectrometry . For immunofluorescence microscopy , cells were plated onto glass-bottom dishes ( MatTek ) and treated as indicated with 10 μM monensin or 500 μM manganese . The cells were then fixed with either ice-cold methanol or 3% formaldehyde , permeabilised where necessary with 0 . 1% saponin and labelled as indicated . The cells were imaged with either a Zeiss Axiovert 200 inverted microscope using a Zeiss Plan Achromat 63× oil immersion objective ( NA 1 . 4 ) , a Hamamatsu OCRA-ER2 camera , and IMPROVISION OPENLAB software or a Zeiss LSM 710 confocal microscope on an inverted AxioImagerZ1 using a Zeiss Plan-Apochromat 63× oil immersion objective ( NA1 . 4 ) and ZEN Black Software , version 2 . 3 . To quantify increased fluorescent spot size or retrieval deficits , we used automated high content screening ( HCS ) microscopes—either an ArrayScan VTI microscope ( Cellomics/Thermo Scientific ) or its upgrade , a Cell Insight CX7 Microscope ( Thermo Scientific ) . On both instruments , for quantifying fluorescent spot size increases we used a SpotDetector V4 Bioapplication , and for the retrieval assays we used the Colocalisation assay V4 Bioapplication . For the Cellomics we show data from Object Total Area , but subsequently we show Object Count . The ArrayScan VTI consists of a modified Zeiss Axiovert 200M inverted microscope , a Zeiss 40×/0 . 5NA LD A-Plan objective , a Hamamatsu ORCA-ER camera , and ARRAYSCAN software . The Cell Insight CX7 consists of a custom designed optical platform , Olympus 40×/0 . 6NA objective , Photometrics X1 camera , and HCS Studio 3 . 0 software . To quantify increases in SPG15-GFP fluorescence , cells were plated onto 96-well Perkin Elmer microplates , formaldehyde fixed , and permeabilised , and then the cells were stained with anti-GFP , followed by Alexa Fluor 488-donkey anti-rabbit IgG and then blue whole cell stain ( Invitrogen ) . To quantify retrieval deficits of GOLIM4 , cells were plated onto 96-well Perkin Elmer microplates , treated with 10 μM monensin for 90 min , washed , recovered for 2 . 25 h in fresh media , and then fixed with methanol . The cells were stained with mouse anti-GOLIM4 and rabbit anti-GM130 , followed by Alexa Fluor 488-donkey anti-mouse IgG and Alexa Fluor 647 anti-rabbit IgG , followed by blue whole cell stain ( Invitrogen ) . For the CIMPR retrieval assay , cells were plated onto 96-well Perkin Elmer microplates , fed with an antibody that recognises the lumenal domain of CIMPR ( Abcam ab8093 , at 30 μg/ml ) for 15 min at room temperature , and then washed and chased for 60 min . The cells were then fixed with formaldehyde , permabilised with 0 . 1% saponin , and stained with rabbit anti-mouse and sheep anti-TGN46 , followed by Alexa Fluor 647 anti-rabbit IgG and Alexa Fluor 488 anti-sheep IgG and then blue whole cell stain . Controls were included for all steps , with the omission of a single antibody in all combinations . For both assays , the whole cell stain allowed a mask to be drawn around the cells , and the anti-GM130 or anti-TGN46 allowed a mask to be drawn around the Golgi or TGN , respectively . The amount of fluorescence that was not retrieved back to the Golgi or TGN was then quantified . More than 1 , 500 cells were scored per knockdown condition , with at least 3 independent repeats . For statistical analysis , data were log transformed prior to analysis by 1-way ANOVA and Tukey-Kramer post hoc test . For pairwise analysis , data were log transformed prior to analysis by paired 2-tailed t test ( *p < 0 . 05; **p < 0 . 01; ***p < 0 . 001 ) . For global proteomic analysis of patient fibroblasts , cells were lysed in lysis buffer ( 2 . 5% SDS/50 mM Tris-HCl pH = 8 ) and heated to 90°C . DNA was sheared by passing the lysates through a QIAshredder ( Qiagen ) . Protein concentrations were estimated by BCA assay ( Pierce ) . Proteins were then acetone precipitated and digested prior to analysis by mass spectrometry [5] . Global proteomic analysis of HeLa cells was performed as described by Itzhak et al . [5] . To compare relative protein levels across samples , mass spectrometry data were processed with the MaxLFQ [14] to yield normalised label-free quantification ( LFQ ) intensities . LFQ intensities were log transformed , and missing data points were imputed from a normal distribution with a downshift of 2 . 2 and a width of 0 . 3 standard deviations . Comparison between control and AP-5–deficient cells was performed with a 2-sided t test . A permutation-based estimated FDR of 0 . 12 and an S0 parameter of 0 . 5 were set to define significance cutoffs ( procedure implemented in Perseus software [57] ) . Organellar maps were prepared as described in detail in Itzhak et al . [5] from control HeLa cells and the 2 independent AP5Z1 knockout cell lines , in triplicate ( 9 maps in total ) . Maps were prepared on 3 separate days , with a complete set of 3 on each occasion ( 1 control map , 1 map from AP5Z1KO_1 , and 1 map from AP5Z1KO_2 ) . In brief , HeLa cells were lysed mechanically , and postnuclear supernatants were subfractionated into 5 fractions by a series of differential centrifugation steps . In parallel , a single membrane fraction was obtained from metabolically ‘heavy’ labelled cells ( SILAC method [58] ) . This fraction served as an internal reference , by spiking it into each of the ‘light’ subfractions . Analysis by mass spectrometry provided a ratio of enrichment/depletion for each protein in each subfraction , relative to the standard . All 5 ratios combined yielded an abundance distribution profile for each protein across the subfractions . Principal component analysis revealed which proteins had similar fractionation profiles ( apparent as organellar clusters in S1 Fig ) . To identify proteins that changed subcellular localization in response to AP-5 knockout , we applied our previously described 2-tiered statistical analysis [5 , 16] . 2 , 046 proteins were profiled across all 9 maps . Briefly , for each protein , the abundance distribution profile obtained in AP-5 knockout cells was subtracted from the profile obtained in the cognate control map . Thus , for each set of 3 maps , 2 sets of delta profiles were obtained ( control–AP5Z1KO_1 and control–AP5Z1KO_2 ) . First , all delta profile sets were subjected to a robust multivariate outlier test to identify proteins with delta profiles that are significantly above experimental scatter . Second , the reproducibility of observed delta profiles across repeats was determined as the Pearson correlation ( replicates 1vs2 , 1vs3 , and 2vs3 ) . Hence , we obtained 2 times 3 p-values for movement , and 2 times 3 profile correlations . We previously described in detail how to analyse such data for triplicate repeats of a control versus ‘treatment’ experiment ( i . e . , 3 p-values and 3 correlations [5 , 16] . In the present study , we modified the analysis to accommodate the second set of ‘treatment’ samples ( i . e . , 2 AP-5 knockout cell lines ) . Because the shifts induced by AP5 KO are very subtle , we used our intermediate stringency scoring for higher sensitivity [5] . For each of the 2 AP5Z1 knockout clones , we selected the median observed p-value for movement and the median observed correlation . To combine the results for the 2 AP-5 knockout clones , we then selected the higher of the 2 median p-values ( i . e . , the less significant one ) and the lower correlation . This correlation corresponds to the protein’s R score . The p-value of movement was then squared ( because a p-value at least as small as this was observed in 2 independent experiments ) and corrected for multiple hypothesis testing , using the Benjamini-Hochberg method . The −log10 of the corrected p-value corresponded to the protein’s M score . As an additional filter , we also scored the correlation of the delta profiles obtained with the 2 AP5Z1 knockout clones ( replicates 1vs2 , 1vs3 , and 2vs3 ) and selected the median clone correlation . Only proteins with a clone correlation >0 . 75 ( i . e . , similar movement in both AP-5 knockout clones ) were considered as candidate movers . We then used our previously published HeLa maps [5] ( 3 pairs of untreated maps with no genuine protein shifts expected ) to control the FDR . As above , we calculated M and R scores from median correlations and p-values of movement . The estimated FDR for a given set of M and R score cutoffs corresponds to the number of hits obtained with the mock experiment data , divided by the number of hits obtained with the AP-5 experiment data , scaled by the relative sizes of the datasets . The cutoffs chosen in Fig 1C ( M > 1 . 5 , R > 0 . 5 ) correspond to an estimated FDR of 23% . Please note that the actual FDR is probably lower than this estimated FDR , because the mock data lack the additional cell line and the clonal correlation filter . Control cells were grown in SILAC Heavy medium and AP5Z1_KO1 ( or KO2 ) cells were grown in SILAC Light medium , mixed , and a vesicle-enriched fraction was isolated . Five confluent 10-cm dishes were scraped into 3 . 5 ml of buffer A ( 0 . 1 M MES , pH 6 . 5 [adjusted with NaOH] , 0 . 2 mM EGTA , and 0 . 5 mM MgCl2 ) . Cells were homogenised with a motorised Potter-Elvehjem homogeniser ( 16 strokes ) and centrifuged at 4 , 100 g for 32 min . Supernatants were treated with ribonuclease A at 50 μg/ml for 60 min . Partially digested ribosomes were pelleted by centrifugation ( 4 , 100 g for 3 min ) and discarded . Membranes were pelleted by centrifugation at 55 , 000 rpm ( 209 , 900 g RCFmax ) for 40 min in an MLA-80 rotor ( Beckman Coulter ) . Membranes were resuspended in 300 μl buffer A using a 1 ml Dounce homogeniser and mixed with an equal volume of FS buffer ( 12 . 5% [wt/vol] Ficoll and 12 . 5% [wt/vol] sucrose , in buffer A ) . Samples were spun at 20 , 000 rpm ( 21 , 700 g RCFmax ) for 34 min to pellet the larger particles ( pellet discarded ) . Supernatants were diluted with 4 volumes of buffer A and centrifuged at 40 , 000 rpm ( 86 , 700 g RCFmax ) in a TLA-110 rotor for 30 min to obtain the vesicle-enriched fraction ( pellet ) . All preparations were performed at 4°C . A maximum total of 50 μg protein was loaded onto precast gels ( NuPAGE 4%–12% Bis Tris Gels; Invitrogen ) and run so that the sample separated into a 5-cm strip . The gel was then washed , stained with Coomassie blue , and cut into 10 slices . Proteins were reduced , alkylated with iodoacetamide ( A3221 , Sigma ) , and in-gel digested with trypsin and the sample analysed by LC-MSMS using a Q-Exactive mass spectrometer ( Q-Exactive [59] ) Protein samples were prepared for mass spectrometry essentially as described [5] . Briefly , following tryptic digest , peptide cleanup and/or fractionation was performed on SDB-RPS Stage tips . Peptides were then loaded onto a 50-cm column ( 75-μm inner diameter , packed in-house with 1 . 8 μm C18 particles ) ( Dr . Maisch GmbH , Germany ) and separated with an EASY-nLC 1000 ( Thermo Fisher Scientific , Germany ) . For organellar map samples , peptide analysis was performed on a Q Exactive HF Hybrid Quadrupole-Orbitrap mass spectrometer ( Thermo Fisher Scientific , Germany ) , without additional peptide fractionation ( 150 min HPLC gradient/sample ) . The first replicate of the patient fibroblast full proteomes was analysed on an Exactive mass spectrometer ( Thermo Fisher Scientific , Germany ) , following peptide fractionation by SAX ( as in [60]; 6 fractions/sample; 240 min HPLC gradients ) . The second replicate was analysed on a Q Exactive HF mass spectrometer , following peptide fractionation by SDB-RPS ( as in [61]; 3 fractions/sample; 150 min HPLC gradients ) . Raw files were processed with MaxQuant [62] using the human reference database ( SwissProt canonical and isoforms data ) downloaded from UniProt . For the AP5Z1 KO , datasets were produced of 2 independent biological repeats of control ( Heavy SILAC ) and both AP5Z1_KO1 ( Light SILAC ) and AP5Z1_KO2 ( Light SILAC ) . The raw data files were processed using MaxQuant . The primary output for each SILAC comparison of vesicle-enriched fraction was a list of identified proteins , a ratio of relative abundance ( Ratio H/L ) , a measure of the variability within each mass spec run ( Ratio H/L variability [%] ) , and the number of quantification events ( Ratio H/L count ) . The raw data identified 2 , 500 proteins across the 4 different datasets . The data were then formatted as follows . Reverse hits , proteins only identified by site , common contaminants , and proteins with no gene names were removed . The ratios of H/L were then determined and linearly normalised based on total intensities , assuming equal protein quantities in both Heavy and Light samples . Because control cells were labelled with Heavy SILAC and knockout cells with Light SILAC , an H/L ratio >1 represents depletion from the vesicle-enriched fraction . The data were then filtered to remove proteins that were identified in <4 experiments , those with variability within each experiment ( >40% average variability over 4 experiments ) , those with large variability between paired repeats ( SD >1 ) , or those with low counts ( <4 average over 4 experiments ) . This left a final list of about 700 proteins , which were then ranked in order of greatest depletion from the vesicle-enriched fraction ( S2 Data ) . Proteomic data transformation , filtering , and statistical analysis were performed in Perseus software [57] , Prism 6 ( GraphPad Software ) , and Microsoft Excel . Principal component analysis was performed in SIMCA 14 ( Umetrics/MKS ) . | Eukaryotic cells contain multiple membrane-bound compartments , each with a distinct function and molecular composition . Proteins are transported from one compartment to another by vesicular carriers . Formation of these carriers requires coat proteins , which both shape the membrane into a vesicle and select the proteins that are to be included as cargo . In many cases , cargo selection is facilitated by an adaptor protein ( AP ) complex , of which 5 have been identified . The most recently identified complex , AP-5 , localises to a late endosomal/lysosomal compartment , and patients with mutations in AP-5 have a form of hereditary spastic paraplegia characterised by aberrant lysosomes . However , the precise function of AP-5 , including its cargo and its pathway , has until now been unclear . In the present study , we have used unbiased subcellular proteomics to look for changes in the localisation of thousands of different proteins in cells from which AP-5 has been deleted by gene editing . We found that there are defects in the retrieval of several proteins from late endosomes back to the Golgi apparatus . Thus , we propose that AP-5 facilitates a novel late-acting retrieval pathway , which contributes to normal lysosomal homeostasis . | [
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| 2018 | Role of the AP-5 adaptor protein complex in late endosome-to-Golgi retrieval |
Local transmission of Chikungunya virus ( CHIKV ) was first documented in Trinidad and Tobago ( T&T ) in July 2014 preceding a large epidemic . At initial presentation , it is difficult to distinguish chikungunya fever ( CHIKF ) from other acute undifferentiated febrile illnesses ( AUFIs ) , including life-threatening dengue disease . We characterised and compared dengue virus ( DENV ) and CHIKV infections in 158 patients presenting with suspected dengue fever ( DF ) and CHIKF at a major hospital in T&T , and performed phylogenetic analyses on CHIKV genomic sequences recovered from 8 individuals . The characteristics of patients with and without PCR-confirmed CHIKV were compared using Pearson’s χ2 and student’s t-tests , and adjusted odds ratios ( aORs ) and 95% confidence intervals ( CIs ) were determined using logistic regression . We then compared signs and symptoms of people with RT-qPCR-confirmed CHIKV and DENV infections using the Mann-Whitney U , Pearson’s χ2 and Fisher’s exact tests . Among the 158 persons there were 8 ( 6% ) RT-qPCR-confirmed DENV and 30 ( 22% ) RT-qPCR-confirmed CHIKV infections . Phylogenetic analyses showed that the CHIKV strains belonged to the Asian genotype and were most closely related to a British Virgin Islands strain isolated at the beginning of the 2013/14 outbreak in the Americas . Compared to persons who were RT-qPCR-negative for CHIKV , RT-qPCR-positive individuals were significantly more likely to have joint pain ( aOR: 4 . 52 [95% CI: 1 . 28–16 . 00] ) , less likely to be interviewed at a later stage of illness ( days post onset of fever—aOR: 0 . 56 [0 . 40–0 . 78] ) and had a lower white blood cell count ( aOR: 0 . 83 [0 . 71–0 . 96] ) . Among the 38 patients with RT-qPCR-confirmed CHIKV or DENV , there were no significant differences in symptomatic presentation . However when individuals with serological evidence of recent DENV or CHIKV infection were included in the analyses , there were key differences in clinical presentation between CHIKF and other AUFIs including DF , which can be used to triage patients for appropriate care in the clinical setting .
Chikungunya virus ( CHIKV; Family Togaviridae , genus Alphavirus ) is the aetiological agent of chikungunya fever ( CHIKF ) , which presents as an acute onset high fever with headache , back pain , muscle and joint pain . The joint pain can vary in intensity but is often very intense , predominates at the ankles , wrists and phalanges and is coupled with swelling . CHIKF is usually a self-limiting disease , and serious outcomes ( e . g . neurological complications ) and fatalities appear to be rare [1 , 2] . However up to 60% of infections are followed by chronic arthritic conditions , with recurrent debilitating joint pain several years post-infection [3 , 4] CHIKV exists as a single serotype thought to confer life-long immunity in recovered individuals . There is , however , sufficient variation to discern three genotypes , namely the enzootic West African ( WAf ) , and East /Central/South African ( ECSA ) , and the epidemic Asian genotypes . The ECSA recently gave rise to the Indian Ocean Lineage ( IOL ) responsible for epidemics in the Indian Ocean islands , mainland India and Europe beginning in 2004 [5] . The expansion of this lineage has been attributed to adaptive mutations in the E1 and E2 envelope glycoproteins that provide a fitness advantage in Aedes albopictus without reducing fitness in A . aegypti [6] and permit rapid lineage diversification [7] [8] . In light of the dramatic re-emergence of CHIKV in Asia , the intensity of global travel and the widespread prevalence of A . aegypti and A . albopictus , the emergence of CHIKV in the Americas was long anticipated [9] . As expected , numerous imported ( travel-related ) cases were subsequently documented in the Americas , none of which resulted in local transmission until December 2013 when autochthonous transmission was documented in the Caribbean island of St . Martin [10] . The etiologic virus , which belongs to the Asian genotype rather than the IOL [11 , 12] , spread rapidly first amongst the islands of the Caribbean archipelago then on to the mainland Americas [13–15] . As of 7 August 2015 CHIKF cases were confirmed in 50 countries / territories in the Caribbean and mainland Americas with approximately 1 . 7 million suspected / confirmed cases since October 2013 [16] . CHIKV belonging to the ECSA genotype was also recently documented in Brazil but has not been confirmed elsewhere in the Americas [17] . Although CHIKF is rarely life threatening , the symptoms can be severely incapacitating , rendering patients unable to perform normal tasks or go to work . As a result , particularly in immunologically naïve populations where attack rates can be as high as 30–50% and the vast majority of infections are symptomatic [18 , 19] [20] , the disease burden , potential to overwhelm public health systems and indirect costs can be very significant . Clinical overlap and confusion with other acute undifferentiated fevers e . g . dengue disease ( which can be fatal ) , are additional concerns in affected regions . The Caribbean twin-island Republic of Trinidad and Tobago ( T&T ) reported its first cases of CHIKV infection in July 2014 ( in Trinidad ) [21] . In this study we report the genetic characterisation of CHIKV and compare clinical , laboratory and epidemiological characteristics of patients with and without confirmed CHIKV or dengue virus ( DENV ) infections who presented at a major hospital in Trinidad , during an acute undifferentiated febrile illness ( AUFI ) surveillance study .
Study participants were enrolled at the Adult Primary Care Facility ( APCF ) of the Eric Williams Medical Sciences Complex ( EWMSC ) during the period of December 25th 2013 to November 5th 2014 . Individuals were recruited , on average , 3 days a week throughout this period , with the exception of October 2014 , when no sample collection was undertaken . Individuals presenting at the APCF on study days with suspected dengue fever ( DF ) or CHIKF were eligible for enrollment . Specifically , patients were eligible for enrollment if they were febrile ( having an axillary temperature of ≥38°C ) or had a reported history of fever within the last 7 days , accompanied by one or more of the following symptoms commonly associated with DF and CHIKF: headache , nausea , vomiting , diarrhoea , aches and pains , eye pain , evidence of haemorrhagic manifestation , rash , joint pain and swollen joints . Individuals who had a readily identifiable focus of infection were excluded from the study . At presentation and enrollment , whole blood was collected in non-additive and/or ethylenediaminetetraacetic acid ( EDTA ) containing blood collection tubes and a questionnaire eliciting relevant demographic and clinical information , medical and recent travel history , and risk factors for exposure to mosquito bites was administered . Whole blood was stored at 4°C and centrifuged at 1500g for 10 minutes at 4°C within 24 hours . Sera and plasma samples were stored at -80°C until use . Nucleic acid extraction was performed using QIAamp Viral RNA Mini Kit ( Qiagen; Valencia , CA ) and carried out according to the manufacturer’s instructions . Extractions were performed using 140μl of serum , or plasma where serum was unavailable . Nucleic acid samples were screened and serotyped for DENV-1-4 using the DENV multiplex RT-qPCR assay previously described by Waggoner [22] . Screening for CHIKV was carried out using the RT-qPCR general assay previously described by Panning [23] . All RT-qPCR screening was done using the SuperScript III Platinum One-Step qRT-PCR kit ( Invitrogen; Carlsbad , CA ) with final reaction volumes amended to 25μl instead of the manufacturer’s recommended 50μl . For ten samples with low Ct values in RT-qPCR assays , viral RNA was extracted then fragmented by incubation at 94°C for eight ( 8 ) minutes in 19 . 5 μl of fragmentation buffer ( Illumina Inc . , San Diego , CA ) . First and second strand synthesis , adapter ligation and amplification of the library were performed using the Illumina TruSeq RNA Sample Preparation kit v2 under conditions prescribed by the manufacturer ( Illumina Inc . , San Diego , CA ) . Cluster formation of the library DNA templates was performed using the TruSeq PE Cluster Kit v3 ( Illumina Inc . , San Diego , CA ) and the Illumina cBot workstation using conditions recommended by the manufacturer . Paired end 50 base sequencing by synthesis was performed using TruSeq SBS kit v3 ( Illumina Inc . , San Diego , CA ) on an Illumina HiSeq 1000 using protocols defined by the manufacturer . Cluster density per lane was 820–940 k/mm2 and post-filter reads ranged from 148–218 million per lane . Base call conversion to sequence reads was performed using CASAVA-1 . 8 . 2 . Reads were filtered for quality and adapter sequences were removed , then viral contigs were assembled de novo using AbySS software [24] . Assembled contigs were checked using bowtie2 to align reads to the contigs [25] followed by visualization using the integrative genomics viewer [26] . Newly generated CHIKV genomic sequences along with sequences from Genbank representing all three genotypes were aligned using the ClustalW alignment tool [27] within Geneious Version 7 . 1 . 7 ( www . geneious . com; [28] ) . Sequences were trimmed to 11 , 259 nucleotides at the boundaries of the open reading frames ( ORFs ) so that ambiguous alignment of the 5’ and 3’ untranslated regions was excluded from analyses . The final data set comprised of 74 of these complete coding sequences from 23 countries isolated during 1953–2014 ( S1 Table ) . Nucleotide and amino acid position numberings are based on NCBI reference sequence NC_004162 . To rule out the presence of recombination within the data set , which could affect the phylogenetic structure , all genomic sequences were screened using SBP and GARD [29] from the HyPhy online package [30] . The best-fit model for subsequent analyses ( GTR+G4 ) was selected using JMODELTEST 2 . 1 . 7 [31 , 32] . Phylogenetic trees were inferred under this model using the maximum-likelihood ( ML ) method in the PhyML program online [33] and the maximum clade credibility ( MCC ) from BEAST version 1 . 8 . 1 [34] . Substitution rates and times to the most recent common ancestor ( TMRCA ) were jointly estimated using BEAST , with the GTR+G4 model of nucleotide substitution , along with a relaxed lognormal molecular clock model [35] and a GMRF skyline tree prior [36] . The analysis ran in duplicate for 50 million generations with 10% removed as burnin , and the convergence of parameters were assessed by calculating the effective sample size ( ESS >200 ) using TRACER v1 . 6 [37] . Panbio Dengue IgM and IgG Capture ELISA ( Brisbane , Australia ) were used to screen sera for the presence of DENV-specific antibodies using manufacturer’s instructions , and all samples were screened in duplicate . Samples were designated as positive for dengue-specific IgM if both duplicates returned a value of >11 Panbio Units and as negative at <9 Panbio Units . Samples were designated as positive for dengue-specific IgG if both duplicates returned a value of >22 Panbio Units and as negative at <18 Panbio Units . For samples returning equivocal results ( 9 to 11 and 18 to 22 Panbio Units ) or where the duplicates fell into different categories ( i . e . one weakly positive or negative and the other equivocal ) , the test was repeated . Samples that remained equivocal were designated as such and for those with that remained with the duplicates falling in different categories , the mean value was taken and the sample classified accordingly . According to the manufacturer ( due to the detection thresholds for the two assays ) , the presence of detectable levels of IgM ( in the absence of detectable IgG ) indicates a probable recent primary infection with DENV , while the presence of detectable levels of IgG indicates probable recent secondary dengue virus infection . The manufacturers note that IgM levels in a secondary infection may be undetectable by the assay . Thus IgM+/IgG- result was interpreted as evidence of a probable recent primary DENV infection , and both IgM+/IgG+ and IgM-/IgG+ results were interpreted as probable recent secondary DENV infections . Euroimmum Chikungunya IgM Capture ELISA ( Leubeck , Germany ) was used to screen sera and plasma for the presence of CHIKV-specific IgM antibodies . ELISAs were carried out in accordance with the manufacturer’s instructions and all samples were screened in duplicate . Samples were designated as positive for CHIKV-specific IgM if both duplicates returned a value of ≥ 1 . 1 and as negative at < 0 . 8 . No sample tested returned an equivocal result ( ≥ 0 . 8 to < 1 . 1 ) . The frequencies of patient demographic and clinical characteristics were determined by questionnaire . The outcome for analysis was having a confirmed infection with CHIKV , defined as a positive result on RT-qPCR . Bivariable associations with a confirmed CHIKV infection were determined using the student’s t-test for continuous variables and the Pearson’s chi-square or Fisher’s exact test for categorical variables . All variables with a p-value less than or equal to 0 . 10 were considered in the final multivariable model , which was determined using binary logistic regression using a forward stepwise selection procedure ( entry: 5% , exit: 10% ) . Adjusted odds ratios ( aORs ) and 95% confidence intervals ( CIs ) are reported . To compare the symptomatic presentation for RT-qPCR positive DENV and CHIKV , a bivariable analysis was performed using the Mann-Whitney U test for continuous variables and the Pearson’s chi-square or Fisher’s exact test for categorical variables . Data were entered using Microsoft Excel 2007 ( Microsoft Corporation , Redmond , WA , USA ) , then transferred to IBM SPSS Statistics v20 ( IBM Corporation , Armonk , NY , USA ) for analysis . All p-values <0 . 05 were considered significant . Positive predictive values ( PPVs ) , negative predictive values ( NPVs ) , and the sensitivity , specificity and likelihood ratios of different combinations of clinical features used to distinguish confirmed and probable DF and CHIKF from other AUFIs were also calculated . Combinations used to differentiate DF from CHIKF were also compared . For both CHIKF and DF these values were calculated using confirmed cases ( RT-PCR positive for virus ) and probable recent cases ( ELISA positive for IgM/IgG antibodies ) . The 8 newly generated CHIKV whole genome sequences are available from Genbank . Accession numbers: KR046227 , KR046228 , KR046229 , KR046230 , KR046231 , KR046232 , KR046233 and KR046234 . The study protocols were approved by the Ethics Committee of the University of the West Indies , St . Augustine and the Trinidad and Tobago Ministry of Health . Permission to carry out the study at the EWMSC was granted by the North Central Regional Health Authority ( NCRHA ) . Written informed consent was obtained from all participants .
There were 158 patients who met the inclusion criteria for AUFI and agreed to participate in the study . Of these , the median age was 32 , ranging from 16–88 years . Half ( 50% ) of the patients were male and , of those with complete data on ethnicity , the majority were Afro- ( 33% ) , Indo- ( 31% ) Trinidadian or Mixed ( 26% ) ( S2 Table ) . Just under a third ( 30% ) had received greater than a secondary level of education , and most ( 69% ) were employed . Most patients ( 79% ) were nationals of Trinidad & Tobago , with relatively few ( 7% ) reporting a travel history outside of Trinidad in the two weeks prior to interview . Twenty-two percent reported having had laboratory-confirmed DF previously , 25% reported that others in their household had been febrile in the two weeks prior to their interview , 55% reported storing water at home and , while 15% reported having screened windows at home , most ( 80% ) reported a history of mosquito bites at their place of residence . At interview , the most commonly reported symptoms were headache ( 83% ) , weakness ( 67% ) , joint pain ( 65% ) and muscle pain ( 65% ) . Seventeen percent reported some degree of haemorrhagic manifestations and 22% reported a rash ( S2 Table ) . Thirty ( 19% ) of the 158 individuals were positive for CHIKV on RT-qPCR . The earliest case was an individual sampled on August 5th , 2014 , about three weeks after the first CHIKV case was confirmed in Trinidad [38] . The number of RT-qPCR confirmed cases peaked in week 38 when the number of individuals enrolled was highest ( Fig 1 ) . Cases were concentrated along the urbanised “East-West corridor” in the north of Trinidad primarily within the boundaries of the North Central Regional Health Authority served by the EWMSC . Additionally , 27 ( 17% ) of the 158 individuals were positive by ELISA for anti-CHIKV IgM antibodies , including 6 of those persons positive by RT-qPCR for CHIKV . Eight of the 158 individuals ( 5 . 1% ) were confirmed as DENV-positive by RT-qPCR ( Fig 1 ) . Of these , 6 were DENV-1 and one each DENV-3 and DENV-4 . For 125 individuals ( including 6 of the aforementioned RT-qPCR positive individuals ) , there was sufficient serum available for both dengue-specific IgM and IgG ELISAs ( S3 Table ) . Using the combined RT-qPCR and ELISA results , 4 . 8% ( n = 6 ) were designated as “confirmed current DENV infections” of which three were primary infections ( i . e . IgM+/IgG- ) and three were secondary infections ( IgM+/IgG+ ) , 18 . 4% ( n = 23 ) were “probable recent primary DENV infections” , 42 . 4% ( n = 53 ) were “probable recent secondary DENV infections” and 3 . 2% ( n = 4 ) were equivocal . In total 66% ( n = 82 ) had evidence of a current or probable recent DENV infection ( S3 Table ) . On bivariable analyses ( Table 1 & S4 Table ) , patients with RT-qPCR confirmed CHIKV infection ( CHIKV+; n = 30 ) were significantly more likely to report joint pain ( 83% , p = 0 . 020 ) than individuals who were RT-qPCR-negative for CHIKV ( not CHIKV+ ) but were less likely to have travelled outside of Trinidad in the 2 weeks prior to interview ( 17% , p = 0 . 044 ) , or to have reported laboratory-confirmed dengue previously ( 7% , p = 0 . 026 ) . They were also more likely to have presented to the APCF earlier ( mean days post onset of fever: 2 . 39 [CHIKV+] vs . 3 . 33 [not CHIKV+] , p = 0 . 021 ) and had lower mean white blood cell counts ( 6 . 52 [CHIKV+] vs . 8 . 36x103/μl [not CHIKV+] , p = 0 . 016 ) . In addition to the above variables , the following also met the criteria for inclusion in the multivariable model: marital status ( p = 0 . 054 ) , nationality ( p = 0 . 080 ) , number of days post onset of illness ( p = 0 . 032 ) , rash ( p = 0 . 084 ) , sore throat ( p = 0 . 015 ) , and abdominal pain ( p = 0 . 006 ) . Due to a highly significant negative correlation between nationality and travel history , only travel history was included in the model . Similarly , due to a strong , positive and highly significant correlation between number of days post onset of fever and days post onset of illness , only the former was considered in the model . Lastly , due to small cell sizes ( n ≤ 2 ) , having had dengue previously , sore throat and abdominal pain were not considered in the final model . The following variables were considered in the final multivariable model: marital status , joint pain , rash , days post onset of fever and white blood cell count . As shown in Table 2 , those who had confirmed CHIKV infection were significantly more likely to have reported joint pain ( aOR: 4 . 52 , 95% CI: 1 . 28–16 . 00 ) , and significantly less likely to be interviewed at a later stage of illness ( days post onset of fever—aOR: 0 . 56 , 95% CI: 0 . 40–0 . 78 ) and have a lower white blood cell count ( aOR: 0 . 83 , 95% CI: 0 . 71–0 . 96 ) . Among the 38 patients positive by RT-qPCR for CHIKV ( n = 30 ) or DENV ( n = 8 ) , there were no significant differences in clinical presentations ( p > 0 . 05 ) between those with CHIKV and those with DENV ( Table 3 and S5 Table ) . However , when compared to DENV , persons with CHIKV more often reported joint pain ( 83% vs . 50% , p = 0 . 071 ) and rash ( 33% vs . 0% , p = 0 . 082 ) , and less often reported abdominal pain ( 7% vs . 38% , p = 0 . 053 ) . Patients with DENV had significantly lower median white blood cell count ( 3 . 50 vs . 6 . 00 x103/μl , p = 0 . 028 ) and platelet ( PLT ) counts ( 1 . 47 vs . 2 . 39x105/μl , p = 0 . 022 ) . The clinical features joint pain and white blood cell count <7 x103/μl were selected as they were significant on bivariable analysis . For DF , PLT count <150 x103/ μl and abdominal pain were also selected as patients with confirmed DENV infection were more likely to display/report these symptoms than persons with confirmed CHIKV infection . Similarly for CHIKF rash was included as patients with confirmed CHIKV infection were more likely to report this sign than persons with confirmed DENV infection ( Table 4 ) . Individually none of the clinical features was able to efficiently differentiate DF or CHIKF from other AUFIs . The results are given in Table 4 . However the combination of rash , joint pain and white blood cell count <7 x103/μl was most efficiently able to differentiate CHIKF from other AUFIs . This combination had a PPV and NPV of 77% and 96% respectively . The three parameters combined had a sensitivity of 94% and a specificity of 84% . Furthermore the positive and negative likelihood ratios were 5 . 9 and 0 . 1 respectively ( Table 4 ) . None of the combinations tested was overall able to efficiently differentiate between DENV and CHIKV . The combination that performed the best was rash , joint pain and white blood cell count <7 x103/μl with a PPV and NPV of 58% and 95% respectively as well as a sensitivity of 88% and a specificity of 78% ( Table 5 ) . Using an Illumina platform , complete genome sequences for CHIKV were determined directly from RNA isolated from the sera of 8 individuals . The overall alignment rate of the reads varied for each sample , ranging from 0 . 45–17 . 5% . Nucleotide identity amongst the consensus sequences from these individuals was 99 . 9–100% and 99 . 8–100% at the amino acid level ( S6 Table ) . Non-synonymous substitutions occurred at nucleotide positions 774 ( T→A ) , 1509 ( C→T ) , 1781 ( C→T ) , 3563 ( G→A ) , 8822 ( C→T ) and 10 , 059 ( G→A ) , resulting in changes at aminoacid residues 233 ( L→Q ) , 478 ( A→V ) , 569 ( R→C ) and 1163 ( V→I ) in the non-structural polypeptide and at residues 419 ( T→I ) and 832 ( G→R ) in the structural polypeptide . The latter two being in the E2 and E1 proteins respectively . Both MCC ( Fig 2 ) and ML phylogenies ( S1 Fig ) estimated from the data set showed that the Trinidad sequences clustered together with the BVI sequence ( Accession no . KJ451624 ) isolated in 2014 near the beginning of the Caribbean outbreak [39] . The most closely related Asian sequences circulated between 2012 and 2013 in China , the Philippines and Micronesia . The clade containing the Trinidad and BVI sequences was defined by the aforementioned amino acid changes and was estimated to be evolving at a rate of 5 x 10−3 substitutions per site per year ( 95% HPD: 1 x 10−4–1 x 10−3 substitutions per site per year ) with the most recent common ancestor ( MRCA ) having arisen about 1 year prior to November 2014 ` ( 0 . 98 years ago [95% higher probability density ( HPD ) : 0 . 49–1 . 63] ) . For three individuals , sequencing was of sufficient depth to allow reliable reporting of within-host variation at individual nucleotide positions within the non-structural and structural polypeptide genes . Within the non-structural polypeptide ORF , for one individual , at nucleotide position 1228 ( i . e . residue 384 of nsp1 ) , 1 . 1% of reads had A instead of the majority T ( p = 2 . 16 x 10−7 ) both of which encode Leu , and at nucleotide position 3276 , 1 . 6% of reads had T instead of G ( p = 1 . 77 x 10−13 ) which would result in a Ser to Ile substitution at residue 1067 of the nonstructural polypeptide ( within nsp2 ) . For the structural polypeptide gene , variation was detected at nucleotide position 9377 ( residue 604 i . e . residue 279 in E2 ) where the minority species was A instead of G . This variant , which was detected in three individuals at frequencies of 2 . 03 , 2 . 08 and 2 . 69% ( p = 1 . 48 x 10−23 , 1 . 15 x 10−4 , 5 . 59 x 10−5 respectively ) , would result in a Gly-to-Glu change . One individual also had a minority variant at nucleotide position 9037 such that 1 . 18% had T instead of G ( p < 6 . 19 x 10−2 ) , which would result in replacement of Glu at amino acid 491 of the structural polypeptide ( residue 165 in E2 ) with a stop codon . None of the four single adaptive mutations in E1 ( A226V ) and E2 ( K252Q; K233E; L210Q ) or the synergistic adaptive mutations in E2/E3 ( R198Q/S18F ) that confer a fitness advantage for IOL strains in A . albopictus [6 , 8 , 40 , 41] were detected .
In this study participants were solicited three days per week from amongst individuals identified by attending physicians as meeting the criteria of having been febrile within the last seven days . We cannot rule out selection bias at this interface and so cannot generalise our findings to those who were not interviewed . However as it was a study of AUFI and was not restricted to persons with a specific infection , selection bias among those who thought they had DENV or CHIKV may be less of a concern . Three interviewers were involved in the administration of questionnaires to participants of the study . In order to ensure consistency , they were trained in the delivery of questions and recording of responses but the possibility of interviewer bias cannot be completely excluded . This study was performed at a tertiary level public healthcare facility , and the distribution of risk factors may be different among those who sought care at public primary care facilities ( i . e . local health centres ) or at other private healthcare facilities . Importantly , despite having a small sample size for the comparison of persons with and without CHIKV , there was sufficient power to detect several meaningful associations on bivariable and multivariable analysis . For the sub-analysis comparing the symptoms of those with CHIKV or DENV , the sample size was restricted to 38 and thus analyses were underpowered . In spite of this , there appeared to be differences in the symptomatic presentation of persons infected with either . In summary , our study confirms the presence of Asian genotype CHIKV in Trinidad and identifies symptoms that distinguish individuals with acute fevers who are viraemic for CHIKV from those who are CHIKV negative by RT-qPCR , and identifies laboratory and clinical features that distinguish between those who are viraemic for CHIKV versus DENV . 51 ( 32 . 3% ) individuals had evidence of CHIKV infection ( positive for CHIKV by RT-qPCR and / or positive for anti-CHIKV IgM antibodies by ELISA ) . Of these , only 30 were positive for CHIKV by RT-qPCR . While all individuals reported being within 7 days of onset of their symptoms ( when viraemia is often detectable ) it is possible that there were individuals in whom viraemia was already cleared or too low to be detected . Additionally , future studies aimed at determining population-level seroprevalence will be essential in order to get an accurate estimate of the impact ( economic and otherwise ) of CHIKV on the Trinidad population and to predict the potential impact of future outbreaks . It will also be important to monitor the genotypes in circulation given the potential for accumulation of adaptive mutations demonstrated by some strains [6 , 8 , 40 , 41] . The rapid spread of the newly emerged CHIKV in the Caribbean in 2014 and the resulting epidemic demonstrate the dramatic effect of the introduction of a “novel” virus on a naïve population , especially where vectors are endemic . Public health authorities in the Americas are , as of August 2015 , on alert for the emergence of Zika virus , a flavivirus known to cause widespread outbreaks of dengue-like illness in Africa and Asia , after being recently detected in the region for the first time . As commercial air travel expands this will continue to facilitate the movement of viruses and ongoing monitoring of individuals with AUFIs is essential to rapidly detect the emergence of viruses to non-endemic areas . | Chikungunya virus ( CHIKV ) recently emerged in the Americas and caused a major epidemic of chikungunya fever ( CHIKF ) . While not usually life threatening , CHIKF is a debilitating and often chronic illness resulting in major morbidity and economic losses . It is difficult to distinguish CHIKF from other viral illnesses that cause acute fevers including dengue fever ( DF ) , an important consideration since DF can be life-threatening and early identification and treatment of cases is key to reducing mortality . In this study we investigated individuals presenting to a major hospital in Trinidad and Tobago ( T&T ) with suspected DF or CHIKF , and identified signs and symptoms that distinguish these two illnesses . We also recovered complete genome sequences for CHIKV and show that the etiologic strain in T&T is a closely related descendent of the strain first isolated from the British Virgin Islands at the beginning of the outbreak in the Americas . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| []
| 2015 | Molecular Characterisation of Chikungunya Virus Infections in Trinidad and Comparison of Clinical and Laboratory Features with Dengue and Other Acute Febrile Cases |
Fasciolosis , due to Fasciola hepatica and Fasciola gigantica , is a re-emerging zoonotic parasitic disease of worldwide importance . Human and animal infections are commonly diagnosed by the traditional sedimentation and faecal egg-counting technique . However , this technique is time-consuming and prone to sensitivity errors when a large number of samples must be processed or if the operator lacks sufficient experience . Additionally , diagnosis can only be made once the 12-week pre-patent period has passed . Recently , a commercially available coprological antigen ELISA has enabled detection of F . hepatica prior to the completion of the pre-patent period , providing earlier diagnosis and increased throughput , although species differentiation is not possible in areas of parasite sympatry . Real-time PCR offers the combined benefits of highly sensitive species differentiation for medium to large sample sizes . However , no molecular diagnostic workflow currently exists for the identification of Fasciola spp . in faecal samples . A new molecular diagnostic workflow for the highly-sensitive detection and quantification of Fasciola spp . in faecal samples was developed . The technique involves sedimenting and pelleting the samples prior to DNA isolation in order to concentrate the eggs , followed by disruption by bead-beating in a benchtop homogeniser to ensure access to DNA . Although both the new molecular workflow and the traditional sedimentation technique were sensitive and specific , the new molecular workflow enabled faster sample throughput in medium to large epidemiological studies , and provided the additional benefit of speciation . Further , good correlation ( R2 = 0 . 74–0 . 76 ) was observed between the real-time PCR values and the faecal egg count ( FEC ) using the new molecular workflow for all herds and sampling periods . Finally , no effect of storage in 70% ethanol was detected on sedimentation and DNA isolation outcomes; enabling transport of samples from endemic to non-endemic countries without the requirement of a complete cold chain . The commercially-available ELISA displayed poorer sensitivity , even after adjustment of the positive threshold ( 65–88% ) , compared to the sensitivity ( 91–100% ) of the new molecular diagnostic workflow . Species-specific assays for sensitive detection of Fasciola spp . enable ante-mortem diagnosis in both human and animal settings . This includes Southeast Asia where there are potentially many undocumented human cases and where post-mortem examination of production animals can be difficult . The new molecular workflow provides a sensitive and quantitative diagnostic approach for the rapid testing of medium to large sample sizes , potentially superseding the traditional sedimentation and FEC technique and enabling surveillance programs in locations where animal and human health funding is limited .
Fasciolosis , due to Fasciola hepatica and Fasciola gigantica , is an important zoonotic production-limiting disease of ruminants [1] . In 2005 , an estimated 91 million people across 8 countries were considered at risk of contracting this neglected tropical disease , with children the most likely to become infected [2] . Despite the number of people at risk , fasciolosis is generally considered a neglected disease of humans due to its chronic nature and subsequent underreporting [3] . Livestock and wildlife act as definitive hosts , although it has been demonstrated that humans may also play a participatory role in the spread of the parasite [4] . Human infection typically occurs through the ingestion of freshwater plants such as watercress , although infection via consumption of metacercariae-contaminated water has also been documented [5] . Infections are traditionally diagnosed via the sedimentation of faecal samples , or more recently through immunological tests such as a commercially available coprological antigen ELISA ( coproELISA ) [6 , 7] . Neither of these methods can differentiate F . hepatica from F . gigantica or vice versa [8] . A high degree of operator error is associated with sedimentation outcomes , with faecal egg count ( FEC ) results often differing between operators , depending on their level of experience . Species identification by extraction of adult flukes requires post-mortem analysis of the liver , which is not possible in human cases and often of limited availability in ruminants unless conducted during abattoir surveillance . Other non-invasive forms of diagnosis in humans and animals , including serological techniques , are unable to provide species confirmation [7] . DNA isolation and real-time PCR analysis have the potential for a preferred diagnostic solution , offering increased throughput , reproducibility , and higher sensitivity , with the added benefit of species differentiation . Despite this potential , no published method currently exists for the reliable , highly sensitive and specific diagnosis of infection from faecal samples . The aim of this study was to develop a new molecular diagnostic workflow for real-time PCR detection of F . hepatica eggs in ruminant faecal samples and to subsequently compare the results to a traditional sedimentation diagnostic test . The workflow involves an optimised disruption protocol to isolate DNA from Fasciola spp . eggs , and a real-time PCR assay that was evaluated for sensitivity and specificity . Additionally , DNA isolation and real-time PCR of a partial and whole pellet from a traditional sedimentation was tested to determine if these additional steps improved the analytical sensitivity of the PCR assay . Finally , the diagnostic sensitivity and specificity of the coproELISA was evaluated in comparison to the traditional sedimentation and newly-developed molecular diagnostic workflow . The positive cut-off threshold was assessed in order to increase the sensitivity and specificity of the ELISA for samples with low egg counts ( ≤10 eggs per gram , EPG ) . The end result is a new molecular workflow for the diagnosis of F . hepatica that was applied to samples from a cohort of beef cattle with constant F . hepatica exposure . Further , the impact of sample storage in 70% ethanol ( EtOH ) on sedimentation outcomes and DNA isolation and amplification were assessed to determine the feasibility of transporting samples in situations where cold chains may not be readily available . The development of a new molecular diagnostic workflow would enable the highly sensitive detection of Fasciola spp . for the quantification of faecal egg load and species identification in regions of parasite sympatry , such as Southeast Asia .
FECs were determined by a standard faecal sedimentation method with minor modifications as follows [9] . Faecal samples ( 3 g and 6 g for sheep and cattle , respectively ) were mixed with distilled water to form a homogenous solution . The solution was hosed with tap water through a 270 μm nylon sieve into a 250 ml conical measuring cylinder , topped with distilled water and allowed to sediment for three minutes . After three minutes the supernatant was aspirated and the sediment poured into a 100 ml conical measuring cylinder , topped with distilled water and allowed to sediment for a further three minutes . Again , the supernatant was aspirated and the remaining sediment poured into a 15 ml centrifuge tube , where it was once more topped with distilled water and allowed to sediment for a final three minutes . The supernatant was aspirated and discarded , leaving 2 ml of sediment which was thoroughly vortexed to ensure homogeneity . To examine presence of fluke eggs , 2 drops of methylene blue ( 1% ) was added to the sediment and examined under an Olympus LG-PS2 stereomicroscope using a 6 . 5×17×1 cm grid tray at 15× magnification . An additional 20 ml of distilled water was added to the tray to allow ease of counting . Each faecal sample was sedimented and counted in duplicate , resulting in a total of 6 g and 12 g being counted for individual sheep and cattle , respectively . All yellow-brown Fasciola spp . eggs were counted . Triplicate clean faecal samples were spiked with a known number of F . hepatica eggs and the percentage lost during the sedimentation process was calculated . The results were in agreement with the original protocol and demonstrated that one third of eggs from the initial sample volume were retained in the sediment after processing [9] . Hence the final number of eggs from individual cattle sedimentations was divided by 2 to obtain EPG ( S2 Table ) . All EPGs are presented as a mean of the two independent sedimentations . All sedimentations were conducted by the same technician to remove any variability in counting . The technician was unaware of the previous results to prevent bias between replicate counts . Clean adult F . hepatica and F . gigantica flukes stored in 70% EtOH from the parasite collection at the Sydney School of Veterinary Science , University of Sydney were used as positive controls . Total genomic DNA from 1/5th of an adult fluke ( 25 mg , cut using a sterile scalpel blade ) from each species was isolated using Isolate II Genomic DNA kit ( BioLine , Australia ) according to the manufacturer’s instructions and eluted in 100 μl of elution buffer ( 10 mM TrisCl buffer , pH = 8 . 5 ) . To monitor DNA isolation efficiency and PCR inhibitors 5 μl of DNA Extraction Control 670 ( Bioline , Australia ) was included and DNA assayed for presence of extraction control signal on CFX96 Touch Real-Time PCR Detection System with the corresponding CFX Manager 3 . 1 software ( BioRad , Australia ) using SensiFAST Probe No-ROX Mix ( BioLine , Australia ) according to the manufacturer's instructions with expected CT values of <31 . Each DNA isolation batch included a blank sample ( ddH2O ) to detect any potential contamination during the extraction process ( extraction negative control ) . Extracted DNA was stored at -20°C prior to molecular analysis . A set of genus-specific primers were used to specifically amplify Fasciola spp . ITS2 rDNA region [10] . The real-time PCR utilised primers SSCPFaF [S0754] ( 5′-TTG GTA CTC AGT TGT CAG TGT G-3′ ) and SSCPFaR [S0755] ( 5′-AGC ATC AGA CAC ATG ACC AAG-3′ ) generating 140 bp amplicons [10] . F . hepatica species specific TaqMan probe ProFh [S0770] ( 5’-ACC AGG CAC GTT CCG TCA CTG TCA CTT T-3’ ) and F . gigantica specific TaqMan probe ProFg [S0771] ( 5’-ACC AGG CAC GTT CCG TTA CTG TTA CTT TGT-3’ ) were then implemented [10] . Use of probes removed non-specific background amplification detected using SYBR chemistry and provided species-specific confirmation ( S1 Fig ) . The real-time PCR does not amplify paramphistome egg DNA isolated from cattle faecal samples ( Herd 2 ) with concurrent infections ( S2 Fig ) . The TaqMan probes were labelled with a 5’-FAM , 5’-HEX reporter dye , respectively , and 3’-BHQ1 quencher . The assay with Australian samples was run only with FAM labelled ProFh probe because F . gigantica is exotic to Australia . All primers and probes were from Macrogen Ltd . ( Seoul , Korea ) . The real-time PCR reactions used SensiFAST Probe No-ROX Mix ( BioLine , Australia ) on CFX96 Touch Real-Time PCR Detection System with the corresponding CFX Manager 3 . 1 software ( BioRad , Australia ) . The volumes of the real-time PCR reactions were made up to 20 μl , including 2 μl of template DNA . The PCR mix included primers and probes at final concentrations of 400 nM and 100 nM , respectively . PCR reactions were initiated at 95°C for 3 min , followed by 40 cycles of 5 s at 95°C and 10 s at 60°C . All runs were performed in duplicate and ddH2O acted as a negative control . The efficiency , limit of detection and limit of quantification of the real-time PCR was determined via seven serial 10-fold dilutions of the positive control ( adult F . hepatica , 174 . 6 ng/μl measured with a NanoDrop Nd-1000 spectrophotometer , Thermo Scientific , Australia ) , representing a range in concentration of 1 . 75 x 101 to 1 . 75 x 10−5 ng/μl . Results were considered to be positive if both replicates displayed CT values <36 . Three conventional PCR assays were used to confirm adult Fasciola spp . ( Adult Fasciola spp . samples ) [11 , 12] . The internal transcribed spacers 1 ( ITS1 ) and 2 ( ITS2 ) were amplified using primers ITS1-F [S0762] ( TTG CGC TGA TTA CGT CCC TG ) and ITS1-R [S0763] ( TTG GCT GCG CTC TTC ATC GAC ) and ITS2-F [S0764] ( TGT GTC GAT GAA GAG CGC AG ) and ITS2-R [S0765] ( TGG TTA GTT TCT TTT CCT CCG C ) , yielding 639-bp and 519-520-bp-long fragments , respectively [11] . DNA fragments of a 577-bp-long 28S rDNA sequence were amplified using primers 28F1 [S0756] ( ACG TGA TTA CCC GCT GAA CT ) and 28R600 [S0757] ( CTG AGA AAG TGC ACT GAC AAG ) [12] . The primers targeting Fasciola spp . ITS2 were also used to amplify DNA from paramphistome eggs collected from cattle faecal samples with concurrent infections ( Herd 2 ) [11] . All PCR amplifications were performed with MyTaq Red Mix ( BioLine , Australia ) in a total volume of 30 μl . Primers were added at a concentration of 250 nM each . The PCR was run using the following cycling conditions: 95°C for 15 s , 55°C for 15 s and 72°C for 20 s for 35 cycles . All reactions were initiated at 95°C for 2 min and concluded at 72°C for 7 min . PCRs were amplified in the Verity PCR cycler ( Thermo Fisher Scientific , Australia ) . Each PCR reaction contained 2 μl of sample DNA . All PCRs were run with negative controls ( ddH2O ) . All PCRs that yielded unambiguous single bands of the expected size were directly and bidirectionally sequenced using amplification primers at Macrogen Ltd . ( Seoul , Korea ) and assembled and compared to reference sequences for F . hepatica and F . gigantica ( AB207139 and AB207143 , respectively ) in CLC Main Workbench 6 . 9 . 1 ( Qiagen , CLC Bio ) [11] . Three sample preparation methods ( Fig 1; workflows red [Method 1] , green [Method 2] and blue [Method 3] ) were used to compare the diagnostic sensitivity and specificity of the real-time PCR on a naturally infected herd with low FECs ( Herd 1 ) . For each animal , DNA from 150 mg ( Method 1 ) pure faeces was isolated using the Isolate Faecal DNA kit ( BioLine , Australia ) and disruption condition iv . Method 2 consisted of a modification to the sedimentation procedure where the final 2 ml of sediment was vortexed to ensure thorough mixing before 150 μl ( ≈150 mg ) was removed and DNA isolated using disruption condition iv . A further modification of the sedimentation procedure was employed in Method 3 where the entire 2 ml ( ≈2 g ) of sediment was centrifuged at 2500 g for 10 minutes to form a pellet of concentrated eggs . The entire pellet was manually removed from the 15 ml centrifuge tube using a combination of Pasteur pipettes and fine wooden applicator sticks for DNA isolation using disruption condition iv . The diagnostic sensitivity and specificity of Method 3 was further confirmed on a herd of cattle with constant F . hepatica exposure ( Herd 2 ) . The analytical sensitivity of the three sample preparation methods was determined by comparing the morphological FEC from the duplicate traditional sedimentations ( henceforth referred to as mEPG ) to the FEC calculated according to the standard curve produced by the optimised real-time PCR ( henceforth referred to as qEPG ) . To determine the effect of storage conditions on sedimentation and DNA isolation outcomes , 6 g aliquots of clean cattle faecal samples spiked with 2000 F . hepatica eggs were stored under the following conditions for one month; i . 4°C , ii . -20°C , iii . -20°C + 70% EtOH , iv . room temperature + 70% EtOH . Samples were spiked and stored in duplicate . After a month , the EtOH was aspirated and samples were placed in an incubator at 37°C to dry . After drying , each sample was sedimented and counted as described in previously and DNA was isolated as described in the section on diagnostic application ( Method 3 ) under disruption condition iv . Presence of F . hepatica antigen in faecal samples was assayed using ‘Monoscreen AgELISA Fasciola hepatica’ ( BIO K 201 , Bio-X Diagnostics S . A . , Belgium ) ( coproELISA ) , an indirect sandwich ELISA kit for the detection of Fasciola spp . antigen in cattle and sheep faeces ( Fig 1 ) . Cattle faecal samples ( Herd 1 and 2 ) were mixed with the kit dilution buffer 1:1 ( 2 g + 2 ml ) in 12 ml centrifuge tubes and vortexed for 30 seconds until thoroughly mixed followed , by centrifugation for 10 minutes at 2500 g . The supernatant ( 0 . 5 ml ) was aspirated and stored in labelled microcentrifuge tubes at 4°C until analysis . There was no effect on positive/negative coproELISA outcomes for samples processed immediately or stored at 4°C for up to 96 h ( S3 Fig; [15] ) . The coproELISA was performed according to manufacturer instructions with 100 μl of supernatant ( prepared as above ) . Each batch included two positive reference samples as controls . Optical densities ( OD ) were read at 450 nm using a SpectraMax 250 plate reader ( Molecular Devices , LLC . , Sunnyvale CA , USA ) . ODs of each corresponding negative well was subtracted from the individual sample ODs ( Net OD ) . The Scaled OD was calculated by dividing the Net OD of the sample by the Net OD of the positive coproELISA controls . Samples were considered positive for F . hepatica antigen if the scaled OD was >0 . 08 ( Monoscreen AgELISA Fasciola hepatica , BIO K 201 batch number FASA16B23 ) . As a post-mortem analysis was unable to be performed to confirm the presence or absence of flukes in the liver , the sedimentation technique was considered the gold standard and the diagnostic sensitivity and specificity of the coproELISA and real-time PCR methods were calculated accordingly . Data was analysed in Microsoft Excel ( 2013 ) and visualised with GraphPad Prism version 6 ( GraphPad Software , USA ) . Nucleotide sequences have been deposited in GenBank ( ITS1: MF678648—MF678649; ITS2: MF678650—MF678652; 28S: MF678653—MF678654 ) . The final protocol has been deposited online on protocols . io and can be found at https://dx . doi . org/10 . 17504/protocols . io . jggcjtw . Raw and supplementary data related to this article have been deposited online on Mendeley Data and can be found under the following DOI’s; https://dx . doi . org/10 . 17632/9zfzv84p8f . 2 and https://dx . doi . org/10 . 17632/4gwjjk47sz . 3 , respectively .
Visual inspection determined that all F . hepatica eggs were disrupted after a single round of homogenisation for 40 seconds at 6 . 0 m/s for both clean eggs and sheep faecal samples . Real-time PCR yielded similar CT values ( 15 . 5–16 . 6 ) for all six egg disruption conditions when applied to 2000 clean F . hepatica eggs in 150 μl PBS ( Table 1 ) . Similarly , when applied to 150 mg of F . hepatica infected sheep faecal samples ( 267 EPG , equivalent to 40 eggs in 150 mg ) , similar CT values ( 21 . 2–22 . 0 ) were observed ( Table 2 ) . The real-time PCR assay was highly efficient ( 100% , R2 = 0 . 995 ) at detecting F . hepatica DNA from adult fluke samples . The initial value of the 10-fold serial dilution of F . hepatica DNA ( 1 . 75 x 101 ng/μl measured with a NanoDrop ND-1000 spectrophotometer , Thermo Scientific , Australia ) gave a corresponding CT value to the 2000 clean eggs in 150 μl PBS ( S4 Fig ) . The standard curve derived from the serial 10-fold dilution gave intervals of 3 . 4 CT values for concentrations of 1 . 75 x 101 to 1 . 75 x 10−5 ng/μl ( S5 Fig ) . Henceforth , the F . hepatica adult fluke DNA dilution was considered a positive reference and was used to determine qEPG values . The assay routinely detected concentrations of 1 . 75 x 10−4 mg pure F . hepatica DNA ( equivalent to a theoretical limit of 2 x 10−2 eggs ) , demonstrating the limit of quantification and occasionally detected 1 . 75 x 10−5 mg ( equivalent to a theoretical limit of 2 x 10−3 eggs ) , giving the limit of detection . This theoretical limit was tested in practice through the isolation of DNA from single F . hepatica and F . gigantica eggs . DNA from five individual eggs from each species was isolated and amplified in duplicate . For both species , 9/10 wells amplified , giving the analytical sensitivity and further demonstrating the value of the bead-beating technique ( Table 3 ) . The three methods of sample preparation were compared for detection of F . hepatica DNA in faecal samples with low FECs ( ≤10 EPG ) . Method 3 proved the most sensitive after disruption at 6 . 0 m/s for 40 seconds ( disruption condition iv . ) , demonstrating 91–100% diagnostic sensitivity in comparison to the traditional sedimentation technique and FEC in cattle ( n = 31 ) with low FECs ( Herd 1 ) ( Fig 2A ) . Method 3 involved isolating DNA from the entire pellet from a traditional sedimentation , which occasionally exceeded the maximum manufacturer-recommended volume of 150 mg . However , no impact of increased sample volume on DNA isolation and amplification was detected ( S3 Table ) . Method 3 was then used to diagnose F . hepatica infection in a cattle herd ( n = 10 ) with constant F . hepatica exposure ( Herd 2 ) where 100% diagnostic sensitivity was observed again ( Fig 2B ) . Additionally , Method 3 showed good correlation ( 0 . 74–0 . 76 ) with FECs for all herds and sampling periods ( Fig 3A–3C ) . In comparison , Methods 1 and 2 show poorer correlation ( 0 . 17 and 0 . 57 , respectively ) ( Fig 3A ) . More than half ( 71% and 58% , October 2016 and February 2017 , respectively ) of Herd 1 ( n = 31 ) was positive for F . hepatica by sedimentation on both collection dates ( Fig 2A and Table 4 ) . The average FEC result for Herd 1 during the Austral spring and summer was 5 EPG ( ±4 . 55 ) and 3 EPG ( ±1 . 34 ) , respectively ( Table 4 ) . For Herd 2 , 80% ( 8/10 ) of the animals were positive for F . hepatica by faecal sedimentation and the average FEC was 61 EPG ( ±70 . 25 ) ( Fig 2B and Table 4 ) . A strong positive correlation ( 0 . 98 ) was observed between duplicate FECs ( Fig 3D ) . A commercially available coproELISA was used to diagnose F . hepatica infection in both Herd 1 and 2 across all sampling periods and the diagnostic sensitivity and specificity at five different thresholds was calculated ( Fig 2 ) . Threshold 2 proved to be the most sensitive cut-off and was calculated by averaging the scaled OD and adding two standard deviations of the known negative samples [13] . Using this cut-off the diagnostic sensitivity ranged from 65–88% for both herds over all sampling periods . In comparison , the manufacturer’s recommended cut-off yielded a larger range in diagnostic sensitivity of 6–63% for both herds over the two sampling periods ( Fig 2 ) . When using the new molecular workflow ( disruption condition iv . and sample preparation Method 3 ) as the gold standard of Fasciola spp . diagnosis , the diagnostic sensitivity and specificity of the traditional sedimentation technique is 90–100% and 80–100% , respectively ( S4C Table ) . In comparison , the diagnostic sensitivity and specificity of the coproELISA at Threshold 2 is 60–88% and 100% , respectively ( S4D Table ) . The DNA isolated using disruption protocol iv . was suitable for the differentiation between F . hepatica and F . gigantica for both conventional and real-time PCR . F . hepatica DNA isolated from cattle faecal samples ( Herd 1 ) was used to successfully amplify and sequence a 28S rDNA gene fragment 100% matching our reference 28S rDNA ( MF678654 ) . The 28S rDNA gene fragment is 99 . 5% identical ( 3 nt differences across 577 nt ) between F . hepatica ( MF678654 ) and F . gigantica ( MF678653 ) . Primers targeting ITS1 and ITS2 are not Fasciola spp . specific i . e . they also amplify paramphistome DNA , therefore were not used in genotyping PCRs with DNA isolated from faecal samples . Storage conditions had no effect on sedimentation , DNA isolation and amplification outcomes ( Table 5 ) . Regardless of the storage condition applied , sedimentation results remained consistent across all replicates and DNA amplification remained unaffected ( S5 Table ) .
A new molecular workflow was developed in response to the need for a specific diagnostic tool for Fasciola spp . in faecal samples [17–19] . The technique enabled medium to large sample throughput with high sensitivity in order to detect changes in faecal Fasciola spp . egg load , with the added benefit of parasite speciation ( i . e . F . hepatica vs F . gigantica ) . In the present study , the extraction of DNA from F . hepatica eggs through the use of a bead-beating approach resulted in consistent DNA isolation . The bead-beating approach ( iv . ) was applied to raw faecal samples with low F . hepatica EPG , but demonstrated decreased diagnostic sensitivity . To improve the diagnostic sensitivity , a Fasciola-egg concentration technique through egg sedimentation ( Method 3 ) was combined with the bead-beating approach prior to DNA isolation . The new molecular workflow was highly sensitive ( 91–100% ) with the real-time PCR results showing good correlation with faecal egg counts ( 0 . 74–0 . 76 ) , enabling robust quantitative detection of Fasciola species-specific eggs in faeces . The hard shell of Fasciola spp . eggs must first be disrupted to ensure access to the inner contents for DNA isolation and subsequent amplification . Although mechanical disruption prior to DNA extraction has been previously employed for the isolation of Fasciola spp . DNA from faeces , demonstration of the efficiency of mechanical disruption is lacking [20–22] . Our study shows consistent mechanical rupture of F . hepatica eggs across a wide range of settings on a high-speed benchtop homogeniser . Our results of mechanical egg disruption are consistent with findings for other parasitic species with notoriously robust eggs , such as Trichuris trichiura and Echinococcus multilocularis [23–24] . The DNA isolation protocol successfully isolated DNA from single F . hepatica and F . gigantica eggs using a single short 40s period of bead-beating ( disruption condition iv . ) . It has previously been shown that isolation of DNA from a single Fasciola spp . egg was possible after vortexing with glass beads for 30 minutes [21] . In a diagnostic laboratory setting , a rapid sample preparation and DNA isolation approach is paramount to take advantage of fast real-time PCR assays to deliver reproducible and accurate diagnostic results . The wide range of settings capable of disrupting Fasciola-eggs ( disruption conditions i . –iv . ) allowed for the selection of a bead-beating protocol that best aligns with other faecal sample molecular diagnostic procedures . As our laboratory utilises a standard protocol that includes 40s at speed of 6 . 0 m/s using FastPrep-24 ( MP Biomedicals , Australia ) , this was incorporated into the new molecular workflow for Fasciola spp . DNA diagnostics . With the demonstration of a wide range of suitable settings able to disrupt Fasciola-eggs for DNA isolation , adaptation of our workflow for other laboratories will be appropriate . Chronic Fasciola spp . infection in animals and humans give variable egg outputs , therefore we optimised our workflow to maximise the analytical sensitivity to <10 EPG [9 , 25–27] . Our initial success with clean eggs demonstrated consistent DNA isolation , although applying this approach to naturally-infected faecal samples with low EPGs ( <10 EPG ) proved challenging . When using the manufacturer-recommended maximum volume of raw faecal material ( 150 mg ) the theoretical sensitivity is 6 . 67 EPG , assuming that each 150 mg of raw faecal material contains 1 Fasciola spp . egg ( Method 1 ) . This is in agreement with other work [28] reporting the analytical sensitivity of one F . gigantica egg in 100 mg faeces , equivalent to 10 EPG . Thus , Method 1 is of limited diagnostic use in chronically infected cattle that frequently report low egg numbers ( ≤10EPG ) [25 , 27] . To improve the approach , a concentration step was included which employed the traditional sedimentation technique for trematode eggs [9] without the microscopic observation and counting ( Method 3 ) . A previously-described alternative method for egg concentration [21] used laborious washing and sieving procedures , including overnight refrigeration of a faecal suspension , making it inappropriate for application in diagnostics [29] . Our new molecular workflow incorporates a Fasciola-egg concentration procedure prior to the optimised Fasciola-egg disruption protocol , leading to highly successful results of 91–100% diagnostic sensitivity in a pilot study in cattle with ≤10 EPG . To ensure that the diagnostic sensitivity of the optimised workflow was consistent across a range of faecal egg loads we tested our approach on ten samples from an endemically infected herd that had received a triclabendazole oral drench six months prior to sampling . Despite the smaller sample size , these samples were considered suitable for proof of principal of the optimised workflow due to their considerably larger EPG range ( max . 221EPG , mean 61EPG ) and our previous success across two time points in a herd with low EPGs . To our knowledge only two other studies [20 , 22] report results testing the capability of molecular diagnostic tools for the identification of Fasciola spp . infection in individual naturally infected animals . In contrast , the diagnostic capacity of our optimised workflow remained high [20] . The clear benefits of the concentration of eggs in samples prior to isolation address the diagnostic sensitivity limitations previously highlighted [20] . No details were provided in other work [22] regarding the sensitivity of the diagnostic approach on naturally infected samples . However , we maintain that our consistent results across different groups of naturally infected animals demonstrate the robustness of our approach to sample preparation for DNA isolation , regardless of the molecular tools employed . Hence the clear benefits of the new molecular workflow addresses the needs of the animal and human health industry in regards to increasing the analytical and diagnostic sensitivity of Fasciola spp . molecular diagnosis . New antigen detection techniques for the diagnosis of Fasciola spp . infection ( coproELISA ) have been used to address the limitations of the traditional sedimentation and FEC approach by detecting infection prior to the completion of the pre-patent period [30] . We compared the diagnostic sensitivity and specificity of all three methods , including our new molecular workflow , by additionally diagnosing all cattle samples with the commercially-available coproELISA . Several studies have reported a decreased sensitivity of the coproELISA since commercialisation when diagnosing samples containing ≤10 EPG and using the manufacturers recommended positive threshold [15–16] . This is despite reporting detection limits of 0 . 6 ng/ml ( cattle ) and 0 . 3 ng/ml ( sheep ) of Fasciola spp . antigen , corresponding to a sensitivity of 100% for cattle harbouring 2 or more flukes , or sheep harbouring 1 fluke , during development [6] . In our study , samples <10 EPG were in agreement with previous reports , and in response we re-evaluated the positive threshold by employing several previously-described methods [13–16] . This re-evaluation increased the diagnostic sensitivity of the coproELISA , particularly in the recently infected herd ( Herd 2 ) . However , the application of arbitrary statistical methods to increase the sensitivity of the assay is problematic , as these methods are unlikely to be applicable across each new population being tested [13] . Hence , a new positive threshold must be calculated for each new batch number and species being diagnosed , resulting in additional costs and an unnecessary waste of time , particularly where large samples sizes are involved . Further , despite the added benefits of earlier diagnosis , the lack of ability to speciate still remains in regions such as Southeast Asia with both Fasciola spp . present . While still being limited by the pre-patent period , the new molecular workflow for the detection of Fasciola spp . in faecal samples maintains the larger throughput associated with the coproELISA at a similar cost , whilst providing the added benefit of species differentiation . The new molecular workflow provides a simple step-wise process for the preparation of faecal samples enabling medium-high throughput for the diagnosis of Fasciolosis . However , the application of this approach is of limited use in locations where the diagnostic capacity may be restricted or if the necessary laboratory equipment is lacking . The ability to preserve samples in 70% EtOH and transport them to areas with increased diagnostic capacity is vital , particularly when conducting epidemiological studies on Fasciolosis in remote and rural areas such as Southeast Asia . The opportunity of sample preservation for transport has been demonstrated previously , particularly when working in areas lacking a continuous cold chain [23 , 31] . Our results were in agreement , demonstrating that regardless of the storage conditions we applied , there was no effect on sedimentation and DNA isolation . The advancement of molecular tools for the differentiation of Fasciola spp . have greatly added to our understanding of their ecology , epidemiology and zoonotic potential [19] . However , no tool currently exists for the identification of the hybrids between F . hepatica and F . gigantica in the field . This is especially important in areas where F . hepatica , F . gigantica and their hybrids exist in sympatry , such as Southeast Asia , where the zoonotic potential of the hybrid forms are largely unknown [19 , 32] . The inclusion of TaqMan probes in our new molecular diagnostic workflow enables the identification of either infection with a hybrid , or a mixed infection with both F . hepatica and F . gigantica within a single animal [10] . However , due to the triploid nature of the hybrids , the differentiation between these two scenarios would require the additional isolation of DNA from single eggs [33] . Our new molecular diagnostic workflow provides this capability , as demonstrated by the repeated successful isolation of DNA from single eggs of both F . hepatica and F . gigantica , adding an additional tool to our diagnostic arsenal . In conclusion , we present a robust approach for the ante-mortem diagnosis of Fasciola spp . infection using faecal samples . The presented workflow is able to differentiate between F . hepatica and F . gigantica species , while also providing a flexible methodology capable of being adapted for use in existing diagnostic laboratory workflows . Although the method maintains the requirement for the completion of the pre-patent period , the additional benefits of fluke species differentiation and increased sample throughput provide clear benefits over the traditional sedimentation and FEC approach . The high diagnostic sensitivity and ability to store samples in 70% EtOH make this approach suitable for use in surveillance programs and epidemiological studies in areas where access to a complete cold chain is lacking or where laboratory capacity is limited . | Fasciolosis caused by infection with F . hepatica , F . gigantica and their hybrid strains is an important health issue in medical and veterinary sciences . The economic impacts of infection and disease on production animals is of particular concern in low-income rural areas of developing countries , including in Southeast Asia where aquatic rice production plus low-input large ruminant husbandry , provides an ideal habitat for parasite proliferation . Here , we describe the development of a robust approach to ante-mortem diagnosis capable of differentiating between the two Fasciola spp . and their hybrid . Previously , such differentiation was based on identification of flukes collected post-mortem , because eggs of the two species have overlapping morphology . The major novelty is the demonstration that Fasciola spp . eggs , released by adults in the bile ducts , can be effectively and reproducibly broken and the content made available for DNA isolation . Coupled with part of a traditional sedimentation technique , this approach presents a new molecular diagnostic workflow capable of the specific detection of F . hepatica DNA in samples with ≤10 eggs per gram of faeces using a real-time PCR TaqMan assay . The presented workflow enables differentiation of F . hepatica and F . gigantica and their hybrid if a duplex TaqMan real-time PCR assay is included . The ability to process medium to large sample sizes in lieu of a continuous cold chain will enable further research into the epidemiology , control and public health concerns associated with Fasciola spp . infection . | [
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| 2017 | Scrambled eggs: A highly sensitive molecular diagnostic workflow for Fasciola species specific detection from faecal samples |
Since 2003 , more than 380 cases of H5N1 influenza virus infection of humans have been reported . Although the resultant disease in these cases was often severe or fatal , transmission of avian influenza viruses between humans is rare . The precise nature of the barrier blocking human-to-human spread is unknown . It is clear , however , that efficient human-to-human transmission of an antigenically novel influenza virus would result in a pandemic . Influenza viruses with changes at amino acids 627 or 701 of the PB2 protein have been isolated from human cases of highly pathogenic H5 and H7 avian influenza . Herein , we have used the guinea pig model to test the contributions of PB2 627 and 701 to mammalian transmission . To this end , viruses carrying mutations at these positions were generated in the A/Panama/2007/99 ( H3N2 ) and A/Viet Nam/1203/04 ( H5N1 ) backgrounds . In the context of either rPan99 or rVN1203 , mutation of lysine 627 to the avian consensus residue glutamic acid was found to decrease transmission . Introduction of an asparagine at position 701 , in conjunction with the K627E mutation , resulted in a phenotype more similar to that of the parental strains , suggesting that this residue can compensate for the lack of 627K in terms of increasing transmission in mammals . Thus , our data show that PB2 amino acids 627 and 701 are determinants of mammalian inter-host transmission in diverse virus backgrounds .
Repeated introductions of avian H5N1 influenza viruses into the human population have resulted in more than 380 reported cases of severe disease since 2003 . Greater than half of these cases have been fatal , highlighting the extreme pathogenicity of H5N1 influenza in humans . Furthermore , high viral loads have been detected in clinical specimens collected from infected patients [1] . Nevertheless , H5N1 influenza viruses do not transmit efficiently from person-to-person . Prior to the 1997 and ongoing 2003 outbreaks of H5N1 zoonoses , it was generally assumed that an influenza virus with the traits of efficient viral growth and pathogenicity in a given host species would transmit between individuals of that species . The phenotype of H5N1 viruses in humans has changed that view: it is now clear that specific viral factors are required to support inter-host transmission . The consequences of human-to-human spread of H5N1 influenza viruses would be of pandemic proportions; for this reason , significant effort has been expended in recent years towards the identification of molecular determinants of transmission . As a result of these efforts , it has become clear that transmissibility among humans is a complex and polygenic trait [2] , [3] . Affinity of the viral hemagglutinin ( HA ) protein for α-2 , 6 linked sialic acid residues has been shown to be necessary [4] but not sufficient [2] , [5] to support transmission between ferrets . Due to its importance in determining viral host range and pathogenicity , the viral polymerase complex has also been suggested to play a role in transmission [3] , [6] , but this hypothesis has not been tested directly . Additional viral proteins as well as host and environmental factors [7] , [8] are also likely to impact the success of human-to-human spread of influenza viruses . Amino acid 627 of the PB2 protein is almost exclusively a lysine in human influenza isolates and a glutamic acid in avian influenza isolates . This residue was first recognized as a determinant of host range in 1993 [9] , and was later shown to contribute to the temperature sensitivity of avian viral replication in mammalian cells [10] . In particular , the introduction of a lysine at position 627 of an avian virus PB2 protein greatly improved replication of a minigenome segment in cells incubated at 33°C [10] . This finding has also been confirmed in the context of infected cell cultures: the growth of A/Viet Nam/1204/04 virus ( VN1204; H5N1 ) in mammalian cell lines is improved at 33°C , but not 37°C or 41°C , by the single amino acid change , PB2 E627K . Furthermore , rVN1204 possessing 627K was found to grow more efficiently in the nasal turbinates of mice than the wild-type virus [6] . A recently reported observation provides some insight into the mechanism underlying the effects of the E to K change: the introduction of E at 627 into the WSN PB2 appears to decrease its association with the NP protein , in mammalian but not avian cells [11] . In addition to affecting host range and tissue tropism , PB2 627K has also been identified as a major pathogenicity determinant of H5N1 and H7N7 subtype influenza viruses in mammalian hosts [12]–[15] . Perhaps the most striking aspect of the polymorphism at PB2 627 is the rapidity with which lysine at this position arises when an avian influenza virus infects either a mouse or a human . A single passage in mice has been reported to be sufficient for an E627K variant to dominate the resultant H5N1 virus population [6] , [16] . The presence of PB2 627K has furthermore been identified in multiple human isolates of H5N1 viruses [1] , [17] , [18] . Similarly to E627K , a change of PB2 amino acid 701 from aspartic acid to asparagine has been implicated in expanding the host range of avian ( or avian-like ) H5N1 and H7N7 subtype viruses to include mice [19] , [20] and humans [1] . In addition , an asparagine at PB2 701 is a common feature of avian-like H3N2 swine viruses circulating in Europe . A mechanism has recently been proposed to explain the contribution of D701N to improved growth of a mouse adapted avian-like H7N7 virus , SC35M , in mammalian cell culture: D701N appears to enhance the binding of PB2 to importin α1 and correspondingly increase PB2 levels in the nucleus in mammalian , but not avian , cells [21] . A potential correlation between the polymorphisms at PB2 positions 627 and 701 was observed in H5N1 viruses collected from humans in 2004–2005 . de Jong et al . reported that , among twelve clinical isolates , eight possessed 627K while a distinct three viruses carried 701N . The authors suggested that 701N may compensate for the lack of 627K in the context of mammalian cells [1] . Herein , we have evaluated two hypotheses: i ) PB2 627K promotes the transmission of influenza viruses between mammals through improved growth in the upper respiratory tract , and ii ) an asparagine at PB2 701 can functionally replace 627K in supporting viral growth and transmission in mammals . We used the guinea pig model to test the effects of polymorphisms at PB2 positions 627 and 701 on aerosol transmission of the recombinant human H3N2 isolate , A/Panama/2007/99 ( rPan99 ) , which we have previously shown to transmit with high efficiency [22] , and on contact transmission of the recombinant human H5N1 isolate , A/Viet Nam/1203/04 ( rVN1203 ) . We show that rVN1203 transmits with moderate efficiency by the contact route in the guinea pig model . Furthermore , the mutation K627E decreases the transmission efficiency of both rPan99 and rVN1203 viruses . Contrary to previous reports and to our first hypothesis , we did not , however , observe a marked difference between the 627K and 627E containing viruses in terms of peak viral titers attained in vivo . Our second hypothesis did prove correct in that combination of glutamic acid at 627 with an aspartic acid to asparagine change at position 701 rescues the phenotype of the 627E viruses: rPan99 virus with 627E+701N transmits with similar efficiency to the parental rPan99 virus , while the rVN1203 627E 701N virus transmits with higher efficiency than the wild-type strain . Our data show that specific adaptations of the viral polymerase to the mammalian host support the transmission of influenza viruses of diverse lineage , revealing one viral trait that could contribute to the making of a pandemic strain .
Reverse genetics systems for Pan99 and VN1203 viruses were used to generate recombinant viruses which encode the mutations K627E alone or K627E and D701N combined , in their respective PB2 segments . All four mutant viruses and the two recombinant parental viruses were successfully recovered from cDNA transfection . Reverse transcription followed by PCR and sequencing of the PB2 gene segment of each virus confirmed that the expected sequence was present . As sequencing was only performed on the PB2 segment , it cannot be formally excluded that random mutation of the remaining segments may have contributed to the phenotypes observed . The PB2 genotype of each virus is summarized in Table 1 . To confirm that the three rVN1203 viruses possessed the polybasic cleavage site associated with high pathogenicity in H5N1 influenza viruses , growth in the absence of exogenous trypsin was assessed by plaque assay on MDCK cells . None of the viruses required trypsin for plaque formation , in contrast to the laboratory-adapted strain A/Puerto Rico/8/34 ( data not shown ) . A preliminary estimate of the fitness of each recombinant virus was obtained by evaluating their plaque phenotypes on MDCK cells . Since previous reports indicate that the polymorphism at PB2 627 impacts viral growth at decreased temperature , plaque assays were performed in duplicate and incubated at either 37°C or 33°C . As seen in Figure 1A and 1B , at both 37°C and 33°C , rPan99 produced the largest plaques of the rPan99 derived viruses . The rPan99 PB2 627E virus formed slightly smaller plaques than either wild-type or rPan99 627E 701N viruses ( p<0 . 001 at 37°C; p<0 . 001 relative to wild-type at 33°C , and p<0 . 01 relative to rPan99 627E 701N at 33°C ) . The rPan99 virus encoding PB2 627E 701N produced plaques which were not significantly smaller than rPan99 wild-type at 37°C ( p>0 . 05 ) and plaques which were slightly smaller than the wild-type at 33°C ( p<0 . 001 , Figure 1A and 1B ) . As was seen with the rPan99 series of viruses , the wild-type rVN1203 ( PB2 627K ) virus produced larger plaques than either the 627E or the 627E 701N mutant viruses ( Figure 1C and 1D ) . The plaque size of all three viruses was reduced at 33°C compared to 37°C , but the reduction was most striking for the rVN1203 virus encoding PB2 627E . This virus produced pinpoint plaques at 33°C , indicating a significant impairment in plaque formation at the lower temperature . At 37°C the average diameter of plaques formed by rVN1203 627E was approximately 2 . 0-fold smaller than the wild-type ( p<0 . 001 ) ; while at 33°C the plaques of rVN1203 627E were approximately 3 . 4-fold smaller than rVN1203 wild-type ( p<0 . 001 ) . Thus , rVN1203 627E was attenuated under both growth conditions , but attenuation was greater at 33°C . Similar to the rPan99 viruses , the rVN1203 virus encoding PB2 627E 701N produced plaques which were closer in size to the rVN1203 wild-type virus ( 1 . 2-fold reduced at 33°C , p<0 . 01 , and 1 . 2-fold reduced at 37°C , p<0 . 001; Figure 1C and 1D ) . To test whether the PB2 627E mutation results in a cold sensitive growth phenotype in the Pan99 background , the multi-cycle growth of the rPan99 based viruses in MDCK cells was compared at 33°C and 37°C . Cells were inoculated at low multiplicity ( 0 . 01 PFU/cell ) and supernatant was sampled at 3 , 10 , 24 , 48 , and 72 h p . i . As shown in Figure 2 , all three rPan99 based viruses grew to higher titers when incubated at 33°C than at 37°C . This result probably reflects the fact that Pan99 virus is highly adapted to growth in the upper respiratory tract of humans . At either temperature , the wild-type virus was found to yield the highest titers . The PB2 627E mutant was attenuated relative to the wild-type virus by greater than 10-fold at both 37°C and 33°C and at all time points except the first . Thus , in the Pan99 background , PB2 627E is an attenuating mutation , but does not appear to result in cold-sensitivity as seen in avian influenza viruses [6] . This lack of cold-sensitivity is most likely due to the presence of multiple adaptations to low temperature growth in the context of a human-adapted virus . At 33°C and 37°C , the rPan99 627E 701N virus grew to intermediate titers relative to the rPan99 and rPan99 627E viruses . These results suggest that , in MDCK cell culture , the attenuated phenotype of a K627E mutant is partially rescued by the D701N mutation . A similar experiment was then performed with the rVN1203-based viruses . MDCK cells were inoculated at an MOI of 0 . 01 with rVN1203 , rVN1203 627E or rVN1203 627E 701N viruses and supernatant was sampled at 2 , 24 , 48 , and 72 h . p . i . As shown in Figure 2 , there were no marked differences in the viral yields attained at either temperature . All three viruses grew to high titers by 24 h . p . i . , although , at 33°C , the 627E mutant virus grew to 8 . 5-fold lower titers than the wild-type . To test whether the phenotypes of rPan99 627E and rPan99 627E 701N seen in vitro are also manifested in vivo , we evaluated the titers of these viruses in the nasal passages and lungs of infected guinea pigs . Growth in the upper respiratory tract was assessed by collecting nasal lavage from intranasally inoculated animals on days 2 and 4 p . i . As shown in Figure 3A , rPan99 wild-type virus grew to a titer of 107 PFU/mL at 2 d p . i . At this time point , the rPan99 627E virus produced a 30-fold lower titer . The kinetics of growth of the 627E mutant virus were delayed relative to the wild-type , with a higher titer ( of 2 . 3×106 PFU/mL ) being reached at 4 d than at 2 d p . i . The rPan99 627E 701N double mutant exhibited an intermediate phenotype: titers on day 2 p . i . were only 4-fold lower ( as compared to the 30-fold reduction for rPan99 627E ) relative to rPan99 wild-type . Thus , moderate differences in viral titers achieved in the upper respiratory tract were observed between the three Pan99-based viruses , but this difference was observed only at day 2 , and not day 4 , post-inoculation . Growth in the lower respiratory tract was assessed by titrating virus harvested from homogenates of guinea pig lung collected on days 2 and 4 p . i . Growth in the lungs was affected to a lesser extent by the K627E and D701N mutations than growth in the nasal passages . Clearance of the wild-type virus from the lungs appeared to be initiated more quickly than for the two mutant viruses; nevertheless , average titers of all three viruses were comparable on day 2 p . i . ( 2 . 4–7×104 PFU/g ) and day 4 p . i . ( 2 . 2–12×104 PFU/g ) ( Figure 3B ) . We used the guinea pig model to characterize the transmission phenotypes of rPan99 wild-type , rPan99 627E and rPan99 627E 701N viruses . Two ( rPan99 wild-type ) or three ( rPan99 627E and rPan99 627E 701N ) independent experiments were performed with each virus , in which we assessed the rate of spread by the aerosol ( large or small respiratory droplet ) route . Each experiment involved eight guinea pigs: four infected intranasally with 1000 PFU of the appropriate virus , and four exposed animals . The rPan99 wild-type virus transmitted to all eight exposed guinea pigs ( Figure 4A ) , as expected based on previous results with the non-recombinant Pan99 virus [7] , [22] . The PB2 mutant virus , rPan99 627E , transmitted less efficiently , with just six of twelve exposed animals contracting infection , and with slower kinetics relative to the wild-type ( Figure 4B ) . Partial rescue of the transmission defect was observed when the PB2 D701N change was also present: rPan99 627E 701N virus transmitted to ten of twelve exposed guinea pigs ( Figure 4C ) . Transmission to contact animals , as measured by isolation of virus in nasal washings , was verified by hemagglutination inhibition assay of paired sera for each guinea pig ( Table 2 ) . To confirm that the mutant viruses had not reverted to the wild-type PB2 sequence prior to transmission , the PB2 genes of isolates from two exposed guinea pigs were sequenced for each virus . All transmitted viruses examined retained the introduced mutations . Thus , in the background of a human H3N2 isolate , mutation of PB2 residue 627 from K to E decreases the efficiency of transmission , while the introduction of D701N alongside K627E results in a phenotype more similar to that of the wild-type virus . To test whether the introduction of K627E or K627E D701N mutations would also alter the transmissibility of the rVN1203 virus , contact transmission experiments were performed in guinea pigs . Each experiment involved eight guinea pigs: four infected intranasally with 104 PFU of the appropriate virus , and four exposed animals . In this case , each exposed guinea pig was placed in direct contact with an inoculated animal by placing them in the same cage at 24 h p . i . It is important to note that a different experimental set up ( aerosol versus contact transmission experiments ) , including a different inoculum dose , was used for the experiments with the rVN1203-based viruses , relative to the experiments with the rPan99-based viruses . Thus , the results shown in Figure 4 should not be compared directly to those presented in Figure 5 . As shown in Figure 5A , the rVN1203 wild-type virus transmitted rapidly to three of four contact guinea pigs . In contrast , the rVN1203 PB2 627E mutant transmitted to only one contact animal ( Figure 5B ) . While this result suggests that this single amino acid change – PB2 K627E – reduces the transmission potential of a highly pathogenic avian influenza virus in a mammalian species ( p = 0 . 09 , Student's t-test ) , an increased number of replicates would be required to prove a statistically significant reduction in transmission . Furthermore , due to the space constraints and high cost of housing animals under BSL3-enhanced containment , we did not retain guinea pigs infected with VN1203 virus for the three weeks required to obtain convalescent sera for HI assays in order to assess transmission to contact animals by seroconversion . The PB2 gene of the transmitted virus was subjected to RT PCR and sequencing and the introduced mutation , K627E , was found to be maintained . In contrast , virus isolated from three of the inoculated animals late in the course of infection ( day 6 p . i . ) exhibited large plaque phenotypes , suggesting reversion had occurred . The sequencing of one of these isolates confirmed the presence of lysine at PB2 627 . As shown in Figure 5C , the rVN1203 627E 701N double mutant virus spread to all four contact guinea pigs . Although slightly more efficient ( 100% compared to 75% for the wild-type virus ) , transmission of the rVN1203 627E 701N double mutant virus appeared to occur with slower kinetics than the wild-type , based on the relatively low titers isolated from contact animals 24 h after the initiation of contact . Taking into account the virus titers shed by the inoculated animals and the 50% infectious doses of the wild-type and double mutant viruses ( see below and Table 3 ) , it is unclear why the kinetics of transmission differ . Again , sequencing of the PB2 gene of one transmitted virus indicated that both the K627E and D701N mutations were retained . Thus , the amino acid change PB2 D701N favors the transmission of an H5N1 virus in a mammalian host and this mutation fully compensates for the lack of a lysine at position 627 in terms of transmission by the contact route . Despite productive growth of all three VN1203-based viruses in the guinea pig respiratory tract , clinical signs were not observed following either intranasal inoculation or infection through contact transmission . The average peak nasal wash titers ( reached on day 2 p . i . ) of guinea pigs intranasally inoculated with the rVN1203 wild-type and 627E mutant viruses were similar , at approximately 2 . 3×105 PFU/ml . The titers of the double mutant virus shed by inoculated guinea pigs at day 2 p . i . were higher , at about 1 . 1×106 PFU/ml . Statistical analysis indicated that only the difference between the wild-type and double mutant virus was significant , with a p value of 0 . 044 ( Student's t-test ) . Overall , the data do not convincingly show that enhanced shedding from the upper respiratory tract contributes to improved transmission of the rVN1203 wild-type virus . In a separate experiment designed to investigate a possible role in transmission of shedding in the feces , we collected rectal swab samples from guinea pigs inoculated with wild-type rVN1203 virus on days 2 , 4 , and 6 post-infection . No virus was detected in these rectal swab samples , despite efficient isolation of virus from the upper respiratory tract of the same animals . 50% In both the rPan99 and rVN1203 backgrounds , the identities of PB2 residues 627 and 701 had a clear effect on transmission . Nevertheless , contrary to our expectations , mutation of these amino acids in the rVN1203 background did not have a striking effect on peak viral growth in the upper respiratory tract of guinea pigs ( Figure 5 ) . In the rPan99 background , differences in shedding titers were seen on day 2 p . i . , which may have contributed to the transmission phenotypes observed . The effect , however , was limited to day 2 p . i . , with similar peak titers being reached by all three viruses by day 4 p . i . ( Figure 3A and Figure 4 ) . We therefore reasoned that the reduction in transmissibility may be due to an effect on the recipient , as well as possibly the donor , host in the transmission equation . In other words , we predicted that the 50% infectious dose ( ID50 ) of the PB2 627E containing viruses would be higher than either the wild-type or the double mutant viruses . To test this prediction , we inoculated groups of four guinea pigs with 10-fold serial dilutions of each of the six recombinant viruses to determine the GPID50 in each case . The results are summarized in Table 3 . In both virus backgrounds , the GPID50 of the wild-type and 627E 701N double mutant viruses were found to be very similar or the same . In contrast , the rPan99 627E virus had a GPID50 of approximately 10-fold higher , and the rVN1203 627E virus had a GPID50 of approximately 7-fold higher than the corresponding wild-type viruses . Although the differences are marginal , it is possible that decreased infectivity contributes to the inefficient transmission phenotypes of both 627E-containing viruses .
In the context of an influenza virus which is well adapted to the human host , rPan99 , and an avian-like influenza virus isolated from a human host , rVN1203 , we have shown that polymorphisms at residues 627 and 701 of PB2 influence transmission among guinea pigs . In addition , we have tested the hypothesis proffered by de Jong et al . [1] that , with respect to adaptation of an avian virus to a mammalian host , D701N can functionally replace E627K . We found that , when PB2 627 is a glutamic acid residue , the D701N mutation not only improves viral growth in mammalian cells , but enhances transmission between guinea pigs . Using minigenome systems and infected cell cultures , the introduction of the E627K mutation into an avian PB2 gene has previously been shown to increase polymerase activity and viral growth at 33°C [6] , [10] . With the mutation of the wild-type K627 to E in the rVN1203 background , we saw a marked reduction in plaque size at both 33°C and 37°C . The degree to which the plaque size decreased relative to the rVN1203 wild-type virus was greater at 33°C , however , as would be expected based on earlier reports . In the rPan99 background , the reduction in plaque size observed with the K627E mutation was again seen at both temperatures and was more subtle than for rVN1203 . The observation that the fully human-adapted virus rPan99 was better able to tolerate the PB2 K627E mutation and maintain plaque formation at 33°C than the more avian-like rVN1203 virus may indicate that Pan99 has developed degenerate mechanisms to permit growth at the lower temperature of 33°C . The in vivo growth of the rPan99 627E virus was impaired relative to the wild-type , but only early ( day 2 ) after infection . Although improved growth in the upper respiratory tract early after infection may act to increase the efficiency of transmission , the fact that – at 4 d p . i . – the rPan99 627E and rPan99 627E 701N mutant viruses reach similar peak titers to the wild-type may argue against this as a mechanism for the improved transmission of the wild-type virus . In addition , contrary to recent findings in mice [12] , we did not observe marked differences in the viral titers reached by rVN1203 wild-type , rVN1203 627E and rVN1203 627E 701N viruses in the upper respiratory tract of guinea pigs . We did , however , see a clear effect of the PB2 mutations on transmission in the rVN1203 background , as well as in the rPan99 background . We hypothesized that the observed transmission phenotypes might arise from differences in infectivity . Determination of the GPID50 values of the three rVN1203- and the three rPan99-based viruses did reveal that higher doses of the 627E mutant viruses are required to productively infect guinea pigs . Although suggestive , the differences observed ( approximately 7-fold for rVN1203 and 10-fold for rPan99 ) may be within the range of error of the GPID50 assay . Thus , the mechanism underlying the decreased transmission efficiencies of rVN1203 PB2 627E and rPan99 PB2 627E viruses relative to the corresponding wild-type and double mutant viruses remains uncertain . Our data suggest that reduced viral growth in the nasal passages or decreased infectivity may play a role , but do not allow us to draw firm conclusions on this point . One additional possibility is that , despite reaching similar peak titers in nasal lavage , the 627E mutant viruses are not shed into the air as efficiently as the wild-type and double mutant viruses . For all three rVN1203 viruses , the viral growth kinetics observed in inoculated animals were markedly different from those in the contact animals . This is not normally observed with human-adapted influenza viruses in guinea pigs [7] , [8] , [22] , but probably reflects a dose-dependent growth phenotype of rVN1203 in this host . In terms of viral growth in vitro and in vivo as well as transmission between guinea pigs , the introduction of N at position 701 was found to at least partially reverse the phenotype of the K627E mutant viruses in both virus backgrounds tested . Further support for the idea that 701N can compensate for a lack of 627K in mammalian hosts is found in the published sequences of swine influenza PB2 genes . Among the avian-like H3N2 viruses circulating in swine , European isolates for which sequences are available in Genbank carry 627E with 701N , while Asian isolates predominantly carry 627K with 701D . Although the number of transmission events is low , it may also be of note that three swine influenza viruses isolated from humans in Asia possessed PB2 627E and 701N [23] , [24] . In wild birds , H5N1 viruses carrying PB2 627K arose during the Qinghai Lake outbreak in 2005; descendants of these viruses retaining possession of 627K continue to circulate among wild waterfowl [25] . Furthermore , the presence of PB2 627K or 701N has been reported in multiple human isolates of H5N1 influenza viruses [1] , [17] , [18] . Despite the relatively high incidence of these mammalian-adaptive mutations in human H5N1 isolates , sustained human-to-human spread has not been observed . Thus , although our data show that PB2 627K or 701N is necessary for optimal transmission of either human- or avian-adapted viruses between guinea pigs , these mutations are not sufficient to support human-to-human spread of H5N1 influenza . Indeed , studies in the ferret model indicate that transmissibility is a complex , multigenic trait [2] , [5] . The appropriate receptor specificity is most likely required [4] , [26]; we have shown that the PB2 protein plays a role; and other viral factors most likely contribute to the transmission phenotype . We have identified PB2 627K and 701N as determinants of transmission between mammals . The observation that these residues contribute to the transmission of both H3N2 and H5N1 influenza viruses between mammals suggests that adaptations in the PB2 protein may be a prerequisite of transmission common to all influenza A subtypes . The identification of the additional viral factors necessary for—and ultimately the set of traits sufficient to support—human-to-human spread will greatly improve our understanding of how a pandemic strain arises .
Madin Darby canine kidney ( MDCK ) cells were maintained in minimum essential medium ( Gibco ) supplemented with 10% fetal bovine serum , 100 units/mL of penicillin , and 100 µg/mL of streptomycin . 293T cells were maintained in Dulbecco's minimum essential medium ( Gibco ) supplemented with 10% fetal bovine serum . Recombinant Pan99 and VN1203 viruses were rescued according to previously reported protocols , with minor modifications [27] , [28] . Briefly , 8 pPOL1-based vRNA expression plasmids and 7 pCAGGS-based support plasmids ( for the expression of NS1 , NA , NP , HA , PA , PB1 and PB2 ) were used to transfect 293T cells . At 24 h post-transfection , growth medium was replaced with serum-free medium containing 1 µg/mL TPCK-treated trypsin ( Sigma ) and MDCK cells were added to the culture . At 72 h post-transfection , rescue supernatant was subjected to plaque assay on MDCK cells in order to obtain clonal isolates . Rescued viruses were propagated in 10 d old embryonated hens' eggs ( Pan99-based ) or MDCK cells ( VN1203-based ) at 37°C . Genotypes of wild-type viruses were verified by RT-PCR and partial sequencing of each of the eight segments . For the rescue of mutant viruses , site-directed mutagenesis was used to introduce the required mutations into the pPol1Pan99-PB2 or pPol1VN1203-PB2 plasmids using the QuikChange II Site Directed Mutagenesis kit ( Stratagene ) . Rescue protocols were as for the wild-type viruses . Genotypes of the mutant viruses were confirmed through RT-PCR and sequencing of the respective PB2 segments . The presence of a multibasic cleavage site in the HA of rVN1203 wild-type and mutant viruses was confirmed by verifying their ability to form plaques in the absence of trypsin ( data not shown ) . Characterization of plaque phenotypes on MDCK cells was performed as described previously [29] . All work with VN1203-based viruses was performed in a USDA and CDC-approved biosafety level 3+ containment laboratory in accordance with institutional biosafety requirements . Polyclonal rabbit anti-A/Puerto Rico/8/34 was used as the primary antibody , and Amersham ECL™-HRP linked anti-rabbit was used as the secondary antibody for immunostaining . To examine multi-step growth , MDCK cells were infected at a multiplicity of 0 . 01 PFU/cell . Following a 45 min incubation , inoculum was removed and monolayers were washed with PBS . Dishes were then incubated at 33°C or 37°C , as indicated , and samples of growth medium collected at 3 , 10 , 24 , 48 and 72 h p . i ( for rPan99-based viruses ) or at 2 , 24 , 48 , and 72 h . p . i . ( for rVN1203-based viruses ) . Titers were determined by plaque assay on MDCK cells . Female Hartley strain guinea pigs weighing 300–350 g were obtained from Charles River Laboratories Inc . ( Wilmington , MA ) . Animals were allowed free access to food and water and kept on a 12 h light/dark cycle . Guinea pigs were anesthetized for the collection of blood and of nasal wash samples , using a mixture of ketamine ( 30 mg/kg ) and xylazine ( 2 mg/kg ) , administered intramuscularly . All procedures were performed in accordance with the Institutional Animal Care and Use Committee guidelines . During guinea pig transmission experiments strict measures were followed to prevent aberrant cross-contamination between cages: exposed animals were handled before inoculated animals , gloves were changed between cages , and work surfaces were sanitized between guinea pigs . Three groups of four guinea pigs were inoculated with 1000 PFU in a 300 µl volume intranasally: group 1 received rPan99 , group 2 received rPan99 PB2 627E , and group 3 received rPan99 PB2 627E 701N . At 2 d p . i . , nasal washings were collected from all 12 guinea pigs . Six guinea pigs were then sacrificed for the collection of lung tissue . At 4 d p . i . , nasal washes were collected from the remaining 6 animals; these 6 guinea pigs were then sacrificed and the lungs removed . As described previously [22] , nasal washing was performed by instilling 1 mL PBS-BA-PS ( PBS supplemented with 0 . 3% bovine serum albumin , 100 units/mL of penicillin , and 100 µg/mL of streptomycin ) into the nostrils and allowing it to drain onto a Petri dish . Samples were collected into 1 . 5 ml tubes and centrifuged for 5 min at 2000×g and 4°C; supernatants were stored at −80°C prior to analysis by plaque assay . For the collection of lung tissue , animals were killed through exposure to CO2 gas . After the lungs were removed , three lobes were detached , placed in separate Petri dishes , and minced . Approximately 300 mg of each lobe was then transferred to 4 ml cold PBS-BA-PS . Thus , three samples were taken from each animal . Extracted lung tissue was weighed , homogenized in PBS-BA-PS and centrifuged at 12 000×g and 4°C for 10 min to pellet debris . Immediately after preparation , supernatants were serially diluted and subjected to plaque assay . Data in Figure 3A represent the nasal wash titers on days 2 and 4 post-infection for all animals inoculated intranasally with the indicated viruses . This data includes titers derived from inoculated animals used in aerosol transmission experiments as well as the 12 inoculated animals used to assess viral growth in the upper and lower respiratory tracts . As in our previous publications , the term “aerosol” is used herein to describe respiratory droplets of all sizes . Aerosol transmission experiments—performed with rPan99 and Pan99-derived mutants—were carried out as described previously [7] . Each experiment involved eight guinea pigs; the data shown in Figure 4 represent cumulative results from two independent experiments for rPan99 wild-type , and three independent experiments for the two rPan99 PB2 mutant viruses . Briefly , the procedure was as follows . On day zero , four of the eight guinea pigs were inoculated intranasally with 1000 PFU of virus . At 24 h p . i . , each of the eight guinea pigs was transferred to a “transmission cage” and then placed into an environmental chamber ( Caron model 6030 ) set to 20% relative humidity and 20°C . Each infected animal was paired on a shelf with a naïve animal; in this arrangement , no contact between the guinea pigs is possible . The animals were housed in this way for seven days , after which they were removed from the chamber and separated . Nasal wash samples were collected from anesthetized guinea pigs on days 2 , 4 , 6 , 8 , and 10 p . i . by instilling 1 mL of PBS-BA-PS into the nostrils and collecting the wash in a Petri dish . Samples were stored at −80°C prior to analysis . Titers in nasal wash samples were determined by plaque assay of 10-fold serial dilutions on MDCK cells . Contact transmission experiments were performed with rVN1203 wild-type and rVN1203-derived PB2 mutant viruses . This work was carried out in a USDA and CDC-approved biosafety level 3+ containment laboratory in accordance with institutional biosafety requirements . For each virus , four of eight guinea pigs were inoculated intranasally with 104 PFU . At 24 h p . i . , each infected animal was placed in the same cage with one naïve animal , and animals were co-housed in this way for a total of seven days . Nasal washes were collected from all guinea pigs on days 2 , 4 , 6 and 8 p . i . Samples were stored at −80°C prior to analysis by plaque assay . Animals were euthanized through intraperitoneal injection of 100 mg/kg sodium pentobarbital ( Nembutal; Ovation Pharmaceuticals , Inc . ) on day 8 p . i . These experiments were performed under ambient conditions ( 19–22°C and 30–70% relative humidity ) . Groups of four guinea pigs were inoculated intranasally with ten-fold serial dilutions of each virus stock . At 2 d . p . i . nasal washings were collected from all guinea pigs . The nasal wash samples were analyzed by plaque assay on MDCK cells to determine which guinea pigs were infected . The GPID50 was then calculated by the method of Reed and Muench [30] and expressed in PFU per guinea pig . PB2 segment of A/Panama/2007/99: Genbank accession number DQ487334 . PB2 segment of A/Viet Nam/1203/04: Genbank accession number AY651719 . | To cause a pandemic , an influenza virus must transmit efficiently from human to human . The viral factors that enable person-to-person spread of influenza viruses remain elusive . Using the guinea pig , an animal which we have previously shown to model the human transmission of influenza , we have identified two specific residues in the viral polymerase , at PB2 positions 627 and 701 , that can contribute to efficient transmission . Interestingly , the two adaptive mutations examined act independently to achieve the same phenotype . Furthermore , these residues impact the transmission of both H3N2 and H5N1 subtype influenza viruses in the context of a mammalian host . The common importance of these amino acids to two diverse virus strains—the human-adapted H3N2 and the more avian-like H5N1—indicates that their mutation may be a common route to the development of a transmission-competent virus . These findings suggest one feature that contributes to the making of a pandemic influenza virus . | [
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| 2009 | Transmission of Influenza Virus in a Mammalian Host Is Increased by PB2 Amino Acids 627K or 627E/701N |
DNA repair is essential to maintain genome integrity , and genes with roles in DNA repair are frequently mutated in a variety of human diseases . Repair via homologous recombination typically restores the original DNA sequence without introducing mutations , and a number of genes that are required for homologous recombination DNA double-strand break repair ( HR-DSBR ) have been identified . However , a systematic analysis of this important DNA repair pathway in mammalian cells has not been reported . Here , we describe a genome-scale endoribonuclease-prepared short interfering RNA ( esiRNA ) screen for genes involved in DNA double strand break repair . We report 61 genes that influenced the frequency of HR-DSBR and characterize in detail one of the genes that decreased the frequency of HR-DSBR . We show that the gene KIAA0415 encodes a putative helicase that interacts with SPG11 and SPG15 , two proteins mutated in hereditary spastic paraplegia ( HSP ) . We identify mutations in HSP patients , discovering KIAA0415/SPG48 as a novel HSP-associated gene , and show that a KIAA0415/SPG48 mutant cell line is more sensitive to DNA damaging drugs . We present the first genome-scale survey of HR-DSBR in mammalian cells providing a dataset that should accelerate the discovery of novel genes with roles in DNA repair and associated medical conditions . The discovery that proteins forming a novel protein complex are required for efficient HR-DSBR and are mutated in patients suffering from HSP suggests a link between HSP and DNA repair .
Mutations in DNA repair genes are associated with different diseases and disorders including cancer [1] , accelerated aging [2] , and neuronal degeneration [3] . Neurons appear to be particularly vulnerable to mutations in DNA repair genes , possibly due to the lack of proliferation and high oxidative stress within these cells . As a consequence , several neurological diseases have been linked to defects in DNA repair such as Ataxia-telangiectasia [4] , Ataxia-telangiectasia-like disorder [5] , Seckel syndrome [6] , Nijmegen breakage syndrome [7] , and Charcot-Marie-Tooth syndrome [8] . A particularly dangerous DNA lesion for a cell is a double strand break ( DSB ) , in which two strands of the DNA are broken in close proximity to one another [9] , [10] . DSBs are repaired mainly via two parallel pathways: homologous recombination and nonhomologous end joining ( NHEJ ) . Repair via homologous recombination typically restores the genetic information , whereas repair via NHEJ often leads to mutations [10] , [11] . Recently , several RNAi screens have addressed different aspects of mammalian DNA repair , such as increased sensitivity towards PARP inhibition [12] , increased sensitivity towards cisplatin [13] , accumulation of 53BP1 foci [14] , [15] , or altered phosphorylation of the histone variant H2AX [8] . These screens have greatly enhanced our understanding of human DNA repair processes and delivered a number of novel genes implicated in various aspects of DNA repair . Here , we report a genome-scale RNAi screen for genes implicated in homologous recombination-mediated DSB repair , uncovering a variety of known and so far uncharacterized genes implicated in this process . In this work , we mine this screen employing a structural bioinformatics approach and identify KIAA0415/SPG48 as a putative helicase that is associated with hereditary spastic paraplegia ( HSP ) .
For a comprehensive search of genes associated with DNA DSB repair , we performed a genome-scale RNAi screen , utilizing an endoribonuclease-prepared short interfering RNA ( esiRNA ) library [16] and employing the well-established DR-GFP assay [17] . First , a stable HeLa cell line with two non-functional GFP alleles was generated , in which GFP expression is efficiently activated only after HR-DSBR ( Figure 1A ) . We then tested the robustness of the assay by co-transfection of these cells with the I-SceI expression plasmid and an esiRNA targeting Rad51 , which is an essential factor for the early stages of homologous pairing and strand exchange [18] . Depletion of Rad51 resulted in a marked reduction of GFP positive cells , and comparisons to negative control transfected cells suggested a high dynamic range for candidate factors influencing HR-DSBR ( Figure 1B and histograms Figure 1C ) . The RNAi screen was carried out in duplicate in 384-well plates by co-transfection of an I-SceI encoding plasmid with the individual esiRNAs targeting over 16 , 000 human genes [16] . The percentage of GFP positive cells was determined by high throughput FACS , providing a sensitive readout for esiRNAs influencing the frequency of HR-DSBR ( Figure 1C ) . Knockdown of 228 and 141 transcripts significantly decreased or increased the percentage of GFP positive cells , respectively ( Figure 1D , Table S1 ) . Among the strongest knockdowns affecting HR-DSBR were genes with well-characterized roles in DNA repair such as Rad51 , BRCA1 , and SHFM1 . Gene ontology enrichment analysis of the candidates revealed a 5-fold enrichment for genes reported to be implicated in DNA repair ( Figure 1E ) , confirming that the screen was efficient . To validate the candidate hits we examined their expression in HeLa cells and resynthesized all esiRNAs for the genes that were expressed . We also generated a second , independent , and non-overlapping esiRNA for these genes and tested all esiRNAs again in the DR-GFP assay in multiple replicates . Using stringent selection criteria ( see Online Methods ) , 45 genes decreased the frequency of homologous recombination , while 17 genes increased it with two independent silencing triggers ( Table 1 ) . To further narrow down the list of these 62 candidates , we tested the esiRNAs for their impact on intracellular GFP levels . EsiRNAs that influence GFP levels , for example by targeting a transcriptional activator for GFP expression , could score in the DR-GFP assay and contaminate the hit list . We therefore transfected the esiRNAs into GFP expressing HeLa cells and assayed GFP levels by FACS . EsiRNAs targeting MKNK2 reduced GFP levels in these cells . Therefore , this gene was excluded from further analysis , reducing the final hit list to 61 genes ( Table 1 ) . The effectiveness of this stringent validation was monitored again by gene ontology enrichment analysis , with an enrichment of now 20-fold for genes annotated in the category DNA repair ( Figure 1E ) . Silencing of 17 genes significantly increased the number of GFP positive cells in the DR-GFP assay . Hence , the knockdown of these genes promoted HR-DSBR , which might be of interest for several biological applications such as increasing the targeting efficiency of genes by homologous recombination [19] . Different reasons might account for the increased number of GFP positive cells observed . One possibility is that the knockdown led to an inhibition of the NHEJ pathway , thereby shifting the ratio of the two possible pathways toward repair via HR . Support for this reasoning comes from experiments in yeast and flies , where the knockout of DNA ligase IV , a gene that is required for NHEJ [20] , significantly increased gene targeting by homologous recombination [21] , [22] . Interestingly , the knockdown of human Lig4 resulted in a striking increase in GFP positive cells in the DR-GFP assay ( Table 1 ) , suggesting that inhibition of the NHEJ pathway can increase the frequency of HR-DSBR also in mammalian cells . This idea is further supported by inspection of other known NHEJ proteins , including XRCC4 , XRCC5 , XRCC6 , PRKDC , and DCLRE1C [23] , [24] . Knockdown of all of these proteins increased the frequency of homologous recombination in the DR-GFP assay ( Table S1 ) . Hence , we speculate that other genes that increased the number of GFP positive cells might be implicated in the NHEJ pathway and that knockdown of these genes could enhance gene targeting by homologous recombination in mammalian cells . The list of genes that decreased the frequency of HR-DSBR was enriched for proteins with well-defined roles in HR-DSBR , such as Rad51 and BRCA1 . In addition , genes , such as E2F1 , that more indirectly influence HR-DSBR were also identified in the screen . E2F1 is involved in cell cycle and apoptosis regulation after DNA damage [25] and has recently been implicated in transcriptional regulation of Rad51 and BRCA1 [26] , possibly explaining why the knockdown of E2F1 scored in our screen . Interestingly , the assay also uncovered a number of genes that have roles in DNA repair processes other than HR-DSBR , such as XPC , which has a role in nucleotide excision repair ( NER ) [27] , and the base excision repair ( BER ) DNA helicase RECQL4 [28] . However , a polymorphism in the XPC gene has recently been shown to correlate with bleomycin-induced chromosomal aberrations [29] , and RECQL4 has been reported to coincide with foci formed by Rad51 after induction of DSBs [30] , suggesting possible links between the different DNA repair pathways . Finally , the gene list is enriched for proteasome subunits , including PSMD4 , PSMD1 , PSMD14 , and SHFM1 . Treatment with proteasome inhibitors has been shown to specifically suppress HR-DSBR possibly because of the lack of proteasome-mediated degradation of chromatin bound proteins blocking the access to the lesion [31] , [32] . Moreover , SHFM1 has been shown to be required for Rad51 foci formation upon DNA damage [33] , implicating a more direct role of this proteasome subunit in HR-DSBR and possibly providing an explanation why SHFM1 was one of the strongest hits in our screen . Based on these results we were encouraged to investigate further the knockdowns that decreased the number of GFP positive cells in the DR-GFP assay . To characterize in detail the 44 knockdowns that decreased the frequency of HR-DSBR , we performed several additional assays . First , we tested the influence on cell viability of these esiRNAs in HeLa cells . Thirteen esiRNAs considerably decreased cell numbers and were excluded from further analyses ( Table 1 ) . Second , we performed mitomycin C ( MMC ) , cisplatin , and ionizing radiation ( IR ) sensitivity assays . MMC predominantly causes interstrand cross-links , which result , among other things , in DSBs due to a block of replication forks [34] . Cisplatin damages DNA in a different way and generates predominantly intrastrand cross-links [35] , whereas IR gives rise to a variety of DNA lesions [36] . Cells with impaired DNA repair pathways might be more sensitive to these treatments , which should manifest in reduced cell viability . Twenty-four hours post-transfection of the esiRNAs , the cells were treated for 1 h with MMC , cisplatin , or exposed to IR and cells were counted after an additional incubation for 48 h . A number of knockdowns increased the sensitivity towards one or more treatments , substantiating a role of these genes in DNA repair , with some of the knockdowns showing an effect for one , but not the other treatment ( Figure 2A , Table 1 ) . For instance , the knockdown of RBBP8 ( also known as CtIP ) , which promotes DNA end resection [37] , did not cause increased sensitivity towards cisplatin . However , substantially less cells were counted after MMC treatment , indicating that RBBP8 depletion primarily sensitized the cells against this drug . Third , we employed a gamma-H2AX removal assay . The histone H2AX is phosphorylated on serine 139 predominantly by ATM/ATR [38] , [39] at sites of DSBs until the lesion is repaired . After successful DNA repair this phosphorylation is reverted by the phosphatase PP2A [40] . Several knockdowns resulted in extended time before gamma-H2AX was removed from irradiated cells ( Figure 2B , Table 1 ) , suggesting a delay in DSBR , and potentially explaining the observed reduction of GFP positive cells in the DR-GFP assay . Surprisingly , a few knockdowns showed overall reduced numbers of gamma-H2AX positive cells , or accelerated removal of gamma-H2AX after irradiation . For example , depletion of ARHGEF1 resulted in a reduced number of gamma-H2AX positive cells 1 h after irradiation ( Figure 2B ) . Potentially , this Rho guanine nucleotide exchange factor [41] is required for efficient recruitment of H2AX phosphorylation factors , which ultimately translates into less efficient HR-DSBR . In contrast , the knockdown of FIZ1 , a Flt3 interacting zinc finger protein [42] , resulted in similar numbers of gamma-H2AX positive cells 1 h after irradiation in comparison to the control transfected cells . However , gamma-H2AX was more rapidly removed in these cells ( Figure 2B ) , potentially compromising effective DSBR . Taken together , these results validate the effectiveness of our screen and serve as an initial classification of molecular pathways for a number of genes that can be explored in future studies . For this work , we mined the screen by performing bioinformatics analyses on the uncharacterized sequences in an attempt to reveal possible molecular functions . KIAA0415 emerged as particularly notable . By applying threading techniques ( see Online Methods for details ) , we identified potential structural homologies of KIAA0415 with proteins belonging to the fold family “P-loop containing nucleoside triphosphate hydrolases” ( SCOP c . 37; Table S2 ) . This fold family contains the so-called “helicase C domain” ( PF00271 ) formed by a tandem repeat of two RecA-like domains ( Tandem AAA-ATPase superfamily ) . Top scoring sequence-to-structure alignments were obtained with the KIAA0415 sequence and the structure of the helicase C domains of SF2 helicases that are involved in DNA repair such as UvrB , Hel308 , RecG , and TRCF ( Figure 3 ) . Visual inspection of the generated 3D model ( see Online Methods ) confirmed the existence of potential SF2 helicase motifs in KIAA0415 ( Figure S1 ) . Molecular dynamics simulations were used to refine the KIAA0415 model and corroborated its stability and its putative ADP and Mg2+ recognition ( Video S1 , Online Methods ) . These results further support the prediction of a helicase-like domain within KIAA0415 and substantiate the conservation in 3D of residues important for its function as a putative SF2 helicase . Based on these results , we decided to further elucidate possible molecular functions of KIAA0415 . We first tested the potency of the employed KIAA0415 esiRNAs in more detail . Both esiRNAs efficiently depleted KIAA0415 mRNA transcripts ( Figure 4A ) and protein ( Figure 4B ) . We then repeated the DR-GFP assay in the HeLa reporter cell line and found 3 . 4 ( esiRNA1 ) and 4 . 3 ( esiRNA2 ) fold decrease in GFP positive cells in comparison to controls , suggesting reduced frequencies of homologous recombination ( Figure 4C ) . We examined the expression levels of I-SceI after the knockdowns to rule out the possibility that I-SceI-generated DSBs are compromised ( Figure S2 ) . To exclude a possible cell-type specific effect , we also tested the knockdowns in a different cell line . U2OS cells carrying a single insertion site of the DR-GFP construct showed a similar reduction of GFP positive cells upon KIAA0415 knockdown ( Figure 4D ) , indicating that this effect was not cell line specific . Finally , we excluded possible off-target effects by performing cross-species RNAi rescue experiments [43] . Stable expression of mouse KIAA0415 in the human DR-GFP cell line rendered this cell line resistant to the human esiRNAs , authenticating a role of KIAA0415 in HR-DSBR ( Figure 4E ) . In summary , these results suggest that KIAA0415 is a novel putative SF2 helicase required for efficient HR-DSBR . To further characterize KIAA0415 , we tagged the gene on a bacterial artificial chromosome ( BAC ) applying the TransgeneOmics approach [44] . This method allows expression of tagged proteins from its native promoter in its genomic context , and therefore , the protein is expressed near physiological levels [44] , [45] . C- and N-terminally tagged KIAA0415 was successfully cloned and expressed in HeLa cells . The fusion protein showed disperse , cytoplasmic , and nuclear localization , which did not change considerably upon induction of DNA damage ( unpublished data ) . Immunoblotting of cell extracts revealed two major protein bands , possibly reflecting two KIAA0415 isoforms ( Figure 4B ) . Cell fractionations showed that the shorter isoform was predominantly nuclear , whereas the longer form was found mostly in the cytoplasm ( Figure 4F ) . Immunoprecipitation experiments followed by spectrometric identification of co-isolated proteins revealed interactions of KIAA0415-LAP with SPG11 , SPG15 , C20orf29 , and DKFZp761E198 ( Figure 5A , B and Table S3 ) . In order to validate these interactions we generated cell lines expressing C-terminally tagged SPG11 , SPG15 , and DKFZp761E198 again using the TransgeneOmics approach . Reciprocal immunoprecipitation experiments followed by mass spectrometry analyses of in-gel and in-solution digests confirmed the existence of a protein complex , which consists of at least five core proteins: KIAA0415 , SPG11 , SPG15 , C20orf29 , and DKFZp761E198 ( Figure 5B and Table S3 ) . In order to test whether protein interaction partners of KIAA0415 would also affect HR-DSBR , we tested esiRNAs targeting these genes in the DR-GFP assay . Interestingly , significant reduction of GFP positive cells were observed upon silencing of C20orf29 and SPG15 with two independent esiRNAs ( Figure 6 ) , suggesting that these proteins are also required for efficient HR-DSBR . Knockdown of SPG11 and DKFZp761E198 , however , did no have an effect on the percentage of GFP positive cells . Together , these experiments reveal a novel protein complex , which at least in part is required for efficient HR-DSBR . The KIAA0415 interaction partners SPG11 and SPG15 , also known as spatacsin and spastizin , are encoded by two genes that have been associated with hereditary spastic paraplegia with thin corpus callosum ( HSP-TCC ) [46] , [47] . HSP-TCC is a subset of hereditary spastic paraplegia ( HSP ) , which are inherited neurological disorders caused by the degeneration of the cortico-spinal tracts leading to lower-limb spasticity . HSP is a highly heterogeneous condition with at least 46 loci identified so far [48] . A potential interaction of SPG11 and SPG15 has been suggested on the basis of similar neurological symptoms [49] , however a physical interaction of SPG11 and SPG15 has not been reported thus far . Because of the physical interaction of KIAA0415 with these two proteins encoded by genes associated with HSP , we decided to investigate if any unexplained HSP cases could be linked to mutations in KIAA0415 . Direct sequencing of KIAA0415 in 166 unrelated HSP patients , including 38 and 64 cases with a recessive or dominant inheritance pattern and 64 sporadic cases ( see Online Methods ) , identified 7 known and 15 new variants , respectively . Most of these variants were not considered causative , because they did not affect protein sequence , were not predicted to alter correct splicing , or were also found frequently in control samples ( Table S4 ) . However , one of these identified variants led to a premature stop codon at position 527 ( c . 1413_1426del14/p . L471LfsX56 , Table S4 ) and was absent in 158 Caucasian and 84 North-African control chromosomes . The mutation was heterozygous and no other mutation or variant was found in the coding sequence of KIAA0415 or in its regulatory regions in this apparently sporadic patient ( FSP-70-1 ) . No other subjects from the family were available for sampling and no copy number variations were detected on chromosome 7 in the affected patient ( unpublished data ) , but small heterozygous rearrangements or mutations in uncovered regions ( unknown exons or introns ) might have escaped detection . More interestingly , we also found a homozygous mutation in two French siblings ( FSP-083 ) , which was not detected in 156 Caucasian and 242 North-African control chromosomes . In these patients , a complex indel in exon 2 ( c . [80_83del4;79_84ins22] , Figure 7C ) generates a frameshift and a stop codon following amino-acid 29 ( p . R27LfsX3 , Figure 7A ) . Interestingly , the insertion is an imperfect quadruplication of the sequence CTGTAA ( A ) , suggesting DNA polymerase slippage during DNA synthesis as the mechanism for introduction of the mutation . Both affected patients presented with progressive spastic paraplegia associated with urinary incontinence since age 50 and 49 , respectively . Cerebral MRI was normal but spinal hyperintensities at C3-C4 and C7 were observed in one . Both parents died at the age of 72 and 77 , respectively , of non neurological causes . They originated from two neighbouring villages , but there was no known consanguinity . However , the analysis of three close microsatellite markers ( D7S531 , D7S517 , and D7S1492 ) and the loss of heterozygosity ( LOH ) search using CYTO_12 microarrays ( unpublished data ) confirmed that the region is homozygous in both affected patients ( Figure 7B ) . To further substantiate a role of KIAA0415 in DNA repair , we compared drug sensitivity in lymphoblast cell lines established from a patient carrying the KIAA0415 mutation ( FSP-083-4 ) and a patient carrying a mutation in SPG15 ( FSP-708-22 [50] ) to control lymphoblast cell lines . Strikingly , the KIAA0415 mutant cells were significantly ( p<0 . 05 ) more sensitive to MMC and bleomycin treatments compared to any of the control cell lines ( Figure 8 ) . In addition , also the SPG15 cell line showed a mild sensitivity to these drugs , phenocopying the results observed in HeLa and U2OS cells . Taken together , these experiments identify KIAA0415 as a novel gene , which is mutated in patients with HSP , and implicate a link between HSP and DNA repair .
Using a well-characterized esiRNA library [16] we performed a genome-scale RNAi screen and identified 61 genes that reproducibly decreased or increased the frequency of DNA repair in an assay for homologous recombination [17] . Secondary assays for processes relevant to DNA repair corroborated many of the initial findings . Hence , we provide a dataset that should accelerate the discovery of novel genes with roles in DNA repair and associated medical conditions . Eighteen out of the 61 candidate genes have been described in other large-scale mammalian DNA repair studies [8] , [13] , [15] , [51] , demonstrating the effectiveness of our screen , but also highlighting that the use of different assays can uncover novel players . Hence , we predict that the development of alternative DNA repair assays for RNAi screens will reveal additional genes implicated in DNA repair . For our screen we co-transfected the “DNA damaging reagent , ” I-SceI , together with the esiRNA silencing triggers . Hence , proteins with long half-lives may have been missed in this screen . Assays in which the DSB is introduced some time after the cells were transfected with the silencing triggers could uncover additional genes playing a role during DNA repair . To prioritize the molecular investigation of the uncharacterized proteins identified in the screen , we employed a structural bioinformatics approach . Based on the prediction that KIAA0415 represents a novel putative helicase we investigated this gene in more detail . Tagging of the gene using the TransgeneOmics approach revealed nuclear as well as cytoplasmic localization and physical interaction with at least four proteins . Investigations of the interaction partners showed that at least two of these proteins are also required for efficient HR-DSBR . Possibly , these proteins form a complex that is required for efficient HR-DSBR . Consequently , the complex would lose its activity when one of the three proteins is depleted . Two of the interaction partners of KIAA0415 are encoded by genes that are associated with spastic paraplegia . This result prompted us to examine whether KIAA0415 mutations can explain spasticity in patient samples not linked with mutations in any of the known spastic paraplegia genes . We report a homozygous mutation in KIAA0415 , responsible for the spastic paraplegia observed in two siblings . Hence , we identify KIAA0415 as a novel spastic paraplegia associated gene . Based on this finding , we propose to rename KIAA0415 to SPG48 according to the HUGO nomenclature . The fact that three proteins that form a protein complex result in similar phenotypic consequences argues that the whole complex is exerting an important function , which is disturbed when one of the proteins is missing or non-functional . It will therefore be interesting to investigate the remaining interaction partners , C20orf29 and DKFZp761E198 , for possible mutations in HSP patients , even though they do not map to known HSP loci [49] . Although only demonstrated for one case , cell lines derived from a patient carrying a SPG48 mutation were more sensitive to DNA damaging drugs than control cells , corroborating a role of SPG48 in DNA repair . Unfortunately , material from other patients with SPG48 mutations was not available . However , we propose that in the future HSP patients be screened for mutations in SPG48 and that cells from these individuals be checked for DNA repair defects . Genes mutated in HSP have been associated with several biological functions , including intracellular transport , axonal pathfinding , mitochondrial functions , cholesterol metabolism , myelin formation/stability , and chaperonin activity [48] . Based on our findings , we propose that HSP might also be a result of impaired DNA repair , adding HSP to the growing list of neurodegenerative diseases caused by DNA repair deficiencies [4] , [5] , [7] , [8] . In agreement with this hypothesis , SPG11 has recently been reported to be phosphorylated upon DNA damage by ATM or ATR [51] . Whether SPG48 ( and its associated proteins ) is a direct component of the HR-DSBR pathway or more indirectly linked to DNA repair remains to be established . Biochemical analysis of the putative helicase domain of SPG48 appears to be an attractive entry point into gaining mechanistic insights into the DNA repair function ( s ) of SPG48 . The technological advances in RNAi screening have increased the speed at which phenotypic data can be obtained . However , interpretation of the resulting genotype-phenotype relationships remains challenging , and approaches that help to decipher the screening data are highly desirable . Approaches that analyze phenotypic data from unrelated RNAi screens and that combine phenotypic- with localization- and proteomic data [52] , [53] have been used successfully to bootstrap phenotype-to-function analyses . Here , we explored the possibility of combining RNAi screening data with structural bioinformatics approaches . The obtained results demonstrate that this combination generates valuable information , which helps to prioritize the follow-up studies of uncharacterized candidate genes . We envision that the design of an automatic pipeline to analyze possible structural and functional features beyond protein sequence similarities will further accelerate the characterization of genes identified in RNAi screens . In the future , it will be important to combine the different “omics” and bioinformatics approaches to understand DNA repair at a systems level and to further accelerate the discovery of genes relevant to human pathology .
Ten µg of the DR-GFP construct [17] were transfected into 2 . 5×106 HeLa cells using 12 µl Enhancer ( Qiagen ) and 14 µl Effectene ( Qiagen ) according to the manufacturer's protocol . Stable cell lines were selected with 3 µg/ml puromycin ( Sigma-Aldrich ) and single clones were obtained by FACS sorting on a FACSAria ( BD Biosciences ) . Colonies derived from individual clones were expanded and tested for their behaviour after transfection with a plasmid encoding the I-SceI endonuclease . A cell line with virtually no GFP positive cells before I-SceI treatment and high number of GFP positive cells after I-SceI treatment was chosen for the screen . Cells were grown on glass coverslips and fixed with 3% paraformaldehyde ( PFA ) as described previously [44] . Immunofluorescence stainings were performed with a primary mouse anti-GFP antibody ( Roche Diagnostics , 1∶4 , 000 dilution ) and a secondary donkey anti-mouse antibody conjugated to Alexa488 ( Molecular Probes , 1∶500 dilution ) . Genomic DNA was counterstained with ProLong Gold antifade reagent containing DAPI ( Invitrogen ) . Images were acquired on an Axioplan II Microscope ( Zeiss ) operated through MetaMorph ( Molecular Devices ) . Western blot analysis was performed as described previously [52] . In this study the following primary antibodies were used: mouse anti-GFP ( Roche Diagnostics , 1∶4 , 000 dilution ) , mouse anti-DM1alpha tubulin ( MPI-CBG Antibody Facility , 1∶50 , 000 dilution ) , and rabbit anti-Histone H3 ( Abcam 1∶25 , 000 dilution ) . The esiRNA library employed has been described elsewhere [16] , [54] . For the screen the I-SceI expression plasmid [17] was co-transfected with individual esiRNAs in an arrayed fashion . Briefly , 50 ng of each esiRNA in 5 µl TE Buffer was pipetted into 384-well tissue culture plates ( BD Biosciences ) and stored at −20°C . Each plate contained four esiRNAs against Rad51 as positive control ( at positions C3 , C21 , M5 , M18 ) and 12 esiRNAs targeting renilla luciferase ( Rluc ) as negative control ( at positions C4 , D3 , D4 , C22 , D21 , D22 , M6 , N5 , N6 , M19 , N18 , N19 as shown in Figure 1C ) . Using a multi-well dispenser ( WellMate , Thermo Scientific ) a mixture of the I-SceI plasmid ( 12 . 75 ng/well ) and the Enhancer ( 0 . 142 µl/well ) in 5 µl/well EC Buffer ( Qiagen ) was dispensed and briefly spun in a Heraeus Multifuge 4KR ( Thermo Electron Corporation ) . After incubation for 5 min , Effectene ( 0 . 12 µl/well ) diluted in 5 µl/well EC Buffer was added to each well and plates were briefly spun again . The transfection mixture was incubated for 5 min to allow complex formation . In the meantime HeLa cells carrying the DR-GFP reporter construct were harvested , counted , and diluted to a final concentration of 40 cells/µl in DMEM ( Invitrogen ) containing 12 . 5% Fetal Bovine Serum ( Invitrogen ) . Fifty µl of the cell suspension was added to each well using a multi-well dispenser ( Wellmate , Thermo Scientific ) . In order to prevent evaporation , plates were sealed with breathable plate sealing foils ( Corning ) and incubated in a tissue culture incubator at 37°C in 5% CO2 . The medium was replaced 24 h post-transfection . After another 72 h cells were washed with PBS and detached by adding 15 µl/well trypsin/EDTA ( Invitrogen ) . After 25 min cells were fixed by addition of 15 µl/well 3% PFA and stored no longer than 48 h at 4°C . Cells were assayed with a FACSCalibur ( BD Biosciences ) equipped with a High Throughput Sampler ( BD Biosciences ) . Data were acquired and analyzed using CellQuest Pro ( BD Biosciences ) . Z-scores were calculated for the percentages of GFP positive cells using the following equation: z = ( x−μ ) σ−1 with: x − percentage of GFP positive cells; μ − mean percentage of GFP positive cells; σ − standard deviation of the number of GFP positive cells . In the primary screen mean and standard deviations were calculated separately for each plate over all samples on the plate excluding controls . Z-scores were calculated for each esiRNA and averaged for duplicates . The transfection of esiRNA targeting Rad51 was used as positive control and as reference for the assay performance . esiRNAs for which the average z-score was below −2 or over 2 were considered as primary hits ( Table S1 ) . In further validation experiments , the z-scores were calculated based on the mean and standard deviation of the negative control ( Rluc transfection ) . EsiRNAs for which the average z-score for 4 replicates were below −2 or over 2 for one esiRNA and below −1 . 5 or over 1 . 5 for a second esiRNA were classified as validated hits . Primer sequences for utilized esiRNAs are presented in Table S5 . Gene enrichment analysis was performed using the Panther Analysis Tools ( http://www . pantherdb . org/tools/ ) . Fifteen ng of each esiRNA diluted in 5 µl Opti-MEM ( Invitrogen ) was pipetted in 384-well tissue culture plates ( Greiner ) . 0 . 2 µl Oligofectamine ( Invitrogen ) was diluted with 4 . 8 µl Opti-MEM , incubated for 5 min and pipetted to each well of the plate . The mixtures were incubated for 20 min to allow complex formation and 1 , 000 cells in 40 µl medium were added to each well . Twenty-four hour post-transfection cisplatin ( 100 ng/ml ) or MMC ( 100 ng/ml ) were added for 1 h or cells were exposed to 10 Gy IR . Cells were washed carefully with PBS and new medium was added . After additional 48 h cells were fixed with −20°C cold methanol for 20 min , washed twice with PBS , and blocked with Blocking Buffer ( 0 . 2% Gelatin from cold water fish skin ( Sigma-Aldrich Chemie ) in PBS ) for 5 min . Cell nuclei were stained with DAPI ( 1 µg/ml ) and cells were preserved with 0 . 02% sodium azide in PBS . Images were acquired on an Olympus IX81 microscope ( Olympus ) and cell numbers were determined using the Scan∧R Analysis software ( Olympus ) . Every knockdown was repeated 3 times . Cell numbers with and without DNA damaging agents were compared to Rluc transfections . HeLa cells were treated with 10 Gy IR 48 h post esiRNA transfection and fixed 1 h or 6 h later . Cells were stained with a phospho-H2AX antibody ( clone JBW301 , Upstate Biotechnology , 1∶600 dilution ) and with donkey anti-mouse TxRed conjugated antibody ( Molecular Probes , 1∶400 dilution ) . DNA was stained with DAPI ( 1 µg/ml ) . Cells were preserved with 0 . 02% sodium azide in PBS and images were acquired on an Olympus IX81 microscope and analyzed by Scan∧R Analysis software ( Olympus ) . Every knockdown was repeated 3 times . Percentages of gammaH2AX positive cells were compared to Rluc transfections . p values were calculated by Student's t test . Sequence-based analysis ( Blast ) failed to identify any statistically significant sequence homology between KIAA0415 and any previously characterized protein . Fold recognition techniques were applied to search for potential structural homologies of KIAA0415 with known protein structures . The threading algorithm ProHit ( ProCeryon Biosciences ) was used to search for structural resemblance of the uncharacterized KIAA0415 sequence with protein structures of the Brookhaven Protein Databank ( PDB ) . Threading calculations were performed with parameters and scoring functions as previously published [55] . A fold library consisting of 19 . 961 protein chains representing the PDB at 95% sequence identity was used . Three-dimensional ( 3D ) models for KIAA0415 were generated by threading its sequence through each fold of the fold library . Inspection of fold coverage , gaps position and content in the sequence-to-structure alignments obtained , together with the analysis of the secondary structure prediction obtained for KIAA0415 by PredictProtein ( http://www . predictprotein . org/ ) were used to discard possible false positives in top scoring folds . A three-dimensional model of KIAA0415 was built based on the threading alignments obtained with high confidence predicted folds and four template structures ( PDBId: 2d7d , 2p6r , 1gm5 , and 2eyq ) by using Modeler in Discovery Studio ( Accelrys v1 . 7 ) . Manual docking of ADP and Mg2+ onto the resulting KIAA0415 3D model was done based on the X-ray structures of 2d7d and 1gm5 . Refinement of the obtained complex was done with AMBER 10 [56] . A first step of energy-minimization by 1 , 000 cycles of steepest descent and 500 cycles of conjugate gradient with harmonic force restraints on protein atoms was followed by 3 , 000 cycles of steepest descent and 3 , 000 cycles of conjugate gradient without constraints . The system was then heated from 0 to 300K for 10 ps . An equilibration step of 30 ps at 300K was followed by a 10 ns MD productive run . The ff03 force field , periodic boundary conditions at constant pressure with Langevin temperature coupling and Berendsen pressure coupling , TIP3P explicit solvent , counterions , 8 Å cut-off for non-bonded interactions , and the SHAKE algorithm for hydrogens were used . BAC recombineering and the generation of BAC-transgenic cell lines was performed as described previously [44] , [57] . A list of all BACs and primers used in this study is provided in Table S6 . A goat anti-GFP antibody ( MPI-CBG Antibody Facility ) immobilized on G-protein sepharose ( GE Healthcare ) or GFP-Trap ( Chromotek ) were used for immunoprecipitation [44] , [52] . Glycine eluated KIAA0415-LAP and SPG11-LAP complexes were analyzed on silver stained SDS PAGE . Excised slices were in-gel digested and analyzed by nanoLC-MS/MS on a LTQ ( Thermo Fisher Scientific ) as previously reported [58] , [59] . Glycine eluates from KIAA0415-LAP , KIAA0415-NFLAP , SPG11-LAP , and DKFZp761E198-LAP immunopurifications were used for in-solution digestion and analyzed by shotgun-LC-MS/MS on a LTQ Orbitrap ( Thermo Fisher Scientific ) [60] . Proteins identified in more than 15% of 193 independent immunoprecipitations performed in ongoing collaborations projects from unrelated baits were considered common backgrounds and further excluded . Cell fractionation was performed with the commercially available ProteoExtract kit ( Novagene , Merck Biosciences ) according to the manufacturer's protocol . We selected 166 unrelated index cases with spastic paraplegia diagnosed according to the Harding's criteria [61]; 109 had a pure form of the disease and 57 had a complex form partially overlapping with the SPG11 typical phenotype . They included 64 index patients from families with dominant inheritance ( mean age at onset: 27 . 0±16 . 6 y ) , 38 index patients with inheritance compatible with an autosomal recessive trait ( mean age at onset: 25 . 6±19 . 9 y ) , and 64 patients with no family history of the disease ( mean age at onset: 31 . 2±16 . 9 y ) . Most patients were French ( n = 137 ) while the remaining patients originated from other countries in Europe ( n = 16 ) , North-Africa ( n = 8 ) , or elsewhere ( n = 5 ) . This study was approved by the local Bioethics committee ( approval No . 03-12-07 of the Comité Consultatif pour la Protection des Personnes et la Recherche Biomédicale Paris-Necker to Drs A . Durr and A . Brice ) . Informed and written consents were signed by all participating members of the families before blood samples were collected for DNA extraction . All clinical evaluations were performed according to a protocol established by the European and Mediterranean network for spinocerebellar degenerations ( SPATAX , coordinator: Dr . A . Durr ) that included: a full medical history and examination , estimation of the age at onset by the patient , observation of additional neurological signs , electroneuromyographic ( ENMG ) studies , and brain MRI , when possible . Disability was assessed on a 7-point scale as previously described [62] , [63] . Mutations in SPAST , SPG3 , SPG6 , and SPG42 were previously excluded in most of the index patients with dominant transmission by direct sequencing and multiplex ligation-dependent probe amplification in the case of SPAST and SPG3 [64] and unpublished data . Among autosomal recessive and sporadic patients , mutations in the CYP7B1/SPG5 gene were excluded in most patients [63] while SPG11 and SPG15 mutations have been excluded in all complex autosomal recessive forms [50] . All coding exons of the gene KIAA0415 ( Ensembl gene ID: ENSG00000164917 ) and its splice junctions were amplified by PCR on a Thermocycler 9700 ( Applied Biosystems , Foster City , CA ) using specific primers ( see Table S7 ) . 3 . 1 Kb on the 3′ and 1 . 5 Kb on the 5′-UTRs were also sequenced in patients with an autosomal recessive transmission carrying a single heterozygote variant . The amplicons were sequenced in both directions using the BIGDYE V3 chemistry in an ABI3730 automated sequencer ( Applied Biosystems ) as recommended by the supplier . The seqscape v2 . 6 ( Applied Biosystems ) software was used to highlight nucleotide variations in comparison to the normal consensus sequence of both genes . In family FSP70 , the mutation was confirmed after subcloning of PCR products into the pcDNA3 . 1/V5-His TOPO TA vector using TOP10 bacteria according to the manufacturer's recommendations ( Invitrogen ) and direct sequencing of at least 5 independent clones of both alleles . After identification of a variant , reamplification and resequencing was systematically performed . Segregation of the mutations/polymorphisms with the disease was verified by direct sequencing in additional family members whose DNA samples were available . In addition , 79 and 121 unrelated healthy Caucasian and North-African subjects were screened to evaluate the frequency of new nucleotide changes . In order to estimate evolutionary conservation , gene sequences of different species were downloaded from the Ensembl genome browser ( www . ensembl . org ) and aligned using the ClustalW algorithm ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) . All variants were systematically tested for their effect on splicing at: http://rulai . cshl . edu/cgi-bin/tools/ESE3/esefinder . cgi , http://rulai . cshl . edu/new_alt_exon_db2/HTML/score . html , http://www . fruitfly . org/seq_tools/splice . html . Predicted effects of missense changes were tested using SIFT and POLYPHEN at http://sift . jcvi . org/www/SIFT_seq_submit2 . html and http://genetics . bwh . harvard . edu/pph/ . Cell lines were obtained from patients by infection with Epstein-Barr-Virus ( Table S8 ) . Lymphoblast were cultured in RPMI medium supplemented with 1% Pen/Strep , 2 mM L-Glutamine , 10 mM Hepes , 1% Fungizone , and 20% FCS . 200 . 000 cells were plated in 6-well plates and cultured without or with 10 ng/ml MMC or exposed to 10 ug/ml bleomycin for 1 h . Growth of the cells was monitored daily by counting the trypan blue negative cells using a Countess Automated Cell Counter ( Invitrogen ) . Four days after incubation 100 . 000 cells were stained with the FITC Annexin V Appoptosis Kit II ( BD Biosciences ) followed by FACS ( BD Biosciences ) analyses following the manufacturer's protocol . Experiments were performed two times in duplicates . | All cells in our bodies have to cope with numerous lesions to their DNA . Cells use a battery of genes to repair DNA and maintain genome integrity . Given the importance of an intact genome , it is not surprising that genes with roles in DNA repair are mutated in many human diseases . Here , we present the results of a genome-scale DNA repair screen in human cells and discover 61 genes that have a potential role in this process . We studied in detail a previously uncharacterized gene ( KIAA0415/SPG48 ) and demonstrated its importance for efficient DNA double strand break repair . Further analyses revealed mutations in the SPG48 gene in some patients with hereditary spastic paraplegia ( HSP ) . We showed that SPG48 physically interacts with other HSP proteins and that patient cells are sensitive to DNA damaging drugs . Our data suggest a link between HSP and DNA repair and we propose that HSP patients should be screened for KIAA0415/SPG48 mutations in the future . | [
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| 2010 | A Genome-Scale DNA Repair RNAi Screen Identifies SPG48 as a Novel Gene Associated with Hereditary Spastic Paraplegia |
Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks , which cohere into extended goal-directed activities . Arranging actions hierarchically has well established benefits , allowing behaviors to be represented efficiently by the brain , and allowing solutions to new tasks to be discovered easily . However , these payoffs depend on the particular way in which actions are organized into a hierarchy , the specific way in which tasks are carved up into subtasks . We provide a mathematical account for what makes some hierarchies better than others , an account that allows an optimal hierarchy to be identified for any set of tasks . We then present results from four behavioral experiments , suggesting that human learners spontaneously discover optimal action hierarchies .
Since the earliest days of psychology and neuroscience , a core objective within both fields has been to understand the formal structure of behavior [1]–[4] . In pursuing this question , both in humans and in other animals , a crucial and recurring observation has been that behavior displays a hierarchical organization . Simple actions fit together into coherent subtasks , which themselves combine to accomplish higher-level goals [5] , [6] . This kind of tiered or nested structure is readily apparent in our everyday activities: Turning on the stove forms part of boiling water , which in turn forms part of cooking pasta . It has also been quantified in detailed formal analyses of behavior , both in the laboratory and in the field [7] , [8] . The ubiquity of hierarchical structure in behavior presumably reflects an adaptive benefit . Consistent with this , computational analyses have revealed at least two important advantages that can be gained by organizing behavior hierarchically . First , hierarchical representations of behavior can be more compact or efficient than non-hierarchical ( flat ) representations , allowing complex behaviors to be encoded more economically at the neural level [9] . Second , hierarchical representations of action can facilitate the discovery of new adaptive behaviors , either through learning or through on-line planning [10]–[12] or problem-solving [13]–[16] . An illustration of this latter point is provided in Figure 1 . The example centers on an artificial reinforcement learning agent [17] that navigates from vertex to vertex in the grid shown in panel A . The agent must learn , through trial and error , to move from the start location highlighted in green to a rewarded goal location , highlighted in red . The black data-series in panel B charts the agent's improvement over successive trials . In contrast , the blue data-series tracks learning in a hierarchical reinforcement learning agent [11] , [18] . This agent is furnished with subtask representations or subroutines for navigating to each of the “doorway” locations marked in blue in panel A ( simulation code available online at www . princeton . edu/~matthewb ) . It can thus behave hierarchically , choosing among subroutines that in turn specify concrete , low-level actions . As is clear from the learning curve , the hierarchical agent converges on shortest-path behavior much more quickly than the flat agent . Another way of interpreting this illustrative simulation is in terms of planning . In many models of planning ( e . g . [19] ) , action plans are gradually refined based on a series of internal simulations , in each of which the outcomes of a potential line of behavior are projected . Interpreting the model in this way , the “trials” in Figure 1B correspond to successive internal simulations , and the effect of hierarchy is to reduce planning time . While this example illustrates the point that hierarchy can facilitate the discovery of new adaptive behaviors , there is an important caveat: Not all hierarchies are created equal . The wrong hierarchical representation can actually undermine adaptive behavior . This point is again illustrated in Figure 1B . The orange data-series in the figure tracks the course of learning for a second hierarchical agent . This agent , like the one just considered , is furnished with a set of subroutines . However , here each subroutine involves navigating not to a doorway but into a corner ( one of the locations highlighted in orange in panel A ) . In contrast to the doorway agent , this corner agent learns much more slowly than the flat agent . Obviously , it is not hierarchy per se that facilitates adaptive behavior . It matters very much which specific set of hierarchical representations an agent carries . These observations bring to the surface a fundamental point concerning behavioral hierarchy: While hierarchy can facilitate learning , it also introduces a new learning problem , the problem of discovering beneficial rather than disruptive subtask representations . Computational work in the area of hierarchical reinforcement learning has given rise to a number of approaches aimed at discovering useful behavioral hierarchies , leveraging ideas from information theory , graph theory , and developmental psychology [10] , [11] , [20]–[22] . For example , Simsek and Barto [20] describe a method based on betweenness , a graph centrality metric which measures the fraction of shortest paths that go through each vertex of a graph . They construct what they call an interaction graph , representing possible state transitions , and compute a weighted betweenness metric that depends on the costs associated with each path . Local maxima , which often appear in “bottleneck” states ( described further below ) , represent subgoal locations that can be utilized in hierarchical representations . Van Dijk and Polani [21] take an information theoretic approach and define subgoals as states in which there is a significant change in the amount of relevant goal information , a measure of the amount of information that needs to be maintained about the goal at each step in order to perform well . Still other work has suggested that useful task decompositions might be learned through analyses of the causal structure of the environment , or via curiosity-driven learning mechanisms [22] . However , such work has never directly confronted the key underlying question of what exactly the agent should learn . Given that some hierarchies are better than others , can one specify for any given behavioral domain the best hierarchy overall ? In other words , what would it mean for a behavioral hierarchy to be optimal ? It is this question that we confront in the present work . Our basic proposal is that the optimal hierarchy is one that best facilitates adaptive behavior in the face of new problems . We show how this notion can be made precise using the framework of Bayesian model selection . After presenting the formal framework , we present results from four behavioral experiments suggesting that human learners are able to discover decompositions deemed optimal in this way .
In order to set the stage , we briefly introduce some additional terminology from the reinforcement learning literature . The goal of a reinforcement learning agent is to find a reward maximizing policy , a mapping from states to actions , in an environment obeying certain Markovian dynamics . In particular , it is assumed the environment consists of a set of states , a set of actions , , a transition function and a reward function , where is expectation and is scalar reward . There are several ways of incorporating hierarchy into reinforcement learning; we adopt the options framework approach [18] in this paper . An option may be thought of as a temporally extended action and consists of: an initiation set containing the states from which it may be invoked , a termination function specifying the probability of terminating the option in each state , and a policy . Once invoked , the agent's behavior is controlled by the option-specific policy until it terminates , at which point the higher level policy again takes over . Options may also be nested , resulting in arbitrarily deep hierarchies . In this paper , we will use the terms option and subtask interchangeably . Root-level policy will refer to the policy at the top level ( outside of all options ) , in contrast to option-level or subtask policies . In any optimization problem , the crucial first step is to identify the objective . In the present case , this means asking: What exactly should an optimal hierarchy optimize ? The rooms example in Figure 1 suggests a sensible answer to this question: An optimal hierarchy should maximize the efficiency with which an agent can discover new reward-maximizing behaviors . To make good on this idea , a method is needed for scoring or ranking candidate hierarchies on this property . In order to solve this problem , we reframe it in terms of Bayesian model selection , where a set of candidate models are compared in their ability to account for a set of target data [23] . In the present case , the set of candidate models comprises all possible combinations of options with which the agent can be furnished . The data , in turn , are a target set of optimal behaviors ( i . e . policies , a series of state–action pairs ) representing the solutions to an ensemble of tasks faced by the candidate agent . That is , the agent is assumed to occupy a world in which it will be faced with a specific set of tasks in an unpredictable order , and the objective is to find a hierarchical representation that will beget the best performance on average across this set of tasks . An important aspect of this scenario is that the agent may reuse subtask policies across tasks ( as well as task policies if tasks recur ) . In what follows , we first describe how Bayesian model selection can be applied in this context . We then explain how model selection achieves the desired optimum , maximizing the ease with which adaptive behaviors can be discovered . In Bayesian model selection , each candidate model is assumed to be associated with a set of parameters , and the fit between the model and the target data is quantified by the marginal likelihood or model evidence: ( 1 ) where is the set of feasible model parameterizations . In the present setting , where the models in question are different possible hierarchies , and the data are a set of target behaviors , the model evidence becomes: ( 2 ) where spans the set of behavioral policies available to the candidate agent , given its inventory of subtask representations ( this includes the root policy for each task in the target ensemble , as well as the policy for each subtask itself ) . The optimal hierarchy is the one that maximizes the model evidence , as formulated in Equation 2 . Note that while the target behavior consists of the optimal ( flat ) policies specified as a series of state–action pairs , the parameter spans the range of all ( hierarchical ) policies the agent may be equipped with . Some of these will be compatible with the data , and some will not . Importantly , multiple settings of may be compatible with the data . In particular , the root-level policy for a state ( in a particular task ) is irrelevant if the state is covered by an option policy . The root policy is , in this setting , unconstrained , and can vary arbitrarily . This “freeing up” of parameters is critical in determining the optimal hierarchy . In order to illustrate this approach , we consider an agent like the one in the rooms example from Figure 1: an agent whose actions equate to deterministic , reversible transitions between discrete states , visualizable as vertices in a graph . We assume , for concreteness , that the ensemble of tasks that the agent faces comprises the set of all shortest-path problems within the graph . In order to build an inventory of subtask representations , the agent is permitted to decompose the graph into a set of connected components ( see Figure 1 ) , defining regions within the state-space of its environment . The agent is then furnished with a subtask representation for each available method of transitioning between regions [24] ( see Methods for further detail ) . For example , given the partitioning shown in Figure 1C , the rooms agent would obtain two subtask representations for each room , each with one doorway as its goal . ( Note that the foregoing exposition assumes that hierarchies are one level deep , and that the termination function for each option is non-zero in a single sub-goal state . This restriction was made for simplicity and for tractability in implementation . However , the general Bayesian model selection framework and optimality guarantees apply to arbitrary hierarchies without change . ) Applying Bayesian model selection under this problem formulation , the data to be modeled take the form of state–action pairs , where the states represent all of the shortest paths within the state-transition graph . In order to mark task boundaries , this concatenation is supplemented by a set of task-unique symbols , associated with indices specifying where each new task begins . The set of models ( behavioral hierarchies ) corresponds to the set of all possible decompositions of the graph . In this context , the model evidence assumes a surprisingly compact form: ( 3 ) where indexes vertex identifiers within the data; is the degree of the vertex appearing as data element i; is plus the number of subtasks initiable at ; and and are indicator functions of , assuming a value ( 1 or 0 ) that indicates whether each element constrains the agent's task-level action policy ( ) or a subtask-level policy ( ) . As detailed under Methods and in the online supplement , each of the terms in Equation 3 can be quantified based strictly on the target data and the graph itself . Figure 1B ( inset ) applies Equation 3 to the rooms domain , plotting the model evidence for four agent hierarchies . The hierarchy with the greatest evidence corresponds to the partition shown in Figure 1C . This partition , with subgoals corresponding to the doors , in fact represents the optimal behavioral hierarchy in this particular domain . Another example is shown in Figure 2F . This shows the task graph for the Tower of Hanoi , a puzzle in which disks must be moved from a start arrangement to a goal arrangement , without ever placing any disk upon a smaller one . The optimal hierarchy for this task divides the state space into three regions , each corresponding to one position of the largest disk . Crucially , by maximizing the model evidence , these hierarchies also turn out to satisfy our original desideratum , maximizing the agent's ability to efficiently discover target behaviors . Specifically , the optimal hierarchy minimizes the geometric mean number of trial-and-error attempts necessary for the agent to discover the optimal policy for any selected task or subtask ( see Figure 1B , inset , for illustrative data ) . An explicit proof of this point is provided in the online supplement . However , the conclusion follows from the fact that every candidate hierarchy induces a probability distribution over behaviors ( see Eq . 2 ) , and that the optimal hierarchy , by definition , places the greatest probability mass on the agent's target behavior . This further implies that the optimal hierarchy will minimize the number of trials needed , on average , to discover the target behavior . It also happens that the optimal hierarchy , by maximizing the model evidence , is guaranteed to minimize the expected number of information-theoretic bits needed to specify a hierarchical policy consistent with the target data . That is , if we treat the target behavior as a stream of data , we can encode this stream using a set of symbols representing the top level and option policies ( see e . g . , [25] , for a related example outside reinforcement learning ) . Depending on the set of options available , some encodings are more compact than others . The hierarchy that maximizes the model evidence induces an encoding that is the most compact . This once again follows directly from the fact that every candidate hierarchy induces a probability distribution over behaviors , and that the optimal hierarchy places the greatest probability mass on the target behavior . The optimal hierarchy will thus accord this behavior the shortest code length under a Shannon code assignment [26] , also implying the shortest expected description for any task-specific behavior ( i . e . , shortest path ) . Figure 1B ( inset ) shows the expected description length for several agent hierarchies in the rooms domain . As is clear from the figure , the hierarchy that maximizes the efficiency of representation also maximizes the efficiency of learning . This is no coincidence: It is a well established result from learning theory , echoed in empirical observations of human behavior , that ease of learning is directly related to descriptive complexity [27] , [28] . Indeed , this connection has inspired previous efforts to identify useful subtask representations through data compression [21] , [29]–[31] . A salient aspect of the specific hierarchies we have considered so far ( Figures 1C , 2F ) , is that they carve the state-space at topological bottlenecks , narrow segments bridging between densely interconnected clusters of vertices . Further examples are shown in Figure 2 , panels A , C , and D . The decompositions discovered here by Bayesian model selection strikingly resemble those arising from graph-theoretic algorithms for community detection , which explicitly aim to isolate tightly connected clusters within complex networks . Indeed , compression of walks on graphs has been employed as one method of community detection [25] . In the present case , where graph decompositions correspond to behavioral hierarchies , the prominence of bottlenecks is intuitive , in the sense that subtask representations are useful precisely to the extent that they carve tasks “at their joints . ” Recognizing this parallel , some work in hierarchical reinforcement learning has used community structure in order to identify useful subtasks [20] , [32] , [33] . The present results place this past research on a normative basis , by showing that the behavioral hierarchies resulting from community or bottleneck detection approximate hierarchies that provably maximize the agent's ability to discover reward-maximizing behaviors . In fact , the two approaches are complementary: While the present work provides a normative basis for understanding which partitions are best , previous work on bottleneck detection offers heuristic algorithms that may find such partitions more efficiently than searching through the entire space of possible hierarchies . Of course , the approaches will not always coincide , and understanding how and when they differ is an interesting challenge for future work . Having introduced a framework for identifying optimal behavioral hierarchies , we turn to the question of whether human learners decompose novel tasks in an optimal fashion . Some encouragement for this possibility comes from previous work in which related formal principles have been proposed to underlie learning in other domains , including vision [34] , [35] , working memory [36] , [37] , language [38] , and others [39] , [40] . Still more germane is a recent study in which participants were asked to parse sequences of visual stimuli whose ordering , unbeknownst to them , was determined by a random walk in the graph shown in Figure 2A [41] . Participants marked the transitions between the five-vertex clusters as natural breaking points , consistent with the idea that human sequence perception spontaneously detects temporal community structure . In order to examine hierarchy learning in the context of goal-directed action , we conducted four new behavioral experiments . In each of these , undergraduate participants learned about and chose actions within graph-like domains . Our general prediction , probed in different ways in each experiment , was that participants would develop a hierarchical representation of each domain aligning with the one predicted by our theoretical framework . As in the rooms domain , the setup in all four experiments is that the agent is able to make deterministic reversible transitions between ( discrete ) states , and that the task ensemble consists of shortest path problems between all pairs of states . Although this is our present focus , it is not a general limitation of the framework . The optimality guarantees outlined above and detailed in the online supplement apply to arbitrary tasks . In our first experiment , a group of forty participants prepared to make a set of “deliveries” by learning the layout of a small town . The town comprised a set of ten locations , each associated with a distinctive visual icon ( Figure 2B ) . Participants were never given a bird's eye view of the town . Instead , during an initial training period , participants were drilled on the adjacency relations among individual locations . On each trial a randomly selected location was highlighted , and the participant's task was to select the three locations immediately adjacent to this probe ( see Figure 2B ) . Following this training period , participants were informed that they would next be asked to navigate through the town in order to make a series of deliveries between randomly selected locations , receiving a payment for each delivery that rewarded use of the fewest possible steps . Before making any deliveries , however , participants were asked to choose the position for a “bus stop” within the town . Instructions indicated that , during the subsequent deliveries , participants would be able to “take a ride” to the bus stop's location from anywhere in the town , potentially saving steps and thereby increasing payments . Participants were asked to identify three locations as their first- , second- and third-choice bus-stop sites . Crucially , the pattern of adjacencies to which participants were exposed was based on the graph shown in Figure 2C . As is obvious upon inspection , the graph has a single bottleneck at its center , and an optimal partition reflecting this fact ( indicated by color in the figure ) . Bayesian model selection identifies two graph vertices , lying at this bottleneck , as optimal subgoal locations . Given the structure of the task and the goal of navigating rapidly to an a priori unknown location , the optimal strategy is to place the bus stop at one of these locations . The objective of the experiment was to evaluate whether participants could detect the bottleneck and exploit it in this way . It is important to stress that participants were never given a bird's-eye view of the town , or even direct information about relative Cartesian positions . The topology of the town graph had to be inferred solely from local adjacency information . Furthermore , all of the locations had exactly three neighbors and received on average equal exposure during training . There was thus nothing specially salient about any of them . Despite this challenge , participants showed a marked tendency to place the bus-stop at the locations predicted ( see Figure 2C ) . After adjusting for chance , the two bottleneck locations were identified as first-choice locations 4 . 4 times as often as the remaining locations ( , ) . Among participants who were able at the end of the experiment to draw the underlying graph perfectly , chose a bottleneck location first ( , ) . The results of this initial experiment are consistent with the notion that human learners identify and exploit optimal task decompositions or behavioral hierarchies . However , it might be argued that the bus stop manipulation prompted a special , task-specific orientation . Two further experiments investigated whether human learners identify and exploit optimal hierarchies spontaneously , without such a prompt . In Experiment 2 , ten participants completed a set of deliveries , with no mention of bus stops , within a town whose layout was based on the bottleneck graph in Figure 2D . Some deliveries were completed step by step , using a graphical interface that showed participants their current location and allowed them to select among adjacent locations . However , on another subset of trials participants were shown all town location icons concurrently and asked either to ( 1 ) indicate all locations lying on the shortest path between a specified start and goal in any order , or ( 2 ) identify any single location lying on this path . In the former condition , participants showed a strong tendency to select the bottleneck location first ( of correct responses on relevant trials; Monte Carlo test , ) . And in the single-location condition , participants again showed a strong tendency to select the bottleneck ( of correct responses on relevant trials; Monte Carlo test , ) . These findings suggest that participants planned their routes hierarchically , “thinking first” of transition-points between subregions , and then planning the specific steps needed to reach those transition points [42] . More importantly , the observed behavior confirms that participants decomposed the task space in an optimal fashion , consistent with the Bayesian model selection account . These conclusions were reinforced by the results of a third experiment . Here , 21 participants made deliveries within a town based again on the graph from Experiment 2 . Interleaved with step-by-step delivery trials like those in Experiments 1 and 2 were trials in which participants were presented with a start location and a goal location , and asked whether a third location would lie on the shortest path from one to the other ( see Figure 2D ) . Correct response times were faster when the probe location lay at the boundary between subregions in the optimal parse than when it lay elsewhere in the graph ( Figure 2D–E; , ) , again consistent with the idea that route planning occurred initially at the level of the regions arising from the optimal decomposition , followed later by finer-grained selection . Further statistical analysis , detailed in the supplement , showed that this main effect was not explained by differences in probe frequency . In a final experiment , we tested whether the predictions of the optimal hierarchy framework extend beyond the domain of spatial navigation . Here , we leveraged the Tower of Hanoi task . As shown earlier , the optimal decomposition of this task separates it into three regions ( Figure 2F ) . Consider the problem defined by the start and goal states shown in Figure 2F . As also shown in the figure , there are two shortest-path solutions to this problem , each involving the same number of steps . The two paths differ , however , in terms of the number of boundaries they traverse between regions: One traverses one such boundary , the other two . Based on the idea that planning occurs first at the level of the regions defined by the optimal hierarchy , and that maintaining subgoals in memory is costly [43] , we predicted that participants faced with this particular problem would prefer the path crossing only a single region boundary . This prediction was confirmed in an experiment involving thirty-five participants , who solved a series of Tower of Hanoi problems . When the problems of interest occurred , participants pursued the single-boundary solution in of cases ( right-tail sign test , ) . Seventeen subjects traversed the single-boundary route most often , while only seven showed the opposite asymmetry ( one-tailed t-test , ) . The results of these four experiments support the conclusion that human learners discover optimal task decompositions and leverage these decompositions in planning action sequences . The data suggest that novel behavioral domains are spontaneously decomposed into subdomains or regions , and that planning initially focuses on transitions between these , typically via topological bottlenecks . More specifically , the decompositions selected are optimal in the sense specified in the Bayesian model selection account . Although our focus has been on a reinforcement learning [17] characterization of learning and planning , this view includes more classic notions of planning both in artificial intelligence [14] and in psychology [16] . Such problems may be cast in the reinforcement learning framework ( i . e . as Markov decision processes ) by encoding the goal state in the reward function ( e . g . by setting reward to be 0 in the goal state and -1 everywhere else ) . Although planning in reinforcement learning is often performed in the forward direction , when the goal state is isolable , one can also perform goal regression , serially satisfying a chain of preconditions . Subgoal discovery at the hierarchical level may help to determine the relevant preconditions , with the same optimality construct applying . In psychology , a number of theorists have attempted to understand planning in the context of broader unified frameworks for cognition , such as ACT-R [13] and Soar [15] . In ACT-R , both goals and subgoals are specified by the task model . In Soar , subgoals specifically related to solving impasses in decision making are automatically acquired . The present paper outlines a normative framework for understanding task decompositions , and this information could in theory be applied to either ACT-R or Soar , specifying the type of decompositions each should strive to achieve . This raises a final issue of note: It is not our proposal that human learners discover optimal hierarchies by literally computing the Bayesian model evidence given foreknowledge of target behaviors , as in Equation 3 . The present experimental results thus raise the important question of what discovery procedure human learners actually employ in order to approximate the same result . One possible answer comes from recent work on statistical learning , which shows that simply learning to predict future events can support discovery of community structure and topological bottlenecks in novel behavioral domains [41] . An inviting direction for further work is to test whether this learning procedure might underlie the kind of hierarchy induction observed in the present experiments .
Ethics statement: All experimental procedures , including procedures for informed consent , were approved by the Princeton University Institutional Review Board . | In order to accomplish everyday tasks , we often divide them up into subtasks: to make spaghetti , we ( 1 ) get out a pot , ( 2 ) fill it with water , ( 3 ) bring the water to a boil , and so forth . But how do we learn to subdivide our goals in this way ? Work from computer science suggests that the way a task is subdivided or decomposed can have a dramatic impact on how easy the task is to accomplish: certain decompositions speed learning and planning compared to others . Moreover , some decompositions allow behaviors to be represented more simply . Despite this general insight , little work has been done to formalize these ideas . We outline a mathematical framework to address this question , based on methods for comparing between statistical models . We then present four behavioral experiments , showing that human learners spontaneously discover optimal task decompositions . | [
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| 2014 | Optimal Behavioral Hierarchy |
Dengue is the most important mosquito-borne viral infection to affect humans . Although it usually manifests as a self-limited febrile illness , complications may occur as the fever subsides . A systemic vascular leak syndrome that sometimes progresses to life-threatening hypovolaemic shock is the most serious complication seen in children , typically accompanied by haemoconcentration and thrombocytopenia . Robust evidence on risk factors , especially features present early in the illness course , for progression to dengue shock syndrome ( DSS ) is lacking . Moreover , the potential value of incorporating serial haematocrit and platelet measurements in prediction models has never been assessed . We analyzed data from a prospective observational study of Vietnamese children aged 5–15 years admitted with clinically suspected dengue to the Hospital for Tropical Diseases in Ho Chi Minh City between 2001 and 2009 . The analysis population comprised all children with laboratory-confirmed dengue enrolled between days 1–4 of illness . Logistic regression was the main statistical model for all univariate and multivariable analyses . The prognostic value of daily haematocrit levels and platelet counts were assessed using graphs and separate regression models fitted on each day of illness . Among the 2301 children included in the analysis , 143 ( 6% ) progressed to DSS . Significant baseline risk factors for DSS included a history of vomiting , higher temperature , a palpable liver , and a lower platelet count . Prediction models that included serial daily platelet counts demonstrated better ability to discriminate patients who developed DSS from others , than models based on enrolment information only . However inclusion of daily haematocrit values did not improve prediction of DSS . Daily monitoring of platelet counts is important to help identify patients at high risk of DSS . Development of dynamic prediction models that incorporate signs , symptoms , and daily laboratory measurements , could improve DSS prediction and thereby reduce the burden on health services in endemic areas .
Dengue is the most important mosquito-borne viral infection affecting humans . Incidence has increased dramatically over the last 50 years , with approximately 100 million symptomatic dengue infections now estimated to occur annually across more than 100 countries [1 , 2] . Although the majority of symptomatic infections manifest as a non-specific self-limited febrile illness , a small proportion of patients progress to more severe and occasionally life-threatening disease . Of particular concern is a vasculopathy characterized by endothelial dysfunction and plasma leakage that becomes apparent several days into the illness , often around the time of defervescence; this phenomenon tends to be more pronounced in children , and may be severe enough to cause hypovolaemic shock , i . e . dengue shock syndrome ( DSS ) [3 , 4] . Other associated phenomena include a ) thrombocytopenia and a coagulopathy that may result in severe bleeding , and b ) organ involvement ( e . g . hepatitis ) that occasionally progresses to major organ failure . As yet no effective anti-viral or immunomodulatory therapy has been identified [5] , but with careful observation and judicious use of intravenous fluid therapy to counteract plasma leakage , most notably urgent volume resuscitation for patients with established DSS , mortality rates have been reduced to less than 1% in specialist centres [6] . However , mortality rates up to 10% are still occasionally reported [7 , 8] . One contributing factor is that early identification of individuals likely to progress to severe disease is difficult . Consequently large numbers of patients with possible dengue are admitted to hospitals in dengue-endemic areas primarily for observation , overburdening the healthcare systems such that limited local resources are not used to maximal advantage for the small proportion of patients who do need expert care . Effective triage to identify high-risk patients early in the illness course should make it possible to improve patient referral strategies to high dependency or intensive care units ( HDU/ICUs ) , a crucial element in maximising the efficiency of healthcare utilisation in low to middle income countries with limited resources . World Health Organization ( WHO ) guidelines for diagnosis and management of dengue now specify a number of warnings signs for likely progression to severe disease , but these are based largely on expert opinion and still require validation in well-designed studies [9 , 10] . Moreover , in practice , physicians often rely on their own clinical experience rather than following WHO guidelines [11] . Identification of objective and evidence-based risk factors , coupled with development of practical tools such as clinical prediction models for severe dengue , could prove very useful to reduce the number of unnecessary hospital admissions , and facilitate HDU/ICU referrals to target care to the patients in greatest need . Several groups have tried to identify risk factors and develop prediction models for DSS [12–19] . Most of these studies were carried out in children [14 , 16 , 17 , 19] , although some focused on or included adults [12 , 13 , 15 , 18] , who may have different underlying pathophysiology compared to children . However , only a few studies were prospective [17–19] , and the number of patients who developed severe disease in these studies was low , ranging from 10 to 55 patients . Furthermore , in all cases data on risk factors were only collected at baseline , i . e . the time of presentation , hospital admission , diagnosis , or initiation of an intervention . Predictions based on baseline risk factors are only valid at a single time-point and tend to become less relevant as a disease progresses [20] . Where longitudinal data are available at intervals during the course of an illness , they present an opportunity to improve predictions by updating the models using sequential information . In this study , we used a comprehensive prospective dataset collected from 2301 children hospitalized during the febrile phase of dengue at the Hospital for Tropical Diseases in Ho Chi Minh City , in order to assess potential risk factors for progression to DSS . We assessed the predictive values of clinical and laboratory candidate risk factors collected at study enrolment , focusing on parameters that are usually available in dengue-endemic countries like Vietnam . In addition we investigated whether longitudinal platelet values and haematocrit levels measured daily during hospitalization might improve prediction of DSS .
The study was approved by the Scientific and Ethical Committee of Hospital for Tropical Diseases ( HTD ) and the Oxford Tropical Research Ethics Committee . A prospective observational study of children hospitalized with suspected dengue at the HTD in Ho Chi Minh City , Viet Nam , was conducted between 2001 and 2009 . The cohort included any child aged between 5 and 15 years admitted to the paediatric dengue ward at HTD with clinically suspected dengue , whose parent/guardian gave written informed consent for them to be enrolled in the study following detailed explanation by a trained study doctor . Consecutive suspected dengue cases identified during the morning ward round were approached by study staff as potential participants; commencing on Monday morning the process continued until up to 10 suspected dengue cases had been enrolled for that week . Of note , the paediatric dengue ward is responsible for managing children with uncomplicated illness only , and HTD policy dictates that any child who develops DSS or about whom there is concern ( typically development of warning signs necessitating monitoring more frequently than 4–6 hourly ) is transferred to the Paediatric Intensive Care Unit ( PICU ) . During the study period all children admitted to PICU with DSS were recruited into a concurrent pediatric cohort [6 , 21] . After enrolment , baseline information including demographic characteristics , clinical history , and examination findings was documented using a structured case report form . The study doctors then followed all patients daily throughout the hospitalization and recorded information on therapeutic interventions , supportive care and major clinical events , in particular the need for transfer to PICU . For those patients who were transferred to PICU additional data was obtained from the case report forms that were completed as part of the PICU DSS study [6] . All patients were asked to return for follow-up assessments one month later . Venous blood samples for dengue diagnostics ( see below ) were obtained on the day of enrolment , the day of discharge or defervescence , and at the follow up visit . In addition , small volume daily blood samples were obtained as part of standard care for dengue patients , to measure haematocrit levels and platelet counts . Clinical management was in accordance with the HTD dengue treatment guidelines , which were based on the WHO 1997 dengue guidelines [22] , and recommendations from the Vietnamese Ministry of Health . There were no substantive changes to the HTD treatment guidelines during the study , and the same group of senior clinicians was responsible for supervision of dengue patient management throughout the whole study period . The study population for this analysis comprised participants enrolled in the observational study who were subsequently confirmed to have dengue . As DSS occurs most frequently on day 5 or 6 of illness [6] , the main analysis was restricted to patients who were enrolled between days 1–4 of illness ( i . e . on the day of fever onset ( day 1 ) or the subsequent 3 days ) and who did not experience DSS on the day of enrolment . RT-PCR was performed on the enrolment specimen using established methodology [23 , 24] . Dengue IgM and IgG capture ELISAs were performed on paired enrolment and early convalescent specimens . During the study period the diagnostic laboratory used a number of different serological tests , following the manufacturer's instructions for commercial kits ( Dengue Duo IgM and IgG Capture ELISA , PanBio , Australia ) , or established standard operating procedures for in-house methods [25] . Laboratory-confirmed dengue was defined by detection of DENV-RNA in plasma by RT-PCR , or by seroconversion on the capture ELISA . A number of different methods to classify primary/secondary immune status are recommended , but all have limitations [26–28] . Given the range of sero-diagnostic tests employed during the 9 years of the study , we elected to use a simple , pragmatic immunologic classification based on capture IgG results only: a probable primary infection was defined by negative dengue-specific IgG results on acute and early convalescent plasma , at least one specimen being obtained during the second week of illness , and a probable secondary infection by a positive dengue-specific IgG identified on either or both the acute and early convalescent specimens . All other cases were considered unclassifiable , in general due to the absence of a suitable specimen at the appropriate time-points . The outcome of interest was DSS , defined as development of narrow pulse pressure ( ≤ 20 mmHg ) or hypotension for age with evidence of impaired peripheral perfusion [10] . Table S1 in S1 Appendix describes the pre-defined candidate predictors assessed at the time of enrolment in the study ( baseline candidate predictors ) . These predictors included the presence of WHO warning signs [10] , as well as other clinical signs and symptoms that have been identified as risk factors for severe dengue in other studies [29 , 30] . As dengue serotype and immune status were missing for a number of participants , and since this information would not normally be available to the treating physician in routine clinical practice , these parameters were only included in univariate analyses but not in the multivariable analysis . Finally , we wished to take into account the evolving nature of acute dengue over several days in the analysis; we defined the day of illness as the number of days up to and including the day of interest , with the day of fever onset defined as day 1 of illness . The platelet count and haematocrit level have long been considered crucial factors to monitor in patients with dengue , but their role as potential predictors of progression to severe disease has rarely been formally investigated [29 , 30] . Using the sequential daily haematocrit and platelet values available for the study participants we were particularly interested to investigate relationships between aspects of the platelet and/or haematocrit dynamics ( baseline value , current value , percentage ( % ) change from the previous day ) with development of DSS . Logistic regression was the main statistical model for all univariate and multivariable analyses . The development and validation of a prediction model for the development of DSS using baseline covariates followed current standard methodology and recommendations for prognostic modeling [31 , 32] , and multiple imputation [33] . Modeling assumptions of logistic regression ( linearity and additivity ) were carefully assessed and the final model was simplified using stepwise backwards model selection . Model performance was summarized using the Brier score , the area under the receiver-operating curve ( AUC ) and calibration measures . All performance measures were corrected for potential over-fitting by 10-times 10-fold cross-validation . Details on model development , variable selection , alternative models , and validation are provided in the S1 Appendix . We investigated the value of daily haematocrit levels and platelet counts to predict subsequent DSS using both graphical and regression methods . For these analyses we included only patients enrolled on day 3 of illness , to avoid potential confounding effects of other time-varying signs and symptoms that were only assessed at baseline , on outcome . For regression analysis , we fitted separate logistic regression models with the occurrence of DSS as the outcome , and different aspects of haematocrit and platelet dynamics ( baseline value , current value , or % change from previous day ) as covariates for each day of illness using only patients who were still at risk on that day ( i . e . those without DSS up to and including that day ) . In addition , these models were adjusted for all baseline covariates ( other than the platelet count ) selected by the final baseline model . The performance of these models was summarized in terms of the AUC and was corrected for optimism using cross-validation . We imputed missing predictor values by multiple imputation using the MICE algorithm [34] , resulting in 20 imputed datasets ( see Additional file 1 for a detailed description ) . All analyses involving baseline risk factors used these imputed datasets , except for univariate analyses that were based on complete-case analyses . We also verified results from analyses based on multiple imputation against results from complete-case analyses [35] . For simplicity , all analyses of longitudinal platelet counts were based on available data only , without imputation . All analyses were performed with the statistical software R version 3 . 2 . 0 ( 2015-04-16 ) [36] and its companion packages [34 , 37–41] .
A total of 3044 children admitted to the paediatric dengue ward between 2001 and 2009 were enrolled in the observational study , of whom 2598 had laboratory confirmed dengue . Among these participants , 2301 were enrolled between days 1 and 4 of illness and formed the main analysis population for evaluating baseline risk factors . For investigating longitudinal haematocrit levels and platelet counts , the analysis was restricted to the 908 patients enrolled on day 3 of illness ( see Fig 1 ) . The main outcome of interest ( development of DSS or not ) was available for all patients but 115/2301 ( 5% ) of participants had at least one missing candidate predictor . Table 1 summarizes the characteristics of these 2301 study participants at enrolment . More males than females were enrolled in the study and the median age was 12 years ( interquartile range ( IQR ) 10–13 years ) . Most patients were still febrile at enrolment , with 96% of the participants having a temperature ≥38°C . Haemodynamic parameters , including pulse rate and systolic blood pressure , were within the normal range , apart from one child with known congenital heart disease . At the time of enrolment , platelet values were already beginning to fall ( median 134 , 000 , IQR 97 , 000–178 , 000 cells/mm3 ) , whereas haematocrit values remained consistent with expected normal values for age ( median 40% , IQR 37–42% ) . Among the 2152/2301 ( 94% ) cases for whom RT-PCR was performed , most patients were infected with DENV-1 ( 956/2152 , 44% ) or DENV-2 ( 553/2152 , 26% ) , with considerably lower representation for DENV-3 and DENV-4 . Among cases whose serology status could be assessed , 94% ( 1938/2053 ) had probable secondary infections . During hospitalization , 179/2301 cases ( 8% ) were referred to PICU for more intense monitoring and management ( Table 1 ) , with 143/2301 ( 6% ) developing DSS . All the DSS cases recovered following appropriate resuscitation . The remaining 36 patients were transferred to PICU due to concerns about warning signs , but all recovered fully without further disease progression . There was no systematic time trend in the incidence of DSS over the study period ( linear trend test: p = 0 . 35 ) . Although DSS was identified on all illness days from 3 to 8 , 92% of the cases occurred between days 4 and 6 . New bleeding was observed after hospitalisation in 955/2288 ( 42% ) cases , but only a small number of patients ( 112/955 ( 12% ) ) experienced mucosal bleeding . The most frequent mucosal bleeding sites were nose ( 58/112 ) and gum ( 34/112 ) , with less frequent sites being gastrointestinal ( 15/112 ) and vaginal ( 7/112 ) . No case was considered severe enough to warrant blood transfusion . The platelet nadir commonly occurred around day 6 of illness with a median nadir of 65 , 200 ( IQR: 41 , 000–99 , 000 ) cells/mm3 . In many patients the maximum haematocrit value was recorded on the same day , with a median level of 44% ( IQR 41–47% ) and a corresponding median maximum haemoconcentration of 13% ( IQR 6–22% ) . Univariate and multivariable associations between baseline candidate predictors and the development of DSS are shown in Table 2 . Male gender , history of vomiting , palpable liver , higher temperature , and lower platelet count , all assessed at enrolment , were significant risk factors for developing DSS in both univariate and multivariable analyses . Enrolment at an earlier day of illness was associated with a higher risk of developing DSS after adjusting for other covariates although not in the univariate analysis . While none of the probable primary cases developed DSS , 138/1938 ( 7% ) of probable secondary and 5/248 ( 2% ) of the inconclusive cases developed DSS . The multivariable analysis results were consistent between multiple imputation and complete-case analyses ( Table S3 in S1 Appendix ) . Age , sex , day of illness , history of vomiting , temperature , palpable liver and platelet count at enrolment were retained in the logistic regression model with stepwise variable selection based on multiple imputation ( Table 3 ) . The same predictors and similar effect sizes were identified in the complete-case analysis ( Table S3 in S1 Appendix ) . The final reduced model had a moderate AUC of 0 . 70 and good calibration , and its performance was comparable to or better than alternative modeling approaches ( Table S4 in S1 Appendix ) . The predicted risk of DSS in study participants based on the reduced model was left skewed with a median ( IQR ) risk of 4 . 6% ( 2 . 6%-8 . 0% ) . Fig 2 displays the number of true positive and false positive cases depending on the chosen risk threshold for classifying subjects as likely to progress to DSS or not . For a low risk threshold , the number of false positive cases was quite high . For example , at a risk threshold of 5% , 108/134 ( 81% ) of cases with DSS would be correctly classified; however , the number of false positive cases would be 8 times higher ( 894 cases ) . For a higher risk threshold , the number of false positive cases decreased at the cost of missing true positive cases . For example , at a risk threshold of 20% , although there were only 46 false positive cases only 17/134 ( 13% ) of the cases with DSS could be detected by the model . In agreement with the findings of the baseline prognostic models , no clear difference in daily haematocrit levels was apparent between study participants who did and did not develop DSS ( Fig 3 , Panel A ) . By contrast , superimposed on the progressive general reduction in platelet counts observed ( as expected ) , in all participants between days 2 and 6 of illness , Panel B demonstrates that platelet counts in the patients who developed DSS tended to be lower than in patients who never progressed to DSS , and that this difference was most pronounced on the day before DSS occurred ( Fig 3 ) . This trend is also apparent in the plots of individual trajectories of haematocrit and platelet count when restricted to participants enrolled on day 3 of illness ( Fig 4 ) . This observation highlights the fact that while a daily platelet count can be helpful for predicting likely progression , the prognostic relevance is time-limited; thus today’s platelet value is useful in assessing the risk for DSS within the next 24 hours , but may not be very informative in predicting development of DSS in two or more days time . Table 4 also shows clear negative relationships between sequential platelet data and the occurrence of DSS: the risk of subsequent DSS decreased for patients with higher current platelet counts and lower relative decreases in platelet counts compared to the previous day’s value . In other words , subjects with a low current platelet count and/or a pronounced decline compared to the previous day’s value are at a higher risk of DSS . Importantly , these relationships persisted on days 3 , 4 , and 5 of illness ( odds ratio of 0 . 91 , 0 . 89 , and 0 . 83 for each 10 , 000 cell/mm3 higher current platelet count , on days 3 , 4 , and 5 , respectively ) . Moreover , prediction models including updated platelet counts demonstrated better ability to discriminate patients who developed DSS from others , compared with the respective baseline prediction model when applied on day 4 or 5 of illness ( AUC of 0 . 72–0 . 74 for updated models vs . 0 . 66 for baseline model on day 4 , and 0 . 67–0 . 73 vs . 0 . 51 on day 5 ) . Similar analysis with daily haematocrit levels did not suggest any potential predictive value ( Table S5 in S1 Appendix ) .
Dengue is a viral infection of increasing global significance that evolves rapidly over a short time-course and displays a wide range of clinical manifestations . Although much has been written from an empirical standpoint about the clinical spectrum of disease few formal descriptions based on prospectively collected data have been published; given the highly variable disease evolution this may reflect the need for enrolment of substantial case numbers to allow meaningful interpretation of the relevance of different clinical events . We present a detailed description of the clinical features of dengue observed in more than 2000 Vietnamese children enrolled within 4 days of fever onset , of whom 143 ( 6% ) went on to develop DSS . Using this unique dataset we were able to identify several baseline factors ( male sex , day of illness at enrolment , history of vomiting , body temperature , palpable liver , platelet count ) that were associated with subsequent development of DSS , as well as to investigate dynamic aspects of serial platelet and haematocrit changes for risk prediction . While the prediction models based on baseline information demonstrated only moderate performance and limited clinical utility , incorporating features of longitudinal platelet count kinetics into the models improved their performance . An important feature of this study is that the study population was enrolled at an early stage of the illness , i . e . all participants were enrolled within the first 4 days after fever onset when they had relatively mild disease . Although the concept of hospitalization based on warning signs has become established in recent years , the strong community perception of dengue as a dangerous disease in childhood , combined with major transport/logistic difficulties during the study years especially at night , resulted in a low threshold for hospitalization at that time . Early enrolment likely explains the lower incidence of DSS in our cohort compared to contemporaneous reports from Ho Chi Minh city that described all hospitalized cases [12] . The finding of a significant association between male gender and a higher risk for DSS , which contrasts with the evidence from previous epidemiological studies [29 , 30] , was somewhat surprising and may also relate to early enrolment . In the cohort of children directly admitted to PICU with DSS during the same time period [6] , females were more likely to be admitted to hospital on the day of DSS than males ( 49% of females compared to 41% of males , chi-squared test p<0 . 001 ) . If severe females presented to hospital later than their male counterparts , this could lead to an underrepresentation of severe females in our study population . Of note , a general male-bias in paediatric admissions has been noted previously , potentially explained by differences in parental health-seeking behaviour , or by true differences in biological characteristics and/or disease susceptibility between genders [12 , 42–44] . Even though males were significantly heavier than females ( with adjustment for age , Table S6 in S1 Appendix ) , it is unlikely that the unexpected association between male gender and higher risk of developing DSS can be explained by weight differences since a ) weight by itself was not a risk factor for DSS , and b ) weight was already adjusted for in the multivariable analysis of risk factors for DSS . Interestingly , we did not find evidence of a relationship between haematocrit and risk of progression to DSS , even though haematocrit but not platelet count was a strong predictor of severe outcome in our study of children with established DSS [21] . Thus the platelet count and haematocrit seem to be relevant for risk prediction during different stages of the disease . We found the absolute platelet count on a given day during the febrile phase to be an important risk factor for developing DSS , and furthermore that changes in the platelet count over time are also related to changes in the likelihood of developing DSS . As the main underlying pathophysiological abnormality in DSS is plasma leakage [3] , these findings suggest a potential role for platelets in the induction of plasma leakage , a phenomenon supported by the recent work by Hottz et al . [45] . Haematocrit levels , by contrast , likely reflect the extent of plasma leakage balanced by a variety of compensatory mechanisms . Haematocrit may be less important in the early phase of disease , as found in this study , because plasma leakage is less pronounced in this stage . However , once a patient has progressed to established DSS , increasing haemoconcentration becomes an important indicator of ongoing or profound leakage . Haematocrit changes are typically monitored every few hours when shock is anticipated and might have proved useful for short-term risk prediction ( hours rather than days ) , but we did not have access to these data . In any case complex modeling would be needed to incorporate such data into an algorithm and a predictive window of less than 24 hours is less relevant from the public health perspective . Several features that we identified at study enrolment—the absolute platelet count , vomiting and a palpable liver—have already been reported in the published literature as predictors of DSS [10 , 29] . We also identified a higher baseline temperature to be independently associated with an elevated risk of DSS , a finding which might be explained by the positive correlation between fever and viral load [46 , 47] . An association between enrolment at an earlier day of illness and a higher risk of DSS was also found , but only in the multivariable not the univariate analyses; this is likely to be an artifact attributable to adjustment for other clinical features assessed at enrolment , notably the platelet count , in the multivariable analyses . By adjusting for platelet count , the reported odds ratio corresponds to the comparison of two subjects who were enrolled on two consecutive days of illness but had the same platelet count on their respective day of enrolment . As platelet counts decrease over time during the illness course [48] , the subject enrolled earlier would have a lower platelet count relative to their day of enrolment and , as platelet count is strongly inversely associated with the risk of DSS development , this might explain the reported effect . The baseline prediction model presented here has an AUC of 0 . 70 and good calibration on internal validation . Unfortunately this moderate performance , combined with the low incidence of DSS , results in a model of only limited clinical utility . To be useful in clinical practice , a prediction model should be able to correctly identify most subjects who subsequently develop DSS . However , as illustrated in Fig 2 , this would mandate a very low risk threshold and the number of true positives would be swamped by the much larger number of false positives . A prediction rule with a high false positive rate implies a significant additional patient burden for high dependency and/or intensive care units to which such cases might be transferred , and this may negatively affect the quality of care that patients who are truly at high risk of DSS receive , especially in endemic countries with scarce resources . The fact that this model was carefully developed using data from over 2300 children hospitalised during the febrile phase of dengue suggests that reliance on readily available baseline characteristics and warning signs is not sufficient for reliable risk prediction for DSS , and that incorporation of novel markers with higher predictive value is likely to be required to achieve better models . In addition to virological , immunological and host genetic factors , novel markers that represent or better characterise microvascular dysfunction and/or intravascular volume status are potential candidates [30] . However , their value still needs to be verified in well-designed prospective studies . Additionally , for practical application in resource-limited settings , inexpensive but reliable systems to measure novel biomarkers that prove to be robust predictors would need to be developed and made widely accessible [30 , 49] , highlighting the current need to capitalise efficiently on parameters that are already available routinely in many centers . Another issue that could affect the applicability of such baseline prediction models , is the lack of transferability of the baseline prediction to later time-points [50 , 51] . In our study , the predictive performance of the baseline platelet count and other baseline risk factors completely deteriorated ( AUC = 0 . 51 ) when aiming to predict DSS occurring more than 2 days later . In contrast , by incorporating dynamic aspects of the platelet count kinetics ( i . e . the current/updated value or the % change from the previous day ) , the models remained relevant to DSS prediction at later time-points . Similar findings have been reported for longitudinal biomarkers in other diseases [20 , 52–55] . The implication of this finding is twofold: first , since the predictive value of a particular platelet count is limited to one day ahead , the necessity to monitor the platelet count daily during the febrile phase is emphasized; second , the potential role for a dynamically updated prediction model to improve triage of dengue patients is suggested . Ideally , such a model would be incorporated into a simple algorithm that could be used by clinicians in endemic areas to monitor patients on a daily basis , adaptively suggesting the appropriate level of observation and/or treatment as new information is collected . For the present however , in settings where measurement of platelet count is not yet available , for example in peripheral facilities in endemic countries with low-resources , it would be beneficial to make this important and simple biomarker accessible to clinicians in order to improve patient triage and management . Unfortunately we were not able to include other potentially relevant risk factors , for example sequential white blood cell counts , as this information was not routinely recorded in the study files at the time the original study was conducted; however it would be interesting to incorporate such information into prospective studies assessing the utility of dynamic prediction modeling for dengue . Another limitation of the present dataset is that the sample size and the number of DSS cases included in the analysis of longitudinal platelet counts , which was restricted to the 908 patients enrolled on day 3 of illness , was too small to draw definite conclusions . This also prevented exploration of more complex models with time-varying coefficients for the longitudinal platelet counts , or assessment of non-linear platelet effects . Moreover , the current study was restricted to hospitalized patients admitted to a single hospital in Vietnam , and the longitudinal data available was limited to daily hematocrit and platelet values . To develop reliable dynamic prediction models , even larger and richer datasets which collect detailed longitudinal information on several candidate risk factors will be required , ideally involving a range of clinical settings in dengue endemic areas [49] . In conclusion , this study has confirmed the value of a number of established risk factors for DSS among children with dengue , and has demonstrated that prediction can be improved by dynamically incorporating sequential platelet values into the models . Although the study was performed among hospitalized children , the structure of the healthcare and transport systems in operation in Ho Chi Minh City at that time resulted in early admission of many children with possible dengue , and the findings may be applicable to the population of children now managed as outpatients during the early phase of their illness in many large cities across southeast Asia . The findings reinforce the view that in the early febrile phase dengue is typically a rather non-specific illness , but also provide strong support for the WHO recommendation to perform daily full blood counts in order to monitor the platelet count closely in these patients [56] . The next step would be to initiate large outpatient based research programs aimed at developing and validating more complex dynamic models which could improve identification and management of cases likely to develop DSS in the early febrile phase , as well as assessing the utility of warning signs that are currently recommended for risk prediction during the transition to the critical phase . Further methodological research on how to build and assess dynamic prediction models in acute infectious diseases will be important to achieve these goals , alongside efforts to acquire and make available suitably rich datasets . | Dengue is a very common , potentially serious , mosquito-borne viral infection . The spectrum of clinical disease is broad . Dengue shock syndrome ( DSS ) , seen primarily in children , is the most serious life-threatening manifestation . Early identification of children presenting with dengue who are likely to develop DSS could improve triage and resource allocation in endemic areas . This study , based on data from 2301 Vietnamese children hospitalized with dengue , aimed to assess the value of readily available clinical and laboratory markers , especially platelet counts and haematocrit levels , in predicting DSS . In addition to risk factors present at the first assessment within 1–4 days from fever onset ( vomiting , higher temperature , palpable liver , lower platelet count ) , we showed that serial daily platelet counts provide useful additional information to identify at an early stage children who are likely to develop shock . Although absolute platelet values were already known to be important , this is the first study to confirm the usefulness of sequential daily platelet counts . It also provides proof of concept for the value of incorporating serial laboratory and clinical signs into future dynamic prognostic models to allow for earlier identification and better management of children at risk of DSS . | [
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| 2017 | The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue |
Cetaceans have a long history of commitment to a fully aquatic lifestyle that extends back to the Eocene . Extant species have evolved a spectacular array of adaptations in conjunction with their deployment into a diverse array of aquatic habitats . Sensory systems are among those that have experienced radical transformations in the evolutionary history of this clade . In the case of vision , previous studies have demonstrated important changes in the genes encoding rod opsin ( RH1 ) , short-wavelength sensitive opsin 1 ( SWS1 ) , and long-wavelength sensitive opsin ( LWS ) in selected cetaceans , but have not examined the full complement of opsin genes across the complete range of cetacean families . Here , we report protein-coding sequences for RH1 and both color opsin genes ( SWS1 , LWS ) from representatives of all extant cetacean families . We examine competing hypotheses pertaining to the timing of blue shifts in RH1 relative to SWS1 inactivation in the early history of Cetacea , and we test the hypothesis that some cetaceans are rod monochomats . Molecular evolutionary analyses contradict the “coastal” hypothesis , wherein SWS1 was pseudogenized in the common ancestor of Cetacea , and instead suggest that RH1 was blue-shifted in the common ancestor of Cetacea before SWS1 was independently knocked out in baleen whales ( Mysticeti ) and in toothed whales ( Odontoceti ) . Further , molecular evidence implies that LWS was inactivated convergently on at least five occasions in Cetacea: ( 1 ) Balaenidae ( bowhead and right whales ) , ( 2 ) Balaenopteroidea ( rorquals plus gray whale ) , ( 3 ) Mesoplodon bidens ( Sowerby's beaked whale ) , ( 4 ) Physeter macrocephalus ( giant sperm whale ) , and ( 5 ) Kogia breviceps ( pygmy sperm whale ) . All of these cetaceans are known to dive to depths of at least 100 m where the underwater light field is dim and dominated by blue light . The knockout of both SWS1 and LWS in multiple cetacean lineages renders these taxa rod monochromats , a condition previously unknown among mammalian species .
Cetacea [dolphins , porpoises , and whales] represents a remarkable example of aquatic specialization within Mammalia [1] . With their return to river and marine environments , the ancestors of modern toothed cetaceans ( Odontoceti ) and baleen whales ( Mysticeti ) underwent extensive modifications that included the evolution of novel structures [e . g . , baleen plates , tail flukes] , major anatomical rearrangements ( e . g . , telescoping of the skull , development of fore-flippers ) , the loss or reduction of typical mammalian traits ( e . g . , olfactory structures , hair , hindlimbs ) , and associated behavioral changes ( echolocation , filter-feeding , deep-diving ) [2]–[4] . At the genetic level this restructuring includes evidence of positive selection in loci related to high-frequency audition [5]–[7] , brain size [8] , [9] , and flipper development [10] , as well as degradation of genes related to olfaction [11]–[13] , taste [14] , tooth enamel formation [15]–[17] , and vomeronasal chemoreception [18] . In the case of vision , aquatic environments impose challenging constraints , and the cetacean eye exhibits both morphological and molecular specializations that enhance underwater sight [19] . Possible morphological adaptations include an extensive reflective tapetum lucidum , a spherical lens with high refractive power , a relatively large cornea , and a rod-dominated retina , all of which enhance visual capabilities under dim light conditions [20] , [21] . At the molecular level , most mammals have dichromatic color vision based on presence of three visual pigments , each of which is a G protein-coupled receptor that consists of an opsin protein moiety linked via a Schiff base to a retinal chromophore [22] . The three opsins that characterize most mammals include a rod opsin ( RH1 ) and two cone opsins , short wavelength-sensitive opsin ( SWS1 ) and long wavelength-sensitive opsin ( LWS ) . Rods mainly function in dim light conditions ( scotopic/night vision ) whereas cones require more light ( photopic vision ) and are necessary with color vision . By contrast with most other mammals , all cetaceans that have been investigated are thought to be L-cone monochromats that possess an inactivated copy of SWS1 and two functional opsins , RH1 and LWS , which are expressed in rod and L-cone cells of the retina , respectively [19] , [23] , [24] . Griebel and Peichl [19] and Peichl [24] suggested that retinal S-cones , which express SWS1 and are sensitive to blue wavelengths , were lost during an early , coastal period of cetacean evolution . Near-shore waters commonly have an underwater light spectrum that is red shifted owing to the absorption of blue light by organic and inorganic debris , and the loss of ‘jobless’ S-cones may have constituted an economical advantage in this environment by simplifying retinal and cortical visual information processing [19] . There are no inactivating frameshift mutations in SWS1 that are shared by all odontocetes and mysticetes [23] , but Griebel and Peichl [19] suggested that an unidentified genetic change , possibly in the promoter region , thwarted expression of the SWS1 protein in the common ancestor of crown Cetacea . Following the knockout of SWS1 , crown cetacean lineages that independently conquered the open ocean were forced to shift λmax [the wavelength of maximal absorption] of RH1 and LWS to bluer wavelengths because SWS1 had previously been inactivated [19] , [24] . By contrast , Bischoff et al . [25] offered an alternative scenario in which RH1 was blue shifted in the common ancestor of Cetacea . Specifically , Bischoff et al . [25] speculated that the ancestral cetacean RH1 possessed 83Asn , 292Ser , and 299Ala at three key tuning sites , as in the deep-diving giant sperm whale [Physeter macrocephalus] , but stopped short of using explicit methods to reconstruct the ancestral RH1 sequence of Cetacea . If RH1 was blue-shifted in the common ancestor of Cetacea , then SWS1 may have been inactivated independently in mysticetes and odontocetes , perhaps due to the inefficiency at S-cones at photon capture in dim light conditions [22] . Another intriguing hypothesis posits rod monochromacy , as opposed to L-cone monochromacy , in at least some cetaceans . McFarland [20] suggested that some cetaceans are probably rod monochromats in which S-cones , L-cones , and their associated opsin genes [SWS1 and LWS , respectively] are lacking , so that vision is based entirely on rods and the rod opsin gene RH1 . Immunocytochemical studies have failed to support this hypothesis and instead demonstrated the presence of L-cones in representative odontocete species belonging to the families Delphinidae and Phocoenidae [26] . More recently , Fasick et al . [21] reported the first partial L-cone opsin sequence ( LWS ) of a mysticete , Eubalaena glacialis ( Atlantic right whale ) , and suggested that the L-cone opsin in this taxon is blue shifted , as are the L-cone opsins of representative odontocetes [27] . A potential shortcoming of this study is that Fasick et al . [21] only sequenced exons 3 and 5 of the E . glacialis LWS gene . In addition , LWS sequences have not been characterized from several other cetacean families including the deep-diving Ziphiidae , Physeteridae , and Kogiidae . Thus , McFarland's [20] suggestion that some cetaceans are rod monochromats remains to be tested by a more complete sampling of opsin gene sequences from a broader array of species . Here , we report complete or nearly complete protein-coding sequences for all three opsin genes ( RH1 , SWS1 , LWS ) from representatives of all extant families of Cetacea and the cetacean sister group , Hippopotamidae . Previous studies have characterized the evolutionary patterns of individual cetacean opsins in isolation , but have not yet integrated information from all three retinal opsin genes ( RH1 , LWS , SWS1 ) into a single , comprehensive analysis . We utilized selection intensity estimates , ancestral sequence reconstructions , shifts in spectral tuning , and shared missense/frameshift mutations to infer the complex history of opsin evolution in Cetacea . Our reconstructions suggest that RH1 was blue-shifted in the common ancestor of Cetacea prior to the independent inactivation of SWS1 on the stem mysticete and odontocete branches . LWS , in turn , was pseudogenized convergently in five different cetacean lineages [right whale plus bowhead , rorquals plus gray whale , Sowerby's beaked whale , giant sperm whale , pygmy sperm whale] , all of which are deep divers that feed on bioluminescent organisms . The tandem inactivation of SWS1 and LWS in these taxa presumably renders them rod monochromats , a condition that was previously unknown within Mammalia .
Maximum likelihood trees based on SWS1 exons plus introns , SWS1 exons , RH1 exons , and LWS exons are shown in Figures S1 , S2 , S3 , S4 . With a few exceptions , clades with high bootstrap support percentages ( >90 ) on individual gene trees are in agreement with the species tree in Figure 1 . All of the gene trees recovered Cetancodonta [Cetacea + Hippopotamidae] , Cetacea , Mysticeti , Balaenidae , Balaenopteroidea , Physeteroidea , Ziphiidae , Iniidae + Pontoporiidae , Phocoenidae , Delphinidae , Delphinoidea , and Iniidae + Pontoporiidae + Delphinoidea [Delphinida] . Odontoceti was only recovered in the SWS1 analyses , but conflicting nodes in the RH1 and LWS trees had low bootstrap support values ( ≤53% ) . Inactivating mutations ( frameshift indels , premature stop codons , disrupted intron splice sites , amino acid replacement at the Schiff's base counterion site ) were apparent for all cetacean species in the SWS1 alignment ( Table 1 ) , but were lacking in SWS1 from the semiaquatic outgroup species , Hippopotamus amphibius ( Figure 1 ) . Although the SWS1 genes of all cetacean species show evidence of mutational decay , no inactivating mutations map to the last common ancestral branch of Cetacea ( Figure 1 , node 26 to node 27 ) . Instead , different molecular lesions define various sublineages of Cetacea ( Table 1 ) . An amino acid replacement ( E113G; bovine RH1 numbering ) at the Schiff's base counterion site that is thought to disrupt opsin-chromophore binding [23] optimizes to the stem branch of Odontoceti ( Figure 1; node 27 to node 35 ) , and a four base-pair frameshift deletion was derived on the stem branch of Mysticeti ( Figure 1; node 27 to node 28 ) . These independent inactivating mutations imply that SWS1 was pseudogenized convergently in the two major subclades of Cetacea ( Figure 1 , Figure S5 ) . Estimates of ω ( dN/dS ) on different branches of the cetacean tree are consistent with parallel knockouts of SWS1 in Odontoceti and in Mysticeti . The ω estimate for SWS1 on the stem Cetacea branch ( Figure 1 , node 26 to node 27 ) , just prior to the two inferred inactivating mutations , suggests a pattern of purifying selection based on analyses with two different codon frequency models ( ω = 0 . 31 , 0 . 35 ) . Likewise , a signature of strong purifying selection ( ω = 0 . 16 , 0 . 17 ) was inferred on the stem Odontoceti branch ( node 27 to node 35 ) ( Table 2 ) . Neutrality is predicted on the stem odontocete branch if SWS1 had previously been inactivated on the stem cetacean branch [19] , but statistical tests rejected this hypothesis ( Table 2 ) . The ω estimate ( 0 . 75 , 0 . 83 ) for the stem mysticete branch ( node 27 to node 28 ) , in turn , is only slightly lower than expected for complete neutrality ( ω = 1 . 0 ) and suggests that pseudogenization occurred very early on this branch . Estimates of ω for crown odontocete + crown mysticete branches are in agreement with expectations for neutrality ( Table 2 ) , and in conjunction with numerous frameshift indels within these clades ( Table 1 ) imply a release from selective constraints after the occurrence of inactivating mutations on the stem odontocete and stem mysticete branches ( Figure 1 ) . No inactivating mutations ( frameshift indels , splice site mutations ) were apparent in the RH1 alignment , implying that RH1 is functional in all of the species that were surveyed ( Figure 1 ) . Ancestral amino acid sequences at key tuning sites ( 83 , 292 , 299 ) in Cetacea [21] , [25] are shown in Table 3 for internal nodes with inferred blue or red shifts in λmax . Amino acid changes from DAS to NSS on the stem cetacean branch [node 26 to node 27] resulted in an inferred blue shift from 501 to 484 nm . Additional blue shifts ( 484 to 479 nm ) are inferred in Caperea , in stem Physeteroidea ( node 35 to node 36 ) , and in stem Ziphiidae ( node 38 to node 39 ) based on an amino acid changes at site 299 ( NSS to NSA ) that occurred independently in these three lineages . Seven red shifts were reconstructed in Cetacea , including three in Mysticeti ( stem Balaenidae [node 28 to node 29] , Megaptera , Eschrichtius ) and four in Odontoceti ( stem Iniidae + Pontoporiidae [node 42 to node 43] , Pontoporia , stem Monodontoidea [node 44 to node 45] , Delphinapterus ) ( Table 3 ) . Among ten other amino acid sites that have been linked to spectral tuning in vertebrates [28] , eight ( sites 96 , 102 , 122 , 183 , 253 , 261 , 289 , 317 ) are invariant among the cetaceans and the hippopotamid that were included in our taxon sampling , site 194 exhibits four amino acid replacements within Cetacea , and site 195 shows an amino acid replacement [L to P] on the stem Cetacea branch and four replacements within Cetacea . Analyses with Codeml rejected site models 2 and 8 , which add an extra category for positively selected sites , in favor of models 1 and 8a , respectively . By contrast , branch-site analyses with two different codon frequency models ( CF ) provided statistically significant support for a bin of five positively selected sites ( 7 , 83 , 123 , 266 , 292 ) on branches with λmax changes ( CF2: P = 0 . 00036 , ω = 5 . 53; CF3: P = 0 . 00018 , ω = 6 . 43 ) . Three of the five positively selected sites ( 83 , 266 , 292 ) have probabilities >0 . 95 of membership in this bin . Inactivating mutations are apparent in LWS sequences from ten cetacean species ( Figure 1 , Figure S6 , Table 1 ) . Reconstructions of ancestral sequences imply eight frameshift indels and three splice site disruptions within Cetacea , with convergent inactivation of LWS on the following five branches ( Figure 1 ) : Physeter macrocephalus , Kogia breviceps , Mesoplodon bidens , stem Balaenidae ( node 28 to node 29 ) , and stem Balaenopteroidea ( node 30 to node 31 ) . All of these separate knockouts of LWS postdate prior inactivations of SWS1 and therefore result in rod monochromacy ( Figure 1 ) . Estimates of ω throughout the species tree generally are consistent with multiple , independent knockouts of LWS within Cetacea . Branches reconstructed as functional for LWS exhibit a strong signature of purifying selection ( ω = 0 . 09 , 0 . 10 ) . By contrast , ω estimates on “transitional” branches [16] , where inactivating mutations in LWS were reconstructed , generally show elevated rates of nonsynonymous substitution ( Physeter: ω = 0 . 38 , 0 . 41 , Kogia: ω = 0 . 73 , 0 . 78 , stem balaenopteroid branch: ω = 0 . 34 , 0 . 37 ) . Exceptions are the short transitional branches for stem Balaenidae ( 1 . 5 to 1 . 7 inferred substitutions , ω = 0 . 0001 ) and Mesoplodon ( 2 . 4 to 3 . 1 inferred substitutions , ω = 0 . 13 , 0 . 19 ) . Branches within crown Balaenopteroidea ( node 31 and descendant branches ) plus crown Balaenidae ( node 29 and descendant branches ) , which are interpreted as pseudogenic based on the prior occurrence of inactivating mutations , have an ω value based on two codon models ( 0 . 69 , 0 . 70 ) that does not deviate significantly from neutral expectations ( ω = 1 . 00 ) based on χ2-tests . Reconstructions of ancestral amino acid sequences at five key tuning sites ( amino acids 180 , 197 , 277 , 285 , 308 ) [21] , [25] are shown in Table 3 for branches with inferred shifts in λmax . For the five tuning sites , AHYTA ( λmax = 552 nm ) is the inferred ancestral condition for Cetancodonta ( node 26 ) and for the last common ancestor of extant cetaceans ( node 27 ) . Three changes at LWS tuning sites were reconstructed within Cetacea . Parallel changes from AHYTA to AHYTS on the stem Mysticeti branch [node 27 to node 28] and on the stem Delphinoidea branch ( node 42 to node 44 ) imply blue shifts from 552 nm to 522–531 nm . A change from AHYTA to AHYTP was reconstructed on the terminal Inia branch , but the functional effect of A308P is unknown ( Table 3 ) . Analyses with Codeml rejected site models 2 and 8 , which add an extra category for positively selected sites , in favor of models 1 and 8a , respectively . Similarly , positively selected sites were not identified in branch-site analyses .
Here , we assembled complete or nearly complete protein-coding sequences for RH1 , SWS1 , and LWS for representatives of all extant cetacean families . These sequences , in combination with molecular evolutionary analyses , permit a detailed , synthetic reconstruction of opsin evolution in Cetacea ( Figure 1 ) . Recent phylogenetic hypotheses imply that the aquatic ancestry of Cetacea extends back to its last common ancestor with the semi-aquatic Hippopotamidae in the early Eocene , >50 Ma [4] , [29] , [30] . Whales and hippos share a variety of “aquatic” specializations including sparse hair , loss of sebaceous glands , and the ability to birth and nurse underwater [4] , [31] , [32] , but these features traditionally have been interpreted as parallel evolutionary derivations in these two lineages . Given the hypothesis that the common ancestor of cetaceans and hippos was aquatic/semi-aquatic ( Figure 1 , node 50 to node 26 ) , shared mutations in opsin genes that enhance vision in aquatic environments might be expected in whales and hippos . ML reconstructions of ancestral opsin sequences imply only two replacement substitutions ( LWS: E41D; RH1: L216M ) on the stem lineage . These replacements are not at key tuning sites and fail to provide compelling evidence for an aquatic shift in opsin properties in the common ancestor of hippos and whales . Following divergence from Hippopotamidae , the unique evolutionary history of Cetacea began on the stem cetacean branch ( Figure 1 , node 26 to node 27 ) . The fossil record indicates that the stem cetacean lineage was marked by a profound transition in anatomy from primitive semi-aquatic forms to obligately aquatic taxa with vestigial hindlimbs [3] , [33]–[35] . Ancestral reconstructions imply that stem cetaceans retained dichromatic color vision with functional SWS1 , LWS , and RH1 as in Hippopotamus and more distantly related artiodactyls; a blue shift in RH1 also occurred on the stem cetacean branch ( Figure 1 ) . Specifically , the amino acid array at three key tuning sites ( 83 , 292 , 299 ) [21] , [25] changed from DAS to NSS , with an inferred λmax shift from 501 to 484 nm . Our ML reconstruction supports Bischoff et al . 's [25] hypothesis that RH1 was blue shifted on the stem cetacean branch , but contradicts their assertion that the ancestral cetacean expressed the amino acids NSA as in deep-diving physeteroids . In addition to replacements at sites 83 and 292 , a change at tuning site 195 ( K to T ) of RH1 occurred on the stem cetacean branch . This change from a polar amino acid to a positively charged residue has been retained in the deep-diving physeteroids ( giant sperm whale , pygmy sperm whale ) . The inferred shift in λmax that results from a K to T replacement at this site , if any , remains to be investigated with mutagenesis studies . Unlike tuning sites 83 , 292 , and 299 , that are situated in transmembrane regions of RH1 and are in close proximity to the chromophore , site 195 is positioned in the luminal face of RH1 [36] . The nature of long distance interactions between this amino acid site and the chromophore are unknown [36] . The basal split in Cetacea defines the separation of Odontoceti from Mysticeti , and also marks the evolution of profound changes in anatomy/feeding strategy in both clades [4] , [37] . Echolocation capabilities and degradation of olfactory structures were derived on the stem odontocete branch ( Figure 1 , node 27 to node 35 ) , whereas the transition to bulk filter feeding with a keratinous baleen sieve evolved on the stem mysticete branch ( Figure 1 , node 27 to node 28 ) . These divergent specializations represent changes in feeding style that would be expected to impact demands on visual systems . Following the blue shift in RH1 on the stem cetacean branch , SWS1 was inactivated independently in stem odontocetes and in stem mysticetes , coincident with the evolution of divergent specializations in these two clades ( Figure 1 ) . Two lines of evidence support this reconstruction and argue against an earlier knockout of SWS1 in the common ancestor of Cetacea . First , comprehensive sequencing of SWS1 exons and introns revealed no shared inactivating mutations common to all extant cetaceans . Odontocetes have a common missense mutation at the Schiff's base counterion site ( E113G ) that disrupts opsin-chromophore binding [23] . Mysticetes , in turn , share a 4-bp frameshift mutation in exon 1 of SWS1 that results in a premature stop codon . Frameshift indels in the same position occur in several odontocetes , but these deletions are most parsimoniously reconstructed as convergent between Mysticeti and multiple odontocete subclades ( Text S1 ) . Several mutations that disrupt intron boundaries were identified , but in all cases these substitutions map to branches within Odontoceti or within Mysticeti . Second , dN/dS values on the stem odontocete and stem mysticete branches should indicate an absence of selective constraints if SWS1 was inactivated earlier in the common ancestor of Cetacea . Estimates of dN/dS ( 0 . 75 , 0 . 83 ) for the stem mysticete branch are consistent with neutral evolution ( dN/dS = 1 . 00 ) , but neutrality was rejected given the low dN/dS estimates ( 0 . 16 , 0 . 17 ) for the stem odontocete branch , indicative of purifying selection and thus functionality after the split between Odontoceti and Mysticeti ( Figure 1 , node 27; Table 2 ) . Together , our reconstructions for the evolution of RH1 and SWS1 contradict the coastal knockout hypothesis [19] , [24] . This scenario postulates that SWS1 was inactivated during an early amphibious phase of cetacean history when semi-aquatic whales occupied coastal waters that absorbed blue light , and that RH1 was subsequently blue shifted in crown cetaceans that moved to open ocean environments dominated by blue light . The coastal knockout hypothesis requires an as yet undiscovered inactivating mutation in SWS1 , perhaps in the promoter region of this gene or at splice sites [23] . Instead , our results fit the hypothesis that RH1 was blue shifted in the common ancestor of Cetacea , and that SWS1 was convergently knocked out in Odontoceti and in Mysticeti after cetaceans had invaded open ocean habitats . This is perhaps surprising given that SWS1 is well suited to detect the blue light that dominates the open ocean . However , the relative scarcity of S cones in the mammalian retina , which diminishes the efficiency of photon capture under dim light conditions , may have predisposed S-cones to eventual loss through relaxed selection [22] . By contrast , rods are much more efficient at photon capture under dim light conditions because of their higher density in the mammalian retina and their integration with large , sparsely distributed ganglion cells that sum photon detection over huge receptive fields [38] . The preferential retention of a functional copy of LWS rather than SWS1 in some cetaceans may reflect the higher density of L-cones and their greater impact on visual acuity [22] . In addition to the blue shift of RH1 in the ancestral cetacean branch , several amino acid replacements in RH1 imply further adjustments in λmax ( Figure 1 ) . These shifts are generally consistent with the photic environments that are occupied by different cetacean species [20] , [25] . Among these changes are blue shifts in deep-diving physeteroids [sperm whales] and in ziphiids [beaked whales] , a red shift in the common ancestor of Inia and Pontoporia , both of which are found in shallow water environments , and red shifts in several mysticetes [e . g . , Eschrichtius , Megaptera] . Bischoff et al . [25] suggested that the red-shifted pigments that occur in some mysticetes are better adapted to relatively shallower foraging environments than the ancestral mysticete pigment . The blue shifts in Physeteroidea and Ziphiidae occured in parallel and in both cases involve amino acid replacements [serine to alanine] at tuning site 299 [21] , [25] . Sperm whales and beaked whales rank among the deepest diving mammals and specialize on a cephalopod-rich diet [39] . Several phylogenetic studies of anatomical evidence grouped these suction feeding species , presumably based on convergent character states related to their deep diving habits [40] , [41] , but most recent work indicates that ziphiids are more closely related to dolphins and porpoises than to physeteroids [37] , [42] , [43] . Nozawa et al . [44] suggested that Yang's [45] codeml program is not useful for identifying adaptive sites in visual pigments . Our results support Nozawa et al . 's [44] finding that site analyses fail to identify adaptive changes in visual pigments . However , branch-site tests identified five codons in RH1 that have evolved under positive selection on branches with inferred changes in λmax . The ω value for the five positively selected sites is well above one ( 5 . 53–6 . 43 ) , and supports the hypothesis that changes affecting λmax in cetacean RH1 proteins are adaptive . The failure of site analyses to detect positively selected sites in RH1 may be a consequence of mixing positive selection on foreground branches with purifying selection on background branches . Nozawa et al . [44] criticized branch-site tests [45]–[47] for their proclivity to generate false positive results based on simulations , but Yang et al . [48] correctly noted that false positives only occurred in 32/14 , 000 cases , which is much lower than the nominal significance level ( 5% ) and demonstrates that the branch-site test is conservative . Among the positively selected sites , two ( 83 , 292 ) are known tuning sites that in part are the basis for inferring changes in λmax ( Figure 1 ) . Changes at site 83 may also be important in dim-light conditions because the amino acid at this position affects the rate at which photoreceptor cells generate electrical signals [49] . The other three sites ( 7 , 123 , 266 ) have not been predicted to affect λmax . Site 7 occurs in the extracellular domain , site 123 occurs in transmembrane helix III , and site 266 occurs in transmembrane helix 6 [50] . The functional consequences of mutations at these amino acid positions in cetacean RH1 sequences remain unknown , although conformational changes associated with transmembrane domains III and VI of G protein-coupled receptors may be important in receptor activation [51] . Changes in LWS spectral sensitivity coincide with deployment of cetaceans to diverse aquatic habitats ( Figure 1 ) . A blue shift in LWS in stem mysticetes co-occurs with an SWS1 frameshift mutation on the same branch , although the sequence of these events is unclear . An additional LWS blue shift in λmax maps to the common ancestor of Delphinoidea [dolphins , porpoises , beluga] , but the most striking feature of LWS evolution in Cetacea is the convergent knockout of this gene in five different lineages: Balaenopteroidea ( rorquals and gray whale ) , Balaenidae ( bowhead and right whale ) , Mesoplodon bidens ( Sowerby's beaked whale ) , Physeter macrocephalus ( giant sperm whale ) , and Kogia breviceps ( pygmy sperm whale ) ( Figure 1 ) . Given that SWS1 is also debilitated in each of these species ( Figure 1 ) , the genetic data imply that these taxa are rod monochromats . This iterated degeneration of cetacean LWS was not apparent in earlier studies because complete protein-coding LWS sequences had been generated for only a few cetacean species [21] . Historically , the pure rod retina has been proposed as the “extreme” adaptation to low light levels [52] . Walls [52] and McFarland [20] suggested the possibility of rod monochromacy in at least some cetaceans . More generally , early studies on retinal anatomy hinted at this condition in a variety of nocturnal and aquatic mammalian species with rod dominated retinas , including night monkeys , lemurs , tarsiers , chinchillas , seals , and bats [52]–[57] . Recent work has shown that representative cetaceans are instead L-cone monochromats and retain a functional copy of LWS [21] , [26] . Similarly , primates , rodents , pinnipeds , and bats that were previously hypothesized to be rod monochromats are now known to be L-cone monochromats with functional LWS or even cone dichromats with functional LWS and SWS1 [22] , [58]–[61] . The present survey of cetacean opsins , which documents pseudogenization of both SWS1 and LWS in multiple cetacean lineages , vindicates McFarland's [20] hypothesis that some cetaceans are rod monochromats ( Figure 1 ) . To our knowledge these are the only known examples of rod monochromacy in Mammalia or even Amniota . The observation that five independent derivations of mammalian rod monochromacy are all clustered within Cetacea is striking , and suggests that one or more features of cetacean biology have been pivotal in driving the degenerative pattern of opsin evolution in this aquatic clade . The naked mole rat ( Heterocephalus glaber ) is the only other mammal , aside from the cetacean species characterized here , that is known to lack a functional copy of LWS , but H . glaber retains an intact SWS1 and is interpreted as a cone monochromat [62] . This condition contrasts with other mammalian cone monochromats [pinnipeds , dolphins , porpoises , some procyonids , some rodents , some bats] that combine a pseudogenic SWS1 with a functional copy of LWS [22] , [23] , [28] , [60] , [63]–[71] . It has been suggested that cetacean cone monochromats [e . g . bottlenose dolphin , Tursiops truncatus] can distinguish colors , possibly via interactions between LWS and RH1 [38] , [72] , but any vestiges of color vision presumably have been lost in the various rod monochromatic cetacean species documented here ( Figure 1 ) . Among other vertebrates , rod monochromatic taxa are rare and to our knowledge have only been documented in bony and cartilaginous fishes [73]–[81] , caecilians [22] , [82] , and the cave salamander Proteus anguinus [83] , although presumed rod monochromacy based entirely on immunocytochemistry , microscopy or spectral analysis does not preclude the possibility that other minor visual pigment classes exist [77] , [81] , [83] . Most of the rod monochromatic fish species inhabit the deep sea or are nocturnal; caecilians are generally fossorial and/or nocturnal with poorly developed eyes; and the cave salamander Proteus lives in a virtually light-free environment . The phylogenetic evidence for multiple , independent knockouts of both SWS1 and LWS within Cetacea raises the question of why convergent pseudogenization and rod monochromacy evolved in this clade but not in other mammalian groups . All rod monochromatic cetacean species that were genetically characterized in our survey are capable of diving to depths that exceed 100 m , with sperm and beaked whales ranking among the deepest diving mammals [39] , [84]–[88] . The selective pressures on mammalian retinal opsins in deep-water habitats are drastically different from those on land . In the open marine environment , the electromagnetic radiation of visible light is weakened with depth due to absorption and scattering [89] . In the mesopelagic zone ( 150–1000 m ) , down-welling sunlight becomes more monochromatic and the spectrum shifts towards shorter , bluer wavelengths with depth [81] , [82] . Below 1000 m ( bathypelagic zone ) , there is no down-welling sunlight and localized bluish bioluminescence becomes the predominant source of light [90] . A rod-dominated retina is advantageous in dim light conditions [38] . Therefore , the convergent pseudogenization of SWS1 and LWS in multiple cetacean lineages may be an adaptation to deep-water habitats and/or feeding at night on bioluminescent invertebrate prey . Cone opsins have a higher rate of thermal activation [i . e . , dark noise] than RH1 [91] and may interfere with rod sensitivity under scotopic conditions . Thus , combined SWS1 and LWS pseudogenization may have increased RH1 sensitivity in physeteroids and ziphiids that feed in the mesopelagic and bathypelagic zones . Echolocation is a key specialization that has enabled odontocete taxa such as these to forage at night and at great depths on individual prey items , in particular cephalopods [92]; rod monochromatism may be an additional adaptive feature that has enabled predation at depth . Balaenopteroid and balaenid mysticetes are not known to feed in the bathypelagic zone , do not echolocate , and instead batch filter aggregations of small prey items . However , baleen whales do feed at night and much of their diet is composed of bioluminescent prey including krill [93] , [94] . The ability to take advantage of this huge resource offers a compelling selective driver on the evolution of visual systems in Mysticeti , and the detection of schools of tiny prey at night would seem to be problematic without echolocation . The reliance of various mysticete species on RH1 might represent one solution for improved night vision given that rods are more useful than cones for contrast detection and hence picking out schools of prey from the background . Along these lines , the parallel pseudogenization of both cone opsins in Cetacea ( Figure 1 ) could be the result of natural selection favoring an all-rod retina , in which case cone opsins were either selected against because of interference with RH1 , or were rendered ‘jobless’ by the elimination of cones and released from selective constraints on color vision in this aquatic clade [22] . The emergence of Cetacea represents a profound macroevolutionary transition that entails comprehensive remodeling at both the genetic and morphological levels [4] . Our results elucidate key events in the evolutionary history of cetacean opsins , including an initial blue shift of RH1 in stem Cetacea , parallel knockouts of SWS1 in Odontoceti and Mysticeti , and five independent inactivations of LWS in deep-diving cetacean lineages . As correctly surmised by McFarland [20] , some cetaceans are rod monochromats and have evolved eyes that are highly specialized for dim-light vision .
Previously published RH1 , SWS1 , and LWS sequences for Cetacea were combined with new sequences that were generated through PCR and dideoxy sequencing . We targeted complete coding regions of all three opsin genes for representatives of Hippopotamidae and all extant cetacean families ( Text S2 ) . RH1 , LWS , and SWS1 sequences for additional cetartiodactyl families [Bovidae , Cervidae , Suidae , Camelidae] were assembled from Ensembl , Pre-Ensembl , and NCBI based on availability with minor augmentation by new sequences ( Table S1 , Text S2 ) . Aligned sequences for Bos taurus , Sus scrofa , Tursiops truncatus , Vicugna pacos , and available GenBank sequences ( Table S1 ) were used to design PCR primers for SWS1 , RH1 , and LWS . SWS1 ( exons 1–4; partial exon 5; introns 1–4 ) was amplified in five overlapping segments . PCR primers for RH1 ( exons 1–5 ) and LWS ( exons 1–6 ) were positioned in the flanking intronic regions of each exon ( see Text S2 for additional details on PCR reactions ) . Accession numbers for new cetartiodactyl sequences are KC676796–KC677023 ( Table S1 ) . Primer sequences are provided in Table S2 . Sequences were aligned manually using Se-Al [95] . The virtual mRNA alignment lengths were 1014 base pairs ( bp ) for SWS1 , 1092 bp for LWS , and 1044 bp for RH1 . The complete alignment for SWS1 , including exons and introns , was 4163 bp . All alignments for phylogenetic and PAML analyses , along with alignments for non-overlapping PCR amplicons ( exons plus partial introns for LWS and RH1 ) , are provided in Text S3 in nexus format . Phylogenetic analyses were performed with RAxML 7 . 2 . 7 [96] and the GTR + Γ model of sequence evolution . Additional details are provided in Text S2 . Opsin alignments were manually inspected for putative inactivating mutations , including substitutions that result in stop codons , changes at intron splice donor/acceptor sites , and frameshift indels . We also examined SWS1 sequences for a missense mutation at Schiff's counterion site ( E113G; bovine RH1 numbering ) that disrupts opsin-chromophore binding [23] . Ancestral DNA sequences for SWS1 , LWS , and RH1 were reconstructed with the Baseml program implemented in PAML 4 . 4b [45] . We used the REV model and a composite species tree based on McGowen et al . [42] for cetaceans and Gatesy [97] for all other cetartiodactyls . Frameshift mutations and other indels were optimized with Fitch parsimony , as implemented in Mesquite [98] . Spectral tuning in RH1 is influenced by at least 13 amino acid sites [28] , although replacements at only three of these sites ( 83 , 292 , 299 ) fully explain the absorbance difference between cow RH1 ( Bos taurus , λmax = 500 nm ) and bottlenose dolphin RH1 ( Tursiops truncatus , λmax = 488 nm ) [99] . These replacements are D83N , A292S , and A299S . Different combinations of ancestral and derived amino acids at these three sites have been tested in mutagenic studies of Bos RH1 to explain the various λmax values that occur in other cetaceans [21] , [25] . For LWS , Yokoyama [36] suggested a “five-sites” rule whereby λmax values between 510 and 560 in vertebrates can be fully explained by amino acid changes S180A , H197Y , Y277F , T285A , A308S and their interactions . Here , we follow Fasick et al . [21] and Bischoff et al . [25] and provide λmax estimates for newly determined RH1 and LWS sequences based on directly determined λmax values from expressed RH1 and LWS pigments that possess identical amino acids at the same key sites for each of these opsins . It will be important in future studies to perform direct measurements of λmax on reconstructions of ancestral RH1 sequences . Even without these experiments , empirical measurements on a diverse array of opsins from cetacean species and Bos taurus ( wild type and mutagenesis variants ) provide a strong foundation for inferring λmax values in ancestral cetacean sequences [21] , [25] . The Codeml program in PAML 4 . 4b [45] was used to estimate the ratio ( ω ) of the non-synonymous substitution rate [dN] to the synonymous substitution rate ( dS ) at individual sites ( RH1 , LWS ) and on branches ( SWS1 , LWS ) . We also performed branch-site analyses [45]–[47] , [100] on RH1 and LWS sequences . In both cases , branches with predicted changes in λmax ( Figure 1 ) of the relevant opsin were assigned to the foreground , and all other branches were assigned to the background . We used a composite species tree for all cetartiodactyl taxa as detailed above . Statistical tests of neutrality [complete absence of functional constraints] for branches and sets of branches were executed as in Meredith et al . [16] . See Text S2 for details . | The emergence of Cetacea ( whales , dolphins , porpoises ) represents a profound transition in the history of life . Living cetaceans have evolved a spectacular array of adaptations in association with their return to aquatic habitats . Aquatic environments impose challenging constraints on sensory systems , including vision , and the cetacean eye exhibits both anatomical and molecular specializations that enhance underwater sight . Most mammals have one photopigment ( RH1 ) for dim-light vision and two photopigments ( long wavelength-sensitive opsin [LWS] , short wavelength-sensitive opsin [SWS1] ) for daytime , color vision . By contrast , cetaceans have an inactivated copy of the gene that encodes SWS1 . Here , we show that LWS is also inactivated in several cetacean lineages including the giant sperm whale , Sowerby's beaked whale , and balaenopteroids ( rorquals plus gray whale ) . These cetaceans dive to depths of at least 100 meters where the underwater light field is dominated by dim , blue light . The knockout of both cone pigments renders these taxa rod monochromats , a condition that is previously unknown among mammalian species . Rod opsin remains functional in these taxa and is blue-shifted to increase its sensitivity to the available blue light that occurs in deep water conditions . These results further elucidate the molecular blueprint of modern cetacean species . | [
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| 2013 | Rod Monochromacy and the Coevolution of Cetacean Retinal Opsins |
Soil-transmitted helminths ( STHs ) are the most prevalent intestinal helminths of humans , and a major cause of morbidity in tropical and subtropical countries . The benzimidazole ( BZ ) drugs albendazole ( ABZ ) and mebendazole ( MBZ ) are used for treatment of human STH infections and this use is increasing dramatically with massive drug donations . Frequent and prolonged use of these drugs could lead to the emergence of anthelmintic resistance as has occurred in nematodes of livestock . Previous molecular assays for putative resistance mutations have been based mainly on PCR amplification and sequencing . However , these techniques are complicated and time consuming and not suitable for resource-constrained situations . A simple , rapid and sensitive genotyping method is required to monitor for possible developing resistance to BZ drugs . To address this problem , single nucleotide polymorphism ( SNP ) detection assays were developed based on the Smart amplification method ( SmartAmp2 ) to target codons 167 , 198 , and 200 in the β-tubulin isotype 1 gene for the hookworm Necator americanus . Diagnostic assays were developed and applied to analyze hookworm samples by both SmartAmp2 and conventional sequencing methods and the results showed high concordance . Additionally , fecal samples spiked with N . americanus larvae were assessed and the results showed that the Aac polymerase used has high tolerance to inhibitors in fecal samples . The N . americanus SmartAmp2 SNP detection assay is a new genotyping tool that is rapid , sensitive , highly specific and efficient with the potential to be used as a field tool for monitoring SNPs associated with BZ resistance . However , further validation on large numbers of field samples is required .
Intestinal helminths cause a major burden on human health in developing countries , infecting more than 2 billion people worldwide [1] . Hookworms are one of the major STHs and the second most prevalent intestinal helminth of humans [2] , infecting an estimated 438 . 9 million people in resource-constrained countries in the tropics and subtropics [3] , and causing 22 . 1 million disability-adjusted life years [4] . Maternal hookworm anemia can complicate pregnancy , placing both mothers and children at higher risk of mortality . Infected children are compromised with anemia , stunted growth , and physical and intellectual growth deficits [5 , 6] . Hookworm infections are mainly caused by N . americanus and Ancylostoma duodenale , with N . americanus being the most prevalent species , representing ~85% of all hookworm infections , and are associated with more morbidity worldwide than any other STH [7] . Current efforts to control morbidity caused by hookworms rely heavily on large-scale administration of ABZ or MBZ in MDA programs [8] which have been greatly expanded in recent years by massive donations of these anthelmintics . However , a single dose of either drug shows suboptimal efficacy against hookworms [9–13] . Treatment with these drugs is the major hookworm control strategy recommended by the World Health Organization ( WHO ) [14 , 15] as there is no vaccine available . A major concern is that MDA over prolonged periods using the same anthelmintics would exert selection pressures on hookworm parasite populations and favour the development of resistance [8 , 16] . In veterinary nematodes , BZ resistance has been developed and is caused by a single nucleotide polymorphism ( SNP ) in the β-tubulin isotype 1 gene that substitutes tyrosine ( Tyr ) for phenylalanine ( Phe ) at codon 167 or 200 ( TTC>TAC ) or alanine ( Ala ) for glutamate ( Glu ) at codon 198 ( GAG>GCG ) [17–21] . Benzimidazole resistance-associated mutations have been found in many veterinary nematodes particularly in nematodes in the same phylogenetic clade as hookworms [12 , 22] . Such SNPs have already been observed in N . americanus and Trichuris trichiura [23 , 24] . Furthermore , the presence of resistance-associated SNPs at codon 200 and 198 increased with treatment and were significantly higher in individuals who showed a poor response to ABZ ( low efficacy ) than in individuals who responded well to ABZ ( good efficacy ) in T . trichiura [24] . With the possibility that BZ susceptibility could be decreased by repeated rounds of MDA , it is important to monitor the level of resistance-associated SNPs in STHs before resistance becomes clinically manifested . The significant success and expansion of MDA programs for the control of human STHs , including hookworms , increases the urgency to monitor for drug resistance [25] . In human medicine , the egg reduction rate ( ERR ) is the gold standard for measuring drug efficacy and detection of resistance [26] . Relying on this efficacy measure alone is likely to be insensitive for detecting the early stages of the development of resistance and may only detect resistance when resistance allele frequencies are at high levels and treatment failures have already occurred [27 , 28] . Furthermore , the ERR could be inappropriately interpreted as evidence of resistance , if standards similar to those used in the guidelines for anthelminthic of the World Association for the Advancement of Veterinary Parasitology were applied [10] . Molecular-based diagnostic tools are accurate and reliable [29 , 30] . Diagnostic sequencing techniques are reliable and sensitive but are lengthy , complex , and too expensive for large-scale screening programs . With the rapid development of mutation-detection methods , several PCR based techniques have been developed to identify anthelmintic resistance , such as allele-specific PCR [18 , 31 , 32] , RFLP-PCR [33 , 34] , real-time PCR [2 , 35–37] and pyrosequencing [24 , 30] . These methods are accurate and highly sensitive; however , they are unsuitable for large-scale screening of BZ resistance due to their complexity and the need for expensive equipment . Therefore , developing a rapid , simple , and accurate molecular method for monitoring for BZ resistance , without the need for expensive equipment , is highly desirable . Here we report the development of a novel genotyping assay to monitor for the presence or absence of β-tubulin polymorphisms in N . americanus , using the SmartAmp2 method ( Smart Amplification Process ) . SmartAmp2 is a unique DNA amplification method for rapid detection of target DNA or genetic polymorphisms under isothermal conditions , in a single step which eliminates the need for PCR amplification or a thermocycler [38] . SmartAmp2 is similar to LAMP , but LAMP technology uses symmetrical primer design . In SmartAmp2 , however , the asymmetrical primer design is a key feature responsible for the suppression of the mismatch amplification , which minimizes the free-primer hybridization , other priming events and alternative mis-amplification pathways [39] . This method can detect a SNP with high specificity and sensitivity within 30 min [38] . The SmartAmp2 method uses Aac DNA polymerase which has strand displacement activity , combined with asymmetric primer design and Thermaus aquaticus MutS ( Taq MutS ) enzyme , which give the assay high specificity [38 , 39] . Taq MutS is a mismatch binding protein [40] , which recognizes a mismatched pair between the target DNA and the discrimination primer . This protein binds to mismatched nucleotides and blocks the dissociation of mismatched DNA by the Aac polymerase , which inhibits further amplification from non-target DNA [39] . The aim of this study was to develop molecular genotyping assays for the detection of the three β-tubulin polymorphisms associated with BZ resistance in other nematodes and validate their specificity and reliability in human hookworm samples and in fecal samples .
Hookworm eggs and larvae were collected during a study conducted in Sri Lanka on the comparative efficacy of different MBZ polymorphs for the treatment of hookworm infections and molecular markers of drug resistance in hookworms . Ethical clearance for the study was granted by the Ethics Review Committee of the Faculty of Medicine , University of Kelaniya ( P39/04/2010 ) . Ethical approval ( study 2535 ) was also obtained by Dr . Patrick Lammie , CDC , Atlanta , GA , and included the collection and examination of fecal samples from Haiti for helminth eggs , and DNA analysis of helminth eggs . Oral informed consent was obtained from all human adult participants and from parents or legal guardians of minors , as described previously [24 , 30] . N . americanus adult worms , larvae and eggs were available in our lab [22 , 28] . Additional larvae ( L3 ) were cultured using the Harada-Mori technique from fecal samples collected in the field in Sri Lanka . All egg and larval samples were preserved in 70% ethanol after collection . Eggs and larvae were isolated under a dissecting microscope using a 10 μl pipette . Genomic DNA was extracted from larvae and eggs as described [41] . Lysis buffer was prepared as follow ( KCl [50 mM] , Tris [10 mM] with pH 8 . 3 , MgCl2 [2 . 5 mM] , 0 . 45% Nonidet P-40 , 0 . 45% Tween 20 and 0 . 01% gelatine ) . Ten μl proteinase-K [10 μg / ml] ( Invitrogen , Life Technologies; Burlington , ON , CA ) and β-mercaptoethanol ( Sigma-Aldrich , ON , CA ) were added to 1 ml of this buffer just before use . Twenty-five μl of lysis buffer mix was added to previously isolated eggs and larvae and then tubes were incubated at 60°C for 2 h . Genomic DNA was extracted from adult N . americanus using QIAamp DNA mini kit ( Qiagen , Hilden , Germany ) according to the manufacture’s protocol . To assist with SmartAmp2 development , control plasmids were engineered by site-directed mutagenesis as previously described [23] . Extracted genomic DNA from individual adult worms was used to amplify a fragment of the N . americanus β-tubulin isotype 1 gene including the codon positions 167 ( exon 4 ) , 198 and 200 ( exon 5 ) . Specific forward primer 5’-AAGAAGCTGAAGGATGTGACTG-3’ and specific reverse primer 5’-GAAGCGA AGACAGGTAGTAACAC-3’ ( Invitrogen ) , were designed in the exonic regions of N . americanus genomic DNA sequence ( GenBank accession no . EF392851 ) . The PCR master mix contained 2 μl 10×PCR buffer , 1 μl ( 50 mM ) MgSO4 , 1 μl dNTP [10mM] , 1 μl of each forward and reverse primer [10 μM] , 1 U Platinum Taq DNA polymerase High Fidelity ( Invitrogen ) , 2 μl genomic DNA and distilled H2O to reach a final volume of 20 μl . Negative controls ( no template ) were also included for quality control . The PCR reaction conditions were 94°C for 3 min , followed by 35 cycles at 94°C for 45 s , 57°C for 45s and 68°C for 1 min and a final extension at 68°C for 10 min . The resulting PCR fragments were Sanger sequenced to confirm the presence of sensitive alleles at codon positions 167 , 198 , and 200 . Plasmids carrying mutations at position 167 , 198 , or 200 ( MT ) were engineered by site-directed mutagenesis . Primers for MT plasmids ( outer primers and inner primers carrying the mutant alleles ) are shown in Table 1 . Amplified WT or MT fragments were cloned into TOPO-TA-Cloning vector ( Invitrogen ) . Plasmid DNAs were extracted and purified using QIAprep miniprep plasmid kit ( Qiagen ) and subsequently sequenced by Sanger sequencing at the McGill University/Genome Quebec Innovation Centre , Montreal , Quebec . The purity and quantity of DNA in clones was measured using a Nano Drop Spectrophotometer , ND-1000 ( Implen , Munich , Germany ) . Diluted WT and MT plasmids were used for assay optimization and development , and to determine the detection limit of each assay . Primer sets were designed to amplify and detect putative β-tubulin mutations in the hookworm N . americanus . The online software version 1 . 1 ( SMAPDNA ) , was made available by KK . DNAFORM , Japan ( http://www . dnaform . jp/en/ ) , and used initially to design the primers . Several primer sets were suggested by the software and further refinement in primer design was made by trials and evaluation tests and the best candidate primer set was selected for each assay . A primer set consists of five specific primers , the folding primer ( FP ) , turn-back primer ( TP ) , boost primer ( BP ) , and two outer primers ( OP1 and OP2 ) , designed to recognize six different sequences on the target sequence . A set of primers has two discrimination primers , specific for either the WT or MT allele , differing only in one nucleotide at the 3’-end or the 5’-end . In this work , BP primer was selected to be the discrimination primer . The location and the sequences of primers for each SNP target are illustrated ( Fig 1 ) . The SmartAmp2 assay was optimized using different concentrations of primers , MgSO4 , and betaine and carried out in 25 μl reactions containing ( 2–3 μM ) TP/FP , ( 1–1 . 5 μM ) BP , ( 0 . 25–0 . 4 μM ) OP1/OP2 ( Invitrogen ) 1 . 4 mM dNTPs ( Invitrogen ) , ( 0 . 8–1 . 4 M ) betaine ( Sigma- Aldrich ) , 1x isothermal buffer ( 20 mM Tris-HCl ( pH 8 . 6 ) , 10 mM KCl , 10 mM ( NH4 ) 2SO4 , ( 4–8 mM ) MgSO4 , 0 . 1% Tween 20 , 1/100 , 000 dilution SYBR Green I ( Invitrogen ) , 1 μg Taq MutS ( Nippon Gene , Toyama , Japan/Wako Chemicals , Richmond , VA , USA ) and 12 U Aac DNA polymerase ( KK . DNAFORM , Yokohama , Japan ) . Control plasmids corresponding to WT or MT alleles were used to develop each assay and to evaluate the accuracy of genotyping between different primer sets . One microliter of WT or MT plasmids ( ~10 ng ) was heated at 95°C for 3 min before being added to the assay . Reactions were incubated at 60°C for 60 min . The Rotor-Gene Q system ( Qiagen ) was used to maintain isothermal conditions and to monitor the change in fluorescence intensity of the intercalating SYBR Green I during the reaction . Assays were evaluated in terms of amplification ( full match ) and non-amplification ( mismatch ) within 60 min . Further optimization was performed to estimate the sensitivity and reproducibility of the assays in individual samples and pools . Assays were tested on individual eggs/larva and pools ( 10–20 eggs/larva per pool ) . After DNA extraction from eggs and larvae using lysis buffer and proteinase K , 3 μl of this crude lysate was added to each reaction and then tubes were incubated at 60°C for 90 min . For evaluation of the sensitivity and the specificity of the assay to detect MT alleles in a background of WT DNA , MT plasmid DNA ( ~5 ng ) was mixed with WT plasmid ( ~5 ng ) in serial dilutions of 1:1 , 1:9 , 1:99 and 0: 100 and assays were carried out using the MT detection primer sets . These experiments were repeated twice and each DNA sample was analysed in duplicate . Validation on field samples was performed using 110 individual samples obtained from Sri-Lanka . Pools of 20 larvae previously collected under microscopy for each individual sample were analyzed . Larval samples were digested using 25 μl of previous lysis buffer mix . From this crude lysate , 3 μl were added to each reaction after a DNA heating step at 95°C for 3 min . Assays were carried out in 25 μl reactions as previously explained . Positive ( adult worm genomic DNA ) and negative controls were always included as a reference in each experiment . Tubes were incubated in a real-time PCR at 60°C for 90 min . PCR amplification of β-tubulin gene around codon positions 167 , 198 and 200 were performed . Primers were designed as follow . For codon 167 , forward and reverse primers were respectively 5’-AAGAAGCTGAAGGATGTGACTG-3’ and 5’-GGGTGGTTCCAGGCT GATGC-3’ . For codons 198/200 ( exon 5 ) , forward and reverse primers were respectively , 5’-GGTTTCCGACACTGTGGTTG-3’ and 5’-GAAGCGAAGACAGGTAGTAACAC-3’ . The PCR master mix contained 5 μl 10×PCR buffer , 1 . 25 μl ( 50 mM ) MgSO4 , 1 μl dNTP [10mM] , 1 μl of each forward and reverse primer [10μM] ( Invitrogen ) , 1 U Platinum Taq DNA polymerase High Fidelity , 3 μl of each DNA sample and distilled H2O up to 50 μl . The PCR reaction conditions were 94°C for 4 min , followed by 35 cycles at 94°C for 45 s , 57°C for 45 s and 68°C for 1 min and a final extension at 68°C for 5 min using a thermocycler ( Biometra , Göttingen , Germany ) . Amplicons were identified on 2% agarose gel and visualized under UV ( Bio-Rad Molecular Imager Gel Doc XR System ) . PCR products were sent to McGill University/Genome Quebec Innovation Centre , Montreal , Quebec for conventional Sanger sequencing to confirm and validate the genotyping results previously obtained with SmartAmp2 assay . Electropherograms were analyzed with Sequencher software ( version 4 . 10 . 1 ) to identify the genotypes . To validate the specificity of the assay on DNA extracted from fecal samples , genomic DNA was extracted from parasite-free fecal samples spiked with N . americanus larvae ( ~1000 ) , or was extracted from the same fecal samples ( with no larvae added ) and used as a negative fecal control . As a positive control , DNA was extracted from purified L3 larvae ( ~1000 ) . A protocol of QIAamp fast DNA stool mini kit ( Qiagen ) was used with some modifications . The fecal suspension ( ~ 1g ) was kept on at 30°C to evaporate the ethanol . Then the InhibitEX buffer ( QIAamp kit ) was added together with approximately 150 mg of 0 . 5 mm glass beads ( Sigma-Aldrich ) and the sample vortexed on a Vortex-Genie 2 at 3000 rpm for 3 cycles of 5 min each , to disrupt the cell wall of the helminth samples . The fecal suspensions were then heat-shocked at 95°C for 5 min and placed in liquid nitrogen for 2 min , for 5 cycles . Then the proteinase K and AL buffer ( QIAamp kit ) were added and heated at 60°C for 1 h . The suspension was centrifuged at 14 , 000 rpm for 1 min and the procedures outlined in the QIAamp kit were then followed . Three μl of the spin column eluate ( 100 μl ) was taken for each SmartAmp assay . The reaction mix for the SmartAmp assay was prepared and carried out as previously mentioned . Negative controls were used in all experiments .
Various sets of candidate primers were designed to genotype the β-tubulin gene at the three SNP positions . Screening of these primer combinations and assay conditions yielded an ideal primer set for each assay , which completed the amplification within 20–30 min from the target DNA sequence in a plasmid . Primer sets were chosen based on first , the speed and yield of the amplification , and second , the primer efficiency to discriminate between the full match and mismatch amplification . In the initial optimization of the assay and without the inclusion of Taq MutS , a 15 min delay for the mismatch amplification was achieved . With the inclusion of Taq MutS , a complete suppression of the mismatch amplification was observed up to 60 min ( S1 Fig ) . All primer sets that displayed late full match amplification or a short delay between the full-match and mismatch amplification were omitted . The location and sequences of primers for each SNP target are shown ( Fig 1 ) . The SNP 167T/A ( Phy167Tyr ) occurs in exon 4 of the β-tubulin gene . A set of primers was designated TP , FP , BP , and OP2 . The 5’-end of the BP discriminates the polymorphism 167T or 167A . In this assay , one of the outer primers was omitted to avoid the design of primers in the intron region . As the assay is highly specific , any polymorphism in the intron regions could affect the reproducibility of the assay . Both 198A/C SNP ( Glu198Ala ) and 200A/T SNP ( Phy200Tyr ) reside on exon 5 of the β-tubulin gene . One set of primers was designed ( TP , FP , OP1 and OP2 ) for both assays but a specific BP ( discrimination primer ) was designed in which the 3’-end of 198BP or 200BP discriminates the polymorphism at 198A/C or 200A/T , respectively . For each assay , BP primers were designed to be specific either for a MT variant or a WT allele . This unique primer design for SNP 198 and 200 makes reaction setup simple and easy to perform within a short time for a large number of samples . Before testing the assay on hookworm samples , constructed WT and MT plasmids were used as DNA templates for assay optimization and development . Sequencing the MT plasmids revealed that the desired mutations at codons 167T/A , 198A/C and 200T/A of the β-tubulin gene were generated . The optimal amplification results were obtained when the reaction mixture contained 2 μM each TP/FP , 1 μM BP and 0 . 25 μM OP1/OP2 with 0 . 8 M betaine and 8 mM MgSO4 . Primer sets with a specific BP for detecting the 167T/A , 198A/C , or 200T/A point mutations rapidly amplified the MT plasmid within 20–30 min , whereas the same primer sets failed to amplify the WT plasmid within 60 min . The WT primer sets amplified the WT plasmid but not the MT plasmid . Each assay was run in duplicate and all negative control reactions included in the experiments showed no amplification . These results confirmed that the SmartAmp2 assays were optimized as they accurately discriminated the full match amplification from the mismatch with complete suppression of the mismatch amplification . As an example , Fig 2 shows 2 different assays using a WT primer set ( 2A ) and a MT primer set ( 2B ) for the 200T/A β-tubulin SNP . Further optimizations on single eggs/larva and pools of eggs or larvae were performed and full-match amplification using the WT primer sets was achieved with complete suppression of the mismatch amplification when the MT primer sets were used . Our assays amplified and genotyped DNA from single eggs and larva with high sensitivity and specificity within 40–50 min ( Fig 3B ) . The WT primer set also amplified DNA from pools of eggs/larvae within 30–40 min with complete suppression of the amplification when the MT primer set was used ( Fig 3B ) . The SmartAmp2 genotyping results and the Sanger sequence were always consistent . To determine the specificity of each assay for detection of the MT alleles in a mix with WT DNA , serially diluted plasmids representing MT alleles at codon 167 , 198 or 200 , and wild-type plasmids , were used . The results of SmartAmp2 assays , using full-match MT primer sets for each SNP target , are illustrated ( Fig 4 ) . The MT alleles were detected even when present at only 1% of the WT DNA approximately at 50 min for the SNP 198 and 200 assays and at 60 min for SNP 167 assay . Up to 90 min , no background amplification was observed from the WT DNA or the negative controls . These results show that the assay is highly sensitive as it allowed detection as low as 1% of the MT alleles in a mix of WT DNA and the ability to genotype single and pooled hookworm eggs and larvae was also demonstrated . High sensitivity and specificity of the assay are particularly important in screening for mutations in individual samples with low levels of STH infections . To validate the accuracy and specificity of the SmartAmp2 assays to handle field samples , we analyzed 110 individual field samples and a pool of 20 larvae per sample was examined . We detected the presence or absence of WT and MT alleles in all the samples within 30–40 min after incubation at 60°C . No background amplification ( mismatch ) was observed within 90 min . No amplification was observed from the negative controls . , Three assays were performed for each sample to screen the three codon positions 167 , 198 and 200 . For codon 167 and 200 , the WT primer set amplified the DNA target within 30–40 min . No amplification was observed with the MT primer set . None of the larval samples revealed significant levels of polymorphisms either at position 167 or 200 . However , a polymorphism was identified at codon position 198A/C in some samples . The MT primer set for codon 198 allowed the amplification of the DNA target within 30–40 min ( full match amplification ) . The SNP198 detection primers recognized the SNP 198A/C of the β-tubulin gene to discriminate homozygous 198A/A ( WT ) , mixed 198A/C ( WT/MT ) , and homozygous 198C/C ( MT ) in genomic DNA samples . From the 110 samples examined by SmartAmp2 for the 198A/C assay , 90 samples were homozygous WT , 12 were mixed , and 8 were homozygous MT . Selected results for each of the three different genotypes using the WT and MT primer sets are illustrated ( Fig 5A ) . The accuracy of our SmartAmp2 results was tested by amplifying the SNP target by PCR . The resultant amplicon was Sanger sequenced and showed concordance with the SmartAmp2 results ( Fig 5B ) . Positive ( spiked with L3 ) and negative fecal samples were assessed in triplicate by SmartAmp2 assay . Full-match amplification was obtained only from positive fecal samples using the WT primer set within 40–45 min . High amplification efficiency was achieved when samples were diluted 1:4 in distilled H2O . The negative fecal samples and the negative control remained at baseline for at least 90 min . DNA extracted from purified L3 ( positive control ) produced a slightly faster amplification signal than genomic DNA from spiked fecal samples ( Fig 6 ) .
Diagnostics are vital to achieve successful elimination of parasitic infections and to aid against emerging pathogen resistance to the limited number of anti-parasitic drugs . The low sensitivity of current field diagnostic tools could miss the early stages of resistance . The lack of rapid , simple and reliable diagnostic tools for intestinal nematodes prevents accurate estimation of the distribution of BZ-resistant populations , the determination of at-risk populations and the burden of disease [42] . In this study , we developed a new SNP genotyping assay based on the SmartAmp2 method for monitoring β-tubulin polymorphisms . Primer sets were selected and optimized specifically to target F200Y , F167Y ( TTC/TAC ) and E198A ( GAG/GCG ) SNPs in the hookworm N . americanus . SNP-detection primer sets were able to efficiently and rapidly discriminate MT and WT genotypes using plasmids as DNA templates . Assays showed high reproducibility and sensitivity for detecting genomic DNA from pools and single egg/larval DNA in a single amplification and detection step . Compared with PCR-based methods , in order to genotype single egg/larval DNA , a nested PCR using the same forward and reverse primers is required , followed by gel electrophoresis and pyrosequencing [24]; a multistep technique that is time consuming and increases the risk of contamination as a result of manipulating PCR products . Additionally , the SmartAmp2 assays showed high specificity and allowed the detection of as little as 1% of the MT alleles in a mix with WT plasmid . Assays detected the E198A ( GAG/GCG ) SNP in the N . americanus larvae . The MT-detection primer set detected and identified the MT alleles in pools of larval samples from Sri Lanka . To our knowledge , this is the first time that 198SNP has been detected in N . americanus . Amplification was evident within 30–40 min using as few as 20 larvae per pool . In addition , genotyping from single larva and eggs was achieved within 40–50 min . Fecal samples spiked with N . americanus larvae were processed in the SmartAmp2 assay and the results showed high tolerance of the Aac polymerase to fecal inhibitors . No significant difference in the amplification efficiency between spiked fecal extracts and purified larval DNA was observed and any slight difference could be explained by the presence of remaining fecal inhibitors in the fecal samples . SmartAmp2 assays for genotyping hookworm samples are highly sensitive and specific using the Aac polymerase which is tolerant to inhibitors in stool samples; however , the assay sensitivity in stool samples could be compromised by the capacity of the extraction method to obtain good quality and quantity of purified DNA , the number of eggs in stool samples particularly in individuals with low level of infection , the amount of stool used for DNA extraction and an uneven distribution of eggs in the stool . To improve the performance of SmartAmp2 assay in fecal samples , sample collection and processing should be simple and ensure high DNA yields . Preliminary enrichment of eggs through sugar flotation followed by repeated cycles of freeze/boiling to crack the egg shell and liberate DNA [43] may significantly improve DNA recovery , assay sensitivity and reproducibility . Commercial DNA extraction methods are reliable and efficient for removing PCR inhibitors present in fecal samples; however , these methods use a small amount of feces and this could affect the sensitivity of the detection assay . SmartAmp2 assay is relatively inexpensive; the main costs are for the Taq MutS and Aac polymerase . SmartAmp2 Primers used in this study were regular primers not HPLC primers . Additionally , reducing the reaction mixture to 10 μl and using in-house prepared buffers and reagents also reduces cost . Our data were generated on a Real-Time PCR system to follow the formation of double stranded DNA in real time , using SYBR green . However , end point detection system for monitoring fluorescence that would allow high throughput analysis of samples in a 96-well microplate format could be employed . Other approaches for visualizing the formation of DNA residues could be applied using fluorescence dyes that allow colorimetric inspection of the results . In a SmartAmp2 assay , the presence of both the WT and MT alleles can be detected in a sample , as amplification would occur with both WT and MT primer sets . The relevance would be that if the MT is detected , as well as the WT , there would be the potential for some parasites being resistant and that with further anthelmintic selection the frequency of the MT allele might increase , increasing the risk of phenotypic resistance . The absence of definitive evidence of anthelmintic resistance in human parasites does not mean that resistance alleles are not present , at least at low frequencies . Such resistance alleles could increase with prolonged and repeated selection pressure . The lack of detection of phenotypic resistance may , in part , be due to the lack of a reliable and sensitive method to monitor for resistance alleles before and after BZ treatment in control programs [44] , a low frequency of resistance alleles , and the probability that BZ resistance is recessive , as it is in veterinary parasites [45] . The present study provides evidence that the SmartAmp2 method targeting β-tubulin polymorphisms in N . americanus allowed direct detection of SNPs of a target DNA sequence in fecal samples . Additionally , these results indicate that our SNP genotyping assays are rapid , simple , very sensitive and highly specific which provide a unique tool for investigating the possibility of developing BZ resistance in the hookworm N . americanus . The development of sensitive and practical methods for early detection of resistance using molecular diagnostic tools that could be adapted to the field is urgently needed in order to sustain the benefits of helminth control programs . | Hookworms are amongst the major STHs and the second most prevalent intestinal helminth of humans . Large-scale treatment with the benzimidazoles ( BZs ) albendazole or mebendazole is the major control strategy against STHs in mass drug administration ( MDA ) programs . Prolonged and repeated treatment with the same anthelmintics has led to the emergence of widespread BZ resistance in veterinary parasites which is caused by a single nucleotide polymorphism at codon 200 , 167 or 198 in the β-tubulin gene . There is a considerable concern that prolonged use of the same anthelmintics with suboptimal efficacy against hookworms , may select for resistant parasites and favour the development of resistance . We developed a novel genotyping assay to screen for β-tubulin polymorphisms in N . americanus , using the SmartAmp2 method . SmartAmp2 is a unique genotyping technology that detects a mutation under isothermal conditions with high specificity and sensitivity . The N . americanus SNP detection assay is rapid , sensitive and highly specific and has the potential to be used in the field for the detection of SNPs associated with BZ resistance . | [
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| 2016 | Isothermal Diagnostic Assays for Monitoring Single Nucleotide Polymorphisms in Necator americanus Associated with Benzimidazole Drug Resistance |
Understanding the molecular basis for phenotypic differences between humans and other primates remains an outstanding challenge . Mutations in non-coding regulatory DNA that alter gene expression have been hypothesized as a key driver of these phenotypic differences . This has been supported by differential gene expression analyses in general , but not by the identification of specific regulatory elements responsible for changes in transcription and phenotype . To identify the genetic source of regulatory differences , we mapped DNaseI hypersensitive ( DHS ) sites , which mark all types of active gene regulatory elements , genome-wide in the same cell type isolated from human , chimpanzee , and macaque . Most DHS sites were conserved among all three species , as expected based on their central role in regulating transcription . However , we found evidence that several hundred DHS sites were gained or lost on the lineages leading to modern human and chimpanzee . Species-specific DHS site gains are enriched near differentially expressed genes , are positively correlated with increased transcription , show evidence of branch-specific positive selection , and overlap with active chromatin marks . Species-specific sequence differences in transcription factor motifs found within these DHS sites are linked with species-specific changes in chromatin accessibility . Together , these indicate that the regulatory elements identified here are genetic contributors to transcriptional and phenotypic differences among primate species .
Understanding the molecular basis of phenotypic differences between humans and other primates has been a priority in medicine , behavior , and evolution research [1]–[3] . The genetic basis for these differences can now be explored genome-wide due in part to the rising number of completely sequenced primate genomes . However , finding genotype-phenotype connections is difficult since the vast majority of sequence changes do not contribute to phenotypic differences across species . It was hypothesized over 40 years ago that phenotypic differences between humans and our closest primate relatives are shaped largely by changes in non-coding regulatory elements [4] . Variation in gene regulation have been indirectly confirmed by studying gene expression differences across matched cell or tissue types isolated from different primates [5]–[12] , but these studies have failed to pinpoint the regulatory elements responsible for these changes [13] . Genome-wide scans of non-coding DNA sequences under branch-specific positive selection have identified putative regulatory elements that have undergone functional changes [14]–[16] . These studies identified hundreds of regulatory regions with evidence of accelerated sequence substitution during human origins , possibly reflecting adaptive changes in gene regulation . Scans for selection do not , however , provide information about the functional or trait consequences of these evolutionary changes . Understanding the relationship between mutation , natural selection , and variation in gene regulation is an important goal in evolutionary genomics . Heritable differences in gene expression must have a genetic basis , but exactly what sequence variants have led to these differences are largely unknown . In this study , we used changes in chromatin configuration to better understand this genotype-phenotype relationship . We identified evolutionary conserved and altered regulatory element activity by performing genome-wide DNase-seq [17] , [18] in primary skin fibroblasts and lymphoblastoid cell lines ( LCLs ) isolated from three human and three chimpanzee individuals ( Figure 1a and Table S1 ) . Each DNase-seq experiment identifies nucleosome-depleted DNaseI hypersensitive ( DHS ) sites that mark all types of regulatory elements , including promoters , enhancers , silencers , insulators , and locus control regions . The comprehensiveness of this assay is supported by ChIP experiments for active histone marks , p300 , CTCF , and other transcription factors [19]–[21] . In addition to human and chimpanzee , we performed DNase-seq on fibroblasts from three Rhesus macaque individuals to polarize human-chimpanzee chromatin differences and to distinguish between gains and losses of regulatory elements on the human and chimpanzee branches ( EBV-derived lymphoblastoid cells are not available for this species ) . We also performed Digital Gene Expression sequence ( DGE-seq ) experiments using the same cell cultures to simultaneously compare levels of mRNA abundance [7] , [22] . Analyses of these data provide insights into the relationship between evolutionary changes in regulatory elements , their tissue-specific activity , and the resulting functional consequences in gene expression .
To directly compare DNase-seq data generated from human and non-human primate fibroblast and lymphoblastoid cell line ( LCL ) samples , we mapped all data to the human genome ( build hg19 ) . Non-human DNase-seq sequences were first aligned to their native primate genome and then converted to human coordinates using liftOver [23] ( Figure 1a ) . We limited analyses to high confidence orthologous regions of the human , chimp , and macaque genomes to eliminate potential artifacts due to mis-aligned , missing sequence , or CNVs ( Materials and Methods ) . Comparisons across individuals within a species and against tiling array DNase-chip [24] , [25] data generated from the same material supported data accuracy and reproducibility ( Materials and Methods and Table S2 ) . DNase-seq signals from individuals within a species were more highly correlated than signals from different species ( Figure 1b ) . Human and chimpanzee DNase-seq signals from fibroblasts were better correlated than human and macaque signals as expected since human and chimp share a more recent common ancestor . Chromatin structure differed more in cell types ( fibroblasts vs . LCLs ) from the same species than in the same cell-type across different species ( Figure 1b ) . For example , human and chimpanzee fibroblast DNase-seq signals are more similar than human fibroblast and human LCL DNase-seq signals . The same correlation patterns were also found in gene expression data generated from the same samples ( Figure 1c ) . We identified genomic regions exhibiting significant differences in DNase-seq signal between species [26] ( Materials and Methods ) . Data from macaque samples were used to classify regions as DHS gains or DHS losses on the human or chimpanzee branch ( Materials and Methods ) . More specifically , we defined a human DHS gain as a region with significantly more DNase-seq signal in human than in either chimpanzee or macaque ( Figure 2a ) , and a human DHS loss as a region with significantly less DNase-seq signal in human than in either chimpanzee or macaque ( Figure 2b ) . In essence , these data identify regulatory regions that originated or disappeared in fibroblasts during human origins . Chimpanzee DHS gains and DHS losses were similarly defined ( Figure S1 ) . For approximately 90% of gains , a corresponding DHS site was completely absent in all three individuals from each of the other species ( Figure 2f ) . For the remaining sites , DHS sites were annotated in multiple species , but a consistently higher DNase-seq signal was present in one species compared to the others ( data not shown ) . We found that the majority of the human DHS gains ( 72–79% ) and chimpanzee DHS losses ( 73–74% ) , and a minority of the human DHS losses ( 11–27% ) and chimpanzee DHS gains ( 8–17% ) , overlapped a DHS site found in one or more of three independently derived human fibroblasts ( Figure 3a , Table S3 ) . We also found similar trends comparing six independently derived LCLs analyzed by our group ( Figure 3b , Table S4 ) , and 20 independently derived human fibroblast samples analyzed by another ENCODE group ( Figure S2a-S2b ) [27] , [28] . These provide evidence that the identified DHS gains and losses represent significant and reproducible functional changes between species . Fibroblasts have been shown to have specific expression profiles associated with different biopsy locations [29] , [30] . We note that DHS gains and losses are not enriched around these genes ( Materials and Methods ) . Identified DHS gains ( Figure 2a ) and losses ( Figure 2b ) deviated in sequence read depth from the general chromatin spectrum ( Figure 2c ) . To more directly compare DHS gains and losses with sites that do not change between species , we also identified a set of DHS regions with similar DNase-seq signal intensity across all three species , which we call Common DHS regions ( Figure 2d , Materials and Methods , Supplemental data file 1 in Dataset S1 ) . Using a false discovery rate ( FDR ) of 1% , we detected 836 human DHS gains , 286 human DHS losses , 676 chimpanzee DHS gains , 211 chimpanzee DHS losses , and 1259 Common regions ( Supplemental data file 1 in Dataset S1 ) . The higher number of DHS gains compared to DHS losses could be due to purifying selection , or more simply may be related to the asymmetry in their detection criteria ( see Materials and Methods for a more complete discussion ) . True species-specific DHS gains and losses could not be identified in LCLs due to the lack of macaque EBV-derived LCL samples . However , we identified 103 DHS sites with higher DNase-seq signals in human ( LCL human DHS gain ) , 181 DHS sites with lower signals in human ( LCL human DHS loss ) , and 1583 DHS sites with similar signals in both ( LCL common DHS; Supplemental data file 1 in Dataset S1 ) . Similar numbers of gains and losses were found when comparing chimpanzee DNase-seq data to data from an independent set of human fibroblasts and LCLs at the same FDR ( Figure S3a–S3b ) . Furthermore , only 66 differential open chromatin sites were detected when comparing human fibroblast data to additional independently derived human fibroblasts . Likewise , only 1 differential DHS site was detected when comparing human LCLs to additional independently derived human LCLs . This is less than 1% of all differential open chromatin sites when comparing human vs . chimpanzee , indicating a low false positive rate ( Figure S3a–S3b ) . As part of the ENCyclopedia Of DNA Elements ( ENCODE ) project [31] , we have generated DNase-seq data from 27 diverse human cell types [32] ( Table S3 , Crawford unpublished ENCODE data ) . We determined the overlap of our identified DHS gains and losses in fibroblasts with DHS sites in these other human cell types . Seven hundred and sixty-seven ( 92% ) fibroblast human DHS gains were found in at least one of three other independently derived human skin fibroblast ENCODE cell lines from normal ( Fibrobl ) and diseased individuals ( Parkinson's: FibroP; Progeria: ProgFib ) supporting the reproducibility of these data ( Figure 4a , Figure S3a ) . Additionally , human DHS gains showed a high level of overlap with some , but not all , non-fibroblast human cell types ( Figure 4a , Table S4 , Figures S4 , S5 ) . This suggests that DHS gains are largely cell-type specific . Few human DHS losses were identified as a DHS site in any of the other human cell types ( Figure 4b and Figures S4 , S5 ) . In contrast , Common DHS sites were detected in most other human cell types ( Figure 4c and Figures S4 , S5 ) suggesting DHS sites active among all three primates have more general roles in regulating transcription . Similar trends were seen when comparing LCL human DHS gain/loss/common regions ( Table S5 ) . This suggests Common DHS sites mark DHS sites present in most or all non-human primate cell types , as can be seen for chimp lymphoblast DHS sites ( Figure S6i ) . Expected chimp and macaque DNase signal intensity are detected in orthologous regions ( Figure 4d–4f ) . Similar to previous analysis of cell-type specific DHS sites [32] , we found species-specific gains and losses of DHS sites depleted in promoter regions relative to Common DHS sites and enriched in distal intergenic regions and within introns ( Figure 2e ) . We also compared chimpanzee DHS gains and losses to DNase-seq results from a diverse set of 27 human cell types . We found that chimpanzee DHS gains did not largely overlap with DHS sites from any of the 27 human cell types ( 5–23% , Figure S6a , Table S4 ) while chimpanzee DHS losses were more likely to overlap human DHS sites , especially those from human fibroblasts ( 73% , Figure S6b , Table S4 ) . Thus , comparisons to diverse cell types indicate that Common DHS sites have been selectively maintained through millions of years of primate evolution suggesting a role in housekeeping function . In contrast , more recently evolved DHS sites unique to humans and chimpanzees are likely functional in a small fraction of cell types with related functions . Species-specific DHS sites were compared to cell-type matched human ChIP-seq data for multiple active histone marks and transcription factor binding sites . We found that human-only DHS sites were better associated with these marks compared to chimpanzee-only DHS sites ( Figure 5 ) . This enrichment was highest for H3K4me1 , H3K4me2 , H3K4me3 , and H3K27ac , consistent with chromatin marks predictive of enhancers [20] , [33] ( Figure 5 , Table S6 ) . H3K4 methylation signals were detected in a higher percentage of LCL human DHS gains compared to Common DHS sites , while CTCF , a known insulator protein , is enriched in LCL Common DHS sites ( Figure 5 , Table S6 ) . The combination of adjacent chromatin marks and their location relative to genes ( Figure 2e ) provides further evidence that species-specific regulatory elements are functional . These data suggest most regulatory elements gained or lost after the human-chimpanzee divergence are preferentially associated with enhancers , while Common regions are preferentially associated with promoters and insulators . We expect species-specific DHS sites that contribute to phenotypic differences would be located near genes differentially expressed across species . To test this , we measured the proximity of fibroblast DHS site gains and losses to genes with variable expression ( Figure 1a ) . From matched fibroblast expression data , we used edgeR [26] analysis to identify 1047 human upregulated genes , 881 human downregulated genes , 785 chimpanzee upregulated genes and 788 chimpanzee downregulated genes ( Supplemental data file 1 in Dataset S1 ) . Human DHS gains were significantly enriched ( permutation test , P value = 0 . 00039 ) near genes with increased expression in human and depleted ( P = 0 . 008 ) near genes with decreased expression in human ( Figure 6a–6b ) . Similarly , human DHS losses were enriched ( P = 0 . 008 ) near genes downregulated in humans and depleted ( P = 0 . 002 ) near genes upregulated in humans ( Figure 6b ) . The same relationships between DNase-seq signal and expression held true for chimpanzee ( Figure 6b , and Table S7 ) . Analogously , we found that significantly upregulated genes were more likely to be near chromatin gains and downregulated genes near chromatin losses in each species compared to genes similarly expressed in both species ( Figure S7a–S7b , Table S8 , Materials and Methods ) . These results support a direct role for species-specific DHS site differences in species-specific gene regulation . The direction of these correlations indicate that DHS site gains and losses are more commonly associated with enhancers than repressors . The LCL DNase-seq and expression data from human and chimp show a similar trend ( Figure S8 ) . Many species-specific expression differences were not readily explained by the presence of a nearby species-specific DHS site . For example , though statistically , genes upregulated in human were enriched near human DHS gains , this was true for only 58 of 1182 higher expressed genes ( Figure 6a , Table S7 ) . This may be partially explained by our strict definition of human DHS gains . Also , long-range interactions may confound the simple way we assigned DHS sites to the nearest gene . Future studies involving chromatin conformation capture ( e . g . , 3C , 4C , 5C ) could be used to better map DHS sites to target gene ( s ) . Lastly , expression differences between species may result from transcription factor binding characteristics that do not alter chromatin structure . Comparative ChIP-seq studies for specific transcription factors will be necessary to determine the extent of this phenomenon . We conducted gene ontology enrichment analysis for both species-specific DHS sites using GREAT [34] and differentially expressed genes using GO ( http://david . abcc . ncifcrf . gov/ ) , but did not find many highly enriched categories in either analysis ( Table S9 ) . This indicates that chromatin gains and losses occur near many different types of unrelated genes representing a broad spectrum of gene ontologies . The functional interpretations of Common and species-specific DHS sites outlined above naturally lead to predictions about the operation of natural selection . We used HyPhy [35] to test for signatures of positive selection within DHS gains and DHS losses on either the human or chimpanzee lineage [15] , [16] ( Materials and Methods ) . Consistent with a functional change unique to humans , both human DHS gains and losses showed significantly more evidence for positive selection on the human branch than on the chimpanzee branch ( Mann-Whitney P = 0 . 03 for gains and P = 0 . 0009 for losses , Figure 6c , Table S10 ) . Similarly , both chimpanzee DHS gains and losses showed increased positive selection on the chimp branch ( P = 0 . 002 for gains and P = 0 . 0004 for losses , Figure 6c , Table S10 ) . Signatures of selection for Common DHS sites were not significant on either branch . These results provide evidence that positive selection contributes to species-specific changes in chromatin , both gains and losses , and in the altered use and activity of gene regulatory elements . Despite this connection with evolutionary pressures , only two DHS gains or losses in fibroblasts overlap previously defined human accelerated conserved non-coding sequences ( HACNSs ) , chimpanzee accelerated conserved non-coding sequences ( CACNSs ) , or human accelerated regions ( HARs; Table S11 ) [14] , [36]–[38] . More generally , few DHS sites from any human cell type we have analyzed , including embryonic stem cells , correspond to genomic regions of accelerated turnover ( Table S11e ) . This lack of overlap may be due to the absence of DNase-seq data from specific developmental cell types since HACNSs , CACNSs , and HARs have been associated with developmental gene regulation , or to regions of accelerated turnover representing a different type of genetic element not detected by DNase mapping . We examined sequence conservation in DHS gains , losses , and Common sites using evolutionarily constrained regions defined by PhastCons [14] , [39] and GERP [40] algorithms with Genome Structure Correction ( GSC ) overlap test statistic [19] , [41] , [42] . By PhastCons analysis , we found that Common DHS sites were the most conserved , a characteristic of regions under negative selection ( Figure S9 ) . Common regions also had the greatest overlap with evolutionarily conserved elements , as defined by GERP ( Figure 6d; Materials and Methods ) . The presence of Common DHS sites in most human cell types ( Figure 4c ) with presumably greater functional demands may contribute to their higher conservation levels relative to gains and losses . Additionally , losses in both species were more conserved and overlapped more with conserved elements than gains ( Figure S9 ) suggestive of relaxed selection and positive selection , respectively . These trends held true even when noncoding genomic regions were partitioned based on their relationship to genes ( promoter , intron , intergenic; Figure 6d ) . In general , higher degree of conservation within specific regions of the genome can result from local differences either in mutation rate or selection [43] . Given that localized decreases in mutation rate below background are unusual , our data suggest that sequence conservation within Common DHS sites is primarily driven by negative selection to maintain function . A large fraction of DHS gains ( ∼70% ) , losses ( ∼60% ) , and Common ( ∼40% ) sites did not overlap any highly conserved elements ( Figure 6d ) . Thus , many DHS sites present in all three species , and possibly many or all of 27 human cell types , are not highly conserved . Understanding how these regions function in all species and cell types without high sequence conservation poses an interesting challenge for evolutionary genomics . Previous studies have shown that individual transcription factor binding sites ( TFBS ) “turn over” rapidly during evolution [44]–[46] . Transposon-mediated shifts in the position of enhancers have also been documented between mouse and human [47] . While these showed evidence of TFBS positional change , the turnover of entire DHS sites have not been shown previously . We identified ten possible instances of regulatory-element shuffling where a human DHS gain maps near ( <50 kb ) a human DHS loss ( Figure S10 ) . These regions were found near genes associated with obesity ( MCR4 , Figure S11 ) , imprinting ( GNAS , Figure S12 ) , and glial cell formation ( METRNL , Figure S13 ) . We also found cases of nearby ( <50 kb ) human and chimpanzee DHS sites that were independently gained ( Figure S14 , Figure S10 ) . One region mapped within an intron of the SRGAP2 gene ( Figure S14 ) , which is involved in neuronal guidance during brain development . Overall , the number of DHS gains and losses that mapped within close proximity to each other was not largely enriched or depleted based on randomized permutation tests , thus we cannot disprove that these findings are due to chance observations . Further detailed functional analyses are needed to determine the biological significance , if any , of these closely mapped regulatory changes . Our analyses above focused exclusively on DHS sites mapped to genome sequences shared between all three primate species . Recently , segments of DNA broadly conserved among mammals were found deleted specifically in the human ( hCONDELs ) or chimpanzee ( cCONDELs ) genome [48] . It has been proposed that these largely gene-desert regions contain regulatory elements that contribute to species-specific phenotypes [41] . We found human and chimp DHS sites mapped to 6% of cCONDELs and 11% of hCONDELs supporting their role in species-specific gene expression ( Table S11 ) . Many human fibroblast DHS sites that overlap cCONDELs were also present in other human cell types ( Figure S15 ) indicating that some CONDELs contain regulatory elements with pleiotropic consequences . We analyzed TFBS motifs found within DHS gain , loss , and common sites across species to identify motifs associated with differences in hypersensitivity . To quantify differences , we determined log ratios of the best position weight matrix ( PWM ) score in a DHS site between species ( Materials and Methods ) . Most motif scores from the JASPAR database were distributed evenly between species ( log ratio near zero ) indicating no species-specificity trend for that motif ( Supplemental data file 2 and 3 in Dataset S1 ) . However , log ratios of AP1 motif scores deviated from zero and correlated with species-specific DHS sites ( Figure 7 ) . For example , in human DHS gains , AP1 motif match scores were higher in the human sequences and lower in the orthologous regions in chimp and macaque ( Figure 7a ) . In contrast , AP1 motif scores in human DHS losses were higher overall in both chimpanzee and macaque sequences compared to human ( Figure 7b ) . Common regions showed even distributions of AP1 motif scores across all three species ( Figure 7c ) . This trend was also found in chimpanzee where chimp DHS gains had higher AP1 motif scores in chimp sequences compared to orthologous regions from human and macaque ( Figure 7d ) , and chimpanzee DHS losses had higher AP1 motif scores in human and macaque ( Figure 7e ) . In a representative human DHS gain , we see that the human allele results in a better match to the canonical AP1 motif than the non-human primate alleles ( Figure 7f ) . These results suggest species-specific sequence changes within the AP1 motif promote hypersensitivity in some species-specific DHS sites in the human , chimp , and macaque genomes . AP1 was the clearest example of this from motifs represented in JASPAR ( Figure 7g , Supplemental data files 2–3 in Dataset S1 ) . Other transcription factors may be acting similarly , but less frequently . For example , we also found that ZNF354C ( Supplemental data file 3 in Dataset S1 , page 81 ) and NFE2L2 ( Supplemental data file 3 in Dataset S1 , page 108 ) showed similar trends to AP1 . In these cases , motif scores positively correlated with the presence of a species-specific DHS sites . In contrast , ZEB1 displayed the opposite trend where higher motif scores correlated with the lack of a species-specific DHS site ( Figure 7g and Supplemental data file 3 in Dataset S1 , page 65 ) . While the mechanism is not yet clear , our findings and ZEB1's known role as repressor [49] is suggestive of its ability to induce a closed chromatin state via binding to CtBP and HDAC [50] .
Precise measurements of transcript abundance enabled by RNA-seq experiments have revealed extensive differences in gene expression among closely related species [51] with 10–20% of transcripts within a given tissue found differentially expressed between humans and chimpanzees [12] , [22] . Many transcripts are tissue-specific , and given the relatively small number of cell types explored , the total number of differentially expressed genes is likely to be considerably larger . An important goal of molecular evolution research is to understand how differences in transcript abundance have evolved , both because the changes are extensive and because some may underlie the origin of functionally significant traits [13] , [52] , [53] . Most gene expression differences across species likely have a genetic basis , but it is difficult to relate expression changes to variation in genome sequences . While many non-coding sequence differences are unlikely to impact transcription , for the subset that do , it is often not clear what genes are directly affected . In addition , a non-coding regulatory mutation may only affect gene expression in a subset of tissues or developmental stages , so many functional consequences have gone unrecognized given the limited number of studies performed thus far . Further complicating analysis , transcription is influenced by environmental factors and by epigenetic modifications . But the lack of a complete regulatory element map across species and tissues is perhaps the most important impediment to understanding gene expression differences in terms of genome sequence evolution . Changes in transcript abundance may be caused by genetic differences within individual regulatory elements in cis that affect transcription factor binding affinity [54] , or within transcription factors that affect binding to many regulatory elements in trans . Even when the genetic basis is known to be in cis , there is no reliable method for identifying the causal mutations from sequence comparisons . As a result , distributions of positive and negative selection genome-wide correlate poorly with changes in transcript abundance [12] , [55] , [56] . In this study , we showed that analyzing chromatin accessibility using DNase-seq provides a powerful approach to link genome sequence changes to species- and tissue-specific differences in gene expression . Chromatin accessible DHS sites have three properties that make them especially valuable for evolutionary analyses of gene expression [17] , [18] . First , DHS sites identify all known functional classes of regulatory elements , including core promoters , enhancers , repressors , boundary elements , and locus control regions , thus revealing all cis components of transcription through a single genome-wide assay . Second , DHS sites are only found when a regulatory element is active or poised , which means that DNase-seq can be used to identify evolutionary changes in tissue- and developmental stage-specific regulatory elements . And third , DHS sites represent only ∼2% of the genome , making it possible to focus analyses on regions that are involved in transcriptional regulation and ignore regions that are not . We performed DNase-seq on fibroblasts from three primate species and identified more than two thousand regulatory elements apparently gained or lost since the divergence of humans and chimpanzees . Turnover of regulatory elements was enriched near genes that display species-specific expression differences , indicating that gains and losses in DHS sites have functional consequences on transcript abundance . To our knowledge , this is the first evidence correlating changes in DNase chromatin accessibility and gene expression across species at a genome-wide scale . We found most expression differences occurred without a detectable change in a nearby regulatory element . One possibility is that mutations within DHS sites affect transcription factor binding without causing large changes in overall chromatin accessibility . Future experiments are needed to identify the specific sequence changes that regulate expression at long distances and/or via post-transcriptional mRNA stability mechanisms . Most regulatory element changes occurred within intergenic regions and introns and were predominantly associated with cell type-specific DHS sites . These results are consistent with expected differences in the extent of pleiotropy: loss of core promoter elements will more likely affect transcription in many tissues and stages of development , while loss of distal enhancers will more likely affect transcription in a subset of tissues . Lower rates of change in core promoter elements and in regulatory elements actively utilized in multiple tissues suggest negative selection is operating to maintain regulatory elements with more critical functions . Analysis of the DNA sequences within regulatory elements provides evidence for the operation of natural selection within these elements . Sequence within DHS sites utilized across all three species show lower rates of substitution than surrounding DNA , which is a proxy for neutral evolution by drift , consistent with negative selection operating to maintain their function . In contrast , regulatory element gains on the human and chimpanzee branches have significantly elevated rates of substitution , consistent with positive selection for altered function , while regulatory element losses show slightly elevated rates , perhaps due to relaxed selection . Explicit tests for positive selection using branch-specific likelihood ratio tests [16] reveal that the highest association is with regulatory element gains and the lowest with common regulatory elements utilized in all three species . Thus , the genome-wide distribution of both negative and positive selection within regulatory elements correlates in predicted ways with the evolutionary conservation and change in their function . Although we are not aware of any previous evidence for such a relationship , it seems likely in principle that the operation of natural selection is often tied to gains , losses , and conservation of regulatory elements . Most instances of inferred positive selection we identified do not overlap previously described HARs [14] or HACNs [36] highlighting that our DHS gains and losses represent a novel set of differential regulatory elements may have played a role in adaptation during human evolution . Many studies have documented evolutionary gains and losses of individual transcription factor binding sites or H3K4me3 histone marks among related species [44]–[46] , [57] , but this is the first evidence showing gains and losses of entire DHS sites . Since we only examined two cell types and applied conservative identification criteria , the full extent of regulatory element changes between humans and chimpanzees is likely to be considerably greater than we report . Nonetheless , the instances of turnover we identified suggest regulatory element gains and losses are a common class of functional change within evolving genomes . We show that sequence differences among species within particular motifs may result in species-specific DHS sites , which suggests one way non-coding regulatory variants can alter chromatin structure . In particular , mutations that produce better matches to the activator protein 1 ( AP1 ) motif on either the human or chimpanzee genome correlate with the presence of species-specific DHS sites , a result detected in human DHS gains and losses as well as chimpanzee gains and losses . Sequence changes increasing the affinity for AP1 motif more likely drive species-specific changes in chromatin structure rather than species-specific coding mutations within the AP1 components , FOS and JUN proteins , altering the sequence-binding preference of AP1 . Since only a minority of species-specific DHS gains and losses has differential AP1 motif scores ( Figure 7 ) , this indicates the majority of factors that govern species-specific DHS sites remain to be discovered . AP1 has been implicated in many aspects of cellular function ranging from proliferation , transformation , differentiation , oncogenesis , apoptosis , hormone activation , to tumor suppression [58]–[61] . We provide evidence that other factors act similarly to AP1 or in the opposite direction as repressors , such as ZEB1 . Mapping DHS sites across a more diverse set of primate samples , as well as using additional de novo motif discovery and performing ChIP-seq to reveal binding sites , will be an important part of identifying additional factors that confer changes in chromatin structure across species .
We obtained two cell types from Coriell for this study: skin fibroblast cells and lymphoblastoid cell lines ( LCLs ) . Primary skin fibroblast cells from three human , three chimpanzee , and three macaque individuals . LCLs , which are B cells immortalized with Epstein-Barr Virus , were obtained from the same three human and three chimpanzee individuals that fibroblasts were isolated from ( Table S1 ) . EBV does not reliably transfect macaque lymphocyte cells , so matched macaque LCLs cells were not available for this study . Importantly , other recent genome-wide studies that used macaque LCLs were of B-Lymphocyte cells transformed with rhesus herpes papio virus , a close relative of human EBV [57] . Cells from all species were grown in standard growth media . Fibroblast growth media consisted of Gibco's MEM ( 10370-021 ) , L-Glutamine ( 25030-081 ) , Pen/Strep ( 15140-122 ) , and 10% FBS ( Hyclone SH30070 ) . LCLs growth media consisted of Gibco's RPMI ( 21870 ) media with L-Glutamine , Pen/Strep , and 15% FBS . We harvested fifty million cells for each individual biological replicate and allocated 35 million cells for DNase assays ( DNase-seq and DNase-chip ) , 10 million for genomic DNA ( used as control for DNase-chip array hybridization ) , and 5 million for RNA DGE-seq expression analysis . DNase-seq libraries we generated as previously described [17] , [18] and sequenced via Illumina's GAII sequencer . DNase-chip library preparations , used for validation of our DNase-seq results were performed as previously described [24] , [25] and were hybridized to 1% ENCODE Nimblegen arrays [19] . Custom arrays were designed to cover the orthologous regions from chimpanzee and macaque . DNase-chip array intensities were compiled and significant DHS sites were called using ChIPOTle [62] ( P<0 . 000001 peak cutoff ) . DNase-seq data generated from each species was aligned to the native genome ( human hg19 , chimpanzee panTro2 , and macaque rheMac2 ) using BWA [63] . To directly compare three different primate species requires that they be aligned to a single reference sequence . Because both the chimpanzee ( panTro2 ) and macaque ( rheMac2 ) reference sequences were built from the existing human reference , we converted all sequences to human coordinates . To do this , we converted each 20-mer DNase-seq sequence from panTro2 or rheMac2 to hg19 with liftOver [23] , using a match setting of 80 percent . After conversion to hg19 coordinates , we used F-seq [64] to identify DNaseI hypersensitive ( DHS ) sites . The F-seq scores from the top 100 , 000 peaks from each sample were used to determine how well chromatin openness correlates among all 15 samples ( Figure 1 ) . This analysis uses a pairwise Pearson correlation to compare the similarity among samples . We used the bioconductor edgeR package to define species-specific hypersensitive regions [26] . EdgeR is designed to detect differences in count data among groups of samples . Briefly , it compares within-group variances to between-group variances using a negative binomial model , and selects entries with significant between-group differences . It was designed for differential expression data such as DGE-seq or RNA-seq , but it is similarly applicable to read counts generated by DNase-seq . One key advantage of edgeR is a normalization procedure specifically designed for high-throughput sequencing studies [26] . To locate significant differences in DNase-seq signal between species , we first identified the union set of the top 100 , 000 DHS sites ( as scored by F-seq ) from all 15 samples ( 9 fibroblasts and 6 LCLs ) . We used bx-python ( https://bitbucket . org/james_taylor/bx-python ) to analyze the data . We divided these regions into windows , attempting to maximize the resolution of the windows while minimizing the number of windows required: Chang et al . ( 2002 ) showed that human skin- and non-skin- fibroblast samples collected from different locations along the body plane showed notable differences in transcriptional profiles [29] . Aware of this issue , we made an effort to use fibroblast cultures made from only skin samples and from the same region of the upper arm . All 3 replicates of macaque Fibroblasts and one human Fibroblast were confirmed from Coriell to be from skin biopsies from the upper arm ( the other two locations were unknown ) . Since our analysis poses a strict requirement of DHS sites to be present in all three human individuals to be called a human DHS gain , having at least one human sample with the biopsy site confirmed to be from the upper arm ensures that the human gains are not a result of human DHS gains being a result of , for example , all human fibroblasts isolated from lower leg . Likewise , to be called a human chromatin loss , DHS sites have to be absent in all three human samples ( but present in all chimp and macaque samples ) ; this biopsy location bias is again mitigated by at least one human sample being from the upper arm . The Yerkes National Primate Center , from where the chimpanzee skin fibroblasts were obtained from , unfortunately do not document the exact location of biopsy . While the standardized skin punch protocol calls for the location of the skin biopsies to be from the ear pinna ( personal communication with Fawn Conner-Stroud from Yerkes ) , we cannot be 100% sure that the samples were isolated from this location . As described above , human DHS losses are a result of signal being present in all three chimpanzee and macaque samples , supporting that these regions are not due to chimp biopsy location . We also want to reiterate that our skin fibroblast results are highly similar in LCL lines that are all uniformly derived from blood samples . We find that none of our chromatin gains and losses overlap the Hox genes described in the Chang et al . , 2002 paper [29] . In addition , a more recent analysis by Rinn et al . [30] , using more comprehensive microarrays and more biopsy sites , identified 337 expression array probes ( 299 unique genes ) that were shown to be highly associated with five different general biopsy site locations . We have compared this list of genes to both species-specific chromatin gains/losses , as well as species-specific gene expression , and find <3% of the species-specific and common DHS overlap with the 299 position specific genes . Similarly , species-specific and commonly expressed genes also show <3% overlap ( Table S12 ) . In order ensure that our tests for selection were meaningful , we wanted to compare chromatin gains and losses with a set of regions that were open in all species . Rather than simply choose DHS sites that have the highest scores , we wanted to mirror the level of hypersensitivity to that of the species-specific regions . This is important because species-specific DHS sites are not necessarily the strongest DHS sites . We also wanted to select a set of regions similar in size to our sets of gains and losses to retain computational tractability . To select a set of matched Common DHS sites , we required that each window be similarly open in all 9 samples from all 3 species . To be considered “similarly open” in a given sample , the number of counts must lie between the 20% and 80% quantiles for that sample in the corresponding species-specific regions . For example , we used the human DHS gains identified by edgeR to define the distribution for each of the three human samples , and similarly for chimp and macaque . As such , our set of Common regions is the set of all windows with DNase counts within this range for each of the 9 samples ( Figure 2d ) . To reduce the number of Common regions we found to the most representative set ( those that most closely match the average signal intensity of the differential DHS sites ) , we narrowed the quantiles until we found a set of around one thousand Common regions , which we reasoned would be a sufficient number to examine summary statistics . To ensure that our results are not biased for a specific set of Common regions , we repeated our experiments on a significantly larger set ( ∼11 , 000 ) of Common DHS sites using less stringent criteria ( 10%–90% quantile ) . This larger set is even more enriched for promoter regions but does not change our conclusions ( data not shown ) . After identifying an initial set of potential Common DHS sites , we filtered out any that appeared to be “appendages” to other hypersensitive sites . Without this step , many Common DHS sites would map to the edge of a strong hypersensitive sites . To ensure that a Common DHS site is a standalone DHS site , we examined the neighboring windows surrounding the initial set of Common DHS sites . If a Common DHS site window contained fewer than 80% of the number of reads in the adjacent window on either side , we filtered it out as most likely an “appendage” to a stronger DHS site . This resulted in a final list of 1259 Common DHS sites matched in intensity to the species-specific DHS sites . We also ran this filter on DHS gains and losses , and found that very few of the gains ( 3–5% ) and losses ( 3–8% ) get flagged as “appendages . ” Of these , many of them are flagged as a result of FDR threshold issues that simply didn't quite highlight a neighboring window , and we would actually still consider this a legitimate gain region . Because gain/loss appendages are relatively rare and are largely due to threshold issues , we elected to retain them in our final list . In every comparison , we reported more species-specific gains than losses . The most important factor in determining the size of these lists is the level of significance we set by choosing a FDR . To obtain lists that match in length , we could simply adjust the FDR value for the lists to yield about the same number of regions . Instead , we decided to keep the FDR constant and select varying numbers of DHS sites for each category . However , it is still constructive to consider the disparity . In other words , “at a constant FDR ( 1% ) , why are there more species-specific increases than decreases ? ” This is possibly a result of purifying selection . Because DHS sites are regulatory ( and therefore tend to be conserved ) , a loss of a DHS site probably confers a fitness disadvantage . In this case , we would expect to see more gains than losses . It is also possible that the prevalence of increases is simply a result of the way we constructed the significance test . A DHS site is a sparse signal ( there are more “closed” regions than “open” regions ) . Combined this with the asymmetry of the evolutionary tree: the chimp and human are more similar to each other than either is to the macaque . A human-specific increase requires both macaque and chimp to be closed ( the default ) , while a human-specific decrease requires both macaque and chimp to be open . This latter scenario will happen less often because the relationship between chimp and human is closer than either to the macaque . In short , the greater number of gains than losses in our analysis may reflect purifying selection on DHS sites; however , it may also simply be a result of the way we constructed the test , particularly due to using an outgroup species to polarize the chromatin structure changes . We tested for evidence of positive selection using the DHS sites indicated as DHS gains , losses , and commons defined by edgeR and common analyses ( see above ) . A branch model test [66] in HYPHY [35] was used to assess evidence for positive selection on each the human and chimp branches . HYPHY uses a likelihood ratio test to compare two opposing models . For the null hypothesis , we specified a composite model that allowed for negative selection , neutral evolution , or relaxed constraint specifically on the branch of interest ( i . e . the human branch ) , with negative or neutral evolution across the rest of the tree . The alternative hypothesis modeled positive selection only on the branch of interest , with negative or neutral evolution on the rest of the tree . For each region , HYPHY performed a likelihood ratio test comparing these hypotheses and output a P value that can be interpreted as a level of evidence for positive selection . In order to test the likelihood of either the null or alternative hypothesis , we specify both the alignment of the region of interest , as well as a background sequence alignment assumed to be evolving neutrally [67] . For the alignment of the region of interest , we used alignments of human , chimp , macaque , and orangutan precomputed at UCSC . For the background sequence , we collected a separate set of local introns for each region to test , following Haygood et al . [16] . To define these background alignments , we started with the UCSC knownGene definition of intron annotations , and then filtered out all first introns , splice junctions , and hypersensitive sites ( in any of the 15 samples in this study ) . In this way , we aimed to select sequences that are evolving neutrally . After defining this set of neutral introns , we used an expanding window centered on the region of interest to collect introns in a region up to 100 kb surrounding the center . We added introns to this collection sequentially as the window expanded until we reached an alignment of 2000 nucleotides . If we were unable to find 2 kb of background introns within 100 kb of sequence , we discarded these regions ( this happens rarely ) . Introns are commonly assumed to be evolving neutrally [43] , [68] , particularly when our filtering steps are taken into account; however , there are still likely to be regulatory sequences present in our background model , either due to sequences containing DNaseI HS sites in other cell types not tested or due to unannotated or mis-annotated transcripts . In order to further correct for this possibility , we performed each likelihood-ratio test 50 times , using 50 different bootstrapped versions of the background model . We then averaged these P values to assign a final P value for each region . This method has the effect of possibly discarding any elements under selection in some of the bootstrap replicates , increasing our ability to detect positive selection even if we inadvertently chose some background regions under selection . To test significance , for a given set of regions ( e . g . human DHS gains ) we ordered the P values for selection on both hg19 and panTro2 , then did a Mann-Whitney test to see if one branch has higher P values than the other ( Table S10 ) . The fibroblast DHS sites where we can polarize the differences using macaque all have significant differences in the direction we expect , while the Common regions do not have significant differences . In the LCLs , where we are unable to polarize ( no macaque LCLs were available ) , we do not detect a significant difference . This is likely due to a combination of two categories ( gains and losses ) that have competing selection ( i . e . LCL human DHS loss = human DHS loss+chimpanzee DHS gain ) . We calculated the observed fraction of overlaps between DHS sites and evolutionarily constrained regions using constrained regions defined by the Genome Evolutionary Rate Profiling ( GERP ) method [40] executed on Enredo , Pecan , Ortheus ( EPO ) [69] , [70] 33-way alignments . EPO alignments and GERP regions are available for download at the Ensembl browser ( http://ensembl . org ) . We then constructed a null distribution of the fraction of expected overlaps by using the conservative Genome Structure Correction ( GSC ) methodology described previously [19] , [41] , [42] . We performed 1000 randomizations and calculated the mean and standard deviation from the null distribution to assess the statistical significance of the observed overlap ( Figure 6d ) . We also used PhastCons to explore degree of sequence conservation . For each region , we extracted the mean and max PhastCons score from the primate PhastCons table at UCSC . We then compared the distribution of these scores across the regions to see how sequence conservation is related to hypersensitivity conservation ( Figure S9 ) . Total RNA purified from 5–10 million cells harvested from the same cell culture used for DNase-seq were also used to generate DGE-seq expression libraries as previously described [12] , [71] . Polyadenylated RNA is captured for enrichment of mRNA and the oligo dT primer is used to make cDNA . Briefly , DGE-seq is similar to Serial Analysis of Gene Expression ( SAGE ) where mRNA abundance is assessed via counting short sequences of their cDNA at specific restriction site locations . These DGE-seq libraries were sequenced using Illumina's GAII sequencer , and averaged 10 million 20mer sequences for each sample , which were then aligned to the samples' native reference sequence using BWA . We used EdgeR to detect differences in tag counts across species by comparing intra-species variances to inter-species variances using a negative binomial model , and selects expressed genes with significant between-species differences [26] . Unlike the analysis performed for comparing cross-species DHS sites , we did not liftOver any non-human expression sequences to human . Instead , we simply compared the DGE-seq sequence counts that aligned to exonic regions within each species' native sequence alignment ( Supplemental data file 5 in Dataset S1 ) . Because of the high level of homology of the exonic regions between the 3 primate species , we directly compared tag count numbers between each of the orthologous genes . For the genome-wide expression correlation comparison ( Figure 1 ) , we normalized tag counts using edgeR to calculate the total library size for each sample and adjusting the tag counts accordingly so that relative differences between the depth of the sequencing reads did not influence the results . Next , we filtered out genes that did not have at least 10 combined tag counts between all of the samples to decrease the noise associated with genes that fall below the meaningful level as recommended for edgeR . Following these filters , we compared the Spearman correlation values between all of the samples and plotted the results as a heatmap with hierarchical clustering to show similarities within and between species and tissue types . Using edgeR , we identified genes that were significantly differentially expressed between the 3 primate species . Comparisons between species were performed on a pairwise manner comparing 3 individuals of one species against 3 individuals from a second species . The same normalization method and filters used in the expression correlation analysis was also used prior to defining the differentially expressed genes . Differential gene expression was defined as genes having a P value cutoff of 0 . 05 . Using the Macaque expression result as an outgroup , we identified genes that displayed high or low expression specifically on the human and chimp branch . For example , genes we defined as highly expressed in human ( human upregulated genes ) are differentially expressed in both human/chimp and human/macaque comparisons , but not differentially expressed in the chimp/macaque comparison . Similar criteria were used to identify genes that display low expression in human compared to chimp and macaque ( human downregulated genes ) . To firmly establish the connection between differential chromatin and differential expression , we tested for significance in overlap in both directions: First , we tested if differential DHS sites tend to be located near differentially expressed genes , and second , we tested if differentially expressed genes tend to have differential DHS sites nearby . To connect sequence changes to species-specific DHS sites , we compared JASPAR motif scores across species . We first extracted the orthologous DNA sequences for human , chimpanzee , and macaque for each of our DHS gain , loss , and common sites . We scanned and scored each of these sequences for all the position weight matrices ( PWMs ) in the JASPAR database . We scored a sequence for a given PWM as the highest motif score anywhere in that sequence . This resulted in a region-by-motif matrix of scores; each score is the highest score for each motif/sequence combination . To compare species , we took the log ratio of scores ( human/chimp , human/macaque , and chimp/macaque ) . Where this score is 0 , the highest score does not differ between species . Deviations from 0 indicate the direction of improvement in motif match ( in a human/chimp comparison , a positive number means the best match in the human sequence scored higher than the best match in the chimp sequence ) . After calculating these scores and each pairwise log-ratio , we plotted the log-ratios ( Supplemental data files 2–3 in Dataset S1 and Figure 7 ) to examine trends . We calculated the pairwise log-ratios for multiple species comparisons and plotted these on different axis to check whether increases over one species correlate with increases over the other . In these two-dimensional plots , each axis quantifies a different pairwise species relationship . Points that cluster in the upper-right quadrant have higher scores than either of the other species; points that cluster in the lower-left have lower scores . The most interesting variation in these plots is whether the points congregate in the upper-right or lower left . To assess significance , we project each data point onto the y = x line to reduce the dimensionality to 1 . We then used the Wilcoxson rank-sum test to compare each distribution to the distribution of the common regions ( Figure 7g and Supplemental data file 3 in Dataset S1 ) . | The human genome shares a remarkable amount of genomic sequence with our closest living primate relatives . Researchers have long sought to understand what regions of the genome are responsible for unique species-specific traits . Previous studies have shown that many genes are differentially expressed between species , but the regulatory elements contributing to these differences are largely unknown . Here we report a genome-wide comparison of active gene regulatory elements in human , chimpanzee , and macaque , and we identify hundreds of regulatory elements that have been gained or lost in the human or chimpanzee genomes since their evolutionary divergence . These elements contain evidence of natural selection and correlate with species-specific changes in gene expression . Polymorphic DNA bases in transcription factor motifs that we found in these regulatory elements may be responsible for the varied biological functions across species . This study directly links phenotypic and transcriptional differences between species with changes in chromatin structure . | [
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| 2012 | Extensive Evolutionary Changes in Regulatory Element Activity during Human Origins Are Associated with Altered Gene Expression and Positive Selection |
The current knowledge of immunological responses to schistosomiasis , a major tropical helminthic disease , is insufficient , and a better understanding of these responses would support vaccine development or therapies to control granuloma-associated immunopathology . CD4+ T cells play critical roles in both host immune responses against parasitic infection and immunopathology in schistosomiasis . The induction of T helper ( Th ) 1 , Th2 and T regulatory ( Treg ) cells and their roles in schistosome infections are well-illustrated . However , little in vivo data are available on the dynamics of Th17 cells , another important CD4+ T cell subset , after Schistosoma japonicum infection or whether these cells and their defining IL-17 cytokine mediate host protective responses early in infection . Levels of Th17 and the other three CD4+ T cell subpopulations and the cytokines related to induction or repression of Th17 cell generation in different stages of S . japonicum infection were observed . Contrary to reported in vitro studies , our results showed that the Th17 cells were induced along with the Th1 , Th2 , Treg cells and the IFN-γ and IL-4 cytokines in S . japonicum infected mice . The results also suggested that S . japonicum egg antigens but not adult worm antigens preferentially induced Th17 cell generation . Furthermore , decreasing IL-17 with a neutralizing anti-IL-17 monoclonal antibody ( mAb ) increased schistosome-specific antibody levels and partial protection against S . japonicum infection in mice . Our study is the first to report the dynamics of Th17 cells during S . japonicum infection and indicate that Th17 cell differentiation results from the integrated impact of inducing and suppressive factors promoted by the parasite . Importantly , our findings suggest that lower IL-17 levels may result in favorable host protective responses . This study significantly contributes to the understanding of immunity to schistosomiasis and may aid in developing interventions to protect hosts from infection or restrain immunopathology .
CD4+ T cells play an important role in the initiation of immune responses against an infection by providing help to other cells and by taking on a variety of effector functions during immune reactions . Upon antigenic stimulation , naive CD4+ T cells activate , expand and differentiate into different effector subsets termed T helper ( Th ) 1 and Th2 cells . The appropriate induction and balance between Th1 and Th2 cellular responses to an infectious agent can influence both pathogen growth and immunopathology [1] . Th17 cells recently emerged as a third independent effector cell subset differentiated from CD4+ T cells upon antigenic stimulation [2]–[5] . Although the functions of these cell subtypes are not completely understood , emerging data suggest that by producing their defining cytokine IL-17 , Th17 cells play an important role in host defenses against extracellular pathogens , such as Klebsiella pneumoniae [6] , Pseudomonas aeruginosa [7] , Porphyromonas gingivalis [8] and Bacteroides fragilis [9] , which are not efficiently cleared by Th1-type and Th2-type immunity . Meanwhile , several studies have shown that Th17 cells and IL-17 also play important roles in immunopathology in some infectious diseases , such as pulmonary tuberculosis [10] , toxoplasmosis [11] and schistosomiasis [12]–[17] . CD4+ T cells can also be induced to differentiate into CD4+CD25+ T regulatory ( Treg ) cells with immunosuppressive activities that down-regulate immune responses , thereby inhibiting immunopathology while promoting parasite survival via direct repression of the induction and responses of the other CD4+ subsets , Th1 , Th2 and Th17 cells [18]–[22] . Since the functional analysis of IL-17 produced by Th17 cells has suggested an important and unique role for this cytokine in both host protection against specific pathogens and immunopathologic damage to the host , much of the research focus has been placed on the factors that either positively or negatively regulate differentiation of Th17 cells . To date , several studies have shown that Th17 cells require specific cytokines for their differentiation , different from those for Th1 and Th2 cells . A combination of TGF-β plus IL-6 was recently described to be essential for initial differentiation [23]–[27] , IL-21 for the amplification [28] , [29] and IL-23 for the subsequent stabilization [25] , [30] , [31] of the Th17 cell subset . On the other hand , both high levels of Th1 and Th2 cells and their respective cytokines , IFN-γ and IL-4 , antagonize the development of Th17 cells [2] , [4] , [5] . Additionally , in the absence of IL-6 , TGF-β alone is clearly favored as the cytokine for differentiation of Treg cells while suppressing the differentiation of Th17 cells [4] , [32] . These findings suggest an intimate link between the Treg and Th17 cell programs of differentiation . However , thus far the notion that CD4+ T cell subsets represent distinct terminally differentiated lineages has been favored on the basis of a series of in vitro experiments , and the suppression of Th17 differentiation by Th1 , Th2 and Treg cells and/or their cytokines has been demonstrated in numerous in vitro studies or under certain simplified or defined conditions [25] , [27] , [32]–[34] . However , there is very little in vivo data available to support such a cross-regulation between Th17 cell differentiation and Th1 , Th2 and Treg cells during multicellular pathogenic infection . Schistosomiasis , a major neglected tropical helminthic disease infecting 200 million people with an estimated 600 million at risk worldwide , is an excellent model for studying the induction and regulation of differentiation of the various CD4+ T cell subsets in response to infection . Infection of Schistosoma japonicum , a multicellular parasite which has an extremely diverse repertoire of antigens , induces the production of bulk cytokines to induce Th1 , Th2 and Treg cells that play important roles in the immune response to infection . In particular , a recently growing number of studies have indicated that IL-17 , a CD4+ T cell-derived cytokine , is most directly associated with the severity of hepatic granulomatous inflammation [12]–[16] , [35]–[37] , suggesting that IL-17-producing T cells are a major force behind severe pathology in schistosomiasis . During schistosome infection , the immune response progresses through at least three phases . ( 1 ) During the first three weeks of the infection , when the host is exposed to migrating immature and mature parasites , the dominant response is Th1-like . The response is induced by non-egg antigens , such as the schistosomula and soluble worm antigen ( SWA ) [38] , [39] . ( 2 ) As the parasites begin to produce eggs ( beginning 4–5 weeks post-infection ) , the response alters , with the emergence of a stronger Th2 response which is primarily induced by egg antigens [38] , [40] . The granulomas that form around the eggs in the liver , which are reported to be positively regulated by Th17 cells and the secreted IL17 cytokine [12]–[16] , [36] , develop to their maximum size around 8–9 weeks post-infection . ( 3 ) During the chronic phase of infection ( beginning 11–13 weeks post-infection ) , the Th2 response is predominant and modulated . The granulomas are also smaller than at earlier times . At this stage , CD4+CD25+Foxp3+ Treg cells are believed to be induced mainly by egg antigens and play an important repressor role in down-regulation of pathologic immune responses [20] . In addition , a recent study reported elevated Th17 levels in response in vaccination against S . mansoni infection in C57BL/6 mice [41] . However , there is very little data available showing the dynamics of Th17 cells after S . japonicum infection as well as whether Th17 cells/IL-17 mediate the host protective responses at the early stage of S . japonicum infection . In the present study , we observed the changes in Th17 cell levels at different stages of S . japonicum infection and investigated the role of IL-17 in the host protective responses .
Animal experiments were performed in strict accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals ( 1988 . 11 . 1 ) , and all efforts were made to minimize suffering . All animal procedures were approved by the Institutional Animal Care and Use Committee ( IACUC ) of Nanjing Medical University for the use of laboratory animals ( Permit Number: NJMU 07-0137 ) . Female 8-week old C57BL/6 mice were purchased from SLAC Laboratory ( Shanghai , China ) and bred in university facilities . All animal experiments were performed in accordance with the Chinese laws for animal protection and in adherence to experimental guidelines and procedures approved by the Institutional Animal Care and Use Committee ( IACUC ) , the ethical review committee of Nanjing Medical University , for the use of laboratory animals . Oncomelania hupensis harboring S . japonicum cercariae ( Chinese mainland strain ) were purchased from the Jiangxi Institute of Parasitic Diseases ( Nanchang , China ) . For kinetic analysis of T cell populations and cytokines , each mouse was infected with 12 cercariae of S . japonicum through the abdominal skin . At 3 , 5 , 8 and 13 weeks post-infection , four mice were randomly chosen from the infected and normal control groups and sacrificed for further study . For challenge experiments , each mouse was infected with 40 cercariae of S . japonicum by abdominal skin exposure . S . japonicum SWA were prepared by harvesting the soluble fraction obtained from sonicated S . japonicum adult worms as previously described [42] . S . japonicum eggs were extracted from the livers of infected rabbits and enriched . The S . japonicum soluble egg antigens ( SEA ) were then prepared from the homogenized eggs as previously described [43] . SWA and SEA were diluted with PBS to a final concentration of 10 mg/ml for immunization . Three independent experiments were carried out in the same manner . In each experiment , C57BL/6 mice were divided randomly into three groups ( two test and one control ) consisting of eight mice per group . Each mouse was injected subcutaneously in the back with 100 µl of a solution containing 50 µg of SEA , 50 µg of SWA or PBS emulsified in incomplete Freund's adjuvant ( IFA , Sigma-Aldrich , ST . Louis , MO ) [44] . Each mouse was immunized two times with a 14-day interval . Two weeks after the last immunization , serum samples were collected , and mice were sacrificed for further study . Single cell suspensions of splenocytes and lymphocytes were prepared by mincing the mouse spleens and mesenteric lymph nodes in PBS containing 1% FBS ( Gibco , Grand Island , NY ) and 1% EDTA . Red blood cells were lysed using ACK lysis buffer . For preparation of single cell suspensions of hepatic lymphocytes , mouse livers were perfused via the portal vein with a PBS/heparin mixture ( 75 U/ml , Sigma Chemical Co . , St . Louis , MO ) . The excised liver was cut into small pieces and incubated in 10 ml of digestion buffer ( collagenase IV/dispase mix , Invitrogen Life Technologies , Carlsbad , CA ) for 30 min at 37°C . The digested liver tissue was then homogenized using a MediMachine with 50 µm Medicons ( Becton Dickinson , San Jose , CA ) for 3 min at low speed [45] . The liver suspension was then centrifuged at low speed to sediment the hepatocytes . The remaining cells were separated on a 35% Percoll gradient by centrifuging at 600×g . The lymphocyte fraction was resuspended in 2 ml of red cell lysis buffer and then washed in 10 ml of complete RPMI 1640 with 0 . 1 M EDTA . The cells were cultured in triplicate in complete RPMI 1640 medium ( Gibco ) containing 10% FBS , 2 mM pyruvate , 0 . 05 mM 2-mercaptoethanol , 2 mM L-glutamine , 100 U of penicillin/ml and 0 . 1 mg/ml streptomycin . Subsequently , 2×105 cells per well in 200 µl of complete media were cultured in 96 well plates ( Nunc , Roskilde , Denmark ) for 72 h at 37°C in the presence of 25 ng/ml phorbol 12-myristate 13-acetate ( PMA ) and 1 µg/ml ionomycin ( Sigma-Aldrich ) [46]–[48] . Alternatively , in some experiments , the cells from S . japonicum infected mice were stimulated with or without 50 µg/ml of SEA for 48 h . Culture supernatants were collected for ELISA after incubation . Cytokines in the culture supernatant were analyzed using mouse cytokine multiplex assay kits for detecting IL-6 and IL-21 ( R&D Systems , Inc . Minneapolis , MN ) and for detecting TGF-β and IL-23 ( Bender MedSystems , Novato , CA ) . IL-17A , IFN-γ and IL-4 levels in the supernatant were measured by ELISA using the eBioscience ELISA Ready-SET-Go kit ( eBioscience , San Diego , CA ) according to the manufacturer's protocol . The SWA and SEA specific IgG , IgG1 and IgG2a antibodies in mouse serum samples were detected by standard ELISA using the SWA and SEA as the coated antigen [42] , [43] . HRP-conjugated rat anti-mouse IgG ( Calbiochem , Darmstadt , Germany ) , IgG1 and IgG2a monoclonal antibodies ( mAbs ) ( BD Pharmingen ) were used . In brief , ELISA plates ( Titertek Immuno Assay-Plate , ICN Biomedicals Inc . , Costa Mesa , CA ) were coated with 0 . 1 mg/ml of SEA or SWA in 50 mM carbonate buffer ( pH 9 . 6 ) and incubated overnight at 4°C . Plates were washed three times with PBS ( pH 7 . 6 ) containing 0 . 05% Tween-20 ( PBS-T ) and blocked with 0 . 3% ( w/v ) bovine serum albumin ( BSA ) in PBS for 1 h at 37°C . The plates were further washed three times with PBS-T and then incubated with the sera diluted with 0 . 3% BSA ( 1∶100 ) at 37°C for 1 h . The plates were washed four times with PBS-T , followed by incubation with HRP-conjugated rat anti-mouse IgG , IgG1 and IgG2a ( 1∶1000 ) for 1 h at 37°C . The plates were then washed five times with PBS-T and developed with tetramethylbenzidine ( TMB ) substrate ( BD Pharmigen ) for 30 min . The optical density ( OD ) of the color developed in the plate was read at 450 nm using a BioRad ( Hercules , CA ) ELISA reader . For detection of Th17 , Th1 or Th2 cells , single cell suspensions of splenocytes , lymphocytes or liver cells from each mouse were prepared , and 1×106 cells from each sample were stimulated with 25 ng/ml PMA and 1 µg/ml ionomycin ( Sigma-Aldrich ) in complete RPMI 1640 medium in the presence of 0 . 66 µl/ml Golgistop ( BD Biosciences PharMingen ) for 6 h at 37°C in 5% CO2 . After 6 h , the cells were collected and surface stained with anti-CD3-APC ( eBioscience ) and anti-CD4-FITC ( eBioscience ) . Subsequently , the cells were washed , fixed , permeabilized with Cytofix/Cytoperm buffer ( BD PharMingen ) and intracellularly stained with PE conjugated antibodies against IL-17A , IFN-γ or IL-4 ( or isotype IgG2a control antibody ) ( eBioscience ) for detection of Th17 , Th1 or Th2 cells , respectively , according to the manufacturer's protocol and analyzed with a FACS Calibur flow cytometer . Cells were gated on the CD3+CD8− population for analysis of Th17 , Th1 or Th2 cells . For detection of Treg cells , the Mouse Regulatory T Cell Staining Kit ( eBioscience ) was used . A single cell suspension of splenocytes from each mouse was prepared , and 1×106 cells were surface stained with anti-CD3-PerCP mAbs ( eBioscience ) , anti-CD4-FITC mAbs and anti-CD25-APC mAbs , followed by fixation and permeabilization with Cytofix/Cytoperm and intracellular staining with anti-Foxp3-PE or IgG2a-PE rat immunoglobulin control antibody , according to the manufacturer's protocol . Cells were gated on the CD3+CD4+ population for analysis of Treg cells . The recombinant mouse IL-17A ( rmIL-17A ) , the neutralizing rat anti-mouse IL-17A mAb ( clone 50104 ) and its control IgG2a mAb were purchased from R&D Systems , Inc . Two independent experiments were carried out in the same manner . In each experiment , 16 mice were randomly assigned in four groups ( four mice per group ) . Each mouse was challenged with 40 cercariae of S . japonicum as described above . For two groups of mice , 70 µg of mAb or its control IgG2a mAb per mouse were administered intraperitoneally ( i . p . ) four days before S . japonicum infection , and the administration of the same dose of mAb was repeated every four days during the infection until two days before the mice were sacrificed [12] . Simultaneously , for the other two groups of mice , 500 ng/mice of rmIL-17A or PBS were administered i . p . two days before S . japonicum infection and repeated every 48 h during the infection until two days before the mice were sacrificed [49] . Forty-two days after the challenge infection ( two days after the last injection of rmIL-17A or anti-IL-17A ) , all four mice in each group were sacrificed . Serum samples were collected for ELISA detection of the levels of SEA or SWA specific antibodies , and the livers were isolated for histopathological examination . The splenocytes were prepared for incubation as previously mentioned for detection of cytokines in the culture supernatant or intracellular staining for detection of Th17 , Th1 , Th2 and Treg cells . Forty-two days after the challenge , all mice injected with neutralizing mAb or rmIL-17A and their controls were sacrificed , and perfusion was performed with saline containing heparin to recover the adult worms . Two grams of each liver were digested with 5% KOH at 37°C overnight , and the numbers of eggs were determined by microscopic examination . The remaining parts of the livers were dissected and immediately fixed in 10% buffered formalin . Liver sections were embedded in paraffin and stained with hematoxylin and eosin ( H&E ) for microscopic examination . The lesions were assessed on coded slides by an observer unaware of the experimental setting . The sizes of the granulomas were measured by computer-assisted morphometric analysis as previously described [44] , and 50 visual fields in the liver section of each mouse ( ten sections for each mouse and five random microscope fields for each section ) were measured under a microscope ( magnification: 100× ) ( Olympus , Tokyo , Japan ) . Granuloma sizes are expressed as means of areas measured in µm2 ± SD . The percentages of neutrophils , eosinophils , lymphocytes and macrophages in the same granulomas were determined by microscopic examination ( 1000× magnification ) of 200 randomly selected cells ( not including hepatocytes ) in each granuloma . Ten sections for each mouse and five microscope fields for each section were counted . Percentages of cells were calculated from microscopic analysis of the same granulomas analyzed for lesion size [14] , [50] , [51] . The worm/egg reduction rate ( percentage of protection ) was calculated according to the following formula: ( 1 - mean of worms/eggs in injected mice/mean of worms/eggs in control mice ) ×100% [52] . Statistical analysis was performed using the SPSS version 10 . 1 ( Statistical Package for Social Sciences , Chicago , IL ) software . Statistical significance was determined by Student's t-test and P<0 . 05 was considered significant .
Consistent with previous studies , the granulomas began to form from five weeks after infection in the mouse liver after egg deposition and continued to develop ( Fig . 1A ) . As shown in Fig . 1B and 1C , in parallel with the development of the granulomas , the proportion of Th17 cells in splenic CD4+ T cells increased very slowly during the first five weeks post-infection compared to that before infection ( week 0 ) and increased rapidly thereafter . Meanwhile , the proportion of the Treg cells in the total splenic CD4+ T cell population showed a continuous increase after infection . Additionally , the proportions of both Th1 and Th2 cells in CD4+ T cells also increased . During the first three weeks post-infection , the proportion of Th1 cells rose much more quickly than that of the Th2 cells . However , after egg deposition , the number of Th2 cells kept increasing rapidly , while the number of Th1 cells reached a plateau by eight weeks post-infection ( Fig . 1B and 1C ) . Compared to the CD4+T cells responses in the spleen , that of the mesenteric lymphocytes showed a more rapid increase of Th17 cells during the first three weeks post-infection and a weaker Th1 response throughout infection ( Fig . 1D and 1E ) . Meanwhile , stronger Th2 but weaker Treg responses were observed in the liver ( Fig . 1F and 1G ) . These results indicated that all of the CD4+ T cell subsets ( Th17 , Th1 , Th2 and Treg cells ) increased as over the course of infection . To further investigate the kinetics of cytokines which affect the differentiation , development and proliferation of Th17 cells during S . japonicum infection in C57BL/6 mice , the splenocytes of infected mice were cultured , and the cytokine levels in the supernatants were detected by ELISA . The results in Figure 2 show that , consistent with the generation of Th17 cells , the level of IL-17 increased very slowly in the first five weeks post-infection compared to that before infection ( week 0 ) . However , IL-17 increased rapidly after five weeks post-infection . Meanwhile , both the inducing cytokines ( TGF-β , IL-6 , IL-23 and IL-21 ) and the inhibitory cytokines ( IFN-γ and IL-4 ) of Th17 cell generation all increased after infection . Taken together , these results suggest that the generation of Th17 cells during infection with S . japonicum may occur as a net effect of the inducing and inhibitory factors . Schistosome parasitic worms are multicellular pathogens which have three different life cycle stages ( schistosomula , adult worm and egg ) in definitive hosts including humans . Among the multitude of schistosome antigens that stimulate host immune responses , the adult worm and egg are two important sources of antigens that are involved induction of different types of Th cell responses or Tregs at different infection stages . Studies show that Schistosoma mansoni eggs induce egg antigen-specific Th17 responses and contribute to the severe immunopathology in murine schistosomiasis [12]–[17] . Consistent with these studies , our data also suggest that the S . japonicum egg antigens have the ability to significantly induce egg antigen-specific Th17 responses ( Figure S1A , S1B and S1C ) . To further investigate the roles of these two major types of S . japonicum antigens on Th17 cell generation , SWA and SEA were used to immunize C57BL/6 mice or to induce CD4+T cells to differentiate in vitro . As shown in Figure 3A and 3B , a significantly higher percentage of Th17 cells was only observed in the SEA immunized group by FACS analysis , suggesting that repeated vaccinations of mice with SEA , instead of SWA , preferentially induced Th17 cells in vivo . Furthermore , the data also suggests that the eggs produced by adult worms in hosts , compared to the adult worms themselves , may more rapidly induce Th17 cells during S . japonicum infection . Meanwhile , additionally , SEA preferentially induced a significant increase of Th2 cells and Tregs , while SWA preferentially induced a significant increase of Th1 cells and caused only a slight increase of Treg and not of Th17 or Th2 cells ( Figure 3A and 3B ) . The profiles of CD4+ T cells differentiation induced by SWA or SEA stimulation in vitro also confirmed the above findings ( Figure S2A and S2B ) . To further investigate the cytokines that were reported to affect the differentiation , development and proliferation of Th17 cells , splenocytes from mice after vaccination with SWA or SEA were isolated and cultured as described in Materials and Methods , and the levels of cytokines in the supernatants were detected by ELISA . As shown in Figure 4 , compared to the SWA and PBS control groups , a significantly higher level of IL-17 was observed in the SEA group , suggesting that repeated vaccination with SEA preferentially induced the production of IL-17 . Compared to the PBS control group , the increase of IL-4 was mainly observed in the SEA group , while the increase of IFN-γ was only observed in the SWA group . Compared to the PBS control group , the levels of TGF-β , IL-6 , IL-23 and IL-21 , which are associated with the generation of Th17 cells , were significantly increased in both the SEA and SWA groups . However , when comparing the SWA group with the SEA group , the data showed that repeated vaccination with SEA preferentially induced higher levels of IL-23 and IL-21 , and further supports that egg antigens are possibly more important in the increase of Th17 cells during S . japonicum infection . To evaluate the role of IL-17 in the host protective responses against S . japonicum infection , C57BL/6 mice were injected with rmIL-17A or anti-IL-17A neutralizing mAb to increase or decrease the level of IL-17 in vivo , respectively , and then challenged with S . japonicum . The protection was measured by the reduction in the worm and egg burden [52] compared between groups injected with rmIL-17A or anti-IL-17A neutralizing mAb and their respective controls . Compared to the control IgG2a mAb group , injection of mice with neutralizing anti-IL-17A mAb led to a 26 . 61% reduction ( P<0 . 01 ) in worm burden ( Fig . 5A ) . On the other hand , compared to the PBS control group , no reduction of worm ( P>0 . 05 ) or egg ( P>0 . 05 ) burden was observed in the rmIL-17A group . Consistent with other reports [12]–[16] , [36] , our results in Figure 5B and 5C also show that elevating IL-17 in vivo by injecting mice with rmIL-17A led to slightly enhanced hepatic granulomatous inflammation ( without statistical significance ) , while decreasing the level of IL-17 by use of an anti-IL-17A neutralizing mAb led to decreased hepatic immunopathology . The percentages of neutrophils and eosinophils in the granulomas , which are thought to be the important populations [14] , were increased when S . japonicum infected mice were injected with rmIL-17A . However , injection with an anti-IL-17A neutralizing mAb led to decreases in the percentages of neutrophils and eosinophils in the granulomas ( Fig . 6 ) . In addition , the results in Figure 6 also show that rmIL-17A decreased while anti-IL-17A neutralizing mAb increased the percentages of lymphocytes and macrophages in S . japonicum infected mice . To further investigate the possible mechanism underlying the effects of IL-17 on the response to anti-schistosome infection and the hepatic immunopathology , we detected the levels of CD4+T cells , cytokines and antibody responses in mice after administration of rmIL-17A or anti-IL-17A neutralizing mAb . The proportions of Th1/Th2/Th17/Treg cells did not change after injection with either rmIL-17A to increase the level of IL-17 or anti-IL-17A neutralizing mAb to decrease the level of IL-17 in vivo ( Fig . 7A and 7B ) . The production of IFN-γ , IL-4 , IL-6 , IL-23 , IL-21 , TGF-β and IL-17 from splenocytes of S . japonicum infected mice increased after injection with rmIL-17A ( Fig . 7C ) . However , injection of S . japonicum infected mice with anti-IL-17A neutralizing mAb resulted in the increase of IFN-γ , IL-4 , IL-6 and IL-21 , but the decrease of TGF-β and IL-17 produced by mouse splenocytes . When compared to the PBS control group , administration of rmIL-17A statistically significantly decreased the level of SEA specific IgG1 antibody but increased the level of SWA specific IgG2a antibody in S . japonicum infected mice ( Fig . 7D and 7E ) . However , when compared to the isotype antibody control group , administration of anti-IL-17A neutralizing mAb statistically significantly increased the levels of SEA specific IgG1 , IgG2a and total IgG antibodies , as well as SWA specific IgG2a antibody in S . japonicum infected mice .
During the past several years , the Th1/Th2 paradigm has been updated to include a third helper subset called Th17 . Through the induction of chemokines and the recruitment of other effector T cell populations [53]–[55] , the responses of Th17 cells dominate in response to certain defined pathogens and play important roles in both host defense against pathogens and immunopathogenesis [11] , [56] . Schistosomiasis is a typical chronic infectious disease . Infection induces the generation of Th1 , Th2 and Treg cells , as well as Th17 cells that are involved in the formation of hepatointestinal perioval granulomas . In this study , we investigated the kinetics of the generation of Th17 cells induced by parasite antigens from different stages of S . japonicum infection in mice as well as the role of Th17 cells in the host protective responses . In our study , as the parasites began to produce eggs , the granulomas formed in the mouse liver and developed continuously . After the eggs were deposited into the liver and the granulomas were beginning to form , the proportion of Th17 cells in the spleen , mesenteric lymph nodes and liver CD4+ T cells increased slowly up to five weeks post-infection but then increased more rapidly between five and eight weeks post-infection while accompanied by the development of the granulomas . Meanwhile , the proportions of Th1 , Th2 and Treg cells in the CD4+ T cells also increased . These findings suggested that the schistosomal antigens induced the simultaneous generation of both Th17 cells and the other CD4+ subsets that are thought to suppress the generation of Th17 cells during infection as reported in many previous studies [2] , [5] , [32]; however , these factors seem to have failed to suppress the generation of Th17 cells in our S . japonicum infection experiments . In addition , the results also suggested it may be the egg antigens of S . japonicum that were responsible for the more rapid increase in the proportion of Th17 cells in total CD4+ T cells from five weeks onward after infection . Schistosome eggs and adult worms are two important sources of antigens exposed to the host during S . japonicum infection [57] . They both have the potential to induce Th1 , Th2 , Th17 and Treg cells and the corresponding cytokines . Therefore , we confirmed the above hypothesis by using to SEA or SWA to immunize mice as well as to stimulate CD4+ cells in vitro , and our data showed that it was SEA that preferentially induced the generation of Th17 cells and production of IL-17 . The generation and suppression of Th17 cells by Th1 , Th2 and Treg cells and/or their cytokines have been demonstrated in numerous studies of in vivo and/or in vitro induction of T cells under defined polarizing conditions [25] , [27] , [32]–[34] . Based on these reports , the current widely accepted differentiation mode of the Th17 cell subset is as follows . In the presence of TGF-β and IL-6 , CD4+ T cells are induced to express the transcription factor RORγt by stimulating the STAT3 and Smad signaling pathway , which leads to the differentiation of Th17 cells . IL-21 together with TGF-β also stimulate the alternative pathway for Th17 differentiation . Meanwhile , the mature Th17 cells amplify themselves by autocrine IL-21 . IL-23 also contributes to the maintenance of Th17 cell stability through engagement of the IL-23R , which is expressed by memory or activated T cells [58] . Simultaneously , IFN-γ and IL-4 can effectively inhibit the generation of Th17 cells [2] , [5] , [59] . Our study clearly showed that during the course of S . japonicum infection , in parallel with the increase of the proportion of Th17 cells , both the inducing ( TGF-β , IL-6 , IL-21 and IL-23 ) and inhibitory ( IFN-γ , IL-4 , Th1 , Th2 and Treg cells ) factors of Th17 cell generation increased as the infection progressed . These results suggested that a multicellular pathogen such as S . japonicum introduced complex sets of antigens at different stages of infection into the host that could promote both the inducible and the inhibitory factors of Th17 cell generation , but the overall net result was an increase in Th17 cells . In another words , the observed increase in Th17 cells during S . japonicum infection was probably due to the ability of the S . japonicum antigens to more strongly upregulate Th17 inducing factors than the Th17 suppressive factors . In addition , immunization of mice with SEA also preferentially induced Th17 cell generation and the production of higher levels of known factors involved in the generation of Th17 cells ( TGF-β , IL-23 and IL-21 ) , while accompanied by the increase in the reported inhibitory factors of Th17 cell generation ( Treg and Th2 cells , as well as IL-4 ) . Many studies have shown that Th17 cells play important roles both in host defenses against extracellular pathogens [6] , which are not efficiently cleared by Th1-type and Th2-type immunity and in immunopathogenesis caused by infection . In S . japonicum infection , it has been reported that Th17 may play an important role in the liver immunopathogenesis and in the formation and growth of granulomas around the eggs produced by the adult worm . The findings indicate that the development of severe murine schistosomiasis correlates with high levels of IL-17 and suggest that the exacerbated egg-induced immunopathology is largely mediated by the subset of SEA-induced Th17 cells which produces IL-17 [12] , [13] . Our study also suggested that the IL-17 level was positively related to the severity of liver pathogenesis , which was possibly due to the enrichment of inflammatory cells including neutrophils and eosinophils in the granulomas . However , there has been no evidence reported yet to indicate whether Th17 and its product IL-17 either improve or impair the anti-schistosome immune response . Considering that the protection against S . japonicum infection is mainly based on the clearance of the schistosomulum at the early stage of infection , we investigated the potential protective effect of IL-17 levels during that period . Our results showed that administration of rmIL-17A to mice failed to reduce the worm and egg burdens , indicating that high levels of IL-17 did not contribute to the protective responses . Instead , decreasing the level of IL-17 in mice by injecting anti-IL-17A neutralizing mAb led to reductions of worm and egg burdens , suggesting that the decrease of IL-17 levels contributed to effective protective responses against S . japonicum infection . Our study further showed that administration of the anti-IL-17A neutralizing mAb increased the levels schistosome specific IgG1 , IgG2a and/or IgG , suggesting that increased antibody-dependent-cell-cytoxicity ( ADCC ) , one of the well accepted mechanisms of killing extra-cellular residing pathogens including schistosome [60] , may at least partially contributed to the more effective protective responses against S . japonicum infection . However , the mechanism underlying the role of IL-17 in the protection against S . japonicum infection needs to be further investigated . In summary , for the first time our study reported on the kinetics of the generation of Th17 cells , which were likely preferentially induced by egg antigens , during S . japonicum infection . We also determined that the proportion of Th17 cells increased together with other CD4+ subsets reported to inhibit them , including Th1 , Th2 and Treg cells , as well as their suppressive cytokines in a S . japonicum infection mouse model . These findings suggest that the generation of Th17 cell is determined by the integrated impact of the inducing and suppressive factors promoted by parasitic antigens . More importantly , our study for the first time indicates that a decrease in the level of IL-17 in the early stage of S . japonicum infection may contribute to the host protective responses . | Th17 immune cells secrete the IL-17 cytokine and contribute to host defenses against certain infections . Recent studies linked IL-17 with the severity of liver inflammation and suggested that Th17 cells contribute to the pathology in schistosomiasis , a serious disease caused by parasitic worms such as Schistosoma japonicum widespread in vertebrates including humans . However , the role of Th17 cells in protection against S . japonicum infection is still unclear . For the first time , we describe here the changes in Th17 cell levels during S . japonicum infection and suggest that the schistosome egg antigens are primarily responsible for stimulating the generation of host Th17 cells after S . japonicum infection . We further show that the level of Th17 cells in the host is determined by a combination of factors , namely exposure to complex parasitic antigens that either induce or suppress their generation . We also suggest that lowering IL-17 levels may favor the host's protective responses against S . japonicum infection . Our findings help to better understand the relationship between the host and parasite in terms of immune protection and pathology in schistosomiasis and may contribute to the future development of vaccination and therapeutic strategies . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
]
| [
"medicine",
"infectious",
"diseases",
"schistosomiasis",
"neglected",
"tropical",
"diseases"
]
| 2011 | Dynamics of Th17 Cells and Their Role in Schistosoma japonicum Infection in C57BL/6 Mice |
Ebolavirus ( EBOV ) , the causative agent of a severe hemorrhagic fever and a biosafety level 4 pathogen , increases its genome coding capacity by producing multiple transcripts encoding for structural and nonstructural glycoproteins from a single gene . This is achieved through RNA editing , during which non-template adenosine residues are incorporated into the EBOV mRNAs at an editing site encoding for 7 adenosine residues . However , the mechanism of EBOV RNA editing is currently not understood . In this study , we report for the first time that minigenomes containing the glycoprotein gene editing site can undergo RNA editing , thereby eliminating the requirement for a biosafety level 4 laboratory to study EBOV RNA editing . Using a newly developed dual-reporter minigenome , we have characterized the mechanism of EBOV RNA editing , and have identified cis-acting sequences that are required for editing , located between 9 nt upstream and 9 nt downstream of the editing site . Moreover , we show that a secondary structure in the upstream cis-acting sequence plays an important role in RNA editing . EBOV RNA editing is glycoprotein gene-specific , as a stretch encoding for 7 adenosine residues located in the viral polymerase gene did not serve as an editing site , most likely due to an absence of the necessary cis-acting sequences . Finally , the EBOV protein VP30 was identified as a trans-acting factor for RNA editing , constituting a novel function for this protein . Overall , our results provide novel insights into the RNA editing mechanism of EBOV , further understanding of which might result in novel intervention strategies against this viral pathogen .
Filoviruses ( ebolaviruses ( EBOV ) and marburgviruses ( MARV ) ) cause severe hemorrhagic fever in humans and nonhuman primates [1] . They contain a non-segmented negative-sense single-stranded RNA genome accommodating seven genes ( NP , VP35 , VP40 , GP , VP30 , VP24 , and L ) to produce seven structural proteins ( nucleoprotein , polymerase cofactor , major matrix protein , transmembrane glycoprotein , transcription activator , minor matrix protein , and RNA dependent RNA polymerase , respectively ) [2] . The transmembrane glycoprotein ( GP1 , 2 ) plays an important role in pathogenesis by dictating viral tissue tropism and initiating infection [2] . Despite similarities in genome and protein functions between EBOV and MARV , one of the major differences is that only EBOV increases its genome coding capacity by producing multiple transcripts from the GP gene using RNA editing [2] , [3] . The EBOV ribonucleoprotein ( RNP ) complex , consisting of NP , VP35 , L , and VP30 , edits the GP gene at an editing site ( seven consecutive uridine ( U ) residues in the genomic vRNA ) by introducing non-template adenosine residues into the mRNA to produce multiple transcript species [3]–[5] . Unedited transcripts ( seven adenosine residues at the editing site ) of the GP gene encode for a soluble form of the glycoprotein ( sGP ) . In contrast , edited transcripts in which an eighth or ninth adenosine residue is inserted at the editing site , resulting in a +1- or +2-shift in the open reading frame ( ORF ) , encode GP1 , 2 and the small soluble glycoprotein ( ssGP ) [3]–[5] . A knockout of the editing site in a recombinant Zaire ebolavirus ( ZEBOV ) resulted in a significant increase in cytopathogenicity compared to wild-type virus , indicating the importance of RNA editing for regulating GP1 , 2 expression and reducing early cytotoxicity during EBOV infection [6] , [7] . Despite this and potential other unknown functions of RNA editing , the mechanism of EBOV RNA editing has not yet been characterized . In particular , it is unknown what regions in the GP gene sequence are required , and whether any viral trans-acting factors contribute to RNA editing . As a first-step to characterize RNA editing we utilized ZEBOV minigenome systems , which allowed us to study viral transcription and replication under biosafety level ( BSL ) 2 conditions [8] , [9] . In particular , we developed a dual-reporter minigenome , with which we were able to show that the conserved editing site in the GP gene as well as neighboring sequences are essential for editing . In addition , VP30 was identified as a trans-acting factor for RNA editing . Finally , we could show that EBOV RNA editing is GP gene-specific , because a sequence located in L gene encoding for seven consecutive adenosine residues did not serve as an editing site , most likely due to the lack of necessary cis-acting sequences .
Minigenomes containing the entire coding region or parts of the coding region of the GP and parts of the coding region of the L gene were inserted into the published ZEBOV minigenome plasmid [8] by replacing the previously used reporter gene using standard cloning techniques . To generate the dual-reporter cassette , the ORFs for the enhanced green fluorescent protein ( eGFP ) and mCherry were cloned up- and downstream , respectively , of 110 nt of the GP gene surrounding the editing site , with the mCherry ORF shifted in a way that functional expression of mCherry would require insertion of an additional residue into the editing site of the mRNA . Subsequently , the dual-reporter cassette was cloned into the minigenome plasmid , as well as into pCAGGS ( mammalian expression vector ) and pTM1 ( T7-driven expression vector ) for control experiments . An altered version of the dual-reporter cassette containing an additional A residue in the editing site for control experiments was generated and cloned into pCAGGS . Point mutations and deletions were introduced into the editing site and surrounding sequences using PCR-driven technology . Two potential stem-loops were predicted in the 45 nt upstream of the editing site using the RNA secondary structure prediction Mfold webserver [10] . The XRNAmute webserver [11] was used to identify point mutations destabilizing these stem-loops , which were introduced into the minigenome plasmid using site directed mutagenesis . All plasmids were sequence verified . Primer sequences and detailed cloning strategies are provided in the supplementary information ( Text S1 ) . 293T ( human embryonic kidney cell line ) cells were cultured in Dulbecco's minimal essential medium ( DMEM ) ( Sigma-Aldrich ) supplemented with 10% ( v/v ) fetal bovine serum ( FBS ) ( heat inactivated ) ( Invitrogen ) , 1% L-glutamine ( 2 mM ) ( Gibco ) and 1% penicillin/streptomycin ( 100 U/ml ) ( Gibco ) under 5% CO2 in a humidified incubator at 37°C . For minigenome rescues , 293T cells were seeded one day before transfection for 50–60% confluency at the time of transfection . Transfection was performed using TransIT-LT1 ( Mirus ) according to the manufacturer's instructions , and 250 ng minigenome plasmid , 1000 ng pCAGGS-L , 125 ng pCAGGS-VP35 , 125 ng pCAGGS-NP , 75 ng pCAGGS-VP30 , and 250 ng pCAGGS-T7 . Cells were analyzed for reporter gene expression 48 hrs post transfection ( unless otherwise stated ) . For quantification of transcripts , minigenome assays were performed as described above , but in vitro transcribed minigenome RNA was used instead of minigenome plasmid DNA to minimize the potential of plasmid contamination in the subsequent transcript quantification steps . Minigenome RNA was in vitro-transcribed using the MAXIscript T7 kit ( Ambion ) , according to the manufacturer's instructions . Briefly , 2 ug minigenome plasmid DNA was linearized using SmaI , precipitated with ammonium acetate , and 1 ug DNA was then used for in vitro-transcription . After in-vitro transcription , residual plasmid DNA was removed using Turbo DNase ( Ambion ) , and transcripts were precipitated with ammonium acetate . For RNA minigenome rescues , 293T cells were transfected as described above , but pCAGGS-T7 and the minigenome plasmids were omitted from the transfection mix . 24 hrs later RNA transfection was performed using the TransIT-mRNA transfection kit ( Mirus ) according to the manufacturer's instructions , using 1 ug of RNA transcript , 2 . 5 ul mRNA boost reagent and 2 . 5 ul mRNA TransIT-mRNA reagent . 48 hrs later , total RNA was extracted from the cells using the RNeasy Kit ( Qiagen ) , including the optional DNAse I treatments , according to the manufacturer's instructions . Transcript quantification was done using a rapid transcript quantification assay ( RTQA ) . Briefly , an oligo-dT based first-strand synthesis was performed on extracted RNA using SuperScript III ( Invitrogen ) according to the manufacturer's instructions . cDNA was purified and then subjected to PCR using a 6 carboxyfluorecein ( FAM ) -labeled forward primer , followed by capillary electrophoresis-based fragment length analysis using the Genetic Analyzer 3730xl ( Applied Biosystems ) . PCR reactions were done in triplicates for each sample . Importantly , controls omitting reverse transcriptase showed the absence of DNA contaminations in our RNA preparations ( data not shown ) . EBOV glycoproteins were detected following sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) under reducing conditions using a rabbit anti-peptide antibody ( peptide TIGEWAFWETKKPH; anti-ssGP/sGP/GP1 , 2 ) ( 1∶1 , 000 dilution ) , which is directed against the the first 12 amino acid residues common to ssGP , sGP , and GP1 , 2 and was purchased from Mimotopes . Dnk anti-Rabbit IgG ( H+L ) ( Jackson ImmunoResearch ) ( 1/10 , 000 dilution ) was used as a secondary antibody and bound antibody was detected using the ECL Plus western blotting detection kit ( GE Healthcare ) . For confocal microscopy experiments , transfection was done in a six-well plate containing coverslips as described above , and 48 hrs after transfection coverslips were washed with phosphate buffered saline ( PBS ) , fixed with 4% paraformaldehyde ( PFA ) , and mounted using ProLong Gold Antifade reagent with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen ) . Confocal images were obtained by a Zeiss LSM710 confocal microscope with a 63× oil immersion objective in sequential excitation mode . Slides were also analyzed using an AXIO Imager M1 epi-fluorescence microscope ( Zeiss ) where applicable . For flow cytometry , cells were fixed with 2% PFA , and washed with cold ( 4°C ) PBS containing 2% FBS . Cells were then analyzed on a LSR II Flow Cytometer ( BD Biosciences ) . Cells were initially gated based on forward and side scatter to exclude dead cells and debris . Mean fluorescent intensity ( MFI ) of eGFP and mCherry was then measured on gated eGFP positive cells . At least 100 , 000 events were analyzed for each sample . Data analysis was performed using FlowJo software version 8 . 3 . 3 ( TreeStar Inc . ) . A three-dimensional model of the GGGAAACU stem-loop structure was constructed based on an NMR-derived structure of a tetraloop-receptor complex [12] . The model was explicitly solvated with TIP3P water molecules and Na+ and Cl- counterions using the VMD program [13] . Molecular dynamics simulations were performed under isobaric-isothermal conditions with periodic boundary conditions using the NAMD [14] program ( v . 2 . 7 ) on the Biowulf Linux cluster at the National Institutes of Health , Bethesda , MD ( http://biowulf . nih . gov ) . Electrostatic interactions were calculated using the Particle-Mesh Ewald summation . The CHARMM27 [15] forcefield was used with CHARMM atom types and charges . Prior to the start of the simulation , an energy minimization was performed using a conjugate gradient method , followed by slow warming to 310 K in 10 K increments . Each increment ran for 5 psec in order to equilibrate the system at a given temperature . Production runs were conducted at 310 K for 18 nsec with data collected every nsec . For all simulations , a 1 fsec integration timestep was used along with a 12 Å non-bonded term cutoff . Langevin dynamics were used to maintain temperature and a modified Nosé-Hoover Langevin piston was used to control pressure .
Minigenome systems model virus transcription and replication , and one particular advantage of these systems is that they allow to study the life cycle of high containment pathogens under BSL2 conditions [9] . To assess whether a minigenome system also models RNA editing , we utilized a previously established ZEBOV minigenome system [8] , and replaced the reporter ORF with the GP ORF , including the editing site encoding for 7 adenosine residues , as it is found in the viral genome ( 7 uridine residues ) . This minigenome was expressed in 293T cells together with the viral RNP complex proteins ( NP , VP35 , L and VP30 ) , leading to minigenome replication and transcription of mRNAs by the viral polymerase complex . Production of viral mRNAs was confirmed by detecting the major products of the GP gene , GP1 , 2 and sGP , by western blot ( Figure 1A ) . Importantly , since GP1 , 2 is only produced after insertion of a non-templated adenosine residue into the mRNA at the GP editing site , detection of GP1 , 2 suggested that editing occurs in the context of a EBOV minigenome system . To show editing at the mRNA level , the unedited and edited transcripts encoding for sGP and GP1 , 2 , respectively , were quantified using RTQA . The percentage of these major transcripts was 80 and 20% , respectively , which corresponds to the percentage of unedited vs . edited transcripts found in EBOV-infected cells , which has been reported to be 70–80% and 20–30% , respectively [3] , [4] ( Figure 1B , left bar ) . To characterize the sequence requirements for editing , a truncated GP minigenome was generated , which contained only 110 nt of the GP translated region surrounding the editing site . RNA editing was readily detectable from this truncated GP minigenome using RTQA , and the degree of editing was comparable to the RNA editing observed with the minigenome containing the whole GP ORF . These results demonstrate that this region in the GP gene is sufficient for editing ( Figure 1B , middle bar ) . Interestingly , the ZEBOV L gene also contains a site encoding 7 consecutive uridine residues ( genomic sense ) , similar to the GP gene editing site . To investigate whether RNA editing is GP gene-specific , the region surrounding the GP editing site was replaced with the corresponding region of the L gene ( 110 nt surrounding the potential editing site in L ) . The minigenome containing the truncated L region could be rescued; however , edited transcripts were undetectable by RTQA , demonstrating that transcriptional editing is GP gene specific ( Figure 1B , right bar ) . To further confirm this finding in the context of viral infection , Vero cells were infected with ZEBOV , total RNA was extracted from infected cells , and mRNA was reverse transcribed using an oligo-dT primer . cDNA was then either subjected to RTQA , or PCR amplified using L-specific primers , followed by cloning of PCR products into the pCR 2 . 1-Topo TA vector for sequencing of positive clones . Both methods detected only unedited L transcripts ( data not shown ) , further demonstrating that transcriptional editing is GP gene specific . To easily quantify RNA editing on the protein level , a dual-reporter cassette was developed which contained the eGFP and mCherry ORFs ( Figure 2A ) . These two reporters were chosen because their excitation and emission spectra are far apart; therefore , no fluorescence resonance energy transfer ( FRET ) based interference and no background noise were observed ( Figure S1 ) . The two ORFs are connected by the 110 nt sequence flanking the GP gene editing site , with the mCherry ORF being frame shifted with respect to the eGFP ORF in such way that mCherry can be expressed only in case of RNA editing ( insertion of a non-templated residue at the editing site ) . As expected , expression of this reporter cassette ( eGFP-45 nt-7A-58 nt-mCherry ) in mammalian cells resulted only in green fluorescence , whereas expression of a control cassette in which the editing site encoded 8 adenosine residues ( eGFP-45 nt-8A-58 nt-mCherry ) resulted in both green and red fluorescence ( Figure 2A , Figure S1 ) . This proved the feasibility of our approach to study RNA editing on protein level using a dual-reporter cassette . The dual-reporter cassette containing the editing site encoding 7 adenosine residues was cloned into a minigenome , which was then expressed in mammalian cells after transcription by the viral polymerase complex . As expected , cells exhibited both green and red fluorescence , indicating that RNA editing occurred ( Figure 3 ) . To investigate the sequence requirements for RNA editing , mutations were introduced into the primary editing site sequence by replacing the A-encoding residue at position 3 or 6 by a G-encoding residue ( Figure 2B ) . RNA editing was completely abolished under these circumstances , as demonstrated by only eGFP expression and no evidence for mCherry expression ( Figure 3; importantly , these results were consistently obtained in numerous fields of view ) . This indicates the importance of the primary editing site sequence for RNA editing . When both the viral sequences upstream and downstream of the editing site were deleted , but the editing site itself was kept unchanged , no editing was observed , indicating that viral sequences flanking the editing site are also required for RNA editing ( Figure 3 ) . To define which sequences surrounding the editing site are important for RNA editing , a number of mutants were generated that contained deletions in regions upstream and/or downstream of the editing site ( Figure 2B ) . After coexpression of these minigenome mutants with the viral RNP complex proteins reporter signals from the dual-reporter minigenome were analyzed by flow cytometry , to allow for easy quantification of editing . This method was first validated by analyzing varying ratios of dual-reporter minigenomes containing an 8A editing site ( surrogate for 100% editing ) or dual-reporter minigenomes containing a mutated 7A editing site that did not allow for any editing to occur , resulting in a very good correlation between the input minigenomes and the measured “edited” and “unedited” mRNAs , although a very small amount of background mCherry fluorescence ( 3 . 7% ) was detected in samples which did not contain mRNAs with an 8A editing site ( Figure S2 ) . When this assay was performed with a dual-reporter minigenome containing only the editing site without surrounding up- and downstream cis-acting sequences , only negligible levels of RNA editing as evidenced by reduced mCherry expression ( encoded only by the edited transcript ) were observed ( Figure 4A , S3 ) , similar to the results from the fluorescence microscopy analysis . Deletion of either the up- or the downstream sequences resulted in reduced RNA editing activity; although in these cases the mCherry signal was higher than that of the minigenome with just the primary editing site sequence and no surrounding viral sequences ( Figure 4A ) . This indicates that both up- and downstream cis-acting sequences are important for RNA editing . Subsequently , the up- and downstream cis-acting sequences surrounding the editing site were consecutively deleted , as shown in Figure 2B . Coexpression of these dual-reporter minigenomes with the viral RNP complex proteins and analysis of reporter protein expression 48 hrs after transfection indicated that the region of 9 nt up- and 9 nt downstream of the editing site is sufficient to support RNA editing ( Figure 4B , S3 ) . Surprisingly , in the case of the minigenome containing only 9 nt of the upstream sequences before the editing site ( 9 nt-7A-58 nt ) , editing was actually increased as compared to the editing observed with the wild-type minigenome ( 45 nt-7A-58 nt ) . It has been previously shown that EBOV minigenome transcription is absolutely dependent on the expression of EBOV L , NP , and VP35 , but that VP30 , while greatly increasing minigenome transcription , is not absolutely required for this process [8] , [16] . We , therefore , investigated the role of VP30 for RNA editing using the dual reporter minigenome . As previously reported , overall reporter expression ( measured by eGFP expression ) was significantly reduced in the absence of VP30 ( Figure 5A ) . However , mCherry expression showed a much more dramatic reduction in absence of VP30 ( p<0 . 001 ) to levels in the range of background fluorescence ( cf . Figure S2 ) , suggesting a role of this protein for RNA editing ( Figure 5A , S3 ) . This observation was confirmed when minigenome expressing cells were analyzed by fluorescence microscopy ( Figure S4 ) . Also , when assessing the amounts of edited and unedited mRNAs using RTQA we observed edited mRNAs only in the presence of VP30 , whereas in its absence no edited mRNAs were detectable , confirming the expression data ( Figure 5B ) . VP30 has been suggested to function in transcription by overcoming a secondary structure ( stem-loop ) prior to the NP transcription start signal [17] . We speculated that VP30 might act in a similar way during RNA editing , and , therefore , analyzed the editing site and its surrounding up- and downstream cis-acting sequences for similar secondary structures using the prediction webserver Mfold [10] . Two secondary structure models were obtained having similar ΔG values ( −9 . 2 and −10 . 5 kcal/mol ) . Both models contain two stem-loops , each model having a stem-loop comprised of the first 24 nucleotides , but differing in the composition of the second stem-loop with one model having a stem-loop consisting of nucleotides 26 to 45 and the other consisting of nucleotides 38 to 45 ( Figure 6A , S5 ) . As we found that the upstream immediate 9 nucleotides ( GGGAAACU ) of the ES are critical for minigenome–driven RNA editing , that this sequence is highly conserved among all EBOV species , and that the location of GAAA resides in the loop likely forming a well-studied and stable GNRA tetraloop motif , the model containing the stem-loop formed by nucleotides 38–45 was examined further . Molecular dynamics of a tertiary structure constructed using the SYBYL program ( Tripos ) and based on a crystal structure of a GNRA tetraloop ( PDB ID 4FNJ ) was performed to determine the integrity of the predicted model . We found that the structure remains intact for the entire 18 nsec simulation with expected flexing of the three adenines in the tetraloop ( Movie S1 ) , which was not the case under similar conditions for the upstream 9 nt ( UAUUUUGG ) of the L gene that contains a GP gene editing site-like sequence ( Movie S2 ) . In addition , we checked for the presence of a pseudoknot for the 45 nt upstream sequence using the RNAstructure webserver [18]; however , no pseudoknot was predicted . To determine the role of the predicted stem-loops , the first putative stem-loop was destabilized by the introduction of several mutations ( C3U , A18C and G24A; positions are relative to the start of the 45 nt upstream of the editing site ) into the dual-reporter minigenome . Dual-reporter minigenomes were expressed in 293T cells together with the viral RNP complex proteins , and reporter protein expression were analyzed by flow cytometry . These mutations did not impair RNA editing , but rather led to a slight increase in editing , indicating that the predicted first stem-loop within the 45 nt upstream of the editing site , but outside of the region shown to be absolutely required for editing ( 9 nt upstream of the editing site ) ( Figure 4 ) , is not required for RNA editing ( Figure 6B ) . Subsequently , mutations were introduced into the second predicted stem-loop of the upstream cis-acting sequences ( either G39A and C44U or G38A and G39A; positions are relative to the start of the 45 nt upstream of the editing site ) . When these minigenomes were expressed in 293T cells in the presence of the viral RNP complex proteins , mCherry expression was dramatically reduced ( Figure 6B , S3 ) , indicating the importance of the second stem-loop ( formed by the 8 nt immediately upstream of the editing site ) for RNA editing . In contrast , in a dual reporter minigenome with a single mutation ( C44T ) in this stem-loop , that does not destabilize its structure , RNA editing was not reduced , supporting the conclusion that the secondary structure rather than the primary sequence of region immediately upstream of the editing site is important for RNA editing ( Figure S6 ) .
The phenomenon of RNA editing of the GP mRNA of EBOV has long been described [4] , [5] , and been suggested to play a role in regulating GP1 , 2 expression , thereby limiting the cytotoxic effects of GP1 , 2 on host cells , and contributing to more efficient virus replication [6] , [7] . Also , RNA editing has been described for other members of the order Mononegavirales , particularly Paramyxoviruses , for which the editing mechanism has been intensely studied [19] , [20] . However , there is no information available on the mechanism of RNA editing for EBOV . Therefore , we have developed several minigenome systems that allow characterization of EBOV RNA editing both on the protein and mRNA levels , and used these systems to study the molecular details of EBOV RNA editing . As a first step to validate this approach , the coding region of the GP gene was cloned into a minigenome system . After transcription by the viral polymerase complex , we observed expression of both GP1 , 2 , which is only expressed after editing , as well as sGP ( Figure 1A ) , similar to what is observed during virus infection . It has been reported that in certain cell lines genomic RNA editing can occur , giving rise to genomes containing an 8A editing site . However , this seems to be a much more rare event than transcriptional editing , since despite obvious increased fitness in Vero cells it takes about 4–5 passages in Vero cells for this mutation to become apparent [21] , and while we cannot totally rule out that in rare cases genomic editing of minigenomes might occur , it is extremely unlikely that this was responsible for the extensive editing observed; rather , this was most likely due to transcriptional editing . Importantly , the ratio of edited vs . unedited mRNAs was similar to what has been observed during virus infection [3] , [4] , both when using either a minigenome containing the full-length GP ORF or a minigenome containing only a 110 nt stretch consisting of the editing site and flanking upstream and downstream sequences from the GP gene ( Figure 1B ) . This confirmed that editing in context of a minigenome assay seems to faithfully model editing in context of viral infection . However , one point in which a minigenome assay differs from the viral life cycle is the necessity for initial transcription of the minigenome RNA from a cDNA plasmid . Our minigenome system utilized bacteriophage T7-polymerase for this step , similar to a T7-driven minigenome system that has previously been used for the characterization of paramyxoviruses RNA editing [22] . Unfortunately , T7-polymerase has previously been shown to occasionally insert non-templated adenosine residues into RNA transcribed from sequences encoding 7 adenosine residues [4] , although at least parts of these observations were performed using infection with a recombinant vaccinia virus as the source for T7-polymerase , which differs from our minigenome assays . While this phenomenon suggests the possibility that analysis of editing is skewed when using T7-driven minigenome systems , several lines of evidence show that this was clearly not the case in our study . First , no editing was observed in a minigenome containing a sequence encoding 7 adenosine residues flanked by parts of the coding region of the L gene ( Figure 1B ) . Second , T7-driven expression of a dual-reporter cassette , which contained the GP editing site along with the flanking regions of GP shown to be sufficient for viral editing , from a pTM1 expression plasmid ( i . e . independent of the viral polymerase complex ) did not result in any editing , as observed by the lack of functional expression of the second reporter . Third , our data show that the editing observed in the minigenome system is dependent on an EBOV protein ( i . e . VP30 ) , which was not required for the low-frequency editing by T7 previously reported . In order to confirm and quantify RNA editing at the protein level , a dual-reporter minigenome was developed . To define the sequence requirements for RNA editing , we first investigated the role of the primary sequence of the editing site itself by introducing point-mutations . A to G mutations at positions 3 or 6 of the editing site sequence completely abolished RNA editing , indicating the importance of the primary editing site sequence ( Figure 3 ) . This is in line with previous reports , where a recombinant EBOV with identical mutations in an 8A-encoding editing site was generated and rescued that was shown to have abolished sGP production , which would have required mRNA editing [6] . Interestingly , when we analyzed RNA editing using a dual-reporter minigenome containing only the editing site without any surrounding up- and downstream viral sequences , RNA editing was dramatically reduced to almost undetectable levels ( Figs . 3 and 4 ) . This clearly indicates a requirement for surrounding cis-acting sequences for RNA editing , and explains why RNA editing does not occur during transcription of the EBOV L gene . While for EBOV such a requirement was not previously known , cis-acting sequences are known to be required for paramyxovirus P gene editing [19] . However , in contrast to paramyxovirus P gene RNA editing , which only seems to require upstream cis-acting sequences [19] , further deletional mutagenesis studies showed that for EBOV RNA editing cis-acting sequences reside on both sides of the editing site ( Figure 4 ) . In particular , 9 nt upstream and 9 nt downstream of the editing site ( a region spanning 25 nt in total ) were identified to contain the sequences required for editing . This region is highly conserved between different EBOV species , further supporting its importance . The observation that the minigenome containing only 9 nt of the upstream sequence resulted in increased editing ( Figure 4B ) was surprising . This could be due to the wider sequence context of the editing site or to a more exposed stem loop ( Figure 6 ) in this particular construct ( see also discussion below ) . Studying trans-acting factors for editing is complicated by the fact that all EBOV RNP proteins contribute to replication and transcription . However , among the EBOV RNP complex proteins VP30 is not absolutely required for minigenome replication and transcription , in contrast to the other RNP complex proteins , even though it greatly increases transcription [8] , [16] . Therefore , it was possible to assess the impact of VP30 on editing by performing minigenome assays in absence of VP30 . Interestingly , while previous publications using luciferase as a reporter gene had reported a 14-fold reduction in reporter activity , we observed only a 4-fold reduction in signal when quantifying the eGFP signal in eGFP-positive cells . This might be due to different properties of the reporter proteins . A more likely explanation , however , stems from our observation that , while the intensity of eGFP signal in positive cells was not greatly reduced , the frequency of eGFP-positive cells in absence of VP30 was much lower than that of minigenome assays performed in the presence of VP30 ( data not shown ) . Since luciferase assays measure the sum of reporter activity from all cells , whereas our analysis was restricted to cells with reporter activity detectable , the difference in reporter activity reduction in the absence of VP30 between the different experimental systems is easily explained . More difficult to explain is the occurrence of two subset of cells , in which minigenome transcription is differentially affected by the absence of VP30; one subset of cells where transcription seems to be completely abolished ( giving rise to the reduced frequency of eGFP-positive cells ) , and another subset where transcription is only moderately affected . A previous study has shown that replication of minigenomes is not affected by the absence of VP30 , which argues against any effect of VP30 on transfection efficacy or expression level of transfected proteins and the minigenome RNA , as well as any effect of VP30 on initial transcription by the T7 polymerase or illegitimate encapsidation of naked minigenome RNAs by NP to produce RNP complexes [16] . Therefore , more likely explanations are: first , these two subsets of cells differ in the exact ratios of the other RNP complex proteins due to differences in the number of plasmids taken up by the cells during transfection , or second , these cells differ in their intracellular environment for viral transcription such as different cell cycle stages or different activation of protein kinase R ( PKR ) , which in turn is actively influenced by EBOV RNP complex proteins [23] , [24] . Surprisingly , analysis of editing both on the protein level and on transcript level showed that VP30 is clearly required for editing ( Figure 5 ) . While further studies will be required to shed light on details of the mechanism by which VP30 supports editing , it is tempting to speculate that VP30 might interact with the editing site or adjacent sequences as VP30 has been shown to directly bind viral RNA [25] . Also , it was shown that VP30 helps the polymerase complex to overcome a stem-loop-structure at the transcription start-site of the NP gene [17] , which led us to the speculation that a similar structure might be involved in transcriptional editing . In silico analysis predicted two secondary structure models with similar ΔG obtained for the 45 nucleotides upstream of the editing site . Although the model containing the stem-loop comprised of GGGAAACU is predicted to have a slightly higher ΔG , it was chosen for further examination as it better fits several observations: first , the sequence upstream of the GGGAAACU is not required for RNA editing , second , the sequence is highly conserved among all EBOV species , and third , the sequence can potentially form a GNRA tetraloop ( known to add to tertiary stability ) , supported by physiologically relevant molecular dynamic simulations ( Movie S1 ) . Together , these observations support the predicted model containing the GGGAAACU stem-loop . Molecular dynamics simulations of stem-loop structures corresponding to the immediate upstream 9 nt of the L gene ES , which does not invoke editing , quickly fell apart ( Movie S2 ) . The chosen model predicted two RNA stem-loops at position 1–24 and 38–45 within the 45 nt sequence upstream of the editing site ( Figure 6 ) . Destabilization of the first stem-loop had no effect on RNA editing , which was to be expected , since this region was dispensable for editing ( Figure 4 ) . In contrast , destabilization of the second stem-loop resulted in impaired RNA editing ( Figure 6 ) . This second predicted stem-loop is formed by the 8 nt immediately upstream of the editing site , and within the 9 nt upstream cis-acting sequence determined to contain regions required for RNA editing . This region along with the editing site is highly conserved , with exception of nucleotide 44 ( two nucleotides upstream of the editing site ) ( Figure 2B ) . Interestingly , mutation of this nucleotide was predicted to not destabilize the stem-loop at position 38–45 and , indeed , did not reduce RNA editing , suggesting that the secondary structure rather than the primary sequence of the upstream sequence is important for editing . This is the first experimental data demonstrating a role of a secondary structure for viral RNA editing . In support of our data , a secondary structure in the editing region of the viral P gene of Simian virus 5 ( SV5 ) has been predicted in silico , and suggested to contribute to RNA editing , although experimental evidence was not provided [26] . For Paramyxoviruses , transcriptional editing has been well studied , and the current model is that upon encountering the editing site the polymerase pauses , and can then backslide on the vRNA-mRNA hybrid [19] , [20] . This allows the penultimate 3′ nucleotide of the nascent mRNA to realign to the upstream residue of the template , resulting in the insertion of a pseudo-templated residue in the mRNA , sometimes referred to as polymerase stuttering [27] . We propose a similar model for ZEBOV RNA editing ( Figure 7 ) with the difference that the sequence surrounding the editing site , in particular the stem loop structure formed by the 8 nt upstream of the editing site in the nascent mRNA , serves as a pause signal for the viral polymerase . Polymerase stuttering at the editing site , which constitutes a slippery sequence , then can occur , leading to the insertion of pseudo-templated adenosine residues into the mRNA . Subsequently , VP30 overcomes the transcription pause , similar to its function in overcoming the transcriptional pause at the beginning of the NP gene , allowing transcription to proceed . While this model is developed based on experiments with a ZEBOV minigenome , the fact that the editing site and surrounding sequences are well conserved among all EBOV species suggest that this mechanism most likely applies to all species of EBOV . It is , therefore , likely that identifying inhibitors to RNA editing might open up the potential for a novel intervention strategy to combat EBOV infections in general . | Ebola virus ( EBOV ) causes severe hemorrhagic fever with case fatality rates of up to 90% and no therapy or vaccine currently available . A better understanding of the EBOV life cycle is important to develop new countermeasures against this virus; however , research with live EBOV is restricted to high containment laboratories . One unique feature of the EBOV life cycle is that its surface glycoprotein is expressed only after editing of the glycoprotein mRNA by the viral polymerase , leading to an insertion of a non-templated nucleotide into the mRNA . While this phenomenon has been long known , the mechanism of mRNA editing for EBOV is not understood . We have developed a unique minigenome system that allows the study of EBOV mRNA editing outside of a high containment laboratory . Using this system we have characterized EBOV mRNA editing and defined the sequence requirements for this process . Interestingly , we could show that signals both up- and downstream of the editing site are important , and that a secondary structure in the RNA upstream of the editing site as well as the viral protein VP30 contribute to editing . These findings provide new detailed molecular information about an essential process in the EBOV life cycle , which might be a potential novel target for antivirals . | [
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| 2013 | Ebola Virus RNA Editing Depends on the Primary Editing Site Sequence and an Upstream Secondary Structure |
Intracellular infection and multi-organ colonization by the protozoan parasite , Trypanosoma cruzi , underlie the complex etiology of human Chagas disease . While T . cruzi can establish cytosolic residence in a broad range of mammalian cell types , the molecular mechanisms governing this process remain poorly understood . Despite the anticipated capacity for fatty acid synthesis in this parasite , recent observations suggest that intracellular T . cruzi amastigotes may rely on host fatty acid metabolism to support infection . To investigate this prediction , it was necessary to establish baseline lipidome information for the mammalian-infective stages of T . cruzi and their mammalian host cells . An unbiased , quantitative mass spectrometric analysis of lipid fractions was performed with the identification of 1079 lipids within 30 classes . From these profiles we deduced that T . cruzi amastigotes maintain an overall lipid identity that is distinguishable from mammalian host cells . A deeper analysis of the fatty acid moiety distributions within each lipid subclass facilitated the high confidence assignment of host- and parasite-like lipid signatures . This analysis unexpectedly revealed a strong host lipid signature in the parasite lipidome , most notably within its glycerolipid fraction . The near complete overlap of fatty acid moiety distributions observed for host and parasite triacylglycerols suggested that T . cruzi amastigotes acquired a significant portion of their lipidome from host triacylglycerol pools . Metabolic tracer studies confirmed long-chain fatty acid scavenging by intracellular T . cruzi amastigotes , a capacity that was significantly diminished in host cells deficient for de novo triacylglycerol synthesis via the diacylglycerol acyltransferases ( DGAT1/2 ) . Reduced T . cruzi amastigote proliferation in DGAT1/2-deficient fibroblasts further underscored the importance of parasite coupling to host triacylglycerol pools during the intracellular infection cycle . Thus , our comprehensive lipidomic dataset provides a substantially enhanced view of T . cruzi infection biology highlighting the interplay between host and parasite lipid metabolism with potential bearing on future therapeutic intervention strategies .
Infection with the protozoan parasite Trypanosoma cruzi underlies the development of human Chagas disease , a progressive and debilitating condition characterized by cardiac and gastrointestinal disturbances [1] . With an estimated 8 million people infected [2] and limited treatment options [3] , this neglected tropical disease remains a significant health and economic burden in Latin America and an emerging immigrant health problem in non-endemic regions of the world [4] . Mammalian infectious forms of T . cruzi are transmitted in the feces of insect vectors ( family Triatominae ) , congenitally , via oral infection by the consumption of contaminated foods and liquids , or in the blood and organs of infected donors [5] , where they colonize a range of cell and tissue types during the acute stage of infection [6 , 7] . Immune control mechanisms are insufficient to eliminate infection [8] , leading to chronic infection with parasite persistence in a variety of tissues including cardiac muscle [9–11] , gastrointestinal smooth muscle [7 , 12 , 13] , and adipose tissue [7 , 14 , 15] . While T . cruzi infection can cause acute myocarditis , parasites often persist asymptomatically for decades before clinical symptoms arise in infected individuals [16] . It is now recognized that tissue infection with T . cruzi is highly dynamic , even during the more tissue-restrictive chronic phase [7] . As such , understanding the mechanisms that underlie the successful intracellular colonization of diverse host cell types by T . cruzi is a crucial step to elucidating processes involved in the development and progression of both the acute and chronic stages of Chagas disease . In the mammalian host , T . cruzi cycles between two morphologically and biochemically distinct forms: non-dividing , motile trypomastigote forms that circulate in the body and actively penetrate cells to establish intracellular infection [17] and obligate intracellular amastigote ( ICA ) forms that replicate in the host cell cytosol [18] . Like other intracellular pathogens ( e . g . , [19] ) , T . cruzi amastigotes must meet their metabolic demands by coupling to host metabolic processes . While such metabolic dependencies are potentially exploitable for pathogen control [20 , 21] , fundamental knowledge of the host pathways that are co-opted by T . cruzi amastigotes during infection is lacking . To address this gap , we previously performed a genome-wide RNA interference screen in human cells to identify host factors that are permissive for intracellular T . cruzi growth [22] . Fatty acid ( FA ) metabolism emerged among the top host cellular pathways associated with an efficient T . cruzi amastigote growth phenotype in human cells , where an increase in parasite proliferation was observed under conditions expected to favor FA uptake and oxidation by infected host cells [22] . Given that T . cruzi upregulates expression of FA oxidation and membrane lipid synthesis enzymes as an early adaptation to the host intracellular environment [23–25] , and the noted tropism of this parasite for tissues with high rates of FA metabolism [26] , intracellular T . cruzi amastigotes appear to be poised to exploit host FA metabolism to meet their metabolic needs . However , biochemical evidence to support this prediction is currently lacking . In this study , we have leveraged comprehensive lipid mass spectrometry to investigate the potential coupling of parasite and host FA metabolism in a cell culture model of T . cruzi infection . We provide several lines of evidence to demonstrate that cytosolically-localized T . cruzi amastigotes co-opt long chain fatty acids ( LCFA ) , predominantly from host triacylglycerol ( TG ) pools , a process that facilitates the replication of this intracellular pathogen .
To assess the potential contribution of host FA metabolism to T . cruzi amastigote development , it was necessary to first establish steady-state lipidomic signatures for the parasites and for their cognate mammalian host cells . T . cruzi is capable of infecting and completing its intracellular life cycle in most nucleated mammalian cell types in vitro [27] . Anticipating the potential influence of the immediate host cellular environment on the T . cruzi amastigote lipidome , parallel lipidomic analyses were performed in two different mammalian cell types that have predicted differences in lipid metabolism [28]: human foreskin fibroblasts ( HFF ) and mouse skeletal myoblasts ( C2C12 ) , as outlined ( Fig 1A ) . T . cruzi amastigotes were isolated from infected cell monolayers at 48 hours post infection ( hpi ) , a time point chosen to reflect a period of active intracellular parasite replication [22] . Isolated parasites were shown to be free of contaminating host cell organelles and membranous material as assessed by transmission electron microscopy ( Fig 1B ) and western blot analysis ( Fig 1C ) . Lipid extracts obtained from mock- and parasite-infected HFF and C2C12 , T . cruzi amastigotes liberated from each host cell type , as well as tissue-culture trypomastigotes ( TCT ) that egress upon completion of the intracellular infection cycle ( Fig 1A ) were subjected to an ultra-high performance reverse-phase liquid chromatography ( UHP-RPLC ) system coupled with a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer operating in high-resolution data-dependent full scan MS/MS in negative and positive ion modes . Representative base peak chromatograms generated from total lipid extracts of uninfected C2C12 , HFF , and T . cruzi amastigotes isolated from these cell types revealed clear peak separation and distinct ion profiles ( Fig 1D; S1 Fig ) . The chromatographic profiles for T . cruzi amastigotes isolated from different mammalian host cell types exhibited strong similarity , particularly in the peak clusters eluting in the 30–36 min range corresponding to triacylglycerols ( TG ) , diacylglycerols ( DG ) , sphingolipids , and cardiolipins ( CL ) , but differ from their respective host cell of origin ( Fig 1D ) . A total of 1079 lipid molecular species were confidently identified after manual curation of LipidSearch-assigned spectra , with coverage of 30 different lipid subclasses , across all samples ( S1 and S2 Tables; Supporting Information: Supplemental Methods ) . Over half of the lipids identified belong to the glycerophospholipid lipid ( GP ) category ( 675 unique molecular species ) , with most of this diversity associated with diacyl glycerophosphocholine ( PC ) or diacyl- and ether-linked glycerophosphatidylethanolamine ( PE ) subclasses ( 236 and 110 unique species , respectively ) . The glycerolipid ( GL ) category comprised approximately one-third of the total lipid diversity , with 326 TG , 58 DG , and 6 monoglycerol ( MG ) species . Sphingolipids , with 58 sphingomyelin ( SM ) and 43 ceramide ( Cer ) species comprised ~10% of the total lipids identified ( S1 Table ) . Next , the proportion of each of the major lipid classes within the lipidomes of T . cruzi and mammalian cells was plotted to determine if the parasite lipidome could be readily distinguished from that of its host and , if so , what major trends in its lipidome would be conserved regardless of the host cell background ( Fig 2 ) . To this end , the relative abundance of each major lipid class identified in T . cruzi and mammalian cells was compared across all cell types and conditions ( Fig 2; S3 Table ) . As expected , the lipidomes of HFF and C2C12 differ markedly , as evidenced by the quantitative differences in the PC and SM content in these disparate cell types ( Fig 2 , uninfected ) . Apart from the increased TG content in infected fibroblasts as compared to the uninfected cell ( Fig 2 , HFF ) , the lipid class distribution of mammalian host cells remained largely unchanged after 48 hours of T . cruzi infection ( Fig 2 , infected ) . The lipid class relative abundance profiles of isolated T . cruzi amastigotes are clearly distinguishable from either mammalian host cell type but display nearly identical profiles independent of host cell of origin ( Fig 2 , ICA ) . This finding suggests that , at least for the phase of the intracellular T . cruzi infection cycle examined here , maintenance of a particular lipid class balance may be critical for the growth and survival of this intracellular pathogen . Notably , this lipid identity includes a relatively enriched TG pool in T . cruzi amastigotes ( ~25% of total lipid area ) , reflecting the capacity for lipid storage in this parasite life cycle stage [29] . The lipidomic signatures of the extracellular trypomastigotes diverge from their amastigote counterparts suggestive of developmental regulation of lipid class ratios in this parasite [30] . In addition , the lipidome profiles of T . cruzi trypomastigotes isolated from distinct mammalian host cell types exhibited marked differences , a feature that may impact future parasite-host interactions . These observations indicate that while the T . cruzi lipidome is dynamic over the course of its life cycle , the lipid composition requirements may be constrained in the intracellular amastigote stage . To compare parasite and host lipidomic signatures in more depth , analyses of the detailed lipid species distributions within each of the major lipid subclasses described above were undertaken . Two-dimensional principal component analyses ( PCA ) were performed to identify overall trends in the data ( Fig 3 ) . When total lipidomes were compared across samples , T . cruzi amastigotes clustered with their cognate mammalian host cells ( Fig 3A ) rather than together , as predicted by the proportional lipid class trends observed for T . cruzi amastigotes isolated from different cell types ( Fig 2 ) . To determine if this relationship might be driven by a sub-compartment of the total lipidome , additional PCA plots were generated for individual lipid subclasses . A more complex relationship between host and parasite lipid signatures emerged in these analyses ( Fig 3B and 3C; S2 Fig ) . Within the TG class , tight clustering between T . cruzi amastigote and cognate mammalian host cell was evident ( Fig 3B ) whereas , clear divergence between parasite and host lipidomes was observed within the PI pool ( Fig 3C ) . Still other lipid classes showed no distinct trend ( S2 Fig ) . Combined , these data suggest that the steady state lipidome of T . cruzi amastigotes is a mixture of parasite-synthesized lipids and host-derived lipids obtained through scavenging . T . cruzi is capable of the synthesis , elongation [31] , and desaturation [32] of FA , although the extent to which the intracellular amastigote stage relies on these FA synthesis capacities has not been determined . With several enzymatic activities absent in mammals , T . cruzi produce some fatty acids that are typically found in much lower abundance in mammalian host cells [32–34] . An example is linoleic acid ( C18:2 cis , cis-Δ9 , 12 ) which , in a gas chromatography with flame ionization detection ( GC-FID ) analysis of the esterified FA content of total lipid pools is found to be highly enriched in isolated T . cruzi amastigotes as compared to mammalian cells ( S3 Fig ) , consistent with previous reports [32 , 33] . Conversely , trypanosomes have been reported to have lower levels of oleic acid and palmitic acid than human cells [33 , 35] . We therefore predict that lipids produced endogenously by T . cruzi , either through de novo synthesis , or via FA remodeling , will be likely enriched in FA species that are abundant in the parasite ( e . g . , C18:2; S3 Fig ) , whereas lipids scavenged from the host and incorporated directly into the parasite lipidome would more closely mirror the FA composition of the mammalian host cell . With these criteria in mind , a deeper analysis of the parasite and host lipidome data was conducted in which detailed comparisons of the FA moiety composition of each lipid subclass of T . cruzi and cognate host cells were performed to determine which , if any , parasite lipid classes display signatures indicative of scavenging by T . cruzi amastigotes . Because of the differences between parasite and mammalian cell FA elongase and desaturase enzymatic machineries mentioned above , emphasis was given to the comparison of long-chain and very long-chain polyunsaturated FA ( LC-PUFA and VLC-PUFA , respectively ) moiety distributions of each of the major lipid subclasses of T . cruzi and host . In most cases , this approach identified T . cruzi-specific trends in the FA moiety distribution of the major GP pools , which were clearly different from the host cells ( Fig 4; S4 Fig ) . For instance , T . cruzi-derived PE and PI pools had high levels of C18:2 FA , and very low levels of C20:3 , C20:4 , and C20:5 FA as compared to the host ( Fig 4A and 4B ) . Also , the parasite-derived PE and PI pools exhibited much higher abundance of ether-bound moieties ( 1-0-alkyl and 1-0-alkenyl , represented as 16:0e and 16:0p , respectively , in Fig 4A and 4B ) as compared to the host . Another parasite-specific trend pertained to the PC and LPC pools , which had markedly higher levels of VLC-PUFA ( C22:4 , C22:5 , and C22:6 FA moieties ) , than the host ( Fig 4C and 4D ) . Taken together , these data suggest that T . cruzi amastigotes either do not readily incorporate GP from host cells , preferring to synthesize these lipids de novo or extensively remodel FA moieties after acquisition from host GP pools . In contrast to these observations , the glycerolipid ( GL ) pools of T . cruzi amastigotes isolated from HFF ( Fig 5 ) or from C2C12 ( S5 Fig ) were more similar to their cognate host cell than to each other , in agreement with the PCA plot of the TG subclass ( Fig 3B ) . These trends were made more evident given the divergence between the HFF and C2C12 host cell GL FA composition . This led us to hypothesize that T . cruzi amastigotes co-opt GL ( TG and/or DG ) or FA derived from the GL pool , directly from their mammalian host cells . To obtain direct biochemical evidence of FA scavenging by T . cruzi ICA and investigate whether amastigotes access long chain fatty acid ( LCFA ) species from their host cells , we used the odd chain fatty acid ( OCFA ) pentadecanoic acid ( C15:0 ) to trace FA incorporation into different lipid pools of the amastigote . This approach took advantage of the fact that OCFA occur naturally in very low abundance in both T . cruzi and mammalian cells ( ~0 . 3%; of total LCFA abundance , S4 Table ) . Using our LC-ESI-MS/MS pipeline , we determined the relative abundance of OCFA substituents in the lipidomes of T . cruzi amastigotes and their host cells after provision of 200 μM C15:0 to T . cruzi-infected cultures for 6 h ( 42–48 hpi ) . This protocol resulted in a striking 30-fold increase in the C15:0 content in total acylated FA in isolated parasites and host cells ( Fig 6A , inset ) , confirming the capacity for C15:0 uptake by mammalian cells , as anticipated [36] . C15:0 was determined to be the most abundant OCFA in the TG subclass ( Fig 6A ) , and was represented in similar levels in host and parasite TG pools , in agreement with the observations from our steady-state lipidomic analyses ( Fig 5B ) . While acylated C15:0 was found in similar abundance ( ~15% ) in uninfected and infected host cells , isolated intracellular amastigotes showed less C15:0 incorporation ( ~8%; Fig 6A , inset ) , Despite the lower C15:0 incorporation in total amastigote FA pool , we observed ~2-fold higher levels of C17:0 and C17:1 in amastigotes than infected or uninfected host cells . This suggests that following import of LCFA from mammalian host cells , T . cruzi amastigotes are able to modify these fatty acids through the action of parasite elongase and desaturase enzymes [33] . We find that OCFA were unequally distributed across lipid classes in the host and parasite lipidomes , with C15:0 being predominantly incorporated into TG pools in T . cruzi amastigotes ( Fig 6A ) . In contrast , T . cruzi amastigote PI was enriched in C17:0 and C17:1 , ( Fig 6B ) , however , this enrichment was not observed in amastigote PE , suggesting that a portion of the acquired C15:0 FA was modified by T . cruzi amastigotes prior to incorporation into different parasite lipid classes . Thus , in conjunction with lipidomic profiling data , metabolic labeling studies provide further evidence that T . cruzi amastigotes acquire LCFA from host lipid pools and incorporate these FA into their own lipid/membrane synthesis pathways . Furthermore , our results implicate host TG metabolism as a critical factor influencing FA incorporation by T . cruzi amastigotes . To investigate the role of host GL metabolism in LCFA scavenging by T . cruzi amastigotes , mouse embryonic fibroblasts ( MEF ) deficient in diacylglycerol acyltransferase ( DGAT1/2 ) -dependent TG synthesis were exploited for parasite infection and metabolic labeling studies . T . cruzi-infected WT , DGAT1/2-/- , and DGAT2-complemented DGAT1/2-/- fibroblasts were pulsed with [14C ( U ) ]-palmitate for 6 h ( 42–48 hpi ) , amastigotes were isolated , and label incorporation into host cell and T . cruzi amastigote neutral lipids and phospholipids was visualized following separation by TLC ( Fig 6D and 6E ) . As expected from prior characterization of the DGAT1/2-/- fibroblasts [37] , the enzyme deficiency resulted in greatly reduced incorporation of exogenous 14C-palmitate into TG pools in DGAT1/2-/- cells as compared to WT cells ( Fig 6D , lane 1 , 2 ) , and was restored with the ectopic expression of DGAT2 ( Fig 6D , lane 3 ) . Decreased 14C-labeling of cholesterol esters ( ChE ) was also noted in the DGAT1/2-/- fibroblasts relative to WT cells , a likely consequence of impaired lipid droplet formation in the knockout cells [37] . Interestingly , we observed a marked increase in TG 14C-labeling in DGAT1/2-/- cells upon infection with T . cruzi , suggesting that a non-DGAT TG synthesis pathway ( as discussed in [37] ) may be upregulated in response to parasite infection . T . cruzi amastigotes harvested from DGAT1/2-/- fibroblasts incorporated less 14C-label into neutral lipid ( Fig 6D ) and phospholipid ( Fig 6E ) classes as compared to the same number of parasites derived from WT fibroblasts or from DGAT2-complemented DGAT1/2-/- fibroblasts ( Fig 6D and 6E ) . Given that the DGAT1/2-/- cells were not generally impaired in 14C-palmitate uptake and incorporation into other neutral lipid or phospholipid classes ( Fig 6D and 6E ) , our results indicate that host DGAT-dependent TG synthesis is a major route of LCFA acquisition by T . cruzi amastigotes . Thus , despite increased TG labeling in parasite-infected DGAT1/2-/- cells our results suggest that the non-DGAT-derived TG pool may be inaccessible to the parasite . We sought to determine the impact of decreased access to host FA through the TG pool on amastigote growth . Using a flow cytometry-based method to follow amastigote proliferation using CFSE-labeled parasites [22] we find that T . cruzi amastigotes undergo fewer divisions in DGAT1/2-/- cells as compared to WT MEF or in DGAT2-complemented DGAT1/2-/- fibroblasts ( Fig 6F ) . These combined lipidomic , metabolic labeling , and proliferation data suggest that T . cruzi amastigotes are capable of scavenging LCFA primarily from the host GL pool and that , specifically , the host TG pool is important for maintaining maximal parasite growth . However , amastigotes are still able to proliferate in host cells lacking the capacity for TG synthesis via the major DGAT-dependent pathway [37] . Such predicted metabolic flexibility may be due to the capacity for parasites to access TG generated via PC in DGAT-independent manner ( discussed in [37] ) or to synthesize their own LCFA de novo [25] via a sequential FA elongase system [38] , and/or parasite access to host FA independent of the host TG pool .
Metabolic coupling to host cellular and biochemical pathways is universally required to sustain the growth and/or survival of obligate intracellular pathogens . In addition to acquiring essential nutrients from their host cells , intracellular pathogens often scavenge metabolites and/or macromolecules that they have the intrinsic capacity to synthesize [39 , 40] . This strategy may be more energetically favorable and offer a level of flexibility that can facilitate pathogen survival under changing environmental conditions . Here , we demonstrate that , despite the predicted capacity for de novo FA synthesis by the kinetoplastid protozoan parasite , Trypanosoma cruzi [31] , the obligate intracellular amastigote stages of this parasite readily incorporate long-chain fatty acids ( LCFA ) , acquired from mammalian host cell glycerolipid ( GL ) pools , into their own lipid storage and synthesis pathways . Our findings expose a biochemical and functional link between parasite and host lipid metabolism and demonstrate the potential or T . cruzi to bypass its own FA and lipid biosynthetic capabilities predicted in expression analyses [23–25] . The unbiased , quantitative lipidomics approach adopted in this study , which focused on FA moiety compositions in different parasite and host cell lipid subclasses , was instrumental in revealing the hybrid nature of the T . cruzi lipidome . A key element of our study design was the generation of parallel comprehensive lipidomic datasets for T . cruzi amastigotes isolated from two distinct mammalian host cell types and the subsequent comparisons made between parasites and their cognate host cells . A deep analysis of the FA moiety distribution within each lipid subclass was conducted with extensive manual curation to facilitate the high confidence assignment of host- and parasite-like lipid signatures . Overall , we find that T . cruzi amastigotes maintain a lipid identity that is distinguishable from that of their mammalian host cells . First , the proportion of several major lipid classes identified in T . cruzi amastigotes remained constant over four independent experiments and was shown to be independent of the host cell type that housed the parasite , despite overt differences between fibroblasts and myoblasts . Additionally , T . cruzi amastigotes display strong parasite-specific signatures within the GP pool with lipids enriched in C18:2 , a FA moiety that is not synthesized by mammalian cells , but differentially produced by T . cruzi via the action of an oleate delta-12 desaturase [32] . This parasite also contains relatively high levels of ether-bound GP and proportionally high levels of VLC-PUFA in their PC pools , as compared to mammalian host cells . Such conservation of class-specific lipid moiety distribution in T . cruzi amastigotes suggests that these molecules play important biological roles in this organism , including differentiation , cell signaling , and modulation of host immune responses , as has been shown for the specific LPC distribution of T . cruzi trypomastigotes and amastigotes [41 , 42] . Another consistent feature among parasites isolated from different mammalian cell types was the relative enrichment of TG , a main storage lipid in cells , which comprised approximately 25% of the T . cruzi amastigote lipidome . However , unlike the parasite signatures noted above , the T . cruzi TG and DG pools , along with the LCFA-containing PC pool , displayed a strong host signature . In fact , the FA moiety profiles for these lipid subclasses were nearly identical to their specific host cell counterparts , suggesting that these lipids were acquired by the parasites from their mammalian host cells . This conclusion is supported by metabolic labeling studies in which incorporation of exogenous FA into amastigote neutral lipids and phospholipids involves flux through host TG pools . Exogenous provision of either odd-chain FA ( C15:0 ) or radiolabeled FA ( C16:0 ) resulted in their incorporation into a range of parasite-associated neutral lipids and phospholipids . However , trafficking of these exogenous FA tracers into host-resident T . cruzi amastigotes was significantly diminished in parasites that were grown in fibroblasts lacking enzymes required for de novo TG synthesis , DGAT1/2 [37] , this trafficking into amastigote was restored upon genetic complementation with DGAT2 . Furthermore , as the proliferative capacity of T . cruzi ICA was significantly reduced in DGAT1/2-deficient fibroblasts and restored with ectopic expression of DGAT2 , we conclude that host TG pools likely serve as an important source of FA needed for different biological activities in the parasite , including lipid and membrane synthesis , β-oxidation , and the generation of bioactive lipid mediators [23–25 , 43] . Together , our observations support a model in which intracellular T . cruzi amastigotes assemble a mosaic lipidome that combines scavenged and de novo synthesized FA . Scavenged lipids may serve as a source of FA during the proliferative phase of the T . cruzi intracellular lifecycle and/or during the intracellular conversion of amastigotes to trypomastigotes , a time when extensive lipid remodeling is anticipated [44] . Evidence that host-derived TG/DG are utilized , at least in part , for the synthesis of parasite phosphatidylinositols ( PI ) was seen in the metabolic incorporation of exogenous FA into parasite PI in a DGAT1/2-dependent manner . In contrast , exogenous FA were not detectably incorporated into T . cruzi PE , a relatively abundant lipid class , suggesting a route of PE synthesis intrinsic to the parasite . Since the bulk of PE in T . cruzi amastigote was shown to be ether-bound ( plasmalogen ) , it is possible that the lack of labeling of these lipids with exogenous FA is a consequence of the compartmentalized biosynthesis of ether lipids in the parasite glycosomes ( organelles which are roughly equivalent to mammalian peroxisomes ) [45] . The lack of flux of exogenous FA into parasite glycosomes would also explain the relatively low labeling of the T . cruzi PI pools , which were also shown to be rich in ether-bound species . While more detailed metabolic flux analyses are required to fully appreciate the contribution of scavenged FA to the biology of intracellular T . cruzi amastigotes , these data suggest that pathways involved in the synthesis of certain parasite lipids may not converge with the exogenously supplied FA obtained from host TG pools . As such , T . cruzi amastigotes may rely on their own FA synthesis capacity to generate a subset of lipids , which may be subject to differential regulation . T . cruzi exploits an uncommon modular synthesis pathway shared with other kinetoplastids to synthesize LCFA de novo , that relies on FA elongases instead of type I or type II FA synthases typically found in other organisms [31 , 46] . This modular synthesis involves 3 elongases ( ELO 1–3 ) , which convert C4:0 to C10:0 ( ELO1 ) , C10:0 to C14:0 ( ELO 2 ) , and C14:0 to C18:0 ( ELO3 ) . A fourth elongase ( ELO4 ) is responsible for the synthesis of very-long chain fatty acids ( VLCFA ) , possibly including VLC-PUFA . Although ELO1-3 are highly expressed in host cell resident T . cruzi amastigotes [25] , it is currently unknown whether these enzymes are active in this life cycle stage [25] or essential . Based on our current finding that T . cruzi amastigotes scavenge and utilize host lipid-derived FA for the synthesis of certain lipids , but not others , we predict that the T . cruzi FA elongase system may be required for this life cycle stage of the parasite . In addition to generating FA de novo , T . cruzi FA elongases , along with parasite desaturases , are likely to be used to modify scavenged LCFA before incorporation into parasite lipids . The relative reliance of T . cruzi on exogenous versus endogenous FA , and whether environmental conditions alter this balance , remains to be determined . Our data strongly suggest that T . cruzi amastigotes scavenge TG , DG , and LCFA-PC from their mammalian host cells during intracellular infection . As the bulk of the cellular TG pool is sequestered in lipid droplets ( LD ) , that are comprised of a neutral lipid core ( TG and DG ) surrounded by a phospholipid monolayer enriched in saturated LCFA-PC [47] , we propose that TG , DG and LCFA-PC are acquired en masse from host lipid droplets . The alternative model , in which FA stored in host TG/DG pools are mobilized through the action of TG lipases [48] , and then taken up by the parasite and reassembled into TG , DG and LCFA-PC with the same FA moiety distribution as existed in the host cell , is unlikely . As lipid droplets are highly dynamic organelles that function as critical hubs for FA trafficking in cells with key roles in cellular lipid and energy metabolism [49–51] , it is not surprising that host LD are frequently targeted by intracellular pathogens . LD accumulation is a common cellular response to pathogen infection [52–55] , which can occur in response to increased oxidative stress [56 , 57] or paracrine signals [58] . With high rates of FA flux , host cell LD represent a readily accessible source of FA for a number of intracellular pathogens , such as Chlamydia trachomatis , Mycobacterium tuberculosis , and M . leprae , that exploit host LD to obtain lipids for energy and membrane biosynthesis [59] . Our demonstration of a biochemical interaction between T . cruzi amastigotes and host TG , which are mainly found in lipid droplets , brings functional insight to earlier descriptions of increased LD content in T . cruzi infected macrophages [60] and the clustering of intracellular parasites in the vicinity of host LD in adipocytes , macrophages , and cardiomyocytes [15 , 61] . During acute Chagas disease , inflammatory macrophages typically exhibit increased formation of LD enriched with arachidonic acid ( AA ) , which is a precursor for the synthesis of proinflammatory eicosanoids such as prostaglandin E2 [61] . Moreover , these LD have been shown to contain eicosanoid-forming enzymes ( cyclooxygenases and lipoxygenases ) that are upregulated during T . cruzi infection [61] . Despite the importance of LD for the storage of AA in inflammatory macrophages and other leukocytes [62 , 63] , we were unable to detect appreciable levels of this FA in the TG pools of either infected or mock-infected host cells , or T . cruzi amastigotes . On the contrary , most of the esterified AA was identified in the PE and PI pools of host cells , while T . cruzi amastigotes had consistently low levels of this FA throughout its lipidome . These differences likely reflect the substantial variation in lipid droplet composition and function between cell types , and even within a homogenous cell population under different environmental conditions [64] . In summary , the application of a comparative lipidomics approach successfully distinguished parasite- and host-specific lipidomic signatures , providing evidence that T . cruzi amastigotes acquire a substantial portion of their lipidome from host TG pools , possibly via the direct acquisition of host lipid droplets . With this strategy , we show that future research on TG and LD metabolism in the context of T . cruzi infection is predicted to yield important information pertaining to the mechanisms of T . cruzi persistence and recrudescence during Chagas disease .
Mammalian cell lines: human foreskin fibroblast ( HFF; provided by S . Lourido , MIT ) , mouse skeletal muscle myoblast ( C2C12; ATCC CRL-1772 ) , African green monkey kidney epithelial ( LLcMK2; ATCC CCL-7 ) , mouse embryonic fibroblast ( MEF ) , and diacylglycerol acyltransferase 1/2-deficient MEF ( DGAT1/2-/-; generously provided by the Walther-Farese laboratory at Harvard T . H . Chan School of Public Health [37] ) . DGAT1/2-/- complementation with OriGene Mouse cDNA ORF Clone of Dgat2 ( NM_026384 ) was performed according with the manufacturer’s protocol for the generation of stable transfectants . Mammalian cells were maintained in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 1mM pyruvate , 25 mM glucose , 2 mM glutamine , 100 U/ml penicillin , 10 μg/ml streptomycin and 10% fetal bovine serum ( FBS ) ( DMEM-10 ) . Cell culture reagents were purchased from Gibco . Trypanosoma cruzi Tulahuén strain parasites ( ATCC PRA-33 ) were selected because of their frequent use in high-throughput screens for novel anti-trypanosomal drug discovery and functional studies [65] and [22 , 66] . Parasites were maintained by weekly passage in LLcMK2 cells in DMEM supplemented with 1 mM pyruvate , 25 mM glucose , 2 mM glutamine , 100 U/ml penicillin , 10 μg/ml streptomycin and 2% FBS ( DMEM-2 ) at 37°C , 5% CO2 as previously described [22 , 66] . Motile extracellular trypomastigotes were collected from infected LLcMK2 supernatants , pelleted at 2 , 000g for 10 min and allowed to swim up from the pellet for a minimum of 2 h at 37°C , 5% CO2 before collection and use for experimental infection . HFF , C2C12 , or MEF , as indicated , were seeded in T-75 flasks and grown to 80% confluence over 2 days in DMEM-10 . To establish intracellular T . cruzi infection , host cell monolayers were incubated with 1 x 107 tissue culture-derived trypomastigotes ( TCT ) for 2 h at 37°C , 5% CO2 in DMEM-2 . Cells were then rinsed twice with PBS to remove extracellular parasites and fresh DMEM-2 medium added to flasks and incubated for a further 46 h . Parallel cultures of mock-infected mammalian cell monolayers were also established . The isolation of the intracellular amastigotes ( ICA ) form of T . cruzi , was conducted using a protocol modified from [67] . Briefly , infected monolayers were washed extensively with PBS , detached from the flask using mild trypsinization ( Gibco , 0 . 05% Trypsin-EDTA ) , resuspended in DMEM-2 and pelleted by centrifugation at 300g for 10 m . After aspirating the supernatant , cells were washed with cytosolic buffer ( 10 mM NaCl , 140 mM KCl , 2 mM MgCl2 , 2 μM CaCl2 , 10 mM HEPES , pH 7 . 4 [68] and suspended in 10 ml cytosolic buffer . Of this infected cell suspension , 0 . 5 ml was retained as the “infected” sample and the remaining 9 . 5 ml was subjected to 2 rounds of cell disruption using a Miltenyi GentleMACS dissociator ( M tubes , Protein_01 protocol ) and host cell lysis was visually confirmed . The lysate was then passed over a 4 ml DEAE-Sephacel ( Sigma ) column ( pre-washed with 10 column volumes cytosolic buffer ) , and cytosolic buffer was added such that three 10 ml flow-through fractions could be collected . Isolated amastigote from each fraction were enumerated by haemocytometer and fractions containing T . cruzi amastigotes were pooled , pelleted , decanted , and then frozen at -80°C until analysis . Quantitation of protein content in host cell and parasite lysates was determined using the ThermoPierce BCA assay reagent kit as per manufacturer’s instructions for microplate assay , using bovine serum albumin as the reference standard . Host cell or parasite lysates were adjusted to 2 μg/μl in 2X Laemmli sample buffer containing 100mM β-mercaptoethanol , heated to 95°C for 3 min in and 10 μl ( 20 μg protein ) and separated by polyacrylamide gel electrophoresis on 4–20% Bio-Rad Mini-PROTEAN TGX Precast Gels and transferred to Immobilon-FL 0 . 45 μm PVDF membrane by semi-dry transfer ( Bio-Rad Trans-Blot SD Semi-Dry Transfer Cell ) for 30 min at 20V . Membranes were blocked for 1 h at ambient temperature with shaking in 1:1 mixture of SEA BLOCK Blocking Buffer ( Thermo Pierce ) and phosphate-buffered saline . Primary antibodies were incubated for 16 h at 4°C at the following dilutions , Abcam ATPB antibody [3D5] , 1:2000; Cell Signaling IRE1 alpha [14C10] , 1:1000; Novus Biologicals Perilipin-3/TIP47 antibody , 1:1000; Sigma FLAG , 1:500 . All secondary antibodies were incubated for 30 m at room temperature at the following conditions AlexaFluor ( 647 goat anti-mouse ) 1:10 , 000; Donkey anti-rabbit DyLight 800 Thermo Fisher 1:10:000 . All antibodies were diluted with a 1:1 mixture of SEA BLOCK Blocking Buffer and phosphate-buffered saline containing 0 . 2% Tween 20 . Membranes were imaged using the Odyssey Infrared Imaging System ( LI-COR Biosciences ) . All solvents used were of HPLC grade or higher . The lipid extraction protocol was modified from [42] . Briefly , cell lysates ( biological replicates , containing the following standard lipid mix per 100 μg protein lysate: 375 pmoles C17:1 lysophosphatidic acid; 225 pmoles C17:0/C20:4 phosphatidic acid; 170 pmoles C17:1 lysophosphatidylserine; 180 pmoles C17:0/C20:4 phosphatidylserine; 86 pmoles C17:1 lysophosphatidylethanolamine; 112 pmoles C17:0/C14:1 phosphatidylethanolamine; 95 pmoles C17:1 lysophosphatidylcholine; 112 pmoles C17:0/C20:4 phosphatidylcholine; 33 . 2 pmoles C17:1 lysophosphatidylinositol; 165 pmoles C17:0/C20:4 phosphatidylinositol; 105 pmoles C17:0/C14:1 phosphatidylglycerol; 180 pmoles C17:0/d18:1 ceramide; 140 pmoles C17:0/d18:1 sphingomyelin; 155 pmoles C12:0/d18:1 β-glucosyl-ceramide; and 60 pmoles C17:1/C17:1/C17:1 triacylglycerol ) were suspended in ice-cold HPLC-grade water , and transferred to 13x100-mm Pyrex tubes with polytetrafluoroethylene ( PTFE ) -lined screw caps . HPLC-grade water , methanol , and chloroform were added to a final chloroform/methanol/water ( C/M/W ) ratio of 1:2:0 . 8 ( v/v/v ) . Samples were vortexed vigorously for 5 min , and centrifuged for 10 min at 1 , 800g at room temperature . After centrifugation , the supernatants were transferred to new Pyrex tubes , and the pellets were dried under N2 stream . The pellets were then extracted with chloroform/methanol ( 2:1 , v/v ) , centrifuged , and the resulting supernatants were combined with the corresponding supernatants from the first step of extraction ( C/M/W 1:2:0 . 8 v/v/v ) , and dried under N2 stream . Samples were then subjected to Folch partitioning [69] by dissolving them in C/M/W ( 4:2:1 . 5 , v/v/v ) , followed by vortexing and centrifuging , as described above . After centrifugation , the lower ( organic ) and upper ( aqueous ) phases were separated into fresh Pyrex tubes . The aqueous upper phase was then re-extracted with C/M ( 2:1 v/v ) , and the resulting organic phase was combined with the organic phase from the preceding step . The pooled organic phases were dried under N2 stream and stored at -20°C until analysis . Extracted lipid samples were diluted in 50 μl of C/M ( 2:1 v/v ) and analyzed by UHPLC-ESI-MS , method modified from [70] . UHPLC-ESI-MS/MS was conducted using a Dionex UltiMate 3000 UHPLC system ( Thermo Scientific ) coupled to a Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer ( Thermo Scientific ) . 5 μl of each sample was injected onto an Accucore ( Thermo Scientific ) C18 LC column ( 2 . 1 mm x 150 mm x 2 . 6 μm particle size ) . Lipids were fractionated by reverse-phase chromatography over a 46 min gradient ( mobile phase A: acetonitrile/water ( 50:50 v/v ) , 10 mM ammonium formate , 0 . 2% formic acid; mobile phase B: methanol/isopropanol/water ( 10:88:20 v/v/v ) , 2 mM ammonium formate , 0 . 01% formic acid ) , with the following program , constant flow rate of 500 μL/min , 35–45% B over 0–5 min , 45–85% B from 5–28 min , 85–100% B from 28–38 min , followed by an immediate drop to 35% B , held constant up to min 46 . The column temperature was set to 50°C , and the autosampler tray temperature was set to 10°C . The HESI II ion source ( Thermo Scientific ) was set as follows: sheath gas flow rate = 60; auxiliary flow rate = 20; sweep gas flow rate = 1; spray voltage ( KV ) = 3 . 00; capillary temperature = 285°C , S-Lens RF level = 45 , and auxiliary temperature = 370°C . The mass spectrometer acquisition settings were as follows for both positive and negative ionization mode: Full Scan–top 15 data-dependent MS/MS . Full scan was set for a range of 250–1800 m/z . The mass resolution was set to 70 , 000; AGC target was set to 1e6 , the C-trap ion accumulation time was set to 120 ms; data dependent MS/MS was set to a mass resolution of 30 , 000 , AGC target was set to 5e5 , the C-trap ion accumulation time was set to 120 ms , select ion exclusion was set to 8 s , and the HCD ( higher-energy collisional dissociation ) fragmentation ramp was set to 15 , 25 , and 35 NCE ( normalized collision energy ) . All data were analyzed using LipidSearch Software Version 4 . 2 ( Thermo Scientific ) and all identified species ( A , B and C quality ) were validated manually as detailed in Supporting Information: Supplemental Methods . Within a lipid class , MainArea values output by LipidSearch were assigned to each FA moiety of a given species and summed using Microsoft Excel PivotTable . Area % is the summed FA value divided by the lipid class total , multiplied by 100 to reflect percentage . Known quantities of each lipid standard were analyzed using the same UHPLC-ESI-MS/MS methods described below to generate a response factor ( RF; peak area/pmol standard injected ) . The RF for each standard was divided by the CerG1 RF to calculate a molar relative response factor ( MRRF ) for each major lipid class . The MRRF for each class was normalized to the CerG1 peak area of each sample , and peak areas adjusted accordingly . Only those classes for which standards were detected in each run were considered for analysis . Each pie chart is the average of at least 3 biological replicates . Mock- and T . cruzi-infected mammalian cell monolayers were incubated with DMEM-2 supplemented with 0 . 3 μCi/ml [14C ( U ) ]-palmitate ( Perkin Elmer ) and 25 nM palmitate ( Cayman Chemical ) [71] for 6 h and monolayers washed extensively in PBS to remove unincorporated label . T . cruzi amastigotes were isolated from infected cells and lipids were extracted from equivalent protein amounts of all labeled samples as described above , without inclusion of internal standards , and lipid extract equivalent to 10 μg protein per sample was subjected to thin layer chromatography ( TLC ) . The web-based server MetaboAnalyst 3 . 0 was used to perform principle component analyses ( PCA ) on median normalized , and log transformed species abundances [73] . Bar and pie charts were generated using GraphPad Prism 7 , version 7 . 0b for Mac OSX . Trypomastigotes were diluted to 5x106 parasites/mL in PBS and stained with a final concentration of 1μM carboxyfluorescein succinimidyl ester ( CFSE ) ( Thermo Fisher ) for 15 min at 37°C . After staining , trypomastigotes were washed and re-suspended in DMEM-2 and allowed to infect host cells as described . At various time points after infection amastigotes were isolated and fixed on ice in 4% paraformaldehyde/PBS for 20 min . After fixation amastigotes were centrifuged at 4 , 000g for 10 min and the resulting pellet was resuspended in PBS and kept at 4°C until preparation for acquisition . Immediately prior to acquisition amastigotes were pelleted at 4 , 000g for 10 min and resuspended in a 0 . 1% Triton X-100/PBS permeabilization solution containing 10 ng/mL DAPI ( Sigma-Aldrich ) for a minimum of 30 min on ice . Events were acquired using a LSR II ( Becton Dickinson ) . Amastigotes were identified based on size and DAPI staining . Proliferation modeling based on signal intensity from undivided parasites collected at 18 hpi were generated using FlowJo ( Tree Star ) proliferation software . Greater than 10 , 000 events in the final amastigote gate were acquired for each sample . | The development of human Chagas disease is associated with persistent intracellular infection with the protozoan parasite , Trypanosoma cruzi , which displays tropism for tissues with characteristically high fatty acid flux , such as heart and adipose tissues . Previous studies have highlighted fatty acid metabolism as likely critical to support the growth and survival of this intracellular pathogen , however biochemical data supporting this prediction is currently lacking . Employing an untargeted lipid mass spectrometry approach , we defined the lipidome of intracellular T . cruzi parasites and their mammalian host cells . Comparative analyses revealed that the fatty acid signatures in the triacylglycerol ( TG ) pools were highly conserved between parasite and host , suggesting a major route of fatty acid acquisition by this pathogen via host TG . Metabolic tracer studies demonstrated intracellular parasite incorporation of exogenous palmitate into both neutral and phospholipid subclasses that was diminished in host cells deficient for TG synthesis . Moreover , parasites grown in these cells displayed reduced proliferation , demonstrating the importance of parasite coupling to host TG pools during the intracellular infection cycle . | [
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| 2017 | Host triacylglycerols shape the lipidome of intracellular trypanosomes and modulate their growth |
Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective . We aim to bring together these two perspectives . So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism . Here we use artificial neural networks to allow for a more open architecture . We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network , and a more complex network that includes the possibility of self-feedback from previous experiences . We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task . Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio . Our results indicate that architectural constraints play an important role for the outcome of evolution . With the simplest network , only genetically determined specialization is possible . This imposes several limitations on worker specialization . Moreover , in order to minimize idleness , networks evolve a biased work ratio , even when an unbiased work ratio would be optimal . By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range . Optimal work ratios are more easily achieved with the self-feedback network , but still provide a challenge when combined with worker specialization .
Division of labor is ubiquitous in nature . The major evolutionary transitions , such as the separation of germ and soma and the transition from prokaryotes to eukaryotes , were accompanied by an increase in division of labor [1] . The transition from solitary to eusocial in insects encompasses the evolution of a reproductive caste and a sterile worker caste . Furthermore , division of labor among sterile workers also evolved , in which different groups of workers specialize in different functions , such as foraging and brood care [2] . Colony growth and survival is strongly dependent on the coordinated interaction of a large number of workers . This non-reproductive division of labor is therefore often considered a major determinant of the ecological success of eusocial insects and will be the focus of the work presented here . Empirical evidence suggests that eusociality has evolved in associations of close kin [3] , [4] . Variation in behavioral tendencies can be found in forced associations of non-social individuals , leading to incipient forms of division of labor [5] , [6] . Undoubtedly , a source of variation is key to generating consistent inter-individual differences and task specialization [7] . The questions that arise are how and why such variation arises among close kin . Here we explore some of the mechanisms and conditions through which task specialization can evolve in groups of related individuals . Recent work on division of labor in insect societies has focused on the self-organization properties of colony behavior . According to a variety of models [8]–[12] colony properties emerge from the behavior of individual workers whose reactions to the environment is governed by simple rules . The behavioral rules leading to emergent specialization are probably shaped by natural selection [10] , [13] , yet only few studies have focused on the evolution of these rules [14] , [15] . Previous work focusing on the benefits of task specialization in other systems ( e . g . enzyme-substrate specialization , coordination in co-viruses ) generally disregard the mechanisms underlying it , viewing instead specialists and generalists as fixed behavioral strategies [16] , [17] . It is thus important to develop models that integrate the evolutionary and self-organization perspective , in order to create a better understanding of division of labor and its evolution [7] . In previous work , we took the response threshold model [1] as a starting point for an evolutionary model for division of labor ( A . Duarte , I . Pen , L . Keller and F . J . Weissing , submitted ) . In the response threshold model , individuals compare an environmental stimulus for a task with their response thresholds; they perform the task if the stimulus is above their threshold , otherwise they remain idle . Using this predefined behavioral architecture , we allowed the evolution of threshold values and showed that division of labor can evolve from a homogeneous population via evolutionary branching , but only if there are clear fitness benefits of individual specialization . Our work also revealed that the response threshold model has the drawback that it imposes severe constraints on the distribution of workers over tasks . Here we look at a more flexible behavioral architecture that is represented by a simple artificial neural network ( ANN ) . ANNs simulate the processing of stimuli by individuals , from stimulus perception by receptor nodes to effector nodes determining the behavioral output [18] , [19] . ANNs have been used in evolutionary robotics to understand the evolution of communication and cooperation [20]–[22] . In a recent paper , Lichocki et al . showed that ANN's , in comparison to response threshold mechanisms , allow for more efficient worker allocation through task switching [23] . Here we examine the effect of the architecture of ANN's in worker specialization and worker allocation , in a context where task switching is detrimental . In the response threshold model , the response to task-associated stimuli is determined by task-associated thresholds . The stimuli , which reflect the colony's need for work on the various tasks , change dynamically due to two factors: there is an inherent tendency for the stimuli to increase , and they are decreased whenever the corresponding task is performed . We keep most assumptions of the threshold model but allow the task-associated stimuli to be processed by an ANN . In principle both the architecture of the network and the way information is processed could evolve [24] , [25] , however , we for simplicity , we focus on predefined architectures ( with a fixed number of receptor and effector nodes ) and allow only for the evolution of connections between the nodes . The stimuli are processed by an ANN consisting of two receptor nodes and two effector nodes ( Figure 1 ) . In a second part of our study , we keep the same network structure but allow for the evolution of a feedback from the effector nodes to the processing of the stimuli ( Figure 1C ) . In other words , an effect of previous experience on current decisions can evolve . An effect of previous experience on task preference , leading to division of labor , has been observed in natural colonies [26] , thus it would be interesting to observe under which circumstances it could evolve . We investigate if these slightly more sophisticated mechanisms for processing input allow for the evolution of adaptive division of labor . More precisely , we study whether task specialization among workers can evolve and moreover , whether an appropriate distribution of workers over tasks can be achieved . Throughout , the main question is whether , and to what extent , the evolution of self-organized division of labor is determined by the underlying architecture of behavior .
The first network studied is a simple feedforward network [19] that consists of two stimulus input nodes and two behavioral output nodes , all four nodes being connected ( Figure 1B ) . Each input node perceives a task-associated stimulus with a certain error ( drawn from a normal distribution with mean 0 and standard deviation 1 ) . The two signals are then processed and transmitted to the output neurons , via connections with weights that are evolvable properties of the network . Output nodes receive a weighted sum of the stimuli , generally designated activation energy . The activation energy of an output node i is thus: ( 1 ) Each output neuron is characterized by a threshold , which is another evolvable property . If the activation energy of an output neuron exceeds the threshold , the neuron is activated , meaning that an individual is willing to perform the respective task . If both output neurons are activated , one task is chosen at random . Note that the response threshold model implemented in previous work is in fact a special case of the feedforward neural network , where and ( Figure 1A ) . The main difference between our feedforward ANN model and the response threshold model is thus the evolution of the connection weights that determine how incoming information is processed and interpreted . The initial values of connection weights in our simulations are: and . Changes in the connection weights and thresholds take place when new individuals are produced , via mutation ( see below ) . During the lifetime of an individual , the parameters of its network are fixed . Thus we do not consider the changing of connection weights with learning , for example . The second network architecture studied is a recurrent network [19] . It includes all previous nodes and connections , and in addition it has two self-feedback loops ( Figure 1C ) . The activation energy in a given time step will affect the activation energy in the next time step: . The connection weight given to the previous activation energy ( from here on called the self-feedback connection ) is also an evolvable property that changes through mutation and natural selection during production of new individuals . During the lifetime of individuals , however , there is no change occurring in the parameters of the networks . Self-feedback connection weights were initialized at zero , which is equivalent to the feedforward network , without any influence of past experience in current decisions . After the work phase , the fitness of each colony is computed based on how much work the workers performed for each task . Fitness is assumed to be proportional to the weighted geometric mean of work done for both tasks: ( 2 ) where is the total number of acts performed for task ( A . Duarte , I . Pen , L . Keller , F . J . Weissing , subm . ) . We take the geometric rather than the arithmetic mean in order to ensure that fitness can only be achieved if both tasks are being performed . The weighing factor allows us to consider the ( realistic ) situation that not all tasks need to be performed equally often . For the fitness function ( 2 ) , fitness is maximized if idleness is eliminated ( i . e . , if is maximal ) and if the workers distribute over tasks according to the ratio . In other words , to maximize fitness the proportion of work allocated to task 1 by the colony should be equal to : ( 3 ) Each generation , 2M reproductive offspring are produced in total in the population . Colonies contribute to the population's pool of sexual individuals in proportion to their fitness . Population size is thus fixed . The reproductive individuals then form M pairs randomly . From each pair one individual will found a new colony with workers , while the old colonies are eliminated . We allowed for the evolution of all connection weights and thresholds of output nodes , giving us in a total 6 ( resp . 8 ) evolving traits . These traits are encoded by 6 ( resp . 8 ) gene loci . The alleles at these loci correspond to real numbers , with threshold alleles being larger or equal to zero , while connection weight alleles may also attain negative values . To keep the genetic assumptions as simple as possible , we assume that all individuals are haploid and that the network of each individual is fully determined by its genotype . Genotypes of workers and sexuals are similarly inherited: Both types of individuals are offspring of the mated colony foundress , and possess alleles for thresholds and connection weights . Our model allows genetic linkage of the threshold loci or linkage of the connection weight loci , but both types of loci are considered to be sufficiently far apart in the genome to make them segregate independently . The degree of linkage is determined by a parameter ( ) that corresponds to a recombination rate . With probability , the threshold alleles ( resp . the connection weight alleles ) are inherited as a block from one of the two parents; with probability , the parent whose allele is transmitted is chosen independently of what happens at the other loci . Mutation occurs with probability at each locus; when a mutation occurs , the genetic value at that locus is changed by adding a real number to it that is drawn from a normal distribution with mean 0 and standard deviation . In our simulations , we typically used and . We evaluate colony-level characteristics such as the proportion of work devoted to each task and the level of individual specialization . For each individual we calculate at the end of a simulation the fraction of time steps that it stayed in the same task from that time step to the next . We average over all workers and normalize this measure by dividing by the probability that individuals stay in the same task merely due to chance . The latter is given by , where is the proportion of work devoted to task . By subtracting 1 from the value thus obtained , we obtain a measure of worker specialization that ranges between −1 and 1 ( A . Duarte , I . Pen , L . Keller and F . J . Weissing , subm . ) : ( 4 ) When is close to 1 , there is a high degree of division of labor , and individuals stay in the same task much more often than expected by chance . If is close to zero , workers switch between tasks at random . If is lower than zero , individuals switch task more often than expected by chance . Worker specialization can be adaptive if there is a cost to switching tasks ( such as a time cost if tasks are confined to different locations , or a cognitive cost ) , or if specialized workers perform their task with higher efficiency [27] . Here we implemented a time cost scenario , by imposing time steps of inactivity whenever an individual chooses to switch from one task to the other .
We tested the behavior of a more complex network , where the activation energy of an output neuron could have a feedback on the activation energy at the next time step ( Figure 1C ) . The self-feedback connections were allowed to co-evolve with the rest of the network . We ran ten replicate simulations for all the parameter combinations tested above .
Here we studied whether and how two different neural network architectures enable the evolution of self-organized division of labor and adaptive task ratios . Our results are summarized in table 1 . With a feedforward network ( table 1 ) , worker specialization evolved more easily ( i . e . at lower switching costs ) in the absence of recombination . In the absence of recombination the connection weights can co-evolve as a tightly linked block of genes , making it easier to evolve specific combinations of connection weights favoring specialization . Recombination pushes populations into a solution where only one connection weight locus branches , the rest of the network being relatively homogeneous in the population . This allows worker specialization to occur , but to a lesser extent than in the absence of recombination , because at least one of the parent networks in a specialized colony behaves as a generalist for a large range of stimulus combinations . A large percentage of colonies showed no worker specialization , hence , no division of labor . This is because random mating allows for couples with similar genotypes to produce colonies where workers are too similar and therefore division of labor cannot emerge . Previous work on the response threshold model ( A . Duarte , I . Pen , L . Keller and F . J . Weissing , subm . ) showed that the work ratio could not easily deviate from 1∶1 , even if a biased work ratio was optimal . In contrast , in the case of the feedforward network , the work ratio was always biased for one of the tasks , even when a symmetric work ratio was optimal ( table 1 ) . Owing to selection for minimizing idleness , the evolved networks maximized the amount of work done by using the stimulus from one of the tasks to stimulate workers to perform the other task . In this way , one of the tasks was performed in excess ( the ‘preferred’ task ) , even when its associated stimulus had been depleted . Although this may seem counter-intuitive , it represents an advantage over networks that attempt to maximize both tasks , because these networks would be limited to the work strictly necessary to reduce stimuli to zero . When , the optimal work ratio was achieved , but only in the absence of switching costs . When switching costs were present , the most common evolved strategy was to increase the proportion of work for task 1 in order to minimize switching among tasks . Some of the limitations of the simple feedforward network were eliminated in the slightly more complex architecture of the recurrent network , where previous activation energies feed back on current activation energies . Worker specialization evolved at low switching costs , now both in the presence and absence of recombination ( table 1 ) , at least for . Interestingly , the presence of recombination favored an outcome where all colonies showed a high degree of specialization . In these populations , specialization does not depend on the presence of two complementary networks in the parents of a colony ( as in figure 3 ) , but on a strengthening of the self-feedback connections . This allows for initial differences between individuals in stimulus perception to be amplified in subsequent time steps and leads to behavioral differentiation through reinforcement of previous experiences . In the presence of recombination , this strategy prevails . However , when no recombination occurs , evolutionary branching of connection weights is still the prevalent strategy through which worker specialization evolves . Why is the experience-based strategy not observed in all simulations ? A likely reason is that to reach this strategy , the values of neural connections must first pass through values where , in the absence of recombination , evolutionary branching is more advantageous . Hence , the evolutionary outcome is dependent on initial conditions . We confirmed this by running simulations where the self-feedback connections were initialized at higher values ( e . g . , ) ; in this case all populations evolved the experience-based strategy rather than evolutionary branching ( results not shown ) . The evolution of an experience-based strategy is affected by stochastic effects at the moment that the population passes the “branching point” , namely on the direction and magnitude of genetic variation , that may lead to local fitness optima . The two strategies may thus represent alternative stable states . The mean population fitness of the genetic specialization ( evolutionary branching ) is noticeably lower than the mean population fitness of the experience-based strategy ( Figure S7 ) . The recurrent network also allowed for the optimal work ratio to be reached in most cases , at least by part of the population ( Table 1 ) , even in the presence of switching costs . When , the self-feedback connections allow the continuous activation of both tasks , stimulating individuals that had previously done a task to do it again , even in the absence of the corresponding task stimulus . With this architecture it is also harder to attain the optimal work ratio when and switching costs are considered , and only few replicate populations show both and high degree of worker specialization . The recurrent network has similarities with the reinforced threshold model , in which individual thresholds are lowered after the performance of the respective tasks and increased when the tasks are not performed [9] , [29] . In both models , initial differences in experience lead to consistent behavioral differentiation , thus bypassing the need of specific genetic combinations for the emergence of task specialization . However , in terms of the distribution of workers over tasks , the reinforced threshold model suffers from the same limitations as the fixed threshold model , with worker distribution being mainly dependent on the parameters of stimulus dynamics ( A . Duarte , T . Janzen , F . J . Weissing and I . Pen , in prep . ) . Our results highlight the importance of considering asymmetries in models of division of labor . In the evolutionary response threshold model by A . Duarte , I . Pen , L . Keller and F . J . Weissing ( subm . ) , we show that a biased -value cannot be obtained through the evolution of thresholds . To achieve a biased -value in this model , asymmetry must be present in the environment ( e . g . in the values of task-associated stimuli [8] ) to which the response-threshold mechanism then responds . However , in reality , asymmetries in the work distribution might also arise from the ability of individuals to perceive and prioritize tasks differently . Here we show that , for both types of networks studied , it is not easy to evolve strict worker specialization together with an asymmetric distribution of workers over tasks . A major difficulty is that in case of genetically determined specialization the work proportion is dependent , to a large extent , on the proportions of different specialists in each colony . Since we only consider single-mated foundresses , colonies in our model show either equal proportions of the two specialist strategies or only one of the specialist strategies . Evolving experience-based specialization enables an asymmetric work distribution and division of labor ( although at a lower degree of worker specialization than under symmetric conditions , and only in the absence of recombination ) , yet the trajectory towards this strategy is subject to stochastic effects that may diverge evolution towards genetically determined specialization or towards an increase of performance of the most needed task beyond its optimal level . The observed difficulty in favoring a specific work ratio under switching costs indicates that the simple behavioral architectures investigated are limited in the ability to evolve efficient solutions to complex optimization problems . In the presence of switching costs , it is important for colonies to maximize worker specialization , while at the same time minimizing the number of idle workers and optimizing the work ratio . The behavioral architectures considered thus far were only able to evolve sub-optimal solutions to this multi-faceted problem . Modelling the evolution of behavioral mechanisms by means of artificial neural networks presents several advantages when compared to a priori chosen behavioral architectures such as a response threshold mechanism . First , mechanisms potentially leading to self-organized division of labor are not built into the model , but must emerge from the model . Second , evolving neural networks transcend some limitation of the human mind . When asked to design plausible mechanisms , the imagination of most modellers is limited to simple and intuitive mechanisms ( like a response-threshold mechanism ) that our mind can easily envisage . For example , it is unlikely that one would envisage a mechanism where a task-associated stimulus does not stimulate the performance of its corresponding task , but of a different one , as it occurs in the feedforward network . By using an independent modelling setup , we can get an idea whether , and to what extent , the results based on the more standard implementations are robust . In our case , the simple feedforward network is too constrained to achieve worker specialization and an appropriate distribution of workers over tasks . By adding a simple elemental feedback the resulting recurrent network had a much higher evolutionary potential . In future models we could consider the evolution of the network's topology , e . g . by allowing the addition and elimination of neurons and connections to an existing network through mutation [24] . The simple feed-forward neural network was constrained by a problem already present with the response threshold mechanism: to get specialization at the colony level , the coexistence of two specialist genotypes is necessary . Random mating and recombination played an important role in the evolutionary outcome . In general we observed that recombination made it more difficult for genetic specialization to evolve . With recombination , evolutionary branching at multiple loci occurred only rarely , at very high switching costs . This is in accordance with the argument that , in constant environments , recombination may destroy favorable allelic combinations [30] , [31] . Our model suggests that in systems where strong genetic task determination and high recombination rates exist , multiple mating would be favored , in order to increase the chance that workers have favorable allelic combinations . This is in accordance to what we observe in honeybees [32] , [33] . Under the recurrent network architecture , recombination may also play a beneficial role by creating more genetic variation in the self-feedback connections , which could favor division of labor emerging through the experience-based strategy . The purpose of our approach was not to represent the behavioral architecture of real organisms , but to present a conceptual model that could shed some light on the role of architectural constraints in the evolution of self-organized division of labor . A limitation of this approach is that the larger the network , the more difficult it is to draw conclusions that are biologically relevant . We have implemented two very simple networks , and yet already have six to eight evolvable parameters . We were able to understand the interaction of the networks with the environment and pinpoint the key connections that allowed for specific behaviors , but this may not be possible for more complex architectures . The fitness function used ( eq . 2 ) favored the minimization of idleness . Although it is not unrealistic to assume that more work will translate to higher colony productivity , in reality social insect colonies contain a large proportion of idle workers [34]–[36] . Examples of circumstances that would allow the presence of idle workers include environmental perturbations that require quick recruitment of “stand-by” workers , advantage of energy-saving strategies under poor resource conditions , and selective neutrality of “incompetent” workers due to highly redundant organization of work [36] ( and references therein ) . As stressed before , here we present a conceptual model for the effect of behavioral architectures in division of labor , and necessarily simplify certain assumptions . A more realistic version of our model would treat fitness as the number of offspring produced by a colony , and explicitly consider the nature of the different tasks ( e . g . foraging and brood care ) . Division of labor is a broad topic , with many aspects that were outside the scope of this study . Previous theoretical work has focused on the evolution of differentiated multicellularity , the evolution of germ and soma in multicellular organisms , and the effect of developmental plasticity in gene expression as a cause of individual differentiation [37]–[40] . Here we focused on the evolution of behavioral task specialization in groups where reproductive altruism ( analogous to germ-soma differentiation ) has already evolved , an assumption which is in line with a recent comparative analysis of the evolutionary history of division of labor [41] . We did not consider the role of developmental plasticity , although this plays an important role in the differentiation of morphological castes [42] . Underlying the different questions concerning division of labor , however , is a problem of functional optimization: Organisms can increase their reproductive success if they perform different tasks efficiently . Dividing tasks among lower-level units within the organism or colony ( often referred to as a superorganism ) is a solution to the problem . What our model suggests is that the particular behavioral rules through which task specialization arises may impact the evolutionary outcome . | In insect colonies , different individuals specialize in different tasks related to colony maintenance and growth . Unveiling why this division of labor evolved and how individuals decide which task to take on is crucial for our understanding of complex group behavior . Here we model the evolution of general behavioral rules for processing environmental signals of task need in social insect colonies , using artificial neural networks . We examine the patterns of individual specialization that arise in the course of evolution . Division of labor is likely to evolve if switching between tasks decreases worker productivity , but the pattern of division of labor across colonies is highly dependent on the architecture of the networks considered . In networks that allow for a feedback of previous experience on future task choice , division of labor can evolve across the whole population of colonies . In networks where this feedback is not allowed , the presence of division of labor is constrained by the specific genetic composition of colonies . Network architecture also affects how fine-tuned the worker allocation to different tasks can be when the tasks have different requirements . | [
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| 2012 | Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor |
Field studies were done of the responses of Glossina palpalis palpalis in Côte d'Ivoire , and G . p . gambiensis and G . tachinoides in Burkina Faso , to odours from humans , cattle and pigs . Responses were measured either by baiting ( 1 . ) biconical traps or ( 2 . ) electrocuting black targets with natural host odours . The catch of G . tachinoides from traps was significantly enhanced ( ∼5× ) by odour from cattle but not humans . In contrast , catches from electric targets showed inconsistent results . For G . p . gambiensis both human and cattle odour increased ( >2× ) the trap catch significantly but not the catch from electric targets . For G . p . palpalis , odours from pigs and humans increased ( ∼5× ) the numbers of tsetse attracted to the vicinity of the odour source but had little effect on landing or trap-entry . For G . tachinoides a blend of POCA ( P = 3-n-propylphenol; O = 1-octen-3-ol; C = 4-methylphenol; A = acetone ) alone or synthetic cattle odour ( acetone , 1-octen-3-ol , 4-methylphenol and 3-n-propylphenol with carbon dioxide ) consistently caught more tsetse than natural cattle odour . For G . p . gambiensis , POCA consistently increased catches from both traps and targets . For G . p . palpalis , doses of carbon dioxide similar to those produced by a host resulted in similar increases in attraction . Baiting traps with super-normal ( ∼500 mg/h ) doses of acetone also consistently produced significant but slight ( ∼1 . 6× ) increases in catches of male flies . The results suggest that odour-baited traps and insecticide-treated targets could assist the AU-Pan African Tsetse and Trypanosomiasis Eradication Campaign ( PATTEC ) in its current efforts to monitor and control Palpalis group tsetse in West Africa . For all three species , only ∼50% of the flies attracted to the vicinity of the trap were actually caught by it , suggesting that better traps might be developed by an analysis of the visual responses and identification of any semiochemicals involved in short-range interaction .
Tsetse flies ( Diptera: Glossinidae ) infest ∼10 million km2 of sub-Saharan Africa where they transmit trypanosomes which cause Human African Trypanosomiasis ( HAT; also known as sleeping sickness ) and African Animal Trypanosomiasis ( AAT; also known as Nagana ) . This complex of diseases has an important impact on health and productivity in sub-Saharan Africa [1] , [2] . HAT occurs in two forms; “rhodesiense” which is caused by Trypanosoma brucei rhodesiense and occurs in eastern and southern Africa; “gambiense” which is caused by T . b . gambiense and occurs in western and central Africa . Currently the latter causes ∼97% of the total number of reported cases of HAT [1] and is transmitted in West Africa by tsetse of the Palpalis group where the most dangerous species are G . palpalis s . l . and G . tachinoides . Means of tackling HAT and AAT differ fundamentally . Control of AAT transmitted by riverine flies is funded and implemented largely by livestock keepers [3] who treat their livestock with trypanocides and insecticides and/or deploy odour-baited traps or targets to control tsetse . Control of HAT is managed and funded by intergovernmental and national agencies and , in the case of the gambiense form , relies mainly on systematic screening , treatment and follow-up of millions of human individuals across the affected region [1] . With a few local exceptions [4] vector control has generally played little role in the management of HAT over the past 80 years . Paradoxically , vector control could contribute significantly to the management of HAT . The relatively low infection rates ( <0 . 1% ) and long incubation period ( ∼25 days ) of T . brucei spp . in the vector [5] , compared to the Trypanosoma spp . of veterinary importance , means that comparable reductions in the density and life-expectancy of tsetse populations would have a relatively greater effect on HAT than AAT . A cost-effective method of tsetse control that could be implemented by local people would complement the efforts of agencies that support mass screening and treatment and hence improve sustainability . Analyses of the history of efforts against sleeping sickness reveal that sustainable solutions have proved elusive [6] , [7] . An integrated approach , based on a combination of interventions directed at both tsetse and trypanosomes , may provide a better way forward . Cost-effective methods of tsetse control exist for the Morsitans group tsetse that spread AAT . Insecticide-treated cattle ( or other domesticated animals ) are valuable where they are present in sufficient numbers and form a major part of the diet of the flies . Where that is not the case or where cattle are not the major component in the fly diet , as is true in much of the cotton belt of West Africa , insecticide treated targets can be substituted . For Morsitans group flies these can be baited with attractants which mimic the odours of the natural host and they can then be deployed at densities of just 4 targets/km2 to eliminate fly populations [8] , [9] . However , far higher densities of traps or targets ( e . g . 30–50/km2 ) are required to eliminate G . palpalis spp . [10] , [11] , [12] , [13] . One reason such high densities of artificial baits are required is that attractants effective against the major tsetse fly vectors of T . brucei gambiense in West Africa have not been identified so far . Ironically , the genesis of modern methods of tsetse control using artificial baits started with the work of Claude Laveissiere and others , working in the HAT foci of Côte d'Ivoire during the 1970s . Their work showed that traps and targets could be used to control HAT [14] but efforts to improve the performance of traps by baiting them with the attractants effective for Morsitans group flies were not successful ( C . Laveissière pers . com ) . Work on G . tachinoides in Burkina Faso [15] showed that natural odour from a human , a pig or a cow increased the catch 1 . 2× . In subsequent studies , they demonstrated that a combination of 3-methylphenol and octenol doubled the catch of this species of tsetse [15] , [16] ) . In the only study of G . p . palpalis [17] , baiting traps with acetone or octenol , both components of cattle odour , doubled the catch of tsetse . To date however , there has not been a comprehensive analysis of the olfactory responses of the Palpalis group species that spread HAT in West Africa . Accordingly , this paper reports the results of studies of the behavioural responses of G . p . palpalis in Côte d'Ivoire and G . p . gambiensis and G . tachinoides in Burkina Faso to host odours . These studies aimed to assess the responses of these three species to ( i ) whole natural odour from pigs , humans and cattle and ( ii ) synthetic host odours known to be effective against other species of tsetse . Identifying attractants effective for these three species would be particularly timely since the African Union is currently initiating a major tsetse control operation in West Africa under the auspices of its Pan African Tsetse and Trypanosomosis Eradication Campaign ( PATTEC ) .
G . p . palpalis . During the first field season studies were carried out between February and April 2008 , when the rainy season begins , at sites near Bingerville ( ∼05 . 35° N , 3 . 82°W ) , ∼25 km East of Abidjan . In the second season studies took place between December 2008 and March 2009 ( the dry season ) at Azaguié ( 05 . 67° N , 04 . 11° W ) , ∼45 km north of Abidjan . Annual rainfall is about 1400 mm . Both areas comprise a mosaic of lagoons , farms where tree crops such as banana , coffee , cocoa , rubber and oil palm are cultivated and the remnants of dense linear forest . Humans , pigs and cattle are present at both sites but wild mammalian hosts are scarce . G . p . palpalis is the only species of tsetse present at these sites . G . tachinoides . Studies were undertaken along the Comoe river at Folonzo ( ∼09° 54′ N , 04° 36′W ) in the Comoe province of southern Burkina Faso . The area receives an annual rainfall of ∼1100mm . Studies took place in the dry season between March to June 2007 and January to May 2008 . In general terms fly numbers were highest in the early parts of the dry season . The study site is in a protected area , and the habitat is Sudanese gallery forest . There are several game species in relatively low abundance in the research area , including warthogs ( Phacochaerus aethiopicus ) , hippopotamus ( Hippopotamus amphibus ) , monitor lizards ( Varanus niloticus ) , hartebeest ( Alcelaphus buselaphus ) , buffalo ( Syncerus cafer ) , Buffon kob ( Kobus kob ) , bushbuck ( Tragelaphus scriptus ) , waterbuck ( Kobus ellipsiprymnus ) and various species of monkey , snake and crocodile . G . p . gambiensis . Studies were performed at the same time and sites as for G . tachinoides , as the two species occur sympatrically along the southern Comoe river . However the Sudanese type gallery found on the Comoe is more favourable for G . tachinoides [18] which occurs at much higher densities than G . p . gambiensis [19] . Additional studies were therefore also conducted at Solenzo ( ∼12°14′ N , 04°23′ W ) , in the Banwa province of western Burkina Faso along the Mouhoun river . Climatic conditions are similar to those along the Comoe river , with an annual rainfall of 1000mm . Studies were undertaken in the dry season between April–June 2007 and January–June 2008 . The habitat along the river , classed as Sudano-Guinean gallery forest [18] , is favourable for the two species , and forms a narrow corridor between agricultural fields and small patches of woodland , but is heavily degraded due to expansion of agricultural fields . Host species in the area include humans , cattle , goats and pigs . At each study site , local cattle , pigs or humans were used as sources of natural host odours . The baits were placed in PVC-coated tents ( ∼3×2×2 m ) from which the air was exhausted at ∼2000 L/min using a 12 v co-axial fan connected to a flexible PVC-coated tube ( 0 . 1 m dia . ) with the outlet placed at ground level , ∼15 m away from the tent , where the various catching devices were placed ( Fig . 1B ) . Studies with Morsitans group flies suggest that the effectiveness of odours from particular host species is related to the gross weight of animals used . Accordingly , to match the weights of different host species , tents normally contained a single ox , two men , or three pigs . Given the approximate weight of the cattle ( ∼150 kg ) , humans ( ∼75 kg ) and pigs ( ∼50 kg ) used , the gross weight of baits within the tent was 150–200 kg unless reported otherwise . When numbers of animals/humans in the tent varied from this it is noted at the relevant point in the text . In most instances the same animals/humans were used in the tent experiments but for logistical reasons this was not always the case . In some experiments , studies were made of the responses of tsetse to chemicals known to be present in cattle odour and known to attract some species of tsetse . Chemicals were dispensed following established methods [20] , [21] . Synthetic cattle odour , as used in these experiments , consisted of carbon dioxide ( ∼1 L/min ) , acetone ( ∼5 mg/h ) , racemic 1-octen-3-ol ( ∼0 . 1 mg/h ) , 4-methylphenol ( ∼0 . 4 mg/h ) and 3-n-propylphenol ( ∼0 . 04 mg/h ) . In some experiments chemicals were dispensed individually or as blends , at rates known to be effective for other species of tsetse . For these experiments the doses of 1-octen-3-ol ( ∼0 . 2 mg/h ) , 4-methylphenol ( ∼0 . 4 mg/h ) and 3-n-propylphenol ( ∼0 . 02 mg/h ) were similar to those used with synthetic cattle odour , but the dose of acetone was increased to ∼500 mg/h . In experiments where 3-methylphenol was used the release rate was ∼0 . 4 mg/h . POCA consisted of P = 3-n-propylphenol ( ∼0 . 02 mg/h ) ; O = 1-octen-3-ol ( ∼0 . 2 mg/h ) ; C = 4-methylphenol ( ∼0 . 4 mg/h ) ; A = acetone ( ∼500 mg/h ) . Other blends and doses are as indicated in the text . Odours that increase the catch of traps may attract more tsetse to the vicinity of the trap and/or increase the proportion of flies that enter and remain within the trap . The number of flies caught by the trap expressed as a proportion of the total flies attracted to the vicinity of the trap is termed the trap's efficiency [26] – exactly how this measured is explained below . To obtain relative measures of ( i ) attraction and ( ii ) trap entry independently , experiments were performed with an E- net ( 0 . 5 m wide×1 m high ) placed adjacent to the trap ( Fig . 1A ) . Two methods were used with G . p . gambiensis to assess whether odours had an effect on trap entry and efficiency . In one experiment the catches from traps operated alone with or without natural host odour were compared with those from traps operated with an adjacent E-net . For this protocol , the mean daily catch from a trap alone ( i . e . without an accompanying E-net ) was expressed as a proportion of the total catch from a trap+flanking E-net . This proportion is termed ‘trap efficiency’ . For the second method , catches from the trap and adjacent E-net were recorded separately , to distinguish flies caught in the trap from those that collided with the net and , using these data , we assessed whether odours had a significant effect on the proportion of tsetse that entered the trap – these data provide the ‘trap entry response’ . Both experiments are necessary since while the second method will detect whether there is an increased propensity for flies to enter a trap , it will underestimate absolute efficiency since the flanking E-net may kill flies that would have otherwise entered the trap . For the remainder of the paper the terms ‘trap efficiency’ and ‘trap entry response’ will be used in the sense given above . All field experiments were carried out for 4 h between 08:00 h and 12:00 h local time when Palpalis group species are most active [27] , [28] . In general , odour baited devices ( i . e . traps , E-nets , E-targets and combinations thereof ) were compared with an unbaited device over 6–12 days in a series of replicated Latin squares of days × sites × treatments . Sites were always >100 m apart . The daily catches ( n ) were normalized and variances homogenized using a log10 ( n+1 ) transformation and subjected to analysis of variance using GLIM4 [29] . To provide a common index of the effect of odours on catches , the detransformed mean catch of tsetse from an odour-baited device was expressed as the proportion of that from an unbaited one . The value is termed the catch index; odours which , say , double or halve the catch from a trap would have catch indices of 2 and 0 . 5 , respectively . In some experiments , the mean catch of tsetse was <1 fly/day and these results were judged to be too low for adequate statistical analysis and are therefore not presented . Logistic regression was used to analyse the effects of odours on the proportions that landed on a target or entered a trap . The total catch ( i . e . target + net , trap + net ) per day from each treatment was specified as the binomial denominator and the daily catches from the target or the trap were specified as the y-variable . The significance of changes in deviance was assessed by either χ2 or , if the data were overdispersed , an F-test following re-scaling [30] . Unless stated otherwise , mean catches are accompanied by the standard error of the difference ( SED ) between means , and the term ‘significant’ denotes that the means differ at P<0 . 05 . To verify that synthetic host odours were dispensed at rates similar to those produced by natural hosts , measurements were made of the concentration of known compounds in host odours . Carbon dioxide was measured routinely using an infra-red gas analyzer ( EGM-1 or EGM-4 , PP Systems , Hitchin , UK ) . For other chemicals , samples were collected from the air exhausted from tents containing cattle ( n = 3 ) , synthetic cattle odour ( n = 2 ) or an empty tent ( n = 3 ) , concurrent with the behaviour studies . Volatiles were entrained ( 1L/min−1 ) for 4 hours onto a porous polymer ( Porapak Q 50/80 ( 50mg ) , Supelco , Bellefonte , USA ) which was held in glass tubing ( 5 mm outer diameter ) by two plugs of silanised glass wool . After collection , the tubes were heat sealed at the field site in glass ampoules and sent to Rothamsted Research , UK where the volatiles were eluted with redistilled diethyl ether ( 750 µl ) . Prior to analysis , the samples were stored at −22°C . The Porapak Q was conditioned by washing with dichloromethane ( 4 ml ) followed by one washing with redistilled diethyl ether ( 4 ml ) and then heating at 132 °C for 2 h under a stream of purified nitrogen ( 90 ml min−1 ) . This conditioning process was repeated three times before use .
For the 35 separate experiments listed in Table 2 ( G . tachinoides , 9 experiments; G . p . gambiensis , 14 experiments; G . p . palpalis , 12 experiments ) we also assessed the landing responses of tsetse exposed to natural or synthetic odours . Just one , G . tachinoides responding to human and cattle odour , showed a significant effect ( Fig . 2A ) . However , in other experiments , these odours did not increase the landing response of G . tachinoides . Similarly baiting an E-target+E-net with POCA had no significant effect on landing response . We therefore conclude that natural host odours have no clear or consistent effect on the landing responses of G . tachinoides , G . p . gambiensis or G . p . palpalis . Illustrative examples of the general landing responses from six experiments ( i . e . , two for each species ) are shown in Fig . 2 . For G . tachinoides and G . p . palpalis , males generally showed a stronger landing response than females but this difference was not apparent for G . p . gambiensis . Natural host odours had no significant effect on the trap entry response of G . tachinoides , G . p . gambiensis or G . p . palpalis ( Table 3 ) . The results show that for G . tachinoides there was a marked difference in the trap entry response of males and females , with 30–38% of males being caught in the trap compared to only 11–16% of females ( Table 3 , experiment 1 ) . For G . p . gambiensis , the percentage of males and females caught in a trap was variable , ranging from 8–15% in one experiment ( Table 3 , experiment 2 ) to 22–35% in another ( Table 3 , experiment 3 ) . The percentage of G . p . palpalis caught from a trap was generally low , ranging between 8 and 27% ( Table 3 , experiments 3 & 4 ) . While host odours did not have any effect on trap entry , the results do show that the total catch ( trap+flanking net ) of G . tachinoides was increased significantly by the POCA blend ( Table 3 , experiment 1 ) . For G . p . gambiensis , host odours had no significant effect on total catch or trap entry response ( Table 3 , experiments 2 and 3 ) . However , the data do suggest that both are increased; analysing the pooled catch of males and females did show that the catch increased significantly ( from 16 to 23 tsetse/day , P<0 . 05 ) and the trap efficiencies increased for both sexes and was significant ( P = 0 . 05 ) for males . The absence of any significant effects of host odours on attraction or trap efficiency for G . p . gambiensis may be an experimental artefact: the E-net may have killed circling flies that would have eventually entered the trap . Accordingly , we also assessed trap efficiency for G . p . gambiensis using the alternative protocol of comparing catches from traps with or without a flanking E-net in the presence or absence of cattle odour . The result showed that host odour had no significant effect , but placing an E-net adjacent to a trap increased the detransformed mean daily catch of both sexes significantly from 2 males and 4 females to 10 males and 13 females . Thus the catch from the trap alone was just 20–25% of that from the trap+E-net . These percentages are broadly consistent with the estimates of efficiency which are collected when using data from a trap+flanking E-net alone . Taken together , the results suggest that the trap entry response is not modulated by natural cattle odour but that total number of flies attracted to the vicinity of the trap is .
The large number of experiments done and the high numbers of flies caught provide firm evidence that G . tachinoides showed consistent increases in catch index of around 2× in response to natural cattle odour , confirming the previous findings [15] , [27] . We obtained slightly higher increases than reported in their studies , particular with traps where cattle odour increased our catches ∼5× . Synthetic cattle odour ( defined in Materials and Methods ) , which contains known kairomones for Morsitans group tsetse , produced greater ( ∼4× ) increases in trap catch ( Table 4 ) than given by the natural cattle odour ( Table 1 ) . The greater catch seen with synthetic cattle odour may be because ( i ) the release rate was ∼5× greater than that in the natural ( determined from Table 5 ) or ( ii ) natural ox odour produces chemicals that ‘repel’ a proportion of the flies . Human and pig odours were not effective with G . tachinoides suggesting that the effective kairomones are found only in cattle odours or that humans and pigs are producing repellents over-riding any kairomones in their odour [34] , [35] . Various combinations of acetone , 1-octen-3-ol , 3-n-propylphenol and 4-methylphenol are used to increase the performance of traps and insecticide-treated targets to monitor and control various Morsitans- and Fusca-group species of tsetse - see review [36] . The results confirm those of earlier studies [15] , [37] showing that the POCA blend , originally developed for use against G . pallidipes [38] is also effective against G . tachinoides . Our data suggests that the incorporation in the blend of 4-methylphenol is about twice as effective as 3-methylphenol ( Table 4 ) . Our results combined with those of earlier studies [16] , [37] , [39] , suggest a blend of POC ( i . e . without acetone ) may be equally effective , producing increases comparable to natural cattle odour . This point is of practical importance as the large volumes of acetone required makes its use in long running control operations particularly difficult . Natural odours from both cattle and humans increased the catch of G . p . gambiensis from traps . But our extensive studies on the effect of baiting electrocuting devices with natural host odours did not show consistently significant effects . For example , for G . p . palpalis , natural odours from five pigs or five humans increased the catch from electrocuting devices but studies with lower numbers of hosts were ineffective . These data for traps and electrocuting devices suggest there is an interaction between odours and visual responses to the catching device . At least part of the difficulty with these studies is caused by the low densities of tsetse – a widespread problem which hampers field studies of G . palpalis [40] . These low densities require that very large numbers of replicates are performed for robust statistical analysis to be possible . As a consequence , the absence of statistically robust effects has perhaps led to the erroneous conclusion that G . palpalis spp . are unresponsive to host odours . In the present study , experiments conducted at times or places where G . p . gambiensis were still low but more abundant than usual did show that baiting traps with natural odours and/or synthetic blends , particularly POCA and POC significantly increased the catches . This is to our knowledge the first published report of improvement in catches using olfactory attractants for this species . Further studies of the responses of G . palpalis spp . are clearly needed to confirm these findings and to identify cost-effective doses and blends . Our results suggest that the three species exhibit a relatively high landing response ( 40–50% ) which was not modulated by natural host odours . However , exhausting volatiles from the tent containing hosts through a long PVC-coated tube to the catching devices could have resulted in a reduced number and concentration of compounds with low volatility compared with those emitted by the host . Compounds with low volatility may be important cues that induce tsetse landing response and this may have caused the lack of difference in landing response to odours from different hosts and control devices . For example , Warnes [41] demonstrated that electrified targets impregnated with ox skin secretions ( sebum ) caught more flies than targets without sebum . The landing response of female G . tachinoides was lower than that of males , confirming previous observations for this species [37] and G . p . palpalis showed a similar trend . This was not the case for G . p . gambiensis where both sexes show similar responses . It should be noted that these responses are all to a single size of target . Laveissière et al . [40] working on G . p . palpalis in Côte d'Ivoire , suggested landing response of males and females varied with changing surface area with more males and less females captured as the black surface area increased . The present results suggest that improvements could be made in the efficiency of traps for Palpalis group tsetse since only 10–30% of tsetse entered a trap . Thus while the biconical trap is the most widely used trap for control and monitoring riverine tsetse in West Africa most of the flies that are attracted to it do not enter immediately [40] . For G . p . gambiensis , odours were most effective when delivered with traps , suggesting the importance of visual responses as well as responses to odours . Thus analysis of visual-olfactory interactions might be the key to improving trap efficiency . For both G . p . gambiensis and G . tachinoides , the overall increases in catch index , landing and entry responses were relatively small in comparison to those found with Morsitans group flies [42] , [43] . In the Palpalis group flies studied here G . tachinoides showed higher responses to natural ox odours than G . p . gambiensis , and also higher responses to POCA . This is consistent with previous observations that this species' behaviour and ecology is intermediate between the savannah-dwelling Morsitans group flies and the more riverine Palpalis group species such as G . palpalis [4] . The smaller increases in catch indices compared to Morsitans group flies that have been observed here for G . palpalis spp . also apply to the other Palpalis group flies G . fuscipes fuscipes and G . f . quanzensis [44] . There is a pressing need to understand why the odours investigated here are seemingly less effective for Palpalis group tsetse . Is the poor response because they rely predominantly on visual cues or because they use odours in a different way to Morsitans group flies ? It has been argued that dense vegetation could be an obstacle to the dispersion of the odour plume [45] . Indeed the riverine species that were studied here ( G . tachinoides and G . p . gambiensis ) live in habitats that differ considerably from the habitats where detailed studies on olfactory cues and host location in savannah tsetse have been conducted . Here their habitats are the linear forests bordering the Comoe or Mouhoun rivers . In recent years , these habitats have become highly fragmented due to human pressure . Hence in these linear and/or fragmented habitats , wind-borne odours may simply be carried to places where few tsetse are found [45] . Such an explanation would not explain the results for G . p . palpalis which is extensively distributed in humid and degraded forest habitats of southern Côte d'Ivoire which are not linear . Although dense vegetation may be an obstacle to the dispersal of volatile chemicals , it is unlikely that this will completely obstruct their movement through such an environment . For example , it has been demonstrated that volatile chemicals release by plants in the rhizosphere can disperse through the soil – an extremely dense environment – and are detected by neighboring plants and nematodes [46] , [47] , [48] , [49] . Another possible explanation for the variability in the responses of G . palpalis spp . to host odours in the present and earlier studies ( eg , [40] ) may center on population structure . There is evidence that in the fragmented habitats typical of populations of G . p . palpalis and G . p . gambiensis the populations may consist of several , genetically-differentiated subunits [50] , [51] , and it has been suggested that these sympatric demes may respond differentially to a given stimulus [52] . Genetically-differentiated demes are associated with trypanosomes from particular host species . One possible explanation for this is that these demes feed preferentially on particular host species . Consequently , the low response to , say , cattle odour may be because only tsetse with a preference for feeding on cattle may respond strongly to cattle odours . Other studies have already reported intraspecific variations in olfactory responses for allopatric populations ( e . g . G . pallidipes - [53] ) . The present results , combined with the earlier studies [15] , [44] contribute to the emerging view that Palpalis-group flies do not show the marked response to host odours exhibited by Morsitans-group tsetse . The relatively low ( ∼2× ) increase in catch observed across a range of habitats suggests that the difference between the Palpalis- and Morsitans-groups is due to their innate host-oriented behavior rather than their particular habitats . We now need to understand better how the Palpalis-group species locate their hosts so that we have a rational basis for developing more cost-effective baits . Despite being lower than for Morsitans group flies , the increases in tsetse catches reported here promise improvements for Palpalis group tsetse control with respect to both human and animal trypanosomiases . There are immediate applications of the use of POCA to improve trapping and control . Indeed , the AU-supported PATTEC program in Burkina Faso has already begun to use this blend for pre-control entomological surveys ( I Sidibe , PATTEC coordinator , Burkina Faso , pers . comm . ) . It is our intention to investigate in more detail the use of POCA blends and individual compounds to enhance control of Palpalis group flies . Regarding costs , we are undertaking further experiments to determine if more cost-effective blends ( e . g . OC ) can be used . In preliminary experiments this blend has been shown to double the catches of G . p . palpalis in Liberia [17] and to double catches of G . tachinoides in Burkina Faso [16] , [37] . Present results suggest that even the relatively modest 2–4× increases in catch indicated by current results could halve the densities of targets required to control Palpalis group tsetse from the current ∼30–50 targets/km2 [54] with consequent significant economic and logistical benefits . Perhaps more importantly in the longer term , the present results show that there is much for improvement in the design and performance of trapping devices . In particular , there is a need to analyse the visual and olfactory responses of riverine species to their reptilian hosts , particularly monitor lizards and crocodiles which constitute an important part of the diet of G . palpalis and G . tachinoides [28] , [55] , [56] , [57] . | Sleeping sickness , otherwise known as Human African Trypanosomiasis , continues to be a serious threat to human health . This disease , which is transmitted by tsetse flies , normally afflicts poor and isolated communities . No vaccines or prophylactic drugs are available to prevent the disease , which , once it has been contracted , is treated with curative drugs that often prove ineffective because of emerging disease resistance in the trypanosomes . These drugs can often have unpleasant and sometimes fatal side effects . Prospects for development of effective vaccines or prophylactic drugs are poor . Killing the tsetse fly vector remains the only method of preventing disease transmission . This can be done at either a local level or regionally . However , a major problem is the cost and logistical difficulty of implementing fly control programmes . To overcome this , we are trying to develop cost-effective insecticide-treated targets by identifying chemicals that will increase the numbers of tsetse that will be lured to a target and killed . Here we show that G . tachinoides is significantly attracted to cow odour , G . p . gambiensis to both cow and human odour , and G . p . palpalis to odours from pigs and humans . This opens the way for further work to identify the attractants present in these natural odours that can then be simply and cheaply incorporated into targets to reduce the cost of control . | [
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| 2010 | Prospects for the Development of Odour Baits to Control the Tsetse Flies Glossina tachinoides and G. palpalis s.l. |
INDUCER OF CBF EXPRESSION 1 ( ICE1 ) encodes a MYC-like basic helix-loop-helix ( bHLH ) transcription factor playing a critical role in plant responses to chilling and freezing stresses and leaf stomata development . However , no information connecting ICE1 and reproductive development has been reported . In this study , we show that ICE1 controls plant male fertility via impacting anther dehydration . The loss-of-function mutation in ICE1 gene in Arabidopsis caused anther indehiscence and decreased pollen viability as well as germination rate . Further analysis revealed that the anthers in the mutant of ICE1 ( ice1-2 ) had the structure of stomium , though the epidermis did not shrink to dehisce . The anther indehiscence and influenced pollen viability as well as germination in ice1-2 were due to abnormal anther dehydration , for most of anthers dehisced with drought treatment and pollen grains from those dehydrated anthers had similar viability and germination rates compared with wild type . Accordingly , the sterility of ice1-2 could be rescued by ambient dehydration treatments . Likewise , the stomatal differentiation of ice1-2 anther epidermis was disrupted in a different manner compared with that in leaves . ICE1 specifically bound to MYC-recognition elements in the promoter of FAMA , a key regulator of guard cell differentiation , to activate FAMA expression . Transcriptome profiling in the anther tissues further exhibited ICE1-modulated genes associated with water transport and ion exchange in the anther . Together , this work reveals the key role of ICE1 in male fertility control and establishes a regulatory network mediated by ICE1 for stomata development and water movement in the anther .
The stamen is the male reproductive organ of flowering plants and at a gross level comprises the filament and the anther [1 , 2] . The late phase of stamen development including filament elongation , anther dehiscence , and pollen maturation , is an essential process in which mature pollen grains are released from locules in the dehiscent anthers , thus enabling pollination and fertilization [3] . Successful fertilization relies on the production and effective release of viable pollen [4] . Failure of anther opening ( dehiscence ) results in male sterility , although the pollen itself can be fully functional [5] . Anther dehiscence is a complex process involving multiple aspects , such as cellular differentiation and degradation , combined with tissue structure alteration as well as dehydration in anthers , which are also regulated by phytohormones [5–6] . A variety of mutants with disturbed anther development in the late stages have been identified in Arabidopsis and the corresponding genes are characterized . The genes characterized so far are categorized into two major functional groups . One is a set of regulators controlling anther structure dynamics including the anther cell layers formation ( e . g . , middle layer [6] , tapetum [5] , septum [7] and stomium [8–11] ) , secondary thickening in the endothecium [12–20] , programmed cell death in sporophyte tissues of anthers ( e . g . , tapetum , septum and stomium ) [4 , 21] , and cell wall degradation ( e . g . , degradation of cell wall components , such as cellulose , hemicellulose and pectin , in anther dehiscence zones catalyzed by cell wall-degrading enzymes ) [22] . The other group includes genes affecting the anther physiological changes , such as water influx [23] , ion homeostasis [24 , 25] and carbohydrate metabolism [26–28] . Notably , most of the genes belonging to this functional group are closely related to anther dehydration . Young anthers take up water for growth during early developmental stages , while at later stages anthers and pollen undergo dehydration before dehiscence [29 , 30] . The dehydration caused by evaporation through stomata and water transport in the vascular bundle promotes pollen grains maturation , anther dehiscence and filament elongation [31–33] . In addition , these two groups of genes are regulated by phytohormones . Studies on jasmonic acid ( JA ) biosynthetic genes [32–36] , JA signaling components including COI1 [37] , MYC and MYB genes [38–43] , and a JA transporter GTR1 [44] have demonstrated that JA plays essential roles in the control of timing of anther dehiscence and pollen maturation . JA positively affects stomium opening [45] and anther dehydration by regulating water transport from anther to filament [32 , 46] . Auxin , generally known as a negative regulator of endothecium lignification , also functions essentially at late anther developmental stages [47–54] . Mutants with disrupted auxin biosynthetic genes or auxin responsive transcription factors are deficient in anther dehiscence , pollen maturation or filament elongation [55–58] . During the modulation of stomium opening in anther dehiscence and pollen maturation , auxin negatively controls the biosynthesis of JA [52 , 56–59] . Deficiency of genes participating in any of these processes can cause anther indehiscence , which is mediated and coordinated by cell layers development and anther dehydration . In comparison , the studies with respect to genes involved in anther dehydration remain relatively limited . INDUCER OF CBF EXPRESSION 1 ( ICE1 ) , also known as SCREAM ( SCRM1 ) , is a MYC-like basic helix-loop-helix ( bHLH ) transcription factor regulating plant responses to chilling and freezing stress and leaf stomata development in normal conditions . Under cold stress , ICE1 is subjected to cold-activated modification [60–63] and subsequently binds to promoters of C-REPEAT BINDING FACTOR ( CBF3 ) [64] to enhance cold tolerance . The identified modification of ICE1 protein includes sumoylation and phosphorylation . In cold exposure , a small ubiquitin-related modifier ( SUMO ) E3 ligase , SAP and Miz 1 ( SIZ1 ) , facilitates SUMO conjugation to ICE1 [60] and a protein kinase , OPEN STOMATA 1 ( OST1 ) , phosphorylates ICE1 to enhance its stability and transcriptional activity [61] . Meanwhile , mitogen-activated protein kinase 3 and 6 ( MPK3/6 ) also phosphorylates but destabilizes ICE1 in response to cold [62 , 63] . ICE1 can be degraded through E3 ubiquitin ligases , high expression of osmotically responsive genes 1 ( HOS1 ) [65] and constitutive photomorphogenic 1 ( COP1 ) [66] . These established a well-characterized regulatory network of ICE1 in low temperature . In ambient temperature , ICE1 directly interacts with three bHLH transcription factors , SPCH , MUTE , and FAMA , to regulate stomatal differentiation in the leaf epidermis [67] . Previous studies also demonstrated that the loss-of-function mutation of ICE1 caused early-flowering with elevated Flower Locus C ( FLC ) gene expression [68] and seed endosperm persistence phenotype that was also observed in the mutant of an endosperm breakdown regulator , ZHOUPI ( ZOU ) [69] . Thus , ICE1 functions in multiple organs at different developmental stages of plants in responses to environmental variations . Here , we illuminate a novel role for ICE1 as a male fertility modulator in Arabidopsis . In the ice1 mutant , the anther wall could not shrink to complete a sufficient anther dehiscence and anthers failed to conduct pollen release . Pollen grains from those indehiscent anthers also showed less viability and lower germination rate . Phenotypic and transcriptomic evidences indicate that the deficient anther dehiscence and pollen germination are associated with water movement and dehydration of anther wall due to the impaired stomatal differentiation as well as altered water transport and ion exchange related genes . Our work brings a new member to anther dehiscence regulators and implicates a potential link among the regulation of environmental responses , vegetative growth , floral transition and fertility development .
In the previously characterized null mutant SALK_003155 in the Columbia ( Col-0 ) background with a T-DNA insertion in the third exon of the ICE1 gene ( Fig 1A ) ( named as ice1-2 ) [67] , we observed reduced fertility ( Fig 1B ) , nevertheless no information with respect to the function of ICE1 in reproductive development has been reported . The extremely low expression level of ICE1 was verified in inflorescences of the ice1-2 ( Fig 1C ) . To investigate the function of ICE1 gene involved in plant fertility , we generated ICE1pro::ICE1 ice1-2 lines , named as c-ice1-2 . Complementation of ICE1 expression and phenotype of reproductive development were confirmed ( Fig 1B and 1C ) . Further characterization revealed that the ice1-2 developed significantly shorter siliques with fewer seeds in each , while c-ice1-2 plants showed restored phenotypes ( Fig 1D–1F ) . In addition , ice1-2 pistils artificially pollinated with Col-0 pollen grains were able to develop into normal siliques , while pollination using ice1-2 pollen was failed in either Col-0 or ice1-2 plants ( S1A Fig ) , demonstrating that the mutant is female-fertile . Together , ICE1 is involved in plant male fertility development and controls seed productivity . Intriguingly , another well characterized mutant ice2-1/scrm2-1 ( SAIL_808_B10 ) disrupting ICE2/SCRM2 , the paralog of ICE1 functioning similarly in cold response and leaf stomata development [70 , 71] , did not show any phenotype in fertility ( S1B Fig ) , which could be due to functional redundancy or the different roles of ICE1-like transcription factors in developmental regulation . After a closer examination of flower anatomy using scanning electron microscopy ( SEM ) , we observed very few pollen grains around the style or on the stigma in ice1-2 ( S2B Fig ) compared with Col-0 ( S2A Fig ) and c-ice1-2 ( S2C Fig ) , thus stigmas of ice1-2 typically were unpollinated . Besides , anthers were only occasionally open while most of them remained indehiscent in ice1-2 . We then compared the floral development in Col-0 , ice1-2 , and c-ice1-2 plants using light microscopy across flower development stages [45 , 72] . At stage 12 , no difference of anther morphology was observed in Col-0 , ice1-2 and c-ice1-2 ( Fig 2Aa , 2Ae and 2Ai ) . In Col-0 and c-ice1-2 , anthers started to dehisce at stage 13 , with concomitant pollen release from the locules after the full expansion of the stigmatic papilla ( stage 13 ) ( Fig 2Ab and 2Aj ) and shriveling of the anther epidermis cell wall ( stage 14 ) ( Fig 2Ac and 2Ak ) , followed by initial stages of silique expansion and floral senescence ( stage 15 ) ( Fig 2Ad and 2Al ) [1] . In contrast , most of ice1-2 anthers did not dehisce at flower stage 13 and later stages ( Fig 2Af–2Ah ) . Majority of the mutant anthers did not dehisce and release pollen grains until the initiation of floral senescence ( stage 15 ) ( Fig 2Ah ) . Based on the flower developmental series , we quantitatively analyzed the process of anther dehiscence in single inflorescences . The youngest flower with visible petals within a flower cluster was labeled as flower 1 and the next elder flower was labeled as flower 2 , and so on [45] ( Fig 2B ) . In Col-0 and c-ice1-2 plants , more than 95% of anthers had dehisced in flower 3 ( 5 . 72 of 6 in Col-0 and 5 . 87 of 6 in c-ice1-2 ) and elder ones , while the dehisced anther number was significantly lower in ice1-2 in flowers 3–5 ( 7 . 7% , 0 . 46 of 6 for flower 3 ) . Even in the oldest flower 5 only 27% ( 1 . 62 of 6 ) of anthers were dehisced ( Fig 2C ) . In fact , even for dehisced anthers in ice1-2 , most of them were still not fully open like that in Col-0 . Therefore , ICE1 is required for dehiscence of anther and the decrease of fertility in ice1-2 is related to indehiscent anthers . Further characterization of anther adaxial surface using SEM provided a closer insight into this phenotype . At stage 12 of anther development in Col-0 and c-ice1-2 flowers , the anthers had locules filled with liquid and an indentation ( stomium region ) in epidermis [72] ( Fig 2Da and 2Di ) . From stages 12 to 13 , the dehiscence program was initiated from the apical toward basal parts . A stomium emerged at the apical of anther and the epidermis cells started to shrink ( Fig 2Db and 2Dj ) . The slit on the stomium begins to widen , resulting in release of pollen at stages 14 ( Fig 2Dc and 2Dk ) and stages 15 ( Fig 2Dd and 2Dl ) . In contrast , in ice1-2 anthers the stomium slit was visible at stage 13 and stage 14 ( Fig 2Df and 2Dg ) . However , the stomium did not rupture sufficiently even at stage 15 and epidermis cells failed to shrink to release pollen from individual anther locules to the stigma ( Fig 2Dh ) . Hence , the ice1 mutation disrupts the shrinkage of anther wall and prevent the release of pollen at the proper stage of pollination . Previous studies have shown that failure of anther dehiscence can be elicited by abnormal cell organization and differentiation of anther tissues [4] . The key processes affecting dehiscence include development of cell layers of the anther [6 , 73] , endothecium secondary thickening [12 , 14] , degradation of middle layer and tapetum [6 , 74] , septum breakdown [33 , 75–77] , and stomium opening [78] . To determine if there was morphological abnormality in the anther tissues , we observed transverse sections of Col-0 and ice1-2 anthers from the emergence of dehiscence to senescence during stamen development . In both Col-0 and ice1-2 , tapetum was visible and started to break down at anther developmental stage 10; at stage 11 endothecium started the lignification for secondary thickening , tapetum was degraded , and septum started to break down; at stage 12 the septum was degraded through a programmed cell death-like lysis to form a single locule ( S3 Fig ) . In Col-0 , stomium was open and epidermis started to shrink to release pollen grains at stage 13 , and epidermis kept shrinking and releasing pollen at stage 14a . Until stage 14b all pollen grains were dispersed . In ice1-2 , although stomium was ruptured , epidermis did not shrink and pollen grains were still covered inside the locules until stage 14b ( S3 Fig ) . The auramine O staining in both semi-thin sections and fresh anthers at anther stage 13 also showed that no obvious difference was between Col-0 and ice1-2 for endothecium secondary thickening that was occurred from stage 11 ( S4A and S4B Fig ) . Whereas at stage 14 very few pollen grains were still inside anthers of Col-0 ( S4Be Fig ) , while the ice1-2 anthers were full of pollen ( S4Bf Fig ) . Taken together , ICE1 may not influence formation of anther cell layers but regulates epidermis shrinkage at the stage of pollen dispersal . Further , the sizes of stamen and pistil tissues were also investigated using light microscopy . The filaments were fully elongated to position the anthers at the height of the stigma at flower developmental stage 14 in Col-0 and c-ice1-2 ( S5Aa and S5Ac Fig ) . In ice1-2 , the stamen and style lengths were slightly shorter and the stamen/style length ratio was smaller ( S5B and S5C Fig ) . The reduced elongation of stamen tissues is also commonly observed in mutants interrupting anther dehiscence [4] . But in ice1-2 , the shorter stamen and pistil may not be the main reason of sterility , since the filaments were able to elongate and allowed anthers to reach stigma ( S5Ab Fig ) . During the dehiscence of the anther , one of the key forces that open the anther comes from the swelling of pollen grains [79] . In mutants such as apy6/7 [80] , yuc6 [81] and ams [82] , delay or lack of anther dehiscence is due to abnormal pollen exine formation or absence of pollen . Here , the pollen development in Col-0 and ice1-2 was examined . Similar with Col-0 , ice1-2 anthers enveloped fully differentiated pollen grains ( Fig 3A ) . The microspores developed into tricellular pollen and the exine structure was normally formed , suggesting an intact meiotic division process and completed trinucleate stage . However , viability of ice1-2 pollen grains was obviously lower than Col-0 and c-ice1-2 shown by fluorescein diacetate ( FDA ) staining ( living cell emits blue-green light [40] ) at anther stage 13 ( Fig 3B and 3C ) , indicating that the pollen maturation was influenced at the final phase . Moreover , ice1-2 pollen grains showed a significantly lower in vitro germination rate compared with Col-0 at stage 13 , and the germination remained poor until stage 15 ( Fig 3D ) . Consistently , the in vivo germination capacity determined through pollination on Col-0 pistils also demonstrated that ice1-2 pollen was deficient in germination ( Fig 3E ) . Most of ice1-2 anthers were manually opened or enlarged for collection of pollen grains . Interestingly , we noticed that when we selected the small proportion of ice1-2 anthers with obviously open stomium and pick pollen grains exposed at the stomium area to do the pollination , the germination was rescued at both stage 13 and stage 15 ( Fig 3E ) . Notably , even for those ice1-2 anthers with open stomium , most of them were still half-dehiscent ( Fig 3F ) . In the in vitro germination assay , hundreds of pollen grains including ones exposed at the stomium area and those enveloped inside epidermis were pooled on media . Thus , it was not surprising to see that pollen grains from ice1-2 anthers possessing open stomium still showed low in vitro germination rate , which was higher than typical ice1-2 anthers though ( Fig 3D ) . Given the fact that pollen structure was intact and pollen grains exposed at the stomium area could germinate in pollination , the impaired pollen viability and germination in ice1-2 might be related to abnormal anther dehiscence and dehydration . Water status is critical for development of pollen grains and anthers . Pollen maturation and anther dehiscence are coordinated processes involving water absorbance and dehydration of anther tissues including endothecium and epidermal cells [4 , 83] . Desiccation of the anther leading to shrinkage of the outer wall provides the final force for anther opening [31] . During pollen development , pollen water content will decrease to a minimum at maturity before dispersal , and rehydrate after pollination [83] . To confirm whether the defects of anther dehiscence and pollen maturation in ice1-2 were due to the issue of dehydration , we examined the anther dehiscence rate in different relative humidity ( RH ) conditions . The 80% RH environment was the normal growth condition of Arabidopsis plants and 40% RH was used as the dehydration treatment . The anther dehiscence rates and phenotypes were recorded at flower stage 13 that is the key stage for anther dehiscence and pollination [1] . Under 80% RH Col-0 showed higher anther dehiscence rate than ice1-2 , while under 40% RH the ice1-2 anther dehiscence rate was significantly increased ( Fig 4A and 4B ) . Moreover , the deficiency of ice1-2 in the pollen viability ( Fig 4C and 4E ) , pollen germination ( Fig 4D and 4F ) , and pollen function indicated by pollination on Col-0 pistils ( Fig 4G and 4H ) were all rescued by 40% RH treatment . Especially for pollen , ice1-2 reached wild type levels in all three indices . As a consequence , the sterility phenotypes of ice1-2 could be rescued by drought treatment as well ( Fig 5A–5C ) . These further demonstrated that in ice1-2 the anther indehiscence and impaired pollen function are due to deficiency in dehydration of anther tissues such as anther wall , which can be derived from abnormal water allocation within the stamen . These are also consistent with the previous studies showing that pollen maturation and anther dehiscence are co-regulated during water movement associated processes [83] . It has been suggested that water moves out of the anther via the transport in the vascular bundle and evaporation of epidermis stomata [28 , 31] . The dehydration of endothecium , connective , and locules can be partially attributable to the evaporation of water through the stomata on the abaxial surface of anthers [31] . Previous studies indicated that ICE1 was expressed in leaf guard cells [67] . We investigated the promoter activity of ICE1 at the stages of floral development involving anther dehiscence program events using β-glucuronidase ( GUS ) report system . Three independent ICE1pro::GUS transgenic lines were assayed and exhibited consistent patterns . The ICE1 promoter showed a strong activity in the inflorescence and floral organs ( S6A Fig ) . At approximately flower stage 10 ( the petals reach the lateral stamens ) [1] , the style , sepals , and filaments showed strong staining , whereas no obvious GUS staining was observed in the anther tissues ( S6B Fig ) . As the flowers developed to stage 12–15 , the GUS staining remained in sepals ( S6C–S6E Fig ) , especially vascular tissues of sepals ( S6F Fig ) , as well as the style ( S6G Fig ) , and turned to be much stronger in connective of anthers ( S6H Fig ) , filaments ( S6I Fig ) , pedicels ( S6J Fig ) , and vascular tissues of petals ( S6K Fig ) . In immature siliques , GUS staining was restricted to the septum , the silique tip , and the base ( S6L Fig ) . Remarkably , although the GUS signal in the adaxial side of anthers was weak in flowers at stage 12–15 , a strong staining was observed in guard cells of stomata in the abaxial side of anthers ( Fig 6A ) , where the ICE1 protein was accordingly accumulated ( Fig 6B ) . The water transport from anther locules to filaments and petals is essential for pollen maturation and anther dehiscence [32] . Multiple genes involved in anther dehiscence were found to be specific expressed in anther guard cells [25 , 45 , 84 , 85] , filaments [6 , 32 , 49] , anthers and filaments junction tissues [27 , 50] , anther wall and vascular bundle [23] . DAD1 strictly expressed in filaments controlling JA biosynthesis and likely water transport also regulates anther dehiscence and pollen maturation [32] . Consistent with the fact that sterile phenotype of ice1-2 can be rescued by dehydration , the high activity of ICE1 promoter in anther stomata and flower vascular bundles suggest a connection of ICE1 function in particular with appropriate dehydration of pollen and/or anthers . At anthesis , endothecium and epidermal cells in anther wall lose most of water via evaporation of stomata on the abaxial side of anthers [86] and osmotic retraction of water through filaments and connective tissue surrounding the vasculature [27] . Actually , in Arabidopsis not much information focused on stomatal development in anthers has been reported , and little attention has been paid to the role of anther stomata in anther dehiscence . Not all plant species possess stomatal pores in anther epidermis and developmental process of anther stomata depends on species [87] . In order to systematically describe the stomata development in the anther of Arabidopsis , we counted the number of anther stomata in flowers at stages from 9 to 12 in Col-0 . The anther stomata increased from 1 . 57 to 5 . 89 at stage 9 to 11 , while at stage 12 much more stomata ( 22 . 38 ) were identified in the anther ( Fig 6C ) . According to the stomatal lineage model in Arabidopsis leaves [88] , stomata differentiate via a series of cell transitions . A group of protodermal cells called meristemoid mother cells can produce meristemoids ( Ms ) through asymmetric divisions . Meristemoids reiterate asymmetric divisions to generate surrounding stomatal lineage ground cells ( SLGCs ) and eventually differentiate into guard mother cells ( GMCs ) . One guard mother cell undergoes one time of symmetric division to produce a pair of guard cells ( GCs ) ( Fig 6D ) . We used scanning electron microscopy ( SEM ) to perform more detailed characterization for stomata lineage in Col-0 anthers of flowers from stage 8 ( before generation of stomatal lineage cells ) to stage 14 ( after anther dehiscence ) . No stomata were observed in the adaxial side of anther epidermis . In the abaxial side , cell number started to increase but no stomatal lineage cells or mature GCs appeared yet at flower stage 8 ( Fig 6Ea ) . At stage 9 , cell types were destined and stomatal lineage cells as well as few mature guard cells within top area were identified ( Fig 6Eb ) . After that , the epidermal cells gradually expanded and more stomata turned to mature . At stage 10 and 11 , mature GCs kept increasing ( Fig 6Ec and 6Ed ) . At stage 12 with a longer duration , the number of mature GCs significantly increased , and most of stomata matured completely at the end of stage 12 ( Fig 6Ee ) . At this moment , the anther shape was changed from oval to round and stomata gradually matured from the top to the bottom . Mature GCs were concentrated in the middle lengthways of the abaxial side in the anther epidermis ( Fig 6Ee ) . From stage 13 to 14 , the enhancing shrinkage of anther wall prompted the rupture in the adaxial side and the pollen dispersed ( Fig 6Ef and 6Eg ) . Stomata were not present in filaments . The accumulation of matured stomata in stage 12 from the top toward the bottom in epidermis coincided the stage at which the anther wall started to shrink and then opened from the top , suggesting the role of stomata in anther dehydration and dehiscence in Arabidopsis . ICE1 has been reported as a regulator of stomatal differentiation at the surface of leaves [67] , but it is unclear whether ICE1 is involved in stomatal differentiation in anthers . Since in mature stomata of anthers ICE1 promoter was strongly active and ICE1 protein was highly accumulated ( Fig 6A and 6B ) , we therefore examined how ice1-2 mutation affected stomatal development in anthers . At flower stage 12 , Col-0 and c-ice1-2 possessed abundant matured guard cells and some stomatal lineage cells , while ice1-2 showed many meristemoids and guard mother cells but not a single mature stoma ( Fig 6F and 6G ) . No stomata clusters or GMC-like tumors were identified either ( Fig 6F ) . In addition , the total number of stomatal lineage cells in ice1-2 were obviously lower than Col-0 in anthers ( Fig 6G ) . These differed from the stomata development in ice1-2 leaves , in which stomata clusters , GMC-like tumors aligned in parallel , and some differentiated GCs expressing mature guard cell marker E994 were present [67] . Consistently , we observed that in ice1-2 leaves more than one third of stomata showed differentiated GCs and nearly half were immature stomata including GMC-like tumors . Stomata clusters were also recorded ( S7A and S7B Fig ) . In comparison , ice1-2 leaves resemble fama leaves in stomata development phenotype showing excessive GMC symmetric divisions and defective terminal differentiation of GCs [67] , but the phenotype in ice1-2 leaves is weaker for they can still form some differentiated GCs [67 , 89] ( S7A Fig ) . Whereas ice1-2 anthers do not exhibit structures indicating unrestricted GMC symmetric divisions and hardly possess differentiated GCs . Thus , ICE1 prompts stomatal differentiation in the anther in a different manner compared with that in leaves , and therefore can regulate anther dehydration to allow the dehiscence . Besides evaporation through stomata , many factors , such as signal of phytohormones , nutrient metabolism and transporters , also influence anther dehydration [23 , 27 , 32] . At present the direct data with respect to water content in the anther remain limited . To further investigate the effect of ICE1 underlying the phenotypes observed , we collected anthers at flower stage 9–13 covering critical time points for dehiscence and performed RNA-Seq to analyze ICE1-regulated genes in anthers . There were 1165 genes differentially expressed in the anther of ice1-2 compared to Col-0 , with 732 up-regulated genes ( UGs ) ( LogFC > 1 , FDR < 0 . 05 ) and 433 down-regulated genes ( DGs ) ( LogFC < -1 , FDR < 0 . 05 ) ( Fig 7A and S1 Table ) . For corroboration of the transcriptome data , three up-regulated genes and three down-regulated genes were subjected to qRT-PCR and these expression changes showed a good agreement between RNA-seq and qRT-PCR data ( S8 Fig ) . Among these differentially expressed genes ( DEGs ) , 574 UGs and 205 DGs were identified as guard cell-expressed genes according to the gene expression database ( http://www . arabidopsis . org/servlets/TairObject ? type=keyword&id=19990 [90] and previously published transcriptome data of the leaf stomatal lineage [91] . Meanwhile , 452 UGs and 146 DGs were detected as stamen-expressed genes through stamen gene expression database ( http://www . arabidopsis . org/servlets/TairObject ? type=keyword&id=20328 [92] ( Fig 7A and S1 Table ) . There were 429 UGs and 114 DGs expressed in both the guard cell and the stamen , indicating the significantly strong overlap between genes expressed in these two tissues for ICE1-regulated DEGs ( p < 8 . 405e-44 for UGs and p < 1 . 560e-20 for DGs by hypergeometric test ) . The overrepresentation of guard cell-expressed genes within ICE-regulated genes in the anther reflects the key role of ICE1 in the regulatory network of stomata development of the stamen , which is in line with the phenotyping results . Eight of these 543 guard cell & stamen DEGs play key roles in leaf stomatal development , including four UGs ( TMM , SPCH , MUTE , bHLH93 ) and four DGs ( FAMA , EPF1 , MPK12 , and MPK14 ) [93] . The results of qRT-PCR also confirmed that the expression of these genes was differentially regulated at flower developmental stage 10–13 of ice1-2 compared with Col-0 [83] ( Fig 7B ) . FAMA and EPF1 controlling guard cell differentiation [67 , 94] were significantly down-regulated , which was in line with the impaired terminal differentiation of anther guard cells in ice1-2 . In leaves the ice1-2 phenotype was close to fama , but for anthers we could not gain fama materials due to its severe developmental defects [89] . The up-regulation of TMM , SPCH , MUTE and bHLH93 in ice1-2 can also be due to feedback effects ( Fig 7C ) . Using FAMApro::FAMA-GFP plants , we observed specific accumulation of FAMA in anther guard cells ( Fig 8A ) . Moreover , while EPF1 promoter does not contain E-box motif ( CANNTG ) that is a typical binding motif of bHLH transcription factors [63] , there are nine E-box elements in the FAMA promoter ( 2 . 5 kb from the transcription start site ) ( Fig 8B and S9A Fig ) . The in vivo dual-LUC assay with transient expression of ICE1 driven by 35S promoter ( used as the effector ) and LUC driven by truncated FAMA promoter fragments ( used as reporters ) demonstrated that in addition to protein interaction , ICE1 activated the FAMA transcription ( Fig 8C and 8D ) . Further investigation using electrophoretic mobility shift assay ( EMSA ) showed two E-box elements located at -582 to -613 bp ( labeled as P3 ) and -629 to -664 bp ( labeled as P4 ) upstream from transcription start site specifically interacted with ICE1 ( S9A and S9B Fig , Fig 8E and 8F ) . P4 exhibited an obviously higher in vitro binding affinity than P3 ( Fig 8G ) . Another E-box element located at -1569 to -1600 bp ( labeled as P7 ) also showed a weak binding with ICE1 but no competitive binding of cold probe was observed ( S9B and S9C Fig ) , suggesting that the shift was due to a non-specific binding or the binding affinity was extremely low . P7 contains the same core sequences with P3 ( S9A Fig ) , thus the flanking sequences may also play an important role in the ICE1 binding affinity . The direct interaction between ICE1 and FAMA promoter is a novel interplay in the regulatory network of guard cell differentiation . It has been reported that FAMA also plays a positive role for ICE1 expression in young seedlings but does not bind to ICE1 promoter [95] . When FAMA is associated with its promoter , it is not necessary for its own expression [89] . Given the weaker developmental defects in ice1 than fama , ICE1 is unlikely necessary for FAMA expression . Rather , ICE1 may enhance the transcription of FAMA with other activators in a redundant manner , which can be a part of the regulatory network in the stomatal lineage development . However , the identification of a novel direct target of ICE1 can be potentially beneficial for breeding application . Gene ontology ( GO ) analysis using singular enrichment provided by agriGO [96] showed that a number of ion transporters , hydrolases and dehydration associated genes were positively regulated by ICE1 in anthers ( Fig 9A and S2 Table ) . Ion gradients or currents are critical for active water movement in the anther and they regulate the anther dehiscence and pollen germination [6 , 24 , 85 , 97 , 98] . Some mutants affecting cation homeostasis , such as mia deficient in a P-type ATPase cation pump [99] and nhx1 nhx2 null in two Na+/H+ antiporters [24 , 25] , also failed in sufficient release of pollen from mature anthers . Twelve transporter genes , in particular genes of sugar transporters , metal transporters as well as ATPases , were down-regulated in ice1-2 anthers ( Fig 9A ) . Among them , STP1 [100] , STP4 [101] , CAX3 [102] and ACA12 [103] were expressed in leaf stomatal guard cells . The number of seeds per silique of aca12 mutant was significantly less than that in the wild type , indicating that ACA12 impacts plant fertility [103] . Accordingly , we observed wilted flower buds in old ice1-2 plants , which resembled the phenotype of nhx1 nhx2 under osmotic stress [25] ( Fig 9C ) , suggesting that ICE1 modulates the ion exchange affecting water movement in flowers . Three glucosinolates hydrolysis related genes , TGG1 , TGG2 , and TGG3 , as well as several glucosinolates biosynthesis genes , were also positively regulated by ICE1 ( S2 Table ) . The glucosinolates are a group of secondary metabolites involved in ABA-regulated stomatal opening [104] and floral development in drought conditions [105] . The tgg1 tgg2 mutant showed stomata with closed aperture in leaves resembling plants in the face of drought stress [106] . Thus , carbohydrate hydrolysis can also be involved in ICE1-regulated anther dehydration . Besides , genes responding to water deprivation and auxin-mediated signaling pathways were enriched ( Fig 9B , S3 Table ) . Two ABA-induced dehydrin genes affecting water use efficiency , RAB18 and LTI30 [107 , 108] , were remarkably repressed in ice1-2 mutant . RAB18 is highly expressed in guard cells , suggesting a role in stomatal function [109] . The downregulated auxin-mediated signaling genes included SAUR41 , GH3 . 5 , GH3 . 6 , BT2 , BT5 , IAA 32 , and MPK12 . BT family proteins are essential during later stages of male gametophyte development [110 , 111] . MPK12 is a MAP kinase that is preferentially expressed not only in leaves but also in anther guard cells [112] , and positively regulates ABA [112] , JA [113] and SA signaling [114] in leaf guard cells of Arabidopsis . It has been shown that auxin represses JA biosynthesis to control the timing of stomium opening and prevent early anther dehiscence [52] . The genes negatively regulated by ICE1 were categorized into two biological processes including JA biosynthesis and response , and flavonoids associated pathway . In the stamens and petals , JA is mainly accumulated in the filaments to regulate water transport , which sequentially triggers flower opening and anther dehiscence [32] . The JA biosynthesis or signaling deficiency can cause profoundly male sterile [4 , 45] . The null mutant of COI1 , a JA receptor , exhibited delayed anther dehiscence and produced sterile pollen [37 , 45] . JA-synthesis related genes , such as LOX2 , AOS and OPR3 , affect water movement in flowers as well [45 , 84] ( Fig 9B and S3 Table ) . The interrupted transport of flavonoids leads to abnormal dehydration and dehiscence of anthers [84] . High amounts of flavonoids are also considered as endogenous auxin transport regulators that affect plant growth [115] . Here , the down-regulation of auxin signaling genes and up-regulation of JA and flavonoid related genes in ice1-2 can be due to either active balance in regulation of water allocation or compensatory feedback consequences of failed stomium enlargement caused by abnormal water movement in the anthers and/or other floral tissues . All the identified enriched pathways in GO analysis of ICE1-regulated genes are related to water transport ( Fig 10 ) . The stomatal differentiation influencing evaporation is also controlled by ICE1 . Together with the fact that dehydration rescued sterility in ice1 , it can be demonstrated that ICE1 participates in the interaction between ambient environmental stimuli and water regulation in the anther tissues . At the same time , it has been reported that CBF3 , a main target of ICE1 , functions in early response to drought in flowers [105] . These can suggest a dual role of ICE1 in water-associated stress resistance and dynamic developmental processes in floral tissues . In summary , ICE1 is identified as a novel male fertility regulator in Arabidopsis and can be a promising target for application of molecular engineering in crop breeding .
All Arabidopsis thaliana plants used were in the Columbia ( Col-0 ) background . The seeds of ice1-2 ( SALK_003155 ) were obtained from the Arabidopsis Biological Resource Center at Ohio State University ( ABRC , http://abrc . osu . edu ) , as previously described [67] . The ICE1pro::GFP-ICE1 ( SCRMpro::GFP-SCRM ) transgenic line is a generous gift from Pro . Keiko Torii ( Department of Biology , University of Washington ) . The FAMApro::FAMA-GFP transgenic line is a generous gift from Ph . D . Xiaolan Chen ( School of Life Sciences , Yunnan University ) . To generate ICE1pro::GUS lines , a 2578bp upstream region from the start codon was amplified by PCR from Arabidopsis Col-0 genomic DNA and cloned into T-vector pMD-19T ( TaKaRa ) . After the DNA sequences were confirmed , the promoter region was cloned into pCAMBIA1301 ( CAMBIA , Australia ) using the method as previously described [63] . Primers were AtPICEF-PstI ( 5’-TActgcagGGACCACCGTCAATAACATCG-3’ ) ; AtPICER-NcoI ( 5’-TTccatggGCCAAAGTTGACACCTTTACC-3’ ) . The ICE1pro::GUS plasmid was electroporated into Agrobacterium tumefaciens strain GV3101 ( WEIDI ) , which was used to transform the Col-0 plants by the floral dipping method [116] . For complementation of ice1-2 mutant , the ICE1 upstream region and open reading frame were amplified and subcloned into pCAMBIA1302 vector using primers AtPICEF-PstI; AtPICER-NcoI; AtICE1F-SpeI ( 5’-ATactagtGATCATACCAGCATACCCTGC-3’ ) ; AtICE1-BstEII ( 5’-TTggtaaccTCAGATCATACCAGCATACCC-3’ ) . The ICE1pro:: ICE1 fusion construct was then introduced into ice1-2/+ plants by the floral dipping method [116] . Plants were grown in greenhouses under long day conditions ( 16 h light/8 h dark ) at 22°C . The dehydration experiments were performed as previously described with some changes [105] . In brief , two treatments were carried out . One was the standard condition with 80% soil moisture and 80% air relative humidity . The other was drought condition with 40% soil moisture and 40% air relative humidity . Pots were arranged according to a randomized design and their positions were changed daily . Seeds were stratified in a cold room for 2 d at 4°C in the dark . Plants were grown in standard condition until the moment just after bolting ( the main shoot was about 1 cm high ) . When the drought treatment was started , plants were transferred into the growth chamber ( RXZ-436B-LED , Ningbo Jiangnan instrument factory , China ) . The soil moisture was maintained by daily weigh and watering until harvest . Pollen germination analysis was conducted mainly as previously described [32] . The in vitro assay was performed on pollen germination media using pollen isolated from flowers at designed stages . For pistil pollination , pollen grains from flowers at designed stages were hand-pollinated on Col-0 pistils . The pollinated pistils were subjected to aniline blue staining or kept growth for characterization of siliques and seeds . For ice1-2 mutant the stomium was manually enlarged for releasing pollen or picking the pollen grains using dissecting needles . Inflorescences of Col-0 and ice1-2 mutant plants were collected , fixed and dehydrated as previously described [117] . The Technovit resin-embedded blocks were sectioned to a thickness of 1 . 0 μm slice using a motorized RM2265 rotary microtome ( Leica ) with a glass knife , and then heat-fixed on glass slides . After staining with 0 . 05% Toluidine Blue for 15–30 min , the sections were photographed under the Microscope Axio Scope . A1 ( Carl Zeiss MicroImaging ) with bright field after rinsing and drying . Lignin in tissue was visualized with 0 . 01% fluorescent brightener ( Sigma ) for 30s , then mounted with 0 . 001% auramine O ( BBI Life Sciences ) and observed by Microscope Axio Scope . A1 ( Carl Zeiss MicroImaging ) under GFP channel . Fluorescence microscopy was performed using a Leica confocal laser-scanning microscope ( Leica TCS SP8 , Leica Microsystems , Wetzlar , Germany ) equipped with a 10× Leica HC PL APO objective . The lignified cells and GFP fusion protein were observed with 488 nm excitation/ 510-540nm emission . Inflorescences and anthers were collected and photographed under a SteREO Discovery V8 dissecting microscope ( Carl Zeiss MicroImaging ) using a SPOT FLEX digital camera ( Diagnostic Instruments ) . Pollen from anthers stage 13–14 [72] were collected and incubated in Fluorescein Diacetate ( FDA ) ( Solarbio ) solution ( FDA ( 5mg/ml ) in acetone and diluted by 20% sucrose to 0 . 1mg/ml ) for 5min [118] , and photographed under Microscope Axio Scope . A1 ( Carl Zeiss MicroImaging ) under DAPI channel with an Axio Cam HRc camera ( Carl Zeiss MicroImaging ) . For SEM analysis , tissues were dissected under anatomical lens ( SMZ-161-BLED , Motic , China ) if needed , then immediately mounted on aluminum stubs for SEM . For leaf tissues , small pieces ( d = 8 mm ) of leaves from about 5-week-old plants were cut , fixed , dehydrated and coated as previously described [106] . These images were taken with scanning electron microscope TM3000 ( TM3000 Tabletop Microscope , HITACHI , Japan ) . For histochemical GUS activity analysis , tissues were immersed in GUS staining buffers with vacuum infiltration and destained with 75% ethanol as previously described [119] . The GUS activity was observed with Microscope Axio Scope . A1 ( Carl Zeiss MicroImaging ) . Coding regions of ICE1 were cloned into the pCAMBIA1302 . The promoter sequences of FAMA were PCR amplified and inserted into the pGreenII 0800-LUC vector , using primer pFAMAF-PstI 5‘-TGCACTGCAGTTTGGAAATTGATTTTGGGA-3’ and pFAMAR-SacII 5’-TCCCCGCGGGAGTAAGCATCACCAA-3’ . After sequencing , all the constructs were transformed into GV3101 Agrobacteria , while the pGreenII-0800 constructs were co-transformed with pSoup-P19 . The mixture of cells containing constructs with protein and promoter was infiltrated according to the published method [120] . The luciferase activity of Nicotiana benthamiana extracts was determined using the dual-luciferase assay kit ( Promega ) and then detected by a Synergy 2 multimode microplate ( BioTek ) as described previously [120] . All tests were performed with three biological replicates and five technical replicates per assay . The electrophoretic mobility shift assay ( EMSA ) was performed as previously described [61] . In brief , the His-ICE1 recombination protein was expressed in E . coli induced by 1 mM IPTG at 37°C for 3 h and purified through sonication and His sepharose beads ( Amersham Biosciences ) . EMSA was conducted using the Lightshift Chemiluminescent EMSA Kit ( Pierce ) with biotin-labeled and cold probes . Probe sequences were listed in S9A Fig . Total RNA was extracted by RNApure Plant Kit ( CWBIO ) according to the manufacturer’s protocol . cDNA was reverse-transcribed using PrimeScript RT reagent Kit with gDNA Eraser ( Perfect Real Time ) ( TaKaRa ) . SYBR Premix Ex Taq II ( TaKaRa ) was used for qPCR on a ABI StepOne Plus real-time system ( Life Technologies ) . qRT-PCR was performed in triplicate and data were collected and analyzed with ABI STEPONETM software version 2 . 1 [121] . Various gene specific signal was normalized relative to ACTIN2 gene ( At3G18780 ) expression . The primer sequences were listed as follows: Anthers at flower stages 9–13 from Col-0 and ice1-2 plants were collected and immediately frozen in liquid nitrogen . Total RNA was extracted using RNAeasy Plant Mini Kit ( Qiagen , Valencia , CA ) according to the manufacturer’s protocol . Around 2 μg of total RNA with an A260/280 value of 1 . 8–2 . 0 was used to prepare the libraries , which were subjected to paired-end ( 2 x 100 bp ) sequencing in the Illumina Hi-seq 2000 system ( Illumina Inc . ) . The RNA-seq analysis was performed as previously described with modifications [121] . In brief , raw reads were cleaned up with Trim Galore ( https://www . bioinformatics . babraham . ac . uk/projects/trim_galore/ ) and mapped to the Arabidopsis genome ( TAIR10 ) by TopHat2 [122] , then further assembled using StringTie and Cufflinks-CuffMerge [123] . The read counts for each gene was calculated by HTSEQ v . 0 . 6 . 0 [124] and the expression level was normalized as Fragments Per Kilobase of transcript per Million mapped reads ( FPKM ) . The differential expression analysis was performed using DEGseq2 [125] . Differentially expressed genes ( DEGs ) were selected when Log2 Fold-Change ( Log2FC ) > 1 or < -1 , and False Discovery Rate ( FDR , Benjamini-Hochberg adjusted P-value ) < 0 . 05 . The RNA-Seq data have been uploaded to the National Center for Biotechnology Information Sequence Read Archive under accession numbers GSE107260 . Gene ontology annotation and enrichment analysis was performed on agriGO , a publicly accessible analysis tool and database ( http://bioinfo . cau . edu . cn/agriGO ) . Genes that express in guard cell or stamen were obtained by matching the annotated accessions to the annotation list under key word ID PO: 000293 ( express in guard cell , http://www . arabidopsis . org/servlets/Search ? type=annotation&action=search&kw_id=19990&kw=guard%20cell&scope=term ) and PO:0006472; PO:0006441 ( express in stamen , http://www . arabidopsis . org/servlets/Search ? type=annotation&action=search&kw_id=20328&kw=stamen&scope=term ) . | INDUCER OF CBF EXPRESSION 1 ( ICE1 ) is a basic helix-loop-helix transcription factor playing multiple roles in Arabidopsis . It was initially identified as the activator of C-Repeat Binding Factor 3 ( CBF3 ) , a core modulator triggering cold acclimation . ICE1 also activates Flowering Locus C ( FLC ) , a major repressor of floral transition , to delay flowering under fluctuating environmental stimuli . In normal conditions , ICE1 participates in control of stomatal development in leaves and endosperm breakdown in seeds . Here we describe a role of ICE1 in male fertility development of Arabidopsis . We provide evidence that ICE1 controls stomatal differentiation in the anther epidermis and thereby anther dehiscence and pollen viability as well as germination . Consequently , fertility of ice1 mutant can be rescued by ambient dehydration . ICE1 regulates FAMA , one key regulator of guard cell differentiation , through direct binding to MYC-recognition elements in FAMA promoter . Moreover , we perform transcriptomic analysis using anther tissues and identify ICE1-regulated genes involved in water transport . These findings reveal a novel role of ICE1 in male fertility regulation through affecting water movement in the anther , which deepens our understanding of coordination between plant development and stress response , and potentially contributes to the pollination controls in crop breeding . | [
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| 2018 | INDUCER OF CBF EXPRESSION 1 is a male fertility regulator impacting anther dehydration in Arabidopsis |
Advances in vertebrate genomics have uncovered thousands of loci encoding long noncoding RNAs ( lncRNAs ) . While progress has been made in elucidating the regulatory functions of lncRNAs , little is known about their origins and evolution . Here we explore the contribution of transposable elements ( TEs ) to the makeup and regulation of lncRNAs in human , mouse , and zebrafish . Surprisingly , TEs occur in more than two thirds of mature lncRNA transcripts and account for a substantial portion of total lncRNA sequence ( ∼30% in human ) , whereas they seldom occur in protein-coding transcripts . While TEs contribute less to lncRNA exons than expected , several TE families are strongly enriched in lncRNAs . There is also substantial interspecific variation in the coverage and types of TEs embedded in lncRNAs , partially reflecting differences in the TE landscapes of the genomes surveyed . In human , TE sequences in lncRNAs evolve under greater evolutionary constraint than their non–TE sequences , than their intronic TEs , or than random DNA . Consistent with functional constraint , we found that TEs contribute signals essential for the biogenesis of many lncRNAs , including ∼30 , 000 unique sites for transcription initiation , splicing , or polyadenylation in human . In addition , we identified ∼35 , 000 TEs marked as open chromatin located within 10 kb upstream of lncRNA genes . The density of these marks in one cell type correlate with elevated expression of the downstream lncRNA in the same cell type , suggesting that these TEs contribute to cis-regulation . These global trends are recapitulated in several lncRNAs with established functions . Finally a subset of TEs embedded in lncRNAs are subject to RNA editing and predicted to form secondary structures likely important for function . In conclusion , TEs are nearly ubiquitous in lncRNAs and have played an important role in the lineage-specific diversification of vertebrate lncRNA repertoires .
There is a growing appreciation that the functional repertoire of metazoan genomes includes much more than protein-coding sequences [1]–[3] . Recent functional genomic studies have revealed , in particular , the widespread occurrence , bewildering diversity , and functional significance of noncoding RNA [4] . In addition to small regulatory RNAs , such as tRNAs or microRNAs , the genome encodes a myriad of long noncoding RNAs ( lncRNAs ) that are greater than 200 nt in length [for review: 5]–[7] . The most recent , though still conservative , catalogues predict between 5 , 000 and 10 , 000 discrete lncRNA loci in the human genome [8]–[10] . The majority of lncRNAs in these manually curated reference sets are intergenic units often referred to as large intergenic noncoding RNAs ( lincRNAs ) because they do not overlap with known protein-coding genes . Comparable numbers of lncRNA loci are expected to occur in the mouse and other vertebrate genomes [9] , [11]–[16] and hundreds of loci with similar properties have also been identified in model invertebrates such as Drosophila melanogaster [17] and Caenorhabidtis elegans [18] , as well as in the model plant Arabidopsis thaliana [19] . Although once dismissed as transcriptional ‘noise’ , there is mounting evidence that many lncRNAs are important functional molecules engaged in diverse regulatory activities . First , the majority of functionally characterized lncRNAs exhibit precise spatiotemporal patterns of expression and , often , discrete cellular localization [9] , [11]–[13] , [20]–[25] . Second , the structure , biogenesis and processing of lncRNAs are very similar to that of protein-coding genes and indicate that most lncRNAs are produced from independent transcription units . For example , lncRNAs are typically transcribed by RNA polymerase II , under the control of diverse combinations of transcription factors that actively bind to promoters and enhancers , with canonical chromatin modifications [10]–[12] , [26] , [27] . LncRNA transcripts are also alternatively spliced , polyadenylated , and subject to other post-transcriptional modifications [10] , [12] , [28] . Third , lncRNA exons generally display a clear signal of purifying selection , implying structural and/or functional sequence constraint , albeit less stringent than on protein-coding exons [10] , [12] , [29]–[33] . Moreover , some lncRNA genes are evolutionarily ancient . A small but increasing number of loci orthologous to human lncRNAs have been identified in the mouse , and the origins of some human lncRNAs can be traced to the common ancestor of mammals , amniotes , or even vertebrates [9] , [10] , [14] , [16] , [34] , [35] . Finally , a growing body of genetic and biochemical work on individual lncRNAs , as well as more systematic approaches to explore lncRNA function and their association with disease , point to crucial regulatory activities , notably in cell differentiation and embryonic development [for review: 7] , [23] , [36]–[44] . While the precise molecular functions of lncRNAs are still poorly understood , even less is known about their origin and evolution . Four non-mutually exclusive hypotheses have been proposed for the emergence of lncRNAs [6] , [14]: ( i ) transformation of a protein-coding genes; ( ii ) duplication of another lncRNA; ( iii ) de novo origin from sequences previously untranscribed or devoid of exonic sequences; ( iv ) emergence from transposable element ( TE ) sequences . Individual examples illustrating each of these mechanisms have been described . For example , Xist , a lncRNA controlling mammalian X inactivation , originated in the eutherian ancestor from a mixture of exons derived from a decayed protein-coding gene [45] together with a variety of transposable elements ( TEs ) progressively accumulated and ‘exonized’ at this locus [46] . However , with the exception of a few emblematic and intensively studied lncRNAs such as Xist , the origins of most lncRNAs remain elusive . In one of the most systematic efforts to trace the origins of lncRNAs , Ulitsky et al . [14] found that a minority ( ∼15% ) of zebrafish lncRNAs showed significant sequence similarity to another lncRNAs or protein-coding genes in the zebrafish genome . Likewise , Derrien et al . [10] reported that human lncRNAs rarely have extensive sequence similarity to each other outside of shared repetitive elements . Collectively these observations suggest that , in contrast to protein-coding genes , novel lncRNA genes do not commonly arise by duplication , but rather may emerge de novo from previously non-exonic sequences and/or from TEs . TEs occupy a substantial fraction of vertebrate genomes ( e . g . at least half of the human genome [47] , [48] ) and are increasingly recognized as important players in the origin of functional novelties [for review: 49]–[52] . Several instances of TEs co-opted for cellular function on a genome-wide scale have been documented , notably as a source of cis-elements regulating adjacent host genes , such as promoters [53] , [54] , transcription factor binding sites [55]–[57] , enhancers [58] , [59] or insulators [60] , [61] . TEs can also be ‘exonized’ into novel coding and non-coding exons [for review: 49] , [62] , [63] . As a source of non-coding exons , TEs have been shown to contribute substantially to untranslated regions [64]–[67] and to alternatively spliced exons of protein-coding genes [66]–[70] , as well as to microRNA genes [71] , [72] . In this study we provide evidence for the widespread involvement of TEs in the assembly , diversification , regulation , and potential function of lncRNAs .
We focus on three vertebrate species -human , mouse and zebrafish- for which extensive lncRNA datasets are available ( Table 1 ) . Each set has been ‘manually’ curated based on a combination of bioinformatics and high-throughput genomics experiments , such as deep sequencing of polyadenylated RNAs ( RNA-seq ) , chromatin state maps and cap-analysis of gene expression ( CAGE ) or paired-end ditags to determine transcript termini . For human , we primarily analyzed the most recent Gencode catalog of lncRNAs ( v13 ) produced from 15 cell lines as part of the ENCODE project [10] , [73] , [74] . We replicated most analyses on another large set of lncRNAs assembled by Cabili et al . [9] from 24 human tissues and cell types . Importantly , the Gencode and “Cabili” sets differ slightly in the way they were curated and they are only partially overlapping [10] . Indeed we found that 64 . 9% of the Gencode v13 genes have no overlap with genes in the Cabili set , and conversely 47 . 3% of the Cabili genes have no overlap with the Gencode v13 set . While the Cabili set only contains “intergenic” ( lincRNA ) units ( no overlap with known protein-coding genes ) , the Gencode catalog includes also “genic” lncRNAs , i . e . those overlapping or nested within protein-coding genes [10] , Figure S1 . Thus , these two sets may be viewed as complementary rather than redundant , acting as “biological replicates” for our study . For mouse , we primarily studied lincRNAs from Ensembl ( release 70 ) and replicated some analyses on lincRNAs from adult liver tissue compiled by Kutter et al [16] . For zebrafish , we merged the sets of developmentally expressed lncRNAs from Pauli et al . [24] and lincRNAs from Ulitsky et al . [14] ( see Methods for more details ) . We inferred the TE content of lncRNAs by calculating the fraction of lncRNA transcripts with exons overlapping at least 10-bp of DNA annotated as TE by RepeatMasker ( see Methods ) . We found that 75% of human ( Gencode v13 ) lncRNA transcripts contain an exon of at least partial TE origin , which is considerably much higher than any other type of RNAs such as small ncRNAs ( tRNAs , sno/miRNAs ) , pseudogenes , coding exons ( less than 1% ) , as well as UTRs , the non-coding parts of mRNAs ( Figure 1A ) . The median length of TE-derived fragments in human lncRNAs is 112 nucleotides and the average is 150 nucleotides . While the majority of human lncRNA transcripts are comprised of a relatively small percentage of TE-derived sequences , 3 , 789 out of 19 , 835 transcripts examined ( ∼19% ) are composed of ≥50% of TE-derived sequences ( Figure 1B ) . Similarly , 68 . 23% and 66 . 5% of mouse and zebrafish lncRNA transcripts , respectively , contain exonic sequences of at least partial TE origin ( Figure 1A ) . To measure the total coverage of TE-derived sequences in lncRNA exons in each species , we intersected TE annotations from RepeatMasker ( with a minimum overlap of 10 bp , see Methods ) with the genomic coordinates of all lncRNA exons , and for comparison , with UTRs and coding exons of RefSeq protein-coding genes . The results show that , in all three species TE coverage is considerably higher for lncRNA exons than for protein-coding exons , but still lower than in the whole genome ( Figure 2 ) . The fraction of lncRNA exon sequence covered by TEs is also at least twice higher than in their UTRs . We noticed that the Cabili set [9] , which consists exclusively of intergenic units ( lincRNAs ) shows greater TE coverage ( 35 . 1%; Figure 2 ) than the Gencode v13 set ( 28 . 9%; Figure 2 ) , suggesting that intergenic lncRNAs may have a higher TE content than ‘genic’ lncRNAs ( i . e . those overlapping protein-coding genes ) . Consistent with this idea , the TE coverage of intergenic lncRNAs in the Gencode v13 set is 31 . 8% , while genic lncRNAs are comprised of 25 . 9% of TE-derived sequences ( Table S1 ) . Thus , human lincRNA transcripts tend to be richer in TE sequences than genic lncRNAs . We wondered whether this trend could merely reflect a higher TE density in intergenic regions in general . It does not appear to be the case because the TE coverage of introns and surrounding sequences of Gencode intergenic lncRNAs is similar to that of protein-coding genes or genic lncRNAs ( Table S1 ) . These observations suggest that TEs are more prevalent in intergenic lncRNAs than in genic lncRNAs . A survey of individual human lncRNAs previously characterized in the literature recapitulates the omnipresence and high prevalence of TE sequences we detect in the ab initio lncRNA catalogs ( Figure 1B , Table 2 and for an expanded version , Table S2 ) . The presence of exonized TEs has been reported for some of these lncRNAs , such as XIST [46] , UCA1 [75] , HULC [76] , PCAT-14 [77] and SLC7A2-IT1A/B [78] . But for the majority , there has been no previous mention of embedded TEs , even though some of these mature lncRNA transcripts are almost entirely composed of TE sequences . For example , the first three exons ( out of four , i . e . ∼86% of the sequence ) of the mature transcript of BANCR , which is involved in melanoma cell migration [79] , are derived from a MER41 long terminal repeat/endogenous retrovirus ( LTR/ERV ) element ( Figure 3A ) . The mature transcript of lincRNA-RoR , which has been shown to modulate the reprogramming of human induced pluripotent stem cells [41] , is made from an assemblage of 6 different TEs together accounting for 2 , 057 nt ( 79 . 7% ) of its length ( Figure 3B ) [see also ref . 7] . Importantly , the structures of BANCR and lincRNA-RoR transcripts have been validated by a combination of RACE and RT-PCR experiments and their function investigated by siRNA knockdowns and rescue experiments [41] , [79] . These transcripts were independently retrieved and their structure accurately predicted in the Cabili and Gencode v13 sets , respectively . In mouse , Fendrr lincRNA , which has a very restricted pattern of expression in lateral mesoderm [80] , initiates within a MTEa ( ERVL-MaLR ) and 4 different TEs account for 808 nt ( 33 . 7% ) of its length ( data not shown ) . In summary , our analyses point to an extensive contribution of TEs to the content of mature lncRNA transcripts , including many of those with established regulatory functions . We next sought to evaluate the functional potential of TEs embedded in lncRNA transcripts . Several studies have reported that lncRNA exons show a signature of evolutionary constraint based on interspecies conservation [10] , [12] , [29] , [30] as well as reduced nucleotide diversity in the human population [31]–[33] compared to randomly sampled regions of the genome or surrounding non-exonic sequences . Nonetheless , the level of constraint acting on lncRNA exons assessed through these analyses was much weaker than on protein-coding exons , presumably reflecting greater malleability of lncRNAs . To compare the level of selective constraint acting on TE-derived sequences to non-TE derived sequences in human lncRNAs ( Gencode v13 ) and to various other types of genomic regions , we aggregated conservation scores per nucleotide calculated by phyloP across an alignment of 10 primate genomes ( see Methods ) . As expected , we found that both TE and non-TE sequences in lncRNA exons were much less conserved than coding exons or UTRs of protein-coding transcripts ( Figure 4A ) . Strikingly though , we found that TE sequences within lncRNA exons were significantly more conserved than either a size-matched random set of genomic regions or a neutral set of TE sequences residing in lncRNA introns ( permutation test , p<0 . 001 ) ( Figure 4A ) . Interestingly , TE-derived sequences are also more conserved than non-TE sequences according to this analysis ( permutation test , p<0 . 06 ) and have significantly less variance in phyloP scores with fewer fast evolving sites than non-TE sequences in lncRNAs ( permutation test , p<0 . 001 ) consistent with greater functional or structural constraints acting on TE-derived sequences in lncRNA genes than non-TE derived sequences . Hence , there appears to be enough functional constraint acting on TE-derived regions of lncRNAs to yield a detectable signal of purifying selection when these sequences are taken as a whole and compared across primate species . These data are consistent with the idea that some of the TE sequences embedded in lncRNAs are evolving under functional and/or structural constraints . To investigate the possible functional contributions of TEs to lncRNAs , we examined where TE segments and exons overlap in lncRNA genes . We defined eight categories of overlap ( Figure 5A ) . For example , a TE may overlap with the internal part of an exon ( called ‘exonized’ in Figure 5 ) , a transcription start site ( TSS ) , a polyadenylation ( polyA ) site , one or multiple splice sites , or a combination of these categories . We found that TE segments frequently overlap with and thereby directly contribute large quantities of these functional features to lncRNAs whereas they only rarely do so in protein-coding transcripts ( Figure 5B , Table S3 ) . For example , 22 . 5% and 29 . 9% of non-redundant TSS and polyA sites , respectively , used by lncRNA transcripts in the human Gencode v13 set are provided by TEs ( 18 . 2% and 19 . 0% in the Cabili set ) . By contrast , TEs contribute only 1 . 7% of TSS and 7 . 9% of polyA sites for full-length cDNAs of protein-coding genes . In total , we identified 29 , 519 and 19 , 214 TE-derived functional features ( TSS , polyA and splice sites ) in Gencode v13 and Cabili lncRNA sets respectively ( Tables S7 and S8 ) . For the Gencode set , this represents 9 times more TE-derived features than in protein-coding transcripts despite having 1 . 5 times more protein-coding transcripts available for analysis . We also retrieved high percentages of non-redundant TSS and polyA sites derived from TEs in the mouse Ensembl lincRNA set ( 18 . 5% and 24 . 7% respectively , see also Table S9 ) , mouse “Kutter” set ( 12 . 3% and 16 . 7% respectively , see also Table S10 ) , as well as in the zebrafish set ( 12 . 4% and 12 . 7% respectively , see also Table S11 ) . We next sought to assess whether the relative contribution of TEs to the different categories of genic features differ from a random model of overlap based on the frequency and coverage of TEs in the genome . In other words , we wondered to what extent the level and type of overlap might reflect the mere abundance of TEs in the genome . To investigate this question , we compared the percentage of exons containing different TE-derived features for lncRNAs and protein-coding transcripts to 5 , 000 simulations where we maintain exon positions but reshuffled randomly the coordinates of TE segments in each genome ( see Methods ) . The results ( Figure 5B and Table S3 ) reveal a similar pattern for all three species: with the exception of the ‘exonized’ and ‘polyA’ categories in mouse , the reshuffled sets yield significantly ( p<0 . 001 or p<0 . 0001 , see Figure 5B and Methods ) greater overlap of TEs with every type of exonic features examined than with the actual TEs observed in the genome ( compare “observed” and “random” profiles in Figure 5B ) . However , the gap between observed and randomized TE sets was much more pronounced for protein-coding transcripts than for lncRNAs ( Figure 5B ) . These data suggest that the contribution of TEs to functional genic features is much greater for lncRNA than for protein-coding loci , but are still less than expected based on their sheer genomic abundance . We presume that this pattern reflects the action of natural selection to preserve lncRNA structure and function . The more pronounced gap between observed and random TE overlaps for protein-coding exons is consistent with the greater functional constraint ( Figure 4 ) and stronger resistance to TE accumulation , in coding and UTR sequences than in lncRNAs ( Figure 1 and Figure 2 ) . Consistent with this idea , TEs inserted in lncRNA exons tend to be older than in the genome , even though here again this trend is not as strong as the one observed for protein-coding exons ( Figure S5 ) . Vertebrate TEs can be divided into four major types: short interspersed elements ( SINEs ) , long interspersed elements ( LINEs ) , LTR/ERV elements , and DNA transposons; and each of these subclasses comprises multiple families . Because each subclass and family of TEs has its own functional properties and evolutionary history , we were interested to see if they have made different contributions to lncRNAs . Overall we observed that all four major TE types contribute to lncRNA exons roughly proportionally to their representation in the genome ( Figure 2 ) . While the human and mouse genomes are largely dominated by SINEs and LINEs , the zebrafish genome is dominated by DNA transposons . These genomic TE landscapes are mirrored in the TE content of their lncRNA repertoires ( Figure 2 ) . The most striking departure from this general trend is apparent in human and mouse lncRNAs , where LINEs seem under-represented and LTR/ERV elements over-represented ( Figure S1 ) . Guided by these preliminary observations , we compared in more detail the content ( nucleotide coverage and copy counts ) of different TE types in exons , introns , and flanking regions of the 3 species lncRNAs and protein-coding genes ( Figure 6B and Figure S2 ) . Consistent with the action of purifying selection to purge TE insertions within or in close proximity to genes , we observe a markedly decreased coverage of TEs in exons and proximate genic regions ( 1 kb upstream or downstream ) compared to introns and more distal regions ( 1–10 kb upstream or downstream ) or to their total coverage of the genome ( Figure 6B and Figure S2 ) . TE depletion in these sensitive genomic areas is much more pronounced for protein-coding genes than for lncRNA genes . This is in agreement with the overall greater contribution of TEs to lncRNA exons ( Figure 2 ) , but it suggests that the proximal flanking regions of lncRNA loci are also enriched in TEs relative to protein-coding genes . This trend is most apparent for LTR/ERV elements in human , which are strongly depleted in the vicinity of protein-coding genes but in relatively high abundance for intergenic lncRNAs in the exons and proximal regions of lncRNAs ( Figure 6B and 6D and Figure S2A and S2B ) . Consistent with this relative enrichment of LTR/ERV elements , we found that nearly all of the statistically most enriched TE families in human lncRNA exons and upstream regions belong to the LTR/ERV class ( both ERV internal regions and their LTRs , Figure S3A and S3B and Figure 7A ) . Moreover , 42 . 5 and 45% of TE-derived TSS in the Gencode v13 and Cabili lncRNA sets respectively map within ERVs ( Table S4 ) . Together these data indicate that LTR/ERV make a greater contribution to human lncRNAs and their upstream flanking regions than other types of TEs . Interestingly , we also found a relative enrichment of a majority of LTR/ERV elements in exons and proximal regions of mouse Ensembl lincRNAs ( Figure S3C and Figure 7 ) . This is similar to human , even though their lncRNAs are largely non-orthologous [9] , [10] and their associated LTR/ERV elements mostly belong to lineage-specific families ( Figure S3 ) . These data therefore point to a convergent process whereby LTR/ERV elements are enriched in exons and upstream regions of human and mouse lncRNA genes . Given the relative abundance of TEs in the first exon and upstream regions of lncRNA genes , we sought to better evaluate the contribution of TEs to the cis-regulation of lncRNA transcription . To do this , we repeated the analysis described above with a subset of human TEs inferred to have cis-regulatory potential based on their positional overlap with DNaseI hypersensitive sites ( DHS ) clusters mapped as part of the ENCODE project [81] , [82] ( see Methods ) . Such DHS clusters are reliable indicators of active chromatin and are enriched for regulatory proteins such as transcription factors [81] , [83] . We identified a total of 35 , 263 TEs with putative cis-regulatory activity , hereafter designated as DHS-TEs , within or in the vicinity ( 10 kb upstream or downstream ) of Gencode v13 lncRNA loci . Consistent with cis-regulatory function , we found that DHS-TEs are significantly enriched in the 1-kb window upstream of lncRNA and protein-coding genes ( compare Figure 6A and 6B ) . DHS-TEs are also enriched in lncRNA exons ( Figure 6B ) , suggesting that these elements are likely involved in cis-regulation of lncRNA transcription . For protein-coding genes , the greatest enrichment of DHS-TEs is observed for SINEs located in the proximal ( <1 kb ) upstream region ( Figure 6B ) . However for lncRNAs , the greatest enrichment of DHS-TEs involve LTRs located in the proximal upstream region , where their density is about twice as high compared to the rest of the genome ( Figure 6B ) . Together these data point to the widespread implication of TEs , and in particular LTRs , to the cis-regulation of human lncRNA genes . To further assess the cis-regulatory activity of TEs upstream of lncRNAs , we assembled subsets of lncRNAs with cell-type specific expression in one of three human cell lines ( 489 lncRNAs in GM12878 , 1008 in H1 and 928 in K562 ) for which RNA-seq data was generated as part of the ENCODE project ( see Methods ) . We examined the level of activity of TE-DHS in the upstream region ( <10 kb ) of these cell-type specific lncRNAs and looked for evidence of cell-type specific regulation . Notably we found that lncRNAs that are highly expressed in a given cell type are also associated with more active TE-DHS mapped in the same cell type ( Figure 8 ) . These results indicate that the opening of chromatin in a TE located in the upstream region of a lncRNA locus correlates with high level of lncRNA transcription in a cell type-specific fashion . Together these analyses suggest that TEs located in the vicinity of hundreds of lncRNA loci carry the hallmarks of cis-regulatory elements and some appear to provide cell type-specific enhancer elements controlling adjacent lncRNA expression . Because transposition represents a major source of lineage-specific DNA , we wanted to evaluate its contribution to the evolution of the vertebrate lncRNA repertoire . Our examination of TE-derived sequences in studied human lncRNAs reveals that many of these elements are restricted to primates ( 36 . 3% for Gencode v13 , Figure S4 ) , suggesting that TEs play an important role in the diversification and possibly the birth of primate-specific lncRNAs . Few of the human lncRNAs functionally characterized have identifiable orthologs in non-primate species , but Xist and cyrano provide solid examples of functional lncRNAs with ancient evolutionary origins . Xist is involved in X-chromosome inactivation and originated in the common ancestor of eutherian mammals [45] , [46] . Previous in silico reconstruction of the Xist locus in the eutherian ancestor suggested that several TEs were already present at the dawn of the Xist gene and likely contributed to the assembly of the first functional Xist transcript [46] . Other TEs embedded in Xist exons are lineage-specific and therefore must have contributed to the diversification of the transcript during eutherian evolution . For example , a primate-specific FLAM_C element makes up nearly half ( 114 nt ) of the first Xist exon in human ( Table S2 ) . cyrano is one of a small subset of zebrafish lncRNAs sharing significant sequence similarity and synteny with apparent orthologs in mouse and human [14] . Most of the sequence similarity between species is limited to a central region of the last exon ( see PhastCons plot in Figure 9 ) . In zebrafish embryos , cyrano is expressed in the nervous system and notochord and morpholino-mediated knockdowns followed by rescue experiments indicate that this lncRNA plays a role in neurodevelopment , a function possibly conserved in mammals [14] . We find that the conserved exonic region of cyrano is flanked by lineage-specific TEs embedded in this orthologous exon in each of the three species examined ( Table 2 , Figure 9 ) . These examples illustrate how TEs can be incorporated long after the birth of lncRNAs to diversify their sequence in a lineage-specific fashion . Among functionally characterized human lncRNAs , we uncovered numerous instances where the TSS resides in primate-specific TEs ( Table S2 ) . In most of those cases , the TE provides the only identified TSS for that lncRNA locus , suggesting a pivotal role for these TEs in the biogenesis and most likely the birth of these lncRNAs during primate evolution . These include six of the eight known lncRNAs containing the largest TE amounts listed in Table 2 , which all have their TSS located within the LTR of an ERV element . Intriguingly , these instances include two different lncRNAs that are highly expressed in human embryonic stem cells ( ESCs ) and have been experimentally shown to be implicated in the maintenance of ESC pluripotency: lncRNA-RoR [41] and lncRNA-ES3 [43] . The transcripts cloned for lncRNA-RoR and lncRNA-ES3 both initiate within LTR7/HERVH elements ( Figure 3B and 3C ) . Furthermore , we found that these same LTR7 elements have donated the DNA binding sites for the ‘master’ transcriptional regulators of pluripotency NANOG , OCT4 , and SOX2 mapped previously to the proximal promoter of lncRNA-RoR [41] ( Figure 3B ) . Ng et al . [39] mapped two binding sites for NANOG in the promoter region of lncRNA-ES3 that we find to reside within the LTR7 driving this locus ( Figure 3C ) . The contribution of LTR7 to the regulation of these lncRNAs in ESCs is consistent with two recent studies showing that TEs , and LTR/ERV elements in particular , play an extensive role in the primate-specific wiring of the core transcriptional network of human ESCs [57] , [84] . In fact , Kunarso et al . [57] identified LTR7/HERVH as one of the most over-represented TE families seeding OCT4 and NANOG binding sites throughout the human genome . Our results indicate that this ERV family also contributed to the recruitment of primate-specific lncRNAs into the pluripotency network of human ESCs [see also ref . 7] . Since lncRNAs act at the RNA level , we hypothesized that TEs may participate in the folding of lncRNAs into secondary structures , which could be important for their function . One prediction of this hypothesis is that lncRNAs with high TE content may fold into more stable structure than those with low TE content . To test this , we selected from the Gencode v13 set the top 100 lncRNAs with highest TE content and the top 100 lncRNAs with lowest TE content ( see Methods ) and compared the minimum free energy ( MFE ) of predicted secondary structures computed by the program randfold [85] for each of these individual lncRNAs . For each input sequence , randfold attributes a p-value to a predicted MFE by comparing it with a MFE obtained for the same sequence randomly reshuffled 99 times ( See ref . [85] and Methods ) . The average p-value for high TE content lncRNAs was significantly lower than the one of low TE content lncRNAs ( p = 0 . 0022 , Wilcox rank sum test ) ( Figure 10A ) . The average length of the lncRNAs in the two datasets was also substantially different ( 913 nt and 1 , 913 nt for high and low TE content respectively ) , but there was no correlation between RNA length and p-value for the 200 lncRNAs examined ( data not shown ) , ruling out a possible bias introduced by lncRNA length . Together these results indicate that TEs generally stabilize lncRNA structure in human , which supports the hypothesis that some of the TEs embedded in lncRNA exons contribute to the folding of lncRNAs into secondary structures . To explore further this hypothesis , we studied a family of DNA transposons in zebrafish , called Angel , which occur in high copy numbers and are known to have the potential to form a stable stem-loop structure at the RNA level due to their long inverted repeats [86] . We reasoned that the incorporation of Angel elements in lncRNAs might in some case have conferred a functional benefit by increasing RNA stability . We identified 71 zebrafish lncRNAs containing exonized Angel elements . As expected , RNA folding programs predict that these lncRNAs have the potential to form a long stem-loop structure by intramolecular pairing of the Angel inverted repeats ( see examples in Figure 10B ) . Furthermore , by comparing the sequence of these elements to that of their ancestral ( consensus ) progenitor , we identified two instances of Angel elements in lncRNAs where base substitutions in one of the arms of the predicted stem-loop structure were accompanied by compensatory substitutions on the other arm allowing the maintenance of base-pairing within the stem-loop structure ( Figure 10B ) . To rule out the possibility that these substitutions occurred not at these loci , but prior to transposon insertion in a progenitor element that would have amplified or duplicated , we used BLAT [87] to search the zebrafish Zv9 genome assembly for paralogous Angel elements that might be sharing the same substitutions . In each case , we found that the compensatory substitutions we identified were unique to the Angel copies residing within the examined lncRNAs ( data not shown ) , suggesting that these mutations occurred after transposon insertion . The probability that these compensatory substitutions would have occurred by chance alone in these two Angel elements is 0 . 001 and 0 . 036 after Bonferroni correction , respectively ( see Methods ) . Furthermore , 12 of the 16 concerted mutations were from A/T to C/G base pairs , which is consistent with the idea that they increased the stability of the stem-loop structure . These data suggest that these Angel elements indeed fold into the predicted secondary structures in vivo and have been maintained over time by natural selection , plausibly for the proper function of the lncRNAs . To seek another , independent line of evidence for the involvement of TEs in forming secondary structures potentially important for lncRNA function , we next looked for sites of adenosine-to-inosine ( A-to-I ) editing in lncRNAs . This form of RNA editing is catalyzed by the ADAR family of adenosine deaminases that act on double-stranded RNA templates [88] . In humans , it has been reported that A-to-I RNA editing occurs predominantly within Alu elements embedded in the 3′ UTR of protein-coding transcripts [89]–[91] . This bias has been explained by the relatively high frequency of Alu elements in transcribed regions of the human genome , which often occur in inverted pairs and thereby can form long RNA duplexes providing templates for ADARs [92] . We used DARNED , a database of RNA editing sites in humans [93] , to identify 2 , 941 A-to-I editing sites in mature lncRNA transcripts . As observed previously for mRNAs , most ( 82% ) of the edited sites in lncRNAs occur within Alu elements , although we also found evidence of A-to-I editing within a wide range of TE types embedded in lncRNAs ( Table 3 and Table S6 ) . This may be explained by the fact that non-Alu TE sequences are much more frequent in lncRNAs than in mRNAs , even when UTRs are considered separately ( Figure 2 and Figure S1 ) . Indeed , we found that the density of edited sites within Alu , non-Alu TE , or non-TE sequences fall within the same order of magnitude in lncRNAs and UTRs ( Table 3 ) . In several cases individually examined , we found that editing sites in TE sequences map preferentially within regions of the lncRNA predicted to form stem-loop structures by virtue of the inclusion of two inverted copies of the same TE family in the lncRNA ( see two examples in Figure 11 ) . The finding that TE sequences , and in particular Alu elements , embedded in lncRNAs are frequent templates for A-to-I editing confirms that TEs are commonly engaged in intra- or inter-molecular base pairing interactions to form stable dsRNA structures .
While TEs are seldom found in protein-coding transcripts ( even in their UTRs , see Figure 1 and Figure 2 ) , they are ubiquitous in lncRNAs of all three vertebrates examined ( Figure 1A ) , accounting for a large fraction of total lncRNA sequence ( Figure 2 ) . Thus , high TE prevalence is probably a common characteristic of vertebrate lncRNA repertoires that distinguish them from mRNAs and smaller ncRNAs , such as tRNAs or microRNAs , which are typically TE-depleted ( Figure 1A ) . We found that all major TE classes are found in lncRNAs in each of the three vertebrate species surveyed , and their relative abundance mirrors that of the entire genome ( Figure 2 ) . Nonetheless , in each species we identified TE families that were statistically enriched ( up to 32 fold ) in lncRNA exons relative to their coverage or density in the whole genome ( Figure 7 and Figure S3 ) . Interestingly , these over-represented families belong to different TE classes in the species examined , for example , LTR/ERV in human and mouse and DNA transposons in zebrafish ( compare colors per species in Figure 7 and Figure S3 ) . The predominance of DNA transposons in zebrafish is expected based on the prevalence of DNA transposons in this genome ( see Figure 2 , Figure 7 and Figure S1 ) . However our results show that LTR/ERVs contribute disproportionally to lncRNAs in human and mouse , which is in agreement with the recent results reported by Kelley and Rinn [94] . Interestingly , human lncRNAs are mostly enriched for the ERV I subclass ( alpharetroviruses ) , compared to mouse where ERV 2 , ERV 3 or ERV K TEs are enriched ( Figure 7 and Figure S3 ) . ERV 1 subclass of elements is less abundant in the mouse genome [95] and strongly repressed in mouse ESCs [96] , [97] . Therefore , it is not surprising that this type of retroviral elements do not contribute more to mouse lincRNAs . While LTR/ERV elements are also generally silenced in most human tissues , a subset of families is known to escape silencing and to become transcriptionally active in some tissues , cell types , or at certain developmental stages [54] , [98]–[100] . These properties may derive from the intrinsic capacity of retroviruses to hijack host transcriptional activators in order to promote their own expression in a cell-type or developmentally restricted fashion [51] , [52] , [55] . For example , hundreds of ERV I elements recruit the pluripotency factors OCT4 and NANOG in human ESCs , but rarely do so in mouse ESCs [57] . This mechanism can readily explain why lncRNA-RoR and lncRNA-ES3 and hundreds of other lncRNAs associated with ERV I elements ( such as LTR7/HERVH ) are highly transcribed in human ESCs ( Figure 3 , Table 2 ) [see also ref . 94] . This trend is also globally apparent through the enrichment of LTR elements ( including LTR7/HERVH ) in 286 human lncRNAs upregulated in ES cells [annotations from Table S1 , 10] ( data not shown ) . In sum , the interspecific variations we observe in the coverage and type of TEs in lncRNAs likely reflect a variety of factors; both methodological , such as the breadth of cell types and tissues examined , and biological such as the abundance and intrinsic properties of certain TEs residing in the genome . Two scenarios can explain the prevalence of TEs in lncRNAs . The first is that TE insertion in pre-existing lncRNAs has relatively little deleterious effect on lncRNA function allowing TEs to accumulate over time as waves of transposition break in the genome . We call this scenario the ‘lncRNA first’ model because it implies that the origin of the lncRNA predates the incorporation of TE ( s ) in their exons . In the second and not mutually exclusive scenario , the ‘TE first’ model , lncRNAs are assembled from TEs that inserted before the birth of the lncRNAs . Several observations and examples outlined below indicate that both models contribute to the pervasive occurence of TEs in lncRNAs . The “lncRNA first” scenario is supported by a comparison of the few lncRNAs known to be of relatively ancient origin , exemplified by Xist or cyrano , which have assimilated lineage-specific TE insertions sequentially during evolution ( see Figure 9 and Table S2 ) . Typically , these exonized TEs correspond to the most variable regions of the transcript sequence flanking more deeply conserved core sequences ( see [14] and Figure 9 ) . On a broader scale , we observe that TEs predominantly contribute to the last exon of lncRNAs ( 56 . 5% of TE amount , see Figure S5 ) . The biased incorporation of TEs to the 3′ region of transcripts is also apparent for mRNAs , where exonized TEs are more abundant in 3′ UTRs ( Figure 1 , Figure 2 and Figure S5 ) , as reported previously [65]–[67] . These data suggest that TE-derived sequences are preferentially acquired at the 3′ end of pre-existing transcripts , either because this region is more permissive to TE insertion and/or because TEs are somehow predisposed for this type of exonization events , for example owing to the presence of cryptic acceptor splice sites facilitating their capture [70] , [101] , [102] . In any case , this 3′ bias is consistent with the ‘lncRNA first’ model whereby TEs are secondarily acquired by existing lncRNAs . On the other hand , several observations support the ‘TE first’ model . First , we identified thousands of lncRNA transcripts that are mostly or entirely composed of TEs ( Figure 1B ) . It is difficult to conceive that these lncRNAs would have emerged from ancestral non-TE regions later replaced or obliterated by secondary TE insertions . More likely , these lncRNAs were born from material providing by pre-existing TE insertions . In support to this idea , we identified 4 , 404 human Gencode v13 lncRNA transcripts with TE-derived TSS , with 1 , 777 of these ( 40 . 4% ) derived from primate-specific TE families ( Table S4 ) . In addition , we found 2 , 213 human lncRNA transcripts whose first exons are entirely derived from TEs , and 965 of these ( 43 . 6% ) are derived from primate-specific TE families ( Table S4 ) . These values are very similar when only genes with a unique TSS are considered and we retrieved comparable numbers in the Cabili set ( Table S4 ) . Since these TEs provide the only TSS assigned for these transcripts , we propose that these lncRNAs were born from the transcriptional activity brought upon TE insertion in the genome . Interestingly , 36 . 8% ( 857/2 , 331 ) of the TE-derived unique TSS map within LTR/ERV elements , while this type of elements account for only 8% of all TEs in the human genome ( see Figure 2 ) . Thus , it appears that the tissue-specific transcriptional activity of LTR/ERV elements [52] , [54] , [100] represents a major force driving the birth of lncRNAs . These data also imply that a substantial fraction of human lncRNAs are of recent origin , because ∼40% of TEs driving human lncRNAs are primate-specific and some are even restricted to hominoids ( e . g . Figure 3A and 3B , Table S2 ) . In summary , our data suggest that , in some instances , TE insertion events have been a source of diversification of ancestral lncRNAs , while in others TE insertions have triggered the emergence of brand new lncRNAs during evolution . In order to better quantify the relative importance of either process to lncRNA evolution , it will be necessary to infer systematically the age of lncRNAs using a comparative RNA-seq approach [16] . It has been extensively documented that mammalian TEs represent an abundant source of cis-regulatory sequences driving or modulating the expression of adjacent protein-coding genes [reviewed in 49] , [56] . Our study provides evidence that TEs located in the vicinity of lncRNAs may also frequently contribute to the transcriptional regulation of these genes . As discussed above , LTR/ERV elements appear to make a disproportionate contribution to lncRNA regulation relative to other TE types and in some cases they may be solely responsible for the cell-type specificity of lncRNA expression . This is exemplified by lncRNA-RoR whose transcription in hESCs is driven by a LTR7/HERVH element occupied by the pluripotency factors OCT4 , NANOG and SOX2 ( Figure 3C and [41] , [94] ) . Thus , much like LTR/ERV elements have been implicated in the wiring of protein-coding genes into specific regulatory networks [55] , [57] , [59] , [84] , they have also recruited lncRNAs serving important developmental function , notably in the pluripotency network [41] , [43] , [94] . Perhaps the most pressing question to address in the future is to what extent TEs may contribute to the function of lncRNA and how ? Our analysis shows that TEs embedded in lncRNAs frequently supply sequences and signals essential for the transcription ( e . g . TSS ) and processing ( e . g . splice , polyA sites ) of the lncRNAs ( Figure 5 ) . However it does not prove that TE sequences per se are indispensable for lncRNA function , if such function even exists . Many studies have used various approaches and statistics to show that lncRNA exons , as a whole , display weak but significant signals of purifying selection suggesting that at least a fraction of lncRNA sequences is subject to functional constraint detectable at the primary DNA level [10] , [12] , [29]–[33] . Our analysis confirms the existence of a signal of purifying selection acting on human lncRNA exons , but more importantly we observe that this signal is higher in TE-derived than in non-TE derived lncRNA sequences ( Figure 4A ) suggesting that a subset of TE sequences in lncRNAs are structurally or functionally constrained . TE-derived sequences could serve as the functional elements of lncRNAs in numerous ways . For example , TE sequences might provide interaction interfaces with proteins involved in post-transcriptional or transcriptional regulation , such as the chromatin modifiers often found in complex with lncRNAs [37] , [103] . Their inclusion may also provide opportunities for base-pairing interaction with single-stranded DNA or RNA containing similar repeats in inverted orientation . Such duplexes might act as a platform to recruit protein effector complexes to genomic or RNA targets . For example , Alu elements embedded within several human lncRNAs form a group called 1/2sbs-RNAs that base-pair with complementary Alu elements located in the 3′-UTR of several protein-coding transcripts to form duplexes creating a binding site for the Staufen1-mediated RNA decay machinery , which in turn promote post-transcriptional repression of the targeted mRNAs [104] . Given the abundance of Alu and other high copy number TEs in lncRNAs , such trans-regulatory effects may be widespread and affecting a large number of mRNAs containing complementary TEs in their UTRs . It was also shown recently that a B2 SINE embedded in a mouse lncRNA antisense to Uchl1 is required for post-transcriptional up-regulation of UCHL1 protein synthesis , an activity that can be transferred to an artificial antisense green fluorescent protein transcript containing the B2 SINE element [105] . We identified 361 mouse lncRNAs containing B2 SINEs ( 16 . 7%; see Tables S5 and S9 ) , raising the possibility that these elements confer similar post-transcriptional regulatory activity to other lncRNAs . Finally , another recent study identified a point mutation associated with a lethal form of infantile encephalopathy within a primate-specific LINE-1 retrotransposon transcribed as part of a lncRNA in the human brain [78] . The precise function of the LINE-1 element in this lncRNA is unknown , but knockdown of the lncRNA resulted in increased neuronal apoptosis , an effect consistent with the encephalopathy phenotype . Interestingly , the point mutation detected in affected individuals was predicted to destabilize a secondary structure in the corresponding lncRNA , suggesting that the LINE-1 element may contribute to lncRNA folding that is important for its function in the brain . Similarly , we identified several instances in zebrafish and in human where TEs embedded in lncRNAs are predicted to be involved in the formation of stem-loop structures that have been maintained in evolution through compensatory mutations and therefore are likely to be functionally significant . We also found that these structures often lead to RNA editing of lncRNAs , which to our knowledge is a novel observation that may be relevant to the function of some lncRNAs [88] , [92] . We also show that human lncRNAs fold into more stable structure than those with low TE content , suggesting that these individual examples of TEs apparently co-opted for the cellular function of lncRNAs likely represent only the tip of a large iceberg . Future work is bound to unravel a variety of mechanisms through which TEs embedded in lncRNAs have become involved in regulating the expression of vertebrate genomes . There is growing evidence that vertebrate genomes contain a large number of long non-coding RNA genes ( lncRNAs ) that play important gene regulation roles , however , remarkably little is known about the origins of these genes . Our study reveals that TEs , through their capacity to move and spread in genomes in a lineage-specific fashion , as well as their ability to introduce regulatory sequences upon chromosomal insertion , represent a considerable force shaping the lncRNA repertoire of human , mouse and zebrafish . These results suggest that the apparent paucity of ancient lncRNA genes may be explained in part by rapid turnover mediated by lineage-specific TEs and imply that the regulatory networks in which lncRNA genes act may be rapidly diverging between species .
The datasets used in this study are as follow: human , Gencode release 13 ( from ftp://ftp . sanger . ac . uk/pub/gencode ) and Cabili et al . ( 2011 ) [9] . Mouse , Ensembl release 70 ( ftp://ftp . ensembl . org/pub/release-70/gtf/mus_musculus/ ) and Kutter et al ( 2012 ) [16] , both sets filtered to keep only intergenic lncRNAs . Coordinates from Kutter et al . were converted from mm9 to mm10 using the liftover tool from UCSC ( http://genome . ucsc . edu/cgi-bin/hgLiftOver ? hgsid=325693955 ) . Zebrafish sets are from Pauli et al . ( 2012 ) [24] and Ulitsky et al . ( 2011 ) [14] . To limit redundancy , in case of overlap of exons between transcripts of the two sets , only transcripts from Pauli et al . ( 2012 ) were kept . Additional descriptors of the datasets are provided in Table 1 . TE annotations used in this study are obtained from the outputs of the RepeatMasker ( RM ) software [106] produced for the following genome assemblies: human , hg19 assembly , RM v . 330 , repbase libraries 20120124 , from RM website ( http://www . repeatmasker . org/species/homSap . html ) . Mouse , mm10 assembly , RM v . 330 , repbase libraries 20110920 , from UCSC website ( http://hgdownload . soe . ucsc . edu/goldenPath/mm10/bigZips/ ) . Zebrafish , danRer7: RM v . 329 , repbase libraries RB20090604 , from UCSC website ( http://hgdownload . cse . ucsc . edu/goldenPath/danRer7/bigZips/ ) . These RM outputs were filtered to remove non-TE elements ( Low Complexity , Satellites , Simple Repeats and ncRNA ) . For mouse , MutSatRep1 , CYRA11_Mm and YREP_Mm are also removed . To minimize multiple counting of single TE copies artificially fragmented in the RepeatMasker outputs we merged consecutive pieces of the same TE separated by less than 10 nt . The TE content of lncRNA transcripts ( datasets described above ) and human RefSeq 57 ncRNAs ( 22 , 486 in total ) , pseudogenes ( 13 , 430 ) , CDS and UTRs ( 20 , 848 protein coding genes ) was determined by intersecting these sets with each species' TE annotations ( described above ) using the ‘Table Browser’ at the UCSC Genome Bioinformatics Site ( http://genome . ucsc . edu/index . html ) [107] . Only overlaps of minimum 10 bp were kept . Protein-coding gene ( pc genes ) models were filtered to retain only those with 5′ and 3′ UTRs , from following releases: human , Refseq 49 ( hg19 , gtf file from UCSC genome browser ) ; mouse , Refseq 57 ( mm10 , gtf file from UCSC genome browser ) ; zebrafish , Ensembl 68 ( danRer7 ) . All nucleotide amounts correspond to genomic amount . Introns and upstream or downstream intergenic regions were processed through Galaxy [108]–[110] to remove all RefSeq genes exons ( CDS and UTRS: Refseq 51 for human and zebrafish , Refseq 57 for mouse ) as well as lncRNA exons of the datasets considered . Intergenic sequences ( upstream or downstream , up to 10 or 1 kb ) correspond to the longest fragment between TSS or polyA and another feature ( RefSeq entries as well as lncRNA exons of the dataset considered ) . These sets ( exons , introns , intergenic sequences ) were then joined in Galaxy with filtered RepeatMasker outputs described above keeping only fragments with at least 10 nt of overlap , to calculate TE coverage of exons . See Tables S7 , S8 , S9 , S10 , S11 for transcripts TE content data . By comparing their PhyloP scores across an alignment of 10 primate genomes , the conservation of human ( Gencode v13 ) TE-derived lncRNA exonic segments was compared to non-TE derived lncRNA segments , RefSeq 57 5′- and 3′-UTRs , protein-coding exons and a set of random genomic fragments size-matched to the TE-derived lncRNA segments . We also generated a set of TE-derived lncRNA intronic segments , non overlapping with splicing sites and corresponding to inactive chromatin to obtain a most neutral set to compare with exonic TE-derived lncRNA segments [32] ( all annotated chromatin marks from 9 cell lines were subtracted: GM12878 , H1-hESC , HMEC , HSMM , HUVEC , HepG2 , K562 , NHEK , NHLF; ENCODE version Jan . 2011 ) . Precompiled PhyloP scores were obtained from the ‘phyloP46wayPrimates’ , ‘phyloP46wayPlacental’ , and ‘phyloP46wayAll’ tracks available from the UCSC Genome Bioinformatics Site ( http://genome . ucsc . edu/index . html ) [107] and intersected with gene annotations using bedtools ( http://code . google . com/p/bedtools/ ) [111] . Boxplots were made in R ( http://www . r-project . org ) . Statistical test used: permutation test with 1000 permutations were performed in R . TEs were not assigned a strand allowing them to overlap genomic features on either strand . TEs found in a genomic feature were classified based on their position in the feature , as schematized in Figure 5A . Both lncRNA and protein-coding genes were filtered before both the random and non-random analyses . In case of multiple splice forms a random mRNA was kept . Additionally protein coding genes that did not have both a 5′ UTR and 3′ UTR were excluded from the analysis . Sets of protein-coding genes are as follows: human , Refseq release 49 , mouse , Refseq release 46 , zebrafish , Ensembl release 68 . For the random sets , all TEs were shuffled within chromosomes ( excluding gaps ) while preventing TE overlap . This process was repeated 5 , 000 times for each set , using a custom perl script ( see http://www . yandell-lab . org/publications/index . html ) . The standard error for the random sets across all categories ( Figure 5A ) always plateaued before 1 , 000 replicates ( data not shown ) . The probability of observing the non-random counts was calculated using the random sets . The p-value represents number of times a lower category count was observed in the random set out of 5 , 000 replicates . With the exception of ‘exonized’ , ‘TSS’ and ‘polyA’ categories in mouse ( p-values = 1 , 0 . 001 and 0 . 298 respectively ) and ‘exonized’ category in zebrafish ( p-value = 0 . 001 ) , the p-values were systematically <0 . 0001 . The GenometriCorr ( Genometric Correlation ) package from R [112] was used to test the degree of overlap between TEs and genomic features ( protein coding exons , lincRNAs ) [Exploring Massive , Genome Scale Datasets with the GenometriCorr Package] . This package uses the Jaccard distance . The Jaccard distance measures the overlap between two sets of genomic ranges ( A & B ) compared to the total genomic range A and B occupy . Jaccard distance ( {A} , {B} ) = |{A}∩{B}|/|{A}U{B}| . To test if the observed overlap is statically significant , one set of genomic features ( TEs ) were shuffled 1 , 000 times and the Jaccard distance was taken for each permutation . We made use of the DNase I clusters track from the integrated ENCODE data sets [81] , [82] , [113] , which was retrieved from the UCSC Genome Browser [107] . The Dnase clusters were intersected with our list of annotated TEs using the program BEDTools [111] and TEs overlapping by more than 10 bp a Dnase cluster were retained . We also retrieved paired 2×75 bp RNA-Seq data sets from ENCODE/Caltech in GM12878 , H1 and K562 cell lines . Expression of lncRNAs was measured using BEDTools by calculating the coverage over the length of the lncRNAs and was normalized by the total number of reads in each cell line . We identified cell-type specific lncRNAs as those having a 10-fold higher expression in a given cell type relative to the average expression in the other two cell-types . Next , to look at cell-type specific regulation we made use of the University of Washington DNase I ENCODE data sets from the same cell lines . Total coverage of reads was calculated over the length of TEs in proximity to lncRNAs ( <10 Kb ) using the program BEDTools to measure accessibility in each cell-type and was normalized by the total number of reads in each library . For each lncRNA , only the most active TE in each cell line was retained for analysis . P-values for the significance of the differences between the distributions were calculated using a Wilcoxon rank sum test . The top 100 lncRNA transcripts with highest TE content ( from 100% to 96 . 74% ) and the top 100 lncRNAs with lowest TE content ( from 2 . 27% to 0 . 49% ) in the Gencode v13 set ( see Table S7 ) were used as input for randfold [85] . They were reshuffled 99 times with dinucleotide shuffling option . Wilcox rank sum test was used to test whether the average p-value of high TE content lncRNAs is smaller than the value of low TE content lncRNAs . All lncRNAs with ANGEL elements coverage in exon region were extracted for compensatory mutation identification . A Perl script was used to compare each ANGEL in lncRNA to the ANGEL consensus in Repbase [114] . lncRNAs with putative compensatory mutations were manually examined and RNA structures predicted using RNAfold [115] . With a transition/transversion ratio of κ , when 2 mutations occur on a same base pair the probability of 2 mutations being compensatory is:κ: the transition/transeversion ratio in zebrafish . We are using κ = 1 . 2 based on SNP analysis in zebrafish [116] . And assume mutations occur on a short stem follows hypergeometric distribution , the probability of having as much as observed compensatory mutation in the pairing stem is:c: compensatory mutations observed; m: mutations observed on a strand in a pairing stem; n: mutations observed on another strand in a pairing stem; N: total pairing nucleotides in the pairing stem . p: probability of compensatory mutation if 2 mutations occur in a pair of bases . Significance was calculated using R language ( http://www . r-project . org ) and the p-value was adjusted by the Bonferroni correction . We intersected genomic coordinates of lincRNAs from Cabili set and protein-coding transcripts ( 5′UTR , coding region and 3′ UTR analyzed separately ) coordinates with human RM output as described above . This allowed annotation of segments as “non-TE” , “Alu-derived” and “non-Alu TE-derived” . that we intersected with editing sites compiled in the DARNED database [93] . Heavily edited lincRNAs were extracted and their secondary structures predicted with RNAfold [115] . | An unexpected layer of complexity in the genomes of humans and other vertebrates lies in the abundance of genes that do not appear to encode proteins but produce a variety of non-coding RNAs . In particular , the human genome is currently predicted to contain 5 , 000–10 , 000 independent gene units generating long ( >200 nucleotides ) noncoding RNAs ( lncRNAs ) . While there is growing evidence that a large fraction of these lncRNAs have cellular functions , notably to regulate protein-coding gene expression , almost nothing is known on the processes underlying the evolutionary origins and diversification of lncRNA genes . Here we show that transposable elements , through their capacity to move and spread in genomes in a lineage-specific fashion , as well as their ability to introduce regulatory sequences upon chromosomal insertion , represent a major force shaping the lncRNA repertoire of humans , mice , and zebrafish . Not only do TEs make up a substantial fraction of mature lncRNA transcripts , they are also enriched in the vicinity of lncRNA genes , where they frequently contribute to their transcriptional regulation . Through specific examples we provide evidence that some TE sequences embedded in lncRNAs are critical for the biogenesis of lncRNAs and likely important for their function . | [
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| 2013 | Transposable Elements Are Major Contributors to the Origin, Diversification, and Regulation of Vertebrate Long Noncoding RNAs |
The objectives of this study were to assess the heterogeneities of estimates and to estimate the seroprevalence of brucellosis in animals and humans in Ethiopia . Data from 70 studies covering 75879 animals and 2223 humans were extracted . Rose Bengal Plate Test ( RBPT ) and Complement Fixation Test ( CFT ) in series were the most frequently used serological tests . A random effects model was used to calculate pooled prevalence estimates . The overall True Prevalence of brucellosis seropositivity in goats and sheep were estimated at 5 . 3% ( 95%CI = 3 . 5 , 7 . 5 ) and 2 . 7% ( 95%CI = 1 . 8 , 3 . 4 ) , respectively , and 2 . 9% for each of camels and cattle . The prevalence was higher in post-pubertal than in pre-pubertal animals ( OR = 3 . 1 , 95% CI = 2 . 6 , 3 . 7 ) and in the pastoral than in the mixed crop-livestock production system ( OR = 2 . 8 , 95%CI = 2 . 5 , 3 . 2 ) . The incidence rates of brucellosis in humans of pastoral and sedentary system origins were estimated at 160 and 28 per 100 000 person years , respectively . The seroprevalence of brucellosis is higher in goats than in other species . Its occurrence is evocative of its importance in the country in general and in the pastoral system in particular . Public awareness creation could reduce the transmission of Brucella spp . from animals to humans and the potential of livestock vaccination as a means of control of brucellosis needs to be assessed .
Brucellosis is one of the neglected zoonotic diseases , and there have been several reports that addressed its global importance [1–3] . The transmission of Brucella spp . of zoonotic importance—Brucella melitensis , Brucella abortus , Brucella suis and Brucella canis—is either horizontal or vertical in animals , and in humans it is mainly related to contact with infected animals or ingestion of unpasteurized milk or products thereof [4] . Eradicated or controlled in most developed nations , it remains important in poverty stricken countries , and reproductive wastage and debilitating illnesses in livestock and humans , respectively , are its major outcomes [1–3] . Moreover , problems associated with its therapeutic management [5] , demographic shifts , and substantial increases in its complications [6] , persistence in pastoral communities [7] , and an increased prevalence in areas where small ruminants predominate [8] have been reported . However , despite reports on its significance , large scale national level prevalence and incidence estimates in sub-Saharan countries where it could have a substantial impact are sparse . Ethiopia is one of the sub-Saharan African countries , with the largest animal and second largest human population . Livestock are the major sources of income and security for a significant segment of the population , and the system of animal production is by and large of an extensive type . The relationship between humans and animals is so close , and the perception of the general population as regards risky practices that favor pathogen transmission is low . Although brucellosis has been reported to be endemic [9] , rigorous country wide statistics are lacking . The objectives of this study were to assess the heterogeneities of estimates , and estimate the pooled seroprevalence of brucellosis in animals , and humans by using meta-analytical methods .
Medline in the Pub Med interface , Google Scholar , Google , African Journals online ( AJOL ) and lists of references of articles were used in the search for studies . The languages of publication were restricted to English and French . Brucell* AND Ethiopia in English , and Brucell* ET Éthiopie in French were the keywords used in electronic searches . To retrieve additional articles , the keywords were combined with other words that included humans , cattle , sheep , goats , camels , pigs , dogs , and horses . The last search was done on August 10 , 2016 . Eligible studies were selected by using inclusion and exclusion criteria . A study was eligible if it fulfilled the following criteria: ( i ) full text and published in English or French , ( ii ) carried out on animals and/or humans after 2000 , ( iii ) cross-sectional/ frequency , ( iv ) employed enzyme immunoassay methods or Rose Bengal Plate Test ( RBPT ) and Complement Fixation Test ( CFT ) in series . Initially , studies with titles and/or abstracts that were not relevant to the outcomes of interest were excluded . In the succeeding steps , reviews , case reports , studies carried out before 2000 and unavailable were excluded , and full text reports were screened for eligibility . Exclusion of screened reports was done by the following criteria: duplication ( articles/data ) , Rose Bengal Plate Test ( RBPT ) only ( to reduce bias/ method related heterogeneity associated with lower specificity of RBPT ) , farm based report , sample size ( < 100 in animal studies ) , and inconsistent data ( data incoherent within a table or tables or in the narrative sections , and could not be figured out ) . The following data were extracted from eligible studies: first author , year of publication , year of study , location ( region ) , altitude ( lowland , midland and highland ) , species ( camel , cattle , sheep , goat , and humans ) , production system ( pastoral/agro-pastoral , mixed crop-livestock , intensive/semi-intensive ) , sample size ( herd/animal ) , sampling methods ( probability/non-probability based ) , breed ( local , cross/exotic ) , age group ( depending on the species ) , sex ( male/female ) , serological test methods , the laboratory where the serological tests were done , and the number of samples examined and positive . To stabilize the variance , the study level estimates were transformed to double arcsine estimates ( t ) by the following formulae: t = sin-1 ( √n/N+1 ) + sin-1 ( √n+1/N+1 ) , and se ( t ) = √1/N+0 . 5 [11] . Logit transformations were used to estimate odds ratios . The data was analyzed by using Stata ( Version 11 . 1 , Stata Corp , College Station , Texas ) , Epi InfoTM ( Version 3 . 5 . 1 , Center for Disease Control , CDC , USA ) and Microsoft Office Excel 2007 . Alpha was set at 0 . 05 , and in multiple comparisons the Bonferroni correction method was applied . Data from animals and humans were analyzed separately . In animal studies , the within study bias was assessed by quality item [12] and quality index measures [13] . The quality items used to measure external validity were sampling methods , sample size , and data reporting quality . A value of one was assigned for reporting each of the following , but none otherwise: probability based sampling method ( herd/flock and animal ) , study period , herd number , laboratory where the serological tests were done , system of production , breed , age and sex as per the set objectives . The worth for sample size was calculated by dividing the sample size ( n ) by 384 . The quality index was calculated by dividing the total score with the highest score as a denominator . Whether prevalence estimates were associated with studies’ qualities were assessed by quality item and quality index regression analyses . A funnel plot was used to visually examine the across study bias ( small study effects ) . The Egger’s regression asymmetry test was used to test the significance of the plot’s asymmetry . Cumulative meta-analyses were performed to examine the magnitude of changes in pooled estimates . Heterogeneity analyses were done to assess the variability among study level estimates . The Galbraith plot was used to graphically examine the heterogeneity of estimates . Inverse variance and Mantel-Haenszel models were used to estimate the heterogeneities of prevalence and odds ratios estimates , respectively . The Cochran’s Chi squared test ( Q-test ) , tau squared ( T2 ) , and Higgin’s statistics/indexes ( H2 and I2 ) [14] were used to measure heterogeneities . I2 estimates less than 25% and greater than 75% , respectively , were considered as low and high heterogeneities . The 95% confidence bounds of I2 were derived from the H statistics: ln ( H ) +/-1 . 965 ( Se ln H ) . Logit estimates of ‘H’ were back transformed to I2 by the following formula: I2 = 1-1/H2 . If Cochran’s Q was greater than the number of studies ( k ) , the standard error ( Se ) of I2 was calculated by the following formula: Se ( ln H ) = 0 . 5 [ ( ln Q-ln k-1 ) / ( √2Q-√2k-3 ) ] . If the number of studies was greater than Cochran’s Q , the standard error of I2 was calculated by the following formula: Se ( ln H ) = √ [ ( 1/2k-4 ) ( 3 ( k − 2 ) 2–1 ) / ( 3 ( k − 2 ) 2 ) ] , [14] . Subgroup analyses were done on the basis of assumed relative homogeneity due to biological or environmental factors , heterogeneity statistics , and data availability . Because of the small number of animal studies that employed enzyme immunoassays , and differences in tests’ performances , all subsequent analyses were performed by using data from studies that employed Rose Bengal Plate Test ( RBPT ) and Complement Fixation Test ( CFT ) in series . The categories and subgroups considered in animal studies were the following: species ( camels , cattle , goats and sheep ) , puberty ( post-pubertal and pre-pubertal ) , sex ( male and female ) , and production system ( pastoral , mixed crop-livestock and semi intensive/intensive ) . Additional subgroup analyses were done on the following: pastoral system by location ( Afar , Borana , Somali and South Omo ) , camel by location ( Afar , Borana and Somali ) , cattle by breed ( local and cross ) and production system ( pastoral , mixed crop-livestock and semi-intensive/intensive ) , small ruminants by production system ( pastoral and mixed crop-livestock ) and by pastoral areas ( Afar , Borana , Somali and South Omo ) . Data from South Eastern Tigray , North Eastern Oromia , Bale and Dire Dawa were grouped in the main pastoral locations depending on proximity ( Afar , Borana and Somali ) . The age effect was assessed in the context of age at puberty . Due to disparities in the categorization of age groups and the difficulty to disaggregate aggregate data , cutoff values of four years for camels , three years for cattle , and one year ( 10 months to 18 months ) for small ruminants were used to group animals as post-pubertal and pre-pubertal . Subgroup analyses by herd/flock size were not done because of lack of uniformity in herd/flock definition , and in numerical categorizations of size ( small , medium and large ) , and due to the difficulties to disaggregate aggregate data as is the case in age groups . Subgroup analyses by agro climatic zones ( lowland , midland and highland ) were not done due to lack of distinct data on the exact number of animals sampled and positive by climatic zone in most cases in point , and on account of the potential climatic heterogeneity in areas where a mixed crop-livestock production is practiced . As the number of eligible human studies was small and the residual heterogeneity of the tests ( CFT vs . other tests ) was not significant ( P > 0 . 05 ) , the overall pooled estimate was calculated by using all data irrespective of serological tests . Subgroup analyses were performed by production system ( pastoral and mixed crop-livestock/semi-intensive/intensive ) and occupation ( slaughterhouse personnel/butchers/animal health and production workers ) . Forest plots were used to graphically display estimates . The DerSimonian and Laird random effects model was used to pool double arcsine estimates . The sensitivities of pooled estimates were assessed by single study omitted influence analyses . Pooled double arcsine estimates were back transformed to prevalence estimates ( p ) by the following formula: p = 0 . 5 {1-sgn ( cos t ) [1- ( N ( sin t ) 2 + ( sin t ) 2–1/ ( N sin t ) ) 2]0 . 5} , [11] , where N = sample size . Log odds estimates were back transformed to odds ratios . The significance of the residual heterogeneity within subgroups was assessed by the Chi distribution ( Overall Q - ∑Q of subgroups , df ) . The Yates’ Chi square was used to test the significance of a difference between pooled estimates . A meta-regression was performed to search for variables that best explain the heterogeneity and predict brucellosis seropositivity in animals . For this purpose , 21 records with data on age at puberty and sex were considered . Due to the limited number of studies considered for the regression analysis , only age at puberty and sex were considered as covariates . Initially , whether the sampling proportions ( post pubertal , and female ) were associated with prevalence estimates were examined . The dependent variable in the regression proper was a double arcsine prevalence estimate , and the covariates were raw prevalence differences in post-pubertal and pre-pubertal animals , in females and males , and an age-sex interaction term . Tau squared ( T2 ) was estimated by the Residual Maximum Likelihood ( REML ) method and the probability was calculated by the Monte-Carlo permutation test at 20000 iterations . To estimate True Prevalence in cattle and small ruminants , the sensitivity and specificity estimates of each of RBPT and CFT predicted by a Bayesian logistic regression model [15] were imputed in the Rogan and Gladen formula [16]: Tp = ( Ap + CSps-1 ) / ( CSes+CSps-1 ) , where Tp = true prevalence; Ap = apparent prevalence; CSes = combined sensitivity of the test series ( Se RBPT × Se CFT ) , and CSps = combined specificity of the test series ( 1- ( 1-Sp RBPT ) × ( 1-Sp CFT ) ) . The sensitivity and specificity estimates of RBPT and CFT in cattle and small ruminants were the following [15]: RBPT in cattle ( Se = 0 . 981 ( 95%CI = 0 . 968 , 0 . 991 ) and Sp = 0 . 998 ( 95%CI = 0 . 997 , 0 . 998 ) ) ; RBPT in small ruminants ( Se = 0 . 925 ( 95%CI = 0 . 916 , 0 . 934 ) and Sp = 0 . 999 ( 95%CI = 0 . 998 , 1 . 000 ) ) ; CFT in cattle ( Se = 0 . 96 ( 95%CI = 0 . 949 , 0 . 970 ) and Sp = 0 . 998 ( 95%CI = 0 . 997 , 0 . 998 ) ) ; CFT in small ruminants ( Se = 0 . 926 ( 95%CI = 0 . 911 , 0 . 939 ) and Sp = 0 . 999 ( 95%CI = 0 . 998 , 0 . 999 ) ) . Estimates of the sensitivities and specificities of each of RBPT and CFT in camels , and humans are divergent and pooled estimates are not available . Both RBPT and CFT are applicable in camels and humans; the sensitivity of RBT in humans is over 99% , and CFT reportedly has a positivity of 91 . 7% [17] . Due to the discrepancy in study level estimates and in the absence of pooled estimates , the performances of the tests were assumed to be as equally good as in cattle and small ruminant sera . Therefore , to estimate True Prevalence in camels , humans , and in subgroups with mixed animal species , the sensitivity and specificity estimates of each of RBPT and CFT [15] were separately pooled by a random effects model -using the median estimates and 95% confidence limits [15] as inputs . The calculated pooled estimates ( Se RBPT = 0 . 953; Sp RPBT = 0 . 998; Se CFT = 0 . 943; Sp CFT = 0 . 999 ) were imputed in the Rogan and Gladen formula . The incidence of brucellosis in humans was calculated by using the relationship between prevalence ( p ) and seroconversion ( a ) , and loss of seropositivity at equilibrium ( b ) : p = a / ( a + b ) , [18 , 19] , where p = calculated True Prevalence , a = 0 . 1 , and b = 0 . 092 [18] .
Fig 1 presents a flow diagram of the search and selection of studies . Of 108 reports identified , 104 were original and 4 were reviews ( 3 narratives , and one systematic on dairy cattle ) . Two articles were in French and the rest were in English . Reports published in languages other than English and French have not been noted . Nine original reports were on non ruminants ( 7 on humans only , one on pigs and one on horses- Brucella abortus titer and fistulous withers ) ; 90 were on domestic ruminants only , and five on ruminants and humans . At the screening stage , 16 reports were excluded: reviews ( 4 ) , studies carried out before 2000 ( 10 ) , and unavailable ( 2 ) . Out of 92 full text reports assessed for eligibility , 22 ( 23 . 9% ) were excluded for diverse reasons: duplication-article/data ( 7 ) , single test ( 9 ) , single farm ( 1 ) , and inconsistent data ( 5 ) , ( S2 Table ) . Altogether , data from 70 studies with 98 species wise records ( 15 camels , 27 cattle , 21 sheep , 25 goats and 10 humans ) were extracted [20–89] . The number , diversity and scope of studies carried out so far are inadequate . Although there has been a substantial increase in the number of studies since 2000 , studies that addressed brucellosis in cattle of the pastoral system and in small ruminants of Somali and South Omo pastoral areas are relatively sparse . Data on livestock of Gambela ( Western Ethiopia ) is not available . Only one study has reported the prevalence of porcine brucellosis [90] . The roles of equines , wildlife and domestic dogs in the epidemiology of brucellosis have not been described . With the exception of one study that reported B . melitensis from slaughtered goats ( 2/14 ) [75] , information on the species and biovars of Brucella is not available . The impact of brucellosis on ruminant productivity , and the potential costs and benefits of livestock vaccination have not been reported . Estimates of the incidence and impact of the disease in humans are non-existent , and the seroprevalence studies are generally limited in number and constrained by sample sizes . The characteristics of the studies are given in S1 Table . The studies have been carried out from 2000 to 2015 in Central , Western , Eastern , Northern and Southern Ethiopia . The species of animals were camels , cattle , goats and sheep , and humans . All animals and humans were unvaccinated against Brucella spp . and above six months of age , but in one study on goats ( >4 months ) [63] . The systems of animal production included pastoral , mixed crop-livestock and semi-intensive/intensive . The sample sizes ranged from 291 to 6201 in animals , and from 38 to 653 in humans . In total , data from 75879 animals and 2223 humans were considered for quantitative syntheses . RBPT-CFT in series were the most frequently used serological tests in animal ( 58/64 ) and human studies ( 7/10 ) . Enzyme immunoassays were used in six animal studies . Immunochromatographic lateral flow assay , 2-mercaptoethanol , and slide agglutination test ( Huma Tex febrile antigens test kit ) have been used in three human studies . In most animal ( 53/64 ) and human ( 7/10 ) studies , the complement fixation tests have been done at the National Veterinary Institute and/or the National Animal Health and Disease Investigation Center , Ethiopia . The quality items’ scores and the quality indexes of animal studies were not associated with prevalence estimates ( P > 0 . 05 ) , ( Fig 2A ) . The funnel plot ( Fig 2B ) , the Egger’s regression asymmetry test ( b = 3 . 19 , 95%CI = -0 . 28 , 6 . 67; p > 0 . 05 ) , and cumulative analyses did not suggest bias . Sample sizes have been calculated by considering expected prevalence and desired precision . In most instances the calculated sample sizes were inflated to account for potential intra-cluster variability ( S1 Table ) . The overall sampling proportions tended to be higher in post-pubertal than in pre-pubertal and in female than in male animals . The operational definitions and classifications of herd/flock size and age groups lack uniformity across studies . Non-reporting of exact sampling methods , incomplete and/or aggregate data reporting were among the shortcomings . The farming practice in the extensive system apparently set hurdles to perform a random sampling in the strictest sense of a probability based sampling . However , as the sampled animals were drawn from different owners or herds/flocks and without a priori knowledge on the seropositivity/ seronegativity status of the units , the bias likely to have been introduced due to ‘non-probability sampling’ could be considered unimportant . Furthermore , most CFT have been done in two certified laboratories , and the bias and heterogeneity due to CFT could be hold negligible . Nonetheless , differences associated with the sampling proportions by age group and sex could have contributed to the between studies’ variability . Most estimates on animal brucellosis lie beyond the confidence limits of the regression line of the Galbraith plot ( Fig 2C ) . The study level estimates were heterogeneous ( P < 0 . 0001 ) , and in a subgroup analysis by serological tests , the residual heterogeneity was significant ( P < 0 . 0001 ) . The subgroup heterogeneity estimates are presented in Tables 1 and 2 . Species wise , the highest between-study variance ( T2 ) was in goats . By system , T2 was higher in cattle of the semi-intensive/intensive system than in the mixed crop-livestock and in small ruminants of the pastoral system than in the mixed crop-livestock . The I2 and/or the lowest 95% uncertainty limits were low to moderate-high in most subgroups with less than eight studies ( 7/11 ) . In all other animal subgroups but animal-pre-pubertal and cattle-mixed crop-livestock subgroups , the I2 estimates including the lower confidence bounds were substantially high ( I2 > 75% ) . In subgroups with less than eight studies , the 95% uncertainty intervals of I2 demonstrate high degree of uncertainties , and the low to moderate estimates do not warrant the absence of substantially high heterogeneities . Furthermore , in all subgroups with less than eight studies but human-sedentary and camel-Somali , the Q test shows the presence of a significant heterogeneity even at a 10% cutoff level . The high heterogeneities could be attributed to multiple factors that may involve biological ( Brucella spp . , host species , breed , age and sex ) , environmental ( agro-ecology , livestock composition and management practice ) and interactions of factors . Differences in the sampling proportions of age groups and female/male could also have contributed to the heterogeneities . Due to the small number of studies , and incomplete and/or aggregate data in several studies , strata based classifications ( sub -sub grouping ) and further assessments could not have been performed . However , the comparatively higher differences between the largest and smallest T2 estimates in species and production system subgroups than in other subgroups ( age , sex and breed ) imply the importance of species and production system as major moderators of heterogeneity . Moreover , as quantification is only one component in variability assessment , and the most important aspect being the implication of the heterogeneity [91] , the species and production system could be considered as epidemiologically most important sources of variability . Each of the measures of heterogeneity has its merits and demerits . The Q-test has a low power to detect heterogeneity in small number of studies but an excessive power when the number of studies is large [14 , 91 , 92] . As sample size ( study precision ) increases , both Q and I2 increase [92] and suggest the presence of unimportant heterogeneity . Furthermore , due to the dependence of I2 on the number and precisions of studies , the 95% uncertainty intervals may not retain the I2 coverage [93] , and the cut off values- low , moderate , and high- are generally arbitrarily defined [14 , 91] . Although , T2 is inherently unaffected by the number and precisions of studies [92] , its interpretation is difficult , and does not facilitate comparison across different outcome measures [14] . On the whole , the median number ( inter quartile range ) of studies included in Cochrane and non Cochrane meta-analyses , respectively , have been reported to be 16 ( 7–30 ) , and 8 ( 4–16 ) [94] . However , what exactly are ‘small’ / ‘large’ numbers of studies and sample sizes in meta-analyses have not been adequately described [93] . Forest plots of the prevalence of brucellosis seropositivity in large and small ruminants are presented in Figs 3 and 4 , respectively . The subgroup prevalence estimates are presented in Tables 3 and 4 . The overall pooled prevalence of animal brucellosis was estimated at 3% ( 95% CI = 2 . 4 , 3 . 6 ) . All single study omitted pooled estimates lie within the 95% confidence bounds of the respective overall means . In most subgroups the 95% confidence intervals of the pooled estimates imply low uncertainties in the location of the respective means . The odds of brucellosis seropositivity was twice higher in goats than in sheep ( OR = 2 , 95%CI = 1 . 8 , 2 . 3 ) . The True Prevalence estimates for cattle and camels did not differ ( 2 . 9% each ) . The pooled estimates show the comparative importance of each of the species as reservoirs of Brucella spp . Although reports on the relative seroprevalence of brucellosis in species kept under a composite ownership are sparse , higher odds ratios of seropositivity in goats [63] , and in camels and cattle [95] kept with one or more other species have been reported . The higher seroprevalence of caprine brucellosis suggests a higher potential of goats as source of Brucella spp . to other species with which they are kept , and this could be more evident in areas with high goat populations . Moreover , despite B . melitensis and B . abortus , respectively , being the classic causes of small ruminant and bovine brucellosis , their host specificity is relative [96] , and elsewhere , there have been increasing evidence of B . melitensis infection in cattle [97] , and small ruminant brucellosis due to B . abortus [96 , 98–100] . Therefore , the general tendency of rearing mixed livestock species , and use of communal grazing resources in both the pastoral and mixed crop-livestock systems are indicative of the significance of both B . melitensis and B . abortus irrespective of the host species . As swine farming is a rare practice limited to urban and peri-urban set-ups , the role of B . Suis in ruminant brucellosis could be ruled out . Forest plots of the odds of brucellosis seropositivity in post-pubertal vs . pre-pubertal animals , in females vs . males , and in improved vs . local cattle are presented in Fig 5 . In the univariable analysis , the odds ratios of brucellosis seropositivity were higher in post-pubertal than in pre-pubertal animals ( OR = 3 . 1 , 95% CI = 2 . 6 , 3 . 7 ) , in females than in males ( OR = 1 . 8 , 95% CI = 1 . 7 , 2 ) , and in improved than in local cattle ( OR = 1 . 2 , 95% CI = 1 . 0 , 1 . 4 ) . In the multivariable analysis-the sampling proportions of each of post-pubertal and female animals were not associated with seroprevalence ( P > 0 . 05 ) -puberty was significantly associated with brucellosis seropositivity ( P < 0 . 001 ) , ( T 2 = 0 . 005 , I2 = 84 . 6% , and R2 = 56 . 94% ) , but not sex ( P > 0 . 05 ) . Age at puberty varies by species , breed , sex and environmental factors . Although puberty is influenced by feed intake , reports on the impact of feed availability under different management scenarios are sparse , and the cutoff values are approximations of the performances reported for camels [101] , cattle [102] goats [103] and sheep [104] . Due to the limited number of studies that met the inclusion criteria for a regression analysis , the model does not adequately explain the heterogeneity variance . Nonetheless , the calculated probability value is suggestive that post-pubertal animals are more likely to be seropositive than pre-pubertal animals . Despite the likelihood of exposure to Brucella spp . and brucellosis seropositivity apparently increase as age advances , the age effect could be modified by sex . The tendency of females to be slightly more seropositive than males could have been due to sampling bias rather than the effect of sex per se . As females are kept longer than males , the population of females is generally higher than that of males and more females have been sampled ( Table 3 ) . However , under natural breeding conditions males often search for females in heat -thus more exposed to infected females within or outside the herd/flock -and being a male may be a risk compared to being a female . Afar , Borana , Somali and South Omo are areas characterized by a non significant crop growing period [105] , and the system of animal production is of a pastoral type ( Fig 6A ) . The mixed crop-livestock production where it exists is practiced in areas typified by single or double crop growing periods . The semi-intensive/intensive system of production ( dairy farming ) is principally found in peri-urban and urban areas of the sedentary system . The populations and average numbers of cattle , camels , goats and sheep per holder vary within and among production systems ( Fig 6 ) , [106] . Forest plots of the overall prevalence of brucellosis seropositivity in livestock by system are presented in Fig 7 . The pooled estimates are given in Table 3 . The prevalence of brucellosis seropositivity was higher in animals of pastoral origin than in animals from the mixed crop-livestock system ( OR = 2 . 8; 95% CI = 2 . 5 , 3 . 2 ) , in small ruminants of the pastoral system than those from the mixed crop-livestock production system ( OR = 4 . 3 , 95% CI = 3 . 5 , 5 . 2 ) , and in cattle under the semi intensive /intensive management system than in cattle from the mixed crop-livestock system ( OR = 1 . 9; 95% CI = 1 . 5 , 2 . 2 ) , ( Fig 8 ) . Of the three systems , the highest prevalence was in pastoral animals . The comparable estimates of brucellosis seropositivity in cattle and small ruminants of the mixed crop-livestock system ( Table 4 ) are suggestive of similar Brucella spp . transmission patterns . These similarities could form a basis on which to build study designs , irrespective of the heterogeneous micro climates within the sedentary system . The production system could influence the occurrence of the disease , and a higher risk in highly mobile and large herds than in less mobile and small herds has been reported [16] . Forest plots of the overall estimates of brucellosis seropositivity in pastoral livestock by location are presented in Fig 9 . The pooled estimates are given in Table 4 . The odds ratios of brucellosis seropositivity were higher in Afar than in Borana livestock ( OR = 3 . 8; 95% CI = 3 . 4 , 4 . 4 ) , in Afar than in Borana camels ( OR = 4 . 3 , 95% CI = 3 . 4 , 5 . 4 ) , and in Afar than in Borana small ruminants ( OR = 4 . 8 , 95% CI = 4 , 5 . 7 ) , ( Fig 10 ) . Compared to other pastoral locations ( Borana , Somali and South Omo ) , the higher prevalence in Afar livestock suggests its importance in the region . The similarities of the estimates in pastoral areas other than Afar imply alike risk factors that may include the range and frequency of animal mobility , livestock species composition and herd/flock size . For instance , in Afar cattle and goats are preferred to sheep and camels , and animals from different localities could intermingle at grazing and watering points , but in Somali sheep and camels are the preferred species and the herding practice and use of resources is clan based [78] . Genetic makeup of animals may also account for the differences in estimates across pastoral locations . It has been reported that , whilst Maltese and South American sheep breeds demonstrated considerable resistance , Southwest Asian and Mediterranean fat-tailed sheep breeds were susceptible to brucellosis [107 , 108] . Prevalence estimates across pastoral areas could also differ depending on the stretch of the grazing area and the occurrence of brucellosis in wild animals . Although the significance of wildlife in the epidemiology of brucellosis has not been adequately described , Brucella spp . have been isolated from a variety of wild life [109] , and an increased prevalence with increased density has been reported [110] . The impact of animal brucellosis is principally a function of prevalence/incidence , population size and effect on the productive /reproductive potential of the host , and its transmission to humans . The seroprevalence estimates ( Tables 3 and 4 ) and the number of animals per holder ( Fig 6A–6D ) are higher in the pastoral than in the mixed crop-livestock system . While the overall proportion of improved cattle ( 0 . 74% ) is lower than that of the indigenous [111] , the herd size is higher in commercial and breeding farms [37] than in the mixed system . Despite reproductive wastage being a feature of ruminant brucellosis , the effect of the disease differs by host species . Most infected cows abort once , but shed the bacteria in subsequent parturitions and milk [96] . The rate of abortion in camels is lower than rates in other ruminants—reviewed by Wernery [112] , and in experimentally infected camels the symptoms were reported to have been mild and transient [113] . In goats , infections vary from short bouts to persistence [107] , and the duration of excretion Brucella spp . lasts longer in goats than in sheep [114] . In sheep , the infection pattern is dose dependent and breed related variations have been recorded [107] . The importance of the species as a source of infection to humans could also differ depending on the utility of the host as a milk source and the raw milk consuming tradition of the community . Whilst cow milk is consumed in all systems , camel milk is common in camel rearing pastoral areas . Small ruminants are also used as sources of milk in the pastoral and some crop-livestock system areas [103] . On the whole , given the comparatively higher seroprevalence estimates and number of animals per holder , the impact of animal brucellosis could be substantially higher in the pastoral than the mixed production system , and sizable in the semi-intensive/ intensive systems . Large scale national level or meta-analytical estimates of brucellosis in sub-Saharan Africa are meager , and comparison of the present estimates with estimates elsewhere is difficult . However , prevalence estimates could differ across countries and time due to factors that may comprise animal , societal , management , and ecological variables [16 , 115] . A review of brucellosis in SSA is given by Ducrotoy et al . 2015 [116] . Forest plot of the prevalence of brucellosis seropositivity in humans is presented in Fig 11 . The overall True Prevalence was estimated at 6 . 7% ( 95% CI = 2 . 4 , 12 . 8 ) . The estimates by subgroup are given in Table 3 . The odds ratio was higher in individuals from the pastoral than from the mixed crop-livestock system ( OR = 6 . 7 , 95% CI = 3 . 6 , 12 . 4 ) . The seroprevalence in occupationally exposed individuals ( slaughterhouse personnel / butchers /animal health and production workers ) was estimated at 1 . 2% ( 95% CI = 0 , 5 . 7 ) . The incidence rates in the pastoral and sedentary systems , respectively , were estimated at 160 and 28 per 100 000 person years . The seroprevalence ratio of brucellosis in humans and animals of the pastoral areas ( 17 . 4/4 . 7 ) is higher than that of the sedentary system ( 3 . 1/1 . 7 ) . This suggests a higher transmission rate of Brucella spp . from animals to humans in the former than in the latter . Similarly , in predominantly pastoral communities of Kenya , a high prevalence of brucellosis seropositivity in humans and livestock , a two to four fold higher prevalence in humans than in animals , and a strong household level association of seropositivity in humans and animals have been described [117] . In rural Ethiopia , livestock delivery is often assisted with bare hands and consuming raw milk is a common practice in a significant segment of the population . Therefore , the seroprevalence estimates in animals and the explicitly low perception of the general population as regards the transmission of zoonotic diseases are evocative of the entrenchment of brucellosis in the pastoral community . Data on the prevalence of brucellosis seropositivity in individuals occupationally exposed ( slaughterhouse personnel / butchers /animal health and production workers ) is limited . Among slaughterhouse personnel elsewhere , seroprevalence estimates of 59% ( 44/75 ) in Iran [118] , 22% ( 78/360 ) in Pakistan [119] and as high as 39% ( 66/170 ) in Nigeria [120] have been documented . In India , 3 . 8% ( 42/1086 ) in veterinarians and 15% ( 8/186 ) in abattoir personnel have been recorded [121] . Differences across countries could be attributed to prevalence differences in animals . The incidence of human brucellosis in the pastoral areas ( 160 per 100 000 person years ) is considerable . The estimate is higher than rates estimated for several countries [1]: the highest in the Middle East ( 52–269 per 100 000 person years ) and the lowest in Western Europe and the USA ( 0 . 02–0 . 9 per 100 000 person years ) . In endemic areas asymptomatic brucellosis is common and patients manifest signs when immunocompromised [122] . In a retrospective analysis of data of two health facilities in Uganda ( 2003–2012 ) , a noticeable prevalence ( 22% , 381/1732 ) was recorded in 2012 , compared to the years before , and this was associated with an increase in the numbers of individuals tested [123] . Childhood brucellosis in Ethiopia has not been described . In one study on febrile children of sedentary origin , 17 ( 2 . 6% ) were reported to have been seropositive [85] . Although milk could have been the main vehicle of transmission , data on milk contamination levels is lacking . However , levels of 12 . 6% in informally marketed milk at purchase in Uganda [124] , and 18 . 1% ( 13/72 ) in sheep’ and 26 . 2% ( 32/122 ) in goats’ milk in Nigeria [125] have been recorded . In Saudi Arabia , children were reported to have constituted for 21% ( 115/545 ) of the total cases of brucellosis , and unpasteurized milk was identified as the main source of infection [126] . In Macedonia , childhood brucellosis ( 18 . 8% , 317/1691 ) was characterized by various organ involvements and a wide spectrum of clinical manifestations [127] . Therefore , given the widespread raw milk consuming habit , it is reasonable to assume that a significant proportion of Ethiopian children , mainly of pastoral origin , could be seroreactors . As most adults are exposed at an early age and do not manifest acute disease , children may account for a higher proportion of patients with acute brucellosis [17] . Human brucellosis in Ethiopia appears to have been under-diagnosed . Thus far there has not been a hospital based retrospective study , and the studies included in this analysis are generally constrained by sample sizes . However , the occurrence of human brucellosis in the rural communities could be linked to the prevalence of brucellosis in animals . The overall data demonstrates the importance of brucellosis and calls for intervention measures -one of which could be livestock vaccination . However , most livestock are kept under an extensive system—where the farming practice is traditional and the road network does not sufficiently go through . In addition , as it currently stands the veterinary sector does not appear to be in a position to carry out a country wide control program . This could be exemplified by the fact that in pastoral areas where brucellosis could have a substantial impact , the animal health care service is for the most part rendered by community based workers . In one instance along the Ethio-Kenya border , the majority of the service providers were reported to have been represented by community based workers ( 140/167 ) , [128] . Of note are the control programs launched elsewhere in middle and low income countries [97] . For example , in Mongolia livestock vaccination reportedly had brought about a decline in the incidence of human brucellosis in the 1970s; it was modeled to be a cost effective measure at the beginning of the 2000s [129] , but in 2010 the rise in the incidence rate of human brucellosis to 229 per 100000 was ascribed to a failure in the functioning of the veterinary sector [130] . In low income countries , ex ante economic analyses alone may not portray the reality on the ground [2] , and several factors may dictate the feasibility and sustainability of a vaccination venture . Therefore , if a countrywide control program is to be a success in Ethiopia , among other factors , the veterinary sector requires a massive investment to strengthen its material and human resources capabilities . Nonetheless , vaccination of livestock in the semi-intensive/ intensive system and breeding farms could be envisaged , albeit the proportion of cattle ( < 0 . 74% ) and small ruminants kept under this system is very small [111] . In areas where vaccination could not be put into effect , improvement of the food value chain is a potential intervention measure [2] , and public awareness creation and policy measures could help reduce its incidence in both animals and humans . For instance , the diminution in seroprevalence in dairy cattle in Addis Ababa has been credited to the screening and culling practices exercised [52] , and implies a potential decrease in the incidence of human brucellosis as well . Similarly , in a stochastic risk assessment of informally marketed milk in Kampala , Uganda , construction of milk boiling centers has been recommended as an intervention measure that could have a substantial impact to shrink the incidence of human brucellosis [124] . This study portrays what has been done hitherto . The pooled estimates could serve the purpose of an input in further studies . Apart from additional seroprevalence studies on human brucellosis , studies on the following would be important to understand the epidemiology of brucellosis and devise control strategies: ( i ) characterization of Brucella spp . , ( ii ) contamination levels of milk with Brucella spp . , ( iii ) roles of equines , wildlife and domestic dogs in the epidemiology of brucellosis , ( iv ) impacts of brucellosis in ruminant productivity and human health , and ( v ) costs/benefits and feasibility of livestock vaccination in animal production systems context . As information on the seroprevalence of brucellosis in humans is sparse , the estimates presented here may not reflect the actual occurrence of the disease . Due to lack of uniformity in data presentation , data from all eligible studies have not been made use of . The regression analysis was performed by using data from a subset of studies on animals , and the model does not adequately explain the heterogeneity nor depict the predictive values of the potential explanatory variables .
The prevalence of brucellosis is higher in goats than in other species . The higher prevalence of brucellosis seropositivity in animals of pastoral origin is suggestive of the importance of human brucellosis in the pastoral systems . The overall data calls for intervention measures that may include public awareness creation , food value chain improvement , and livestock vaccination as appropriate to the systems of animal production . Further studies aimed at generating additional data on issues unaddressed so far , and uniformity in data reporting would be important to further explore and understand the epidemiology of the disease in a National context . | Brucellosis is one of the neglected zoonotic diseases with major outcomes of reproductive wastage in livestock and debilitating illnesses in humans . Ethiopia is one of the sub-Saharan African countries with the largest animal and second largest human population . The aim of this study was to estimate the occurrence of brucellosis in animals and humans . To this effect , data from 70 published studies were extracted , and meta-analytical methods were employed . Of the domestic animals , the highest prevalence was in goats ( 5 . 3% ) , and the lowest in sheep ( 2 . 7% ) . Brucellosis seropositivity was higher in animals of the pastoral than the mixed crop-livestock production system . In humans , the prevalence was estimated at 17 . 4% in the pastoral and 3 . 1% in the sedentary system , and the incidence rates , respectively , were 160 and 28 per 100 000 person years . The high occurrence of the disease is evocative of its importance in the country in general and in the pastoral system in particular . Public awareness creation could help reduce its occurrence in humans , and the potential of livestock vaccination as a means of control needs to be assessed . | [
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| 2016 | Brucellosis Seropositivity in Animals and Humans in Ethiopia: A Meta-analysis |
A tight regulation of transcription factor activity is critical for proper development . For instance , modifications of RUNX transcription factors dosage are associated with several diseases , including hematopoietic malignancies . In Drosophila , Myeloid Leukemia Factor ( MLF ) has been shown to control blood cell development by stabilizing the RUNX transcription factor Lozenge ( Lz ) . However , the mechanism of action of this conserved family of proteins involved in leukemia remains largely unknown . Here we further characterized MLF’s mode of action in Drosophila blood cells using proteomic , transcriptomic and genetic approaches . Our results show that MLF and the Hsp40 co-chaperone family member DnaJ-1 interact through conserved domains and we demonstrate that both proteins bind and stabilize Lz in cell culture , suggesting that MLF and DnaJ-1 form a chaperone complex that directly regulates Lz activity . Importantly , dnaj-1 loss causes an increase in Lz+ blood cell number and size similarly as in mlf mutant larvae . Moreover we find that dnaj-1 genetically interacts with mlf to control Lz level and Lz+ blood cell development in vivo . In addition , we show that mlf and dnaj-1 loss alters Lz+ cell differentiation and that the increase in Lz+ blood cell number and size observed in these mutants is caused by an overactivation of the Notch signaling pathway . Finally , using different conditions to manipulate Lz activity , we show that high levels of Lz are required to repress Notch transcription and signaling . All together , our data indicate that the MLF/DnaJ-1-dependent increase in Lz level allows the repression of Notch expression and signaling to prevent aberrant blood cell development . Thus our findings establish a functional link between MLF and the co-chaperone DnaJ-1 to control RUNX transcription factor activity and Notch signaling during blood cell development in vivo .
Proper blood cell development requires the finely tuned regulation of transcription factors and signaling pathways activity . Consequently mutations affecting key regulators of hematopoiesis such as members of the RUNX transcription factor family or components of the Notch signaling pathway are associated with several blood cell disorders including leukemia [1 , 2] . Also , leukemic cells often present recurrent chromosomal rearrangements that participate in malignant transformation by altering the function of these factors [3] . The functional characterization of these genes is thus of importance not only to uncover the molecular basis of leukemogenesis but also to decipher the regulatory mechanisms controlling normal blood cell development . Myeloid Leukemia Factor 1 ( MLF1 ) was identified as a target of the t ( 3;5 ) ( q25 . 1;q34 ) translocation associated with acute myeloid leukemia ( AML ) and myelodysplastic syndrome ( MDS ) more than 20 years ago [4] . Further findings suggested that MLF1 could act as an oncogene [5–8] or a tumor suppressor [9] depending on the cell context and it was shown that MLF1 overexpression either impairs cell cycle exit and differentiation [10] , promotes apoptosis [11 , 12] , or inhibits proliferation [13 , 14] in different cultured cell lines . Yet , its function and mechanism of action remain largely unknown . MLF1 is the founding member of a small evolutionarily conserved family of nucleo-cytoplasmic proteins present in all metazoans but lacking recognizable domains that could help define their biochemical activity [15] . Whereas vertebrates have two closely related MLF paralogs , Drosophila has a single mlf gene encoding a protein that displays around 50% identity with human MLF in the central conserved domain [16 , 17] . In the fly , MLF was identified as a partner of the transcription factor DREF ( DNA replication-related element-binding factor ) [16] , for which it acts a co-activator to stimulate the JNK pathway and cell death in the wing disc [18] . MLF has been shown to bind chromatin [18–20] , as does its mouse homolog [21] , and it can either activate or repress gene expression by a still unknown mechanism [18 , 20] . MLF also interacts with Suppressor of Fused , a negative regulator of the Hedgehog signaling pathway [19] , and , like its mammalian counterpart [13] , with Csn3 , a component of the COP9 signalosome [22] , but the functional consequences of these interactions remain elusive . Interestingly the overexpression of Drosophila MLF or that of its mammalian counterparts can suppress polyglutamine-induced cytotoxicity in fly and in cellular models of neurodegenerative diseases [17 , 23–25] . Moreover phenotypic defects associated with MLF loss in Drosophila can be rescued by human MLF1 [17 , 26] . Thus MLF function seems conserved during evolution and Drosophila appears to be a genuine model organism to characterize MLF proteins [15] . Along this line , we recently analyzed the role of MLF during Drosophila hematopoiesis [26] . Indeed , a number of proteins regulating blood cell development in human , such as RUNX and Notch , also control Drosophila blood cell development [27] . In Drosophila , the RUNX factor Lozenge ( Lz ) is specifically expressed in crystal cells and it is absolutely required for the development of this blood cell lineage [28] . Crystal cells account for ±4% of the circulating larval blood cells; they are implicated in melanization , a defense response related to clotting , and they release their enzymatic content in the hemolymph by bursting [27] . The Notch pathway also controls the development of this lineage: it is required for the induction of Lz expression and it contributes to Lz+ cell differentiation as well as to their survival by preventing their rupture [28–31] . Interestingly , our previous analysis revealed a functional and conserved link between MLF and RUNX factors [26] . In particular , we showed that MLF controls Lz activity and prevents its degradation in cell culture and that the regulation of Lz level by MLF is critical to control crystal cell number in vivo [26] . Intriguingly , although Lz is required for crystal cell development , mlf mutation causes a decrease in Lz expression but an increase in crystal cell number . In human , the deregulation of RUNX protein level is associated with several pathologies . For instance haploinsufficient mutations in RUNX1 are linked to MDS/AML in the case of somatic mutations , and to familial platelet disorders associated with myeloid malignancy for germline mutations [1] . In the opposite , RUNX1 overexpression can promote lymphoid leukemia [32 , 33] . Understanding how the level of RUNX protein is regulated and how this affects specific developmental processes is thus of particular importance . To better characterize the function and mode of action of MLF in Drosophila blood cells , we used proteomic , transcriptomic and genetic approaches . In line with recent findings [20] , we found that MLF binds DnaJ-1 , a HSP40 co-chaperone , as well as the HSP70 chaperone Hsc70-4 , and that both of these proteins are required to stabilize Lz . We further show here that MLF and DnaJ-1 interact together but also with Lz via conserved domains and that they regulate Lz-induced transactivation in a Hsc70-dependent manner in cell culture . In addition , using a null allele of dnaj-1 , we show that it controls Lz+ blood cell number and differentiation as well as Lz activity in vivo in conjunction with mlf . Notably , we found that mlf or dnaj-1 loss leads to an increase in Lz+ cell number and size due to the over-activation of the Notch signaling pathway . Interestingly , our results indicate that high levels of Lz are required to repress Notch expression and signaling . We thus propose a model whereby MLF and DnaJ-1 control Lz+ blood cell growth and number by promoting Lz accumulation , which ultimately turndowns Notch signaling . These findings thus establish a functional link between the MLF/Dna-J1 chaperone complex and the regulation of a RUNX-Notch axis required for blood cell homeostasis in vivo .
To better characterize the molecular mode of action of MLF , we sought to identify its partners . Accordingly , we established a Drosophila Kc167 cell line expressing a V5-tagged version of MLF close to endogenous levels in a copper-inducible manner ( Fig 1A ) . After anti-V5 affinity purification from whole cell extracts of control or MLF-V5-expressing cells , isolated proteins were identified by mass spectrometry . Five proteins reproducibly co-purified with MLF and were either absent or at more than 4 fold lower levels in each control purification ( Fig 1B ) : the Hsp40 co-chaperone DnaJ-1 ( also known as DROJ1; [34] ) , the constitutively expressed Hsp70 chaperones Hsc70-4 and Hsc70-3 , the RNA binding protein Squid ( Sqd ) , and the retrotransposon-encoded protein Copia . Of note , as this manuscript was in preparation , Dyer et al . also identified DnaJ-1 and Hsc70-4 as partners of MLF using a similar proteomic approach in the Drosophila S2 cell line [20] . Since DnaJ-1 was the strongest hit in our analysis , we focused on this candidate and we further characterized its interaction with MLF as well as its function both in cell culture and in vivo . First , we confirmed the interaction between MLF and DnaJ-1 by co-immunoprecipitation assays in Kc167 cells transfected with expression plasmids for tagged versions of these proteins using anti-tag antibodies ( Fig 1C and 1D , and S1 Fig ) or an anti-MLF antibody ( S1C Fig ) . In addition , consistent with the hypothesis that these proteins interact in the cell , immunostainings showed that DnaJ-1 and MLF co-localize in the nuclei of Kc167 transfected cells ( S1D Fig ) . Finally , we also observed a specific interaction between MLF and DnaJ-1 by in vitro GST pull down assays ( S1E Fig ) . We then mapped the domains required for the interaction between DnaJ-1 and MLF . Hsp40/DnaJ co-chaperones play a crucial role in the regulation of protein folding and degradation; they chiefly act by delivering substrates to Hsp70/DnaK chaperones and stimulating their ATPase activity [35 , 36] . DnaJ-1 belongs to the DnaJB/class II subfamily of Hsp40/DnaJ proteins , which are characterized by an N-terminal J-domain required to stimulate Hsp70 ATPase activity ( amino acids 4 to 57 in DnaJ-1 ) , a central glycine/phenylalanine ( G/F ) -rich region ( amino acids 64 to 144 ) , and a conserved C-terminal region ( amino acids 157 to 320 ) that contains the client proteins binding domain followed by a dimerization interface [36] . Immunoprecipitations of GFP-MLF expressed with different HA-tagged DnaJ-1 variants indicated that the DnaJ-1 C-terminal region mediates MLF binding ( Fig 1C ) . In contrast , a point mutation ( P32S ) in the highly conserved HPD loop crucial for Hsc70 activation [36] , deletion of the J-domain or deletion of the J and G/F domains did not affect the interaction between DnaJ-1 and GFP-MLF . MLF does not harbor characteristic domains apart from a central “MLF homology domain” ( MHD , amino acids 96 to 202 ) conserved between MLF family members [15] . Using GFP-DnaJ-1 as bait and MLF deletion mutants as preys , we found that the MHD was sufficient for binding DnaJ-1 , while MLF N- and C-terminal regions were dispensable ( Fig 1D ) . Finally , consistent with the above results , the C-terminal region ( amino acids 157 to 334 ) of DnaJ-1 bound to the MHD of MLF ( S1F Fig ) . In sum , these data indicate that MLF and DnaJ-1 specifically bind each other through their conserved central and C-terminal region , respectively . We have previously shown that MLF is required for Lz stability and transcriptional activity [26] . Interestingly , Dyer et al . reported that the knockdown of DnaJ-1 or of its chaperone partner Hsc70-4 leads to a destabilization of exogenously expressed Lz in S2 cells [20] . However , the relationships between DnaJ-1 , MLF and Lz were not further explored . We thus asked whether DnaJ-1 also controls Lz activity . As shown in Fig 2A , transfection of a Lz expression plasmid in Kc167 cells induced a robust activation of the 4xPPO2-Fluc reporter gene [37] , which was significantly decreased when either mlf or dnaj-1 expression was knocked down by dsRNA treatment . Consistent with previous results [20 , 26] , Western blot analyses showed that mlf and dnaj-1 knockdowns caused a drop in Lz protein level ( Fig 2B ) . Moreover , RT-qPCR experiments showed that mlf and dnaj-1 knockdowns did not affect the expression of each other or decrease lz transcript level , while they did cause a significant reduction in the expression of Lz target gene ppo2 ( S2A–S2D Fig ) . Hence , like MLF , DnaJ-1 controls Lz protein stability and activity in Kc167 cells . Next , we tested the effect of DnaJ-1 overexpression on Lz’s activity and protein level . Reminiscent of MLF [26] , we observed that DnaJ-1 over-expression was associated with an increase in Lz-induced transactivation and Lz level ( Fig 2C and 2D ) . The overexpression of C-terminally-truncated DnaJ-1 proteins did not affect Lz-induced transcription or its expression . In contrast , the overexpression of DnaJ-1 carrying the P32S point mutation or a deletion of its J-domain caused a decrease in Lz-induced transcription and a drop in Lz level ( Fig 2C and 2D ) , suggesting that the activation of Hsc70 by DnaJ-1 is required for Lz’s stable expression and activity . In line with this hypothesis , knocking down Hsc70-4 , which interacts with DnaJ-1 and MLF [20] , caused a strong decrease in Lz-induced transactivation and a concomitant reduction in Lz protein level ( S2E and S2F Fig ) . In sum , our results support the idea that MLF acts with DnaJ-1 in a Hsc70 chaperone complex to promote Lz stability and activity . Given the impact of MLF and DnaJ-1 on Lz activity , we then asked whether these two proteins bind this RUNX transcription factor . Upon transfection of the corresponding expression plasmids , both HA-DnaJ-1 and HA-MLF were co-immunoprecipitated by GFP-tagged Lz but not by GFP alone ( Fig 2E and 2F ) . Furthermore , in vitro translated Lz bound to E . coli-purified GST-MLF and GST-DnaJ-1 but not to GST alone in pull down assays ( S2G Fig ) . Using different MLF variants in co-immunoprecipitation assays , we found that the N-terminal part of the MLF homology domain ( amino acids 96 to 147 ) was crucial for the interaction with Lz ( Fig 2G ) . Similarly the C-terminal domain of DnaJ-1 was required for binding Lz , while its J domain was dispensable ( Fig 2H ) . Therefore it appears that MLF and DnaJ-1 interact with Lz through conserved domains and our results suggest that the MLF/DnaJ-1 complex regulates Lz stability and activity in Kc167 cells by binding it . Since DnaJ-1 interacts with MLF and controls Lz level ex vivo , we then sought to analyze DnaJ-1 function in circulating larval crystal cells , whose proper development requires Lz stabilization by MLF [26] . Given that no mutant for dnaj-1 was available , we used a CRISPR/Cas9 strategy to generate dnaj-1 null alleles ( S3 Fig ) [38] . In the following experiments , we used an allelic combination between two mutant lines obtained from independent founder flies ( dnaj-1A and dnaj-1C ) , which harbor a complete deletion of the dnaj-1 coding sequence ( S3 Fig ) . Around 65% of the dnaj-1A/C mutants reached the larval stage and 15% emerged as adult flies but they did not show obvious morphological defects . Reminiscent of mlf phenotypes [26] , bleeding of third instar larvae revealed that dnaj-1 mutants exhibited a ±1 . 8-fold increase in the number of circulating lz>GFP+ blood cells as compared to wild-type ( Fig 3A ) . In addition , as in the mlf mutant , crystal cells from dnaj-1 mutant larvae still expressed the differentiation marker PPO1 and were capable of melanization upon heat treatment ( Fig 3C–3H ) . A closer examination also revealed the presence of unusually large lz>GFP+ cells in the dnaj-1 mutant and quantitative analyses confirmed that dnaj-1 loss caused a significant increase in lz>GFP+ cell size whereas lz>GFP- cells were unaffected ( Fig 3B ) . Interestingly , a similar phenotype is observed in mlf mutant larvae ( Fig 3B ) , suggesting that both genes not only control crystal cell number but also their differentiation ( see below ) . Importantly , lz>GFP+ cell number and size was restored to wild-type when DnaJ-1 was re-expressed in the crystal cell lineage of dnaj-1A/C mutant larvae using the lz-GAL4 driver ( Fig 3A and 3B ) . This demonstrates not only that these phenotypes are specifically caused by the dnaj-1 mutation , but also that DnaJ-1 acts cell autonomously and after the onset of lz expression in the crystal cell lineage . Of note , we did not observe a rescue when we expressed a DnaJ-1 protein lacking its J-domain , suggesting that the interaction with Hsp70 chaperones is critical for the function of DnaJ-1 in the crystal cell lineage ( S3C and S3D Fig ) . Furthermore , the increase in crystal cell number and size was also observed when we monitored crystal cell presence by immunostaining against PPO1 in larvae carrying a dnaj-1A or dnaj-1C homozygous mutation or over a deficiency uncovering the dnaj-1 locus ( S3E and S3F Fig ) . Overall , these results demonstrate that , like mlf , dnaj-1 controls circulating larval lz>GFP+ cell number and size . Since MLF and DnaJ-1 bind to each other , we tested whether they genetically interacted to regulate crystal cell development . While heterozygous mutation in either mlf or dnaj-1 did not significantly alter circulating lz>GFP+ cell number or size , mlfΔC1/+ , dnaj-1A/+ transheterozygote larvae displayed a significant increase of both parameters ( Fig 3I and 3J ) . We thus conclude that DnaJ-1 and MLF act together to control crystal cell development . In sum , these results reveal a functional interaction between MLF and DnaJ-1 in vivo . Next we assessed whether DnaJ-1 affects Lz stability in vivo as it does in cell culture . Unexpectedly , immunostaining against Lz did not reveal a decrease in Lz expression in dnaj-1 mutant crystal cells while the level of Lz was clearly lower in the mlf mutant ( Fig 4A–4C ) . Actually quantitative analyses revealed a slight ( 30% ) but significant ( p = 0 . 006 ) increase in Lz level in dnaj-1 mutant as compared to wild-type , whereas Lz level dropped by more than 2 folds in mlf mutant ( Fig 4D ) . Thus , unlike mlf , dnaj-1 loss is not sufficient to destabilize Lz in vivo . We then tried to understand the reason for this discrepancy . One potentially important difference between Kc167 cells , in which DnaJ-1 is required to stabilize Lz , and crystal cells , in which it is not , is MLF expression . Indeed , in Kc167 cells , MLF is mainly detected in the cytoplasm and is present at low levels in the nucleus ( S4A Fig ) . In contrast , MLF is present at high levels in the nucleus of larval crystal cells ( S4B Fig ) . Moreover , MLF expression in this lineage is not affected by dnaj-1 loss ( S4C and S4F Fig ) . We thus surmised that the presence of high levels of nuclear MLF might prevent Lz degradation in the absence of DnaJ-1 . To test this hypothesis , we designed two complementary experiments . On the one hand , we assessed whether MLF over-expression in Kc167 cells could protect Lz from degradation following dnaj-1 knockdown . Lz level dropped when Kc167 cells were treated with a dsRNA targeting dnaj-1 ( Fig 4G ) and increased upon over-expression of MLF ( Fig 4F ) . Strikingly though , and in line with the observations in dnaj-1 mutant crystal cells , the level of Lz was not reduced but further increased when dnaj-1 was knocked down in MLF-overexpressing cells ( Fig 4H and 4I ) . On the other hand , we asked whether Lz would still be stable in dnaj-1 mutant crystal cells if MLF level is decreased . Accordingly , we expressed a dsRNA directed against mlf in lz>GFP+ cells , which caused a significant and similar knock-down of MLF in wild-type and dnaj-1 mutant larvae ( S4D–S4F Fig ) . Remarkably , we found that the drop in Lz protein level caused by mlf down-regulation was significantly enhanced in dnaj-1 deficient larvae , while the dnaj-1 mutation alone increased Lz level ( Fig 4J–4N ) . Hence it appears that in the absence of DnaJ-1 , high levels of MLF prevent Lz degradation . Given that chaperones are important for proper protein folding [35 , 36] , we postulated that Lz proteins accumulating in crystal cells in the absence of DnaJ-1 might be less active . Thus increasing Lz expression might be sufficient to rescue lz>GFP+ cell number and size . In addition , although re-expressing Lz is sufficient to restore lz>GFP+ cell number in mlf mutant larvae [26] , it is not known whether this also rescues lz>GFP+ cell size . Interestingly , lz>GFP+ cell count and cell size were restored to wild-type levels when we enforced Lz expression in this lineage either in mlf or dnaj-1 mutant larvae ( Fig 4O and 4P ) . We thus conclude that DnaJ-1 and MLF act together to control crystal cell development by regulating Lz activity in vivo In parallel , to gain further insights into the function of MLF in the control of crystal cell development , we established the transcriptome of circulating lz>GFP+ blood cells in wild-type and mlf larvae . Heterozygous lz-GAL4 , UAS-mCD8-GFP L3 larvae carrying or lacking a mlf null mutation were bled , lz>GFP+ cells were collected by FACS and their gene expression profile was determined by RNA sequencing ( RNAseq ) from biological triplicates . Using Drosophila reference genome dm3 , we detected the expression of 7399 genes ( 47% of the total fly genes ) in each of the 6 samples ( Fig 5B and S1 Table ) . Consistent with the role of the crystal cells as the main source of phenoloxidases [39] , the two most strongly expressed genes were PPO1 and PPO2 . In addition , lz expression as well as that of several other crystal cell markers was readily detected ( see below ) . It was recently shown that larval circulating Lz+ cells derive from plasmatocytes , which express Hemolectin ( Hml ) and Nimrod C1 ( NimC1 ) , and transdifferentiate into crystal cells [40] . Accordingly , we detected the expression of these genes , as well as other “plasmatocytes” markers such as peroxidasin and croquemort ( which were actually shown to be also expressed in crystal cells [41 , 42] ) in lz>GFP+ cells . Using DESeq2 to identify differentially expressed genes between wild-type and mlf mutant lz>GFP+ cells , we found 779 genes with significantly altered expression ( adjusted p-value <0 . 01 ) : the transcript level of 469 genes was decreased and that of 310 genes was increased in the absence of MLF ( Fig 5A and 5B , and S2 Table ) . In line with our previous in situ hybridization results [26] , RNAseq analysis did not reveal a significant modification of PPO1 or PPO2 expression in the absence of mlf . However , the lz transcript level was reduced by ±2 fold ( p-value = 0 . 0018 ) , which could be due to defective maintenance of the lz auto-activation loop [43] . To assess whether other crystal cell markers were affected by mlf , we established a compilation of genes expressed in ( embryonic or larval ) crystal cells based on Flybase data mining and re-examination of Berkeley Drosophila Genome Project in situ hybridizations ( http://insitu . fruitfly . org/cgi-bin/ex/insitu . pl ) ( S3 Table ) . Among these 129 genes ( i . e . excluding mlf itself ) , 44 ( 34% ) were differentially expressed in the absence of mlf ( 19 repressed and 25 activated ) ( Fig 5C ) , indicating a strong over-representation of deregulated gene in the “crystal cell” gene set as compared to all expressed genes ( p-value = 2 . 6x10-13 , hypergeometric test ) and showing that mlf plays a crucial role for proper crystal cell differentiation . To substantiate these results , we analyzed by in situ hybridization the expression of 4 genes that were either down-regulated ( CG7860 and Oscillin ) or up-regulated ( CG6733 and Jafrac1 ) in the mlf mutant . CG7860 and Oscillin were specifically expressed in lz>GFP+ but not in the surrounding lz>GFP- hemocytes in wild-type conditions ( Fig 5D and 5G ) . Consistent with our RNAseq data , the expression of CG7860 and Oscillin was strongly reduced in mlf mutant larvae . Although CG6733 is expressed in embryonic crystal cells [43] , we did not detect its expression in circulating hemocytes of wild-type larvae , but it was expressed in the lz>GFP+ lineage in mlf larvae ( Fig 5J and 5K ) . Finally , Jafrac1 expression increased in lz>GFP+ cells of mlf mutant larvae as compared to wild-type , whereas its ( lower ) expression in lz>GFP- blood cells seemed similar ( Fig 5M and 5N ) . These data thus confirm the RNAseq results and demonstrate that MLF controls the expression of several crystal cell markers . Since the above results indicate that MLF functionally interacts with DnaJ-1 during crystal cell development , we also tested whether these four genes were deregulated in the dnaj-1 mutant . As for mlf , we observed that a dnaj-1 mutation caused a down-regulation of CG7860 and Oscillin and an up-regulation of CG6733 and Jafrac1 expression in lz>GFP+ blood cells ( Fig 5F , 5I , 5L and 5O ) . In sum it appears that the loss of mlf or dnaj-1 leads to a deregulation of the crystal cell gene expression program characterized both by the overexpression and the downregulation of crystal cell markers . Therefore mlf and dnaj-1 are required for proper differentiation of the Lz+ blood cell lineage . Interestingly , the levels of Notch receptor transcripts were significantly higher in the mlf mutant ( p = 1 . 3x10-6 ) ( Fig 5C ) . Notch signaling plays a key role in crystal cell development [27]: Notch is first activated by its ligand Serrate to specify Lz+ cells ( crystal cell precursors ) and its activation is subsequently maintained in Lz+ cells in a ligand-independent manner to promote crystal cell growth and survival [29–31 , 40 , 44] . The rise in lz>GFP+ cell number and size observed in mlf and dnaj-1 mutant could thus be due to increased ligand-independent Notch signaling . However , the role of Notch signaling in crystal cell growth and survival has been mainly investigated in the larval lymph gland [30 , 31] . In agreement with these investigations , inhibiting the Notch pathway in circulating Lz+ cells , either by down-regulating the expression of Suppressor of Hairless [Su ( H ) ] , the core transcription factor in the Notch pathway , or by overexpressing Suppressor of Deltex [Su ( dx ) ] , a negative regulator of Notch [45] , resulted in a decrease in lz>GFP+ cell number and impaired their growth , whereas the overactivation of Notch signaling consecutive to the expression of a constitutively active Su ( H ) -VP16 fusion protein [46] , caused a strong increase in lz>GFP+ cell number and size ( S5 Fig ) . Then we further investigated the level of Notch expression and activation in mlf and dnaj-1 mutant blood cells . Immunostaining using an antibody against the Notch extracellular domain ( NECD ) showed that Notch accumulated at higher levels in lz>GFP+ cells of mlf and dnaj-1 mutant larvae than in wild-type conditions ( Fig 6A–6C ) . Quantitative analyses confirmed that mlf loss caused a significant increase in Notch level in lz>GFP+ cell , whereas the ( lower ) expression of Notch in lz>GFP- blood cells was not affected ( Fig 6D ) . Similar results were obtained when we measured Notch protein levels using an antibody directed against its intra-cellular domain ( NICD ) ( Fig 6E and S6 Fig ) . Thus Notch level is specifically increased in lz>GFP+ cells of mlf and dnaj-1 mutants . Next , we tested whether this resulted in increased signaling by monitoring the expression of two Notch signaling pathway reporter genes expressed in larval crystal cells: Klumpfuss-Cherry [31] and NRE-GFP [47] . Both mlf and dnaj-1 loss were associated with a strong increase in the expression of these reporters ( Fig 6F–6J ) . Thus mlf and dnaj-1 are required to tune down Notch signaling in the crystal cell lineage . Finally , we asked whether the rise in lz>GFP+ cell size and/or number observed in mlf and dnaj-1 mutants depends on Notch . Strikingly , when we reduced Notch dosage by introducing one copy of the N55e11 null allele in these mutants , both parameters were restored to control levels , while N55e11 heterozygote mutation had no effect per se ( Fig 6K and 6L ) . Collectively , these data strongly support the hypothesis that the increase in Notch level underlies lz>GFP+ cell expansion in mlf and dnaj-1 mutants . It was shown that crystal cells tend to increase their size as they mature in response to Notch signaling [31 , 40] , which is consistent with the results we obtained by manipulating Notch signaling activity in Lz+ cells ( S5 Fig ) . To better characterize the defects associated with mlf or dnaj-1 loss , we analyzed the distribution of lz>GFP+ cells as well as Notch level according to lz>GFP+ cell size categories . Whereas cells more than 1 . 3-fold larger than the mean wild-type cell size represented a small fraction ( ±10% ) of the lz>GFP+ population in wild-type larvae , they constituted the prevalent population in mlf or dnaj-1 mutant ( respectively 49 . 6 and 37% ) ( Fig 7A ) . Interestingly , Notch protein level was maximum in the population of lz>GFP+ cells of mean cell size but lower in larger cells of wild-type larvae ( Fig 7B ) , whereas it continued to increase in the larger cell populations of mlf or dnaj-1 larvae ( Fig 7B–7D ) . Actually we observed a similar trend when we monitored Notch expression by in situ hybridization . In wild-type larvae , Notch transcripts were readily seen in small/medium lz>GFP+ cells but barely detectable in large lz>GFP+ cells ( Fig 7E and 7F ) . In contrast , Notch transcripts continued to accumulate in large lz>GFP+ cells from mlf or dnaj-1 mutant larvae ( Fig 7H and 7J ) . Hence , MLF/DnaJ-1 loss is associated with the accumulation of large crystal cells exhibiting aberrant maintenance of Notch expression . Since the Notch pathway is activated in a ligand-independent manner in Lz+ cells [30] , a tight regulation of the level of Notch is particularly critical to control crystal cell growth and number . All together , our data suggest that in mlf or dnaj-1 mutant larvae , Notch expression fails to be turned down when lz>GFP+ cells reach a critical size , leading to the maintenance of a high level of Notch signaling and thus to increased crystal cell growth and survival . We showed above that forcing the expression of Lz rescues the increase in crystal cell number and size caused by mlf or dnaj-1 loss . It is thus plausible that this RUNX transcription factor directly participates in down-regulation of Notch signaling . To explore this hypothesis , we asked whether a reduction in lz activity might cause an expansion of the Lz+ cell lineage associated with an over-activation of the Notch pathway . Accordingly , we introduced the lzr1 null allele into the lzGAL4 context . This hypomorphic allelic combination caused a decrease in Lz expression ( Fig 8B ) and resulted in an increase in lz>GFP+ cell number and size ( Fig 8E and 8F ) . Interestingly , lzGAL4/Y hemizygous larvae displayed similar phenotypes ( Fig 8C , 8E and 8F ) , indicating that this P{GAL4} insertion in lz alters its expression in the crystal cell lineage . As an alternate strategy , we interfered with Lz activity by expressing a fusion protein between Lz partner Brother ( Drosophila CBFß homolog ) and the non-muscular myosin heavy chain SMMHC [48] . This chimera mimics the CBFß-MYH11 fusion protein generated by the Inv ( 16 ) translocation in human AML and can sequester RUNX factors in the cytoplasm [1 , 49] . Bro-SMMHC expression in lz>GFP+ cells titrated Lz from the nucleus and also caused an increase in lz>GFP+ cell number and size ( Fig 8D–8F ) . Importantly , the expression of the Notch pathway reporters NRE-GFP and Klu-Cherry was strongly increased in lzGAL4/lzR1 mutant or upon Bro-SMHCC expression in the Lz+ blood cell lineage ( Fig 8G and 8H ) . Moreover , knocking down Su ( H ) or over-expressing the Notch protein inhibitor Su ( dx ) was sufficient to prevent the rise in lz>GFP+ cell number and size of lzGAL4/Y hemyzigotes ( S5 Fig ) . Thus , a reduction in lz activity causes similar defects as the mlf or dnaj-1 mutations and likely involves the overactivation of the Notch pathway . Then we analyzed the relathionship between Lz and Notch levels . In Lz+ cells of increasing size , Lz levels continuously increased while Notch became less abundant ( S7A Fig ) . This suggested that Lz level rises as crystal cells grow/mature and , in view of the above results , we surmised that this increase might participate in the down-regulation of the Notch receptor . Indeed , we found that the Notch receptor level was significantly augmented in lz>GFP+ cells of hypomorphic lzGAL4/Y hemyzigote mutant larvae , whereas it was reduced when Lz was over-expressed ( Fig 9A–9E ) . In addition , the increase in Notch expression observed in lzGAL4/Y larvae was suppressed by forcing Lz expression . Moreover , in situ hybridization experiments revealed that , unlike in control larvae , Notch expression was not repressed in large lz>GFP+ cells in lzGAL4/Y larvae ( S7 Fig ) . Therefore Notch might be a direct transcriptional target of Lz . By analyzing the expression of different GAL4 lines that cover potential Notch regulatory regions [50] , we identified two lines that drive expression in circulating Lz+ blood cells ( Fig 9F and S7 Fig ) . The regulatory elements carried by these two lines ( GMR30A01 and GMR30C06 ) overlap on a 668bp DNA segment that contains two consensus binding sites for RUNX transcription factors conserved in other Drosophila species ( S7A Fig ) , suggesting that Lz might directly regulate Notch transcription by targeting this region . We thus tested the effect of Lz dosage manipulation on the activity of this enhancer-GAL4 line . Strikingly , a hypomorphic lozenge mutation ( lzg/Y ) [51] or the expression of Bro-SMMHC caused an increase in the expression of this enhancer , whereas the over-expression of Lz resulted in its down-regulation ( Fig 9G–9K ) . These findings strongly argue that Lz directly represses Notch expression . All together , these results demonstrate that high levels of Lz are required to prevent the accumulation of over-grown lz>GFP+ cells as well as over-activation of the Notch pathway , and we propose that Lz-mediated repression of Notch transcription is critical during this process .
Members of the RUNX and MLF families have been implicated in the control of blood cell development in mammals and Drosophila and deregulation of their expression is associated with human hemopathies including leukemia [1 , 9 , 15 , 52] . Our results establish the first link between the MLF/DnaJ-1 complex and the regulation of a RUNX transcription factor in vivo . In addition , our data show that the stabilization of Lz by the MLF/DnaJ-1 complex is critical to control Notch expression and signaling and thereby blood cell growth and survival . These findings pinpoint the specific function of the Hsp40 chaperone DnaJ-1 in hematopoiesis , reveal a potentially conserved mechanism of regulation of RUNX activity and highlight a new layer of control of Notch signaling at the transcriptional level . In line with results published as this manuscript was in preparation [20] , we found that MLF binds DnaJ-1 and Hsc70-4 and that these two proteins , like MLF , are required for Lz stable expression in Kc167 cells . In addition , our data show that MLF and DnaJ-1 bind to each other via evolutionarily conserved domains and also interact with Lz , suggesting that Lz is a direct target of a chaperone complex formed by MLF , DnaJ-1 and Hsc70-4 . Of note , a systematic characterization of Hsp70 chaperone complexes in human cells identified MLF1 and MLF2 as potential partners of DnaJ-1 homologs , DNAJB1 , B4 and B6 [53] , a finding corroborated by Dyer et al . [20] . Therefore , the MLF/DnaJ-1/Hsc70 complex could play a conserved role in mammals , notably in the regulation of the stability of RUNX transcription factors . How MLF acts within this chaperone complex remains to be determined . In vivo , we demonstrate that dnaj-1 mutations lead to defects in crystal cell development strikingly similar to those observed in mlf mutant larvae and we show that these two genes act together to control Lz+ cells development by impinging on Lz activity . Our data suggest that in the absence of DnaJ-1 , high levels of MLF lead to the accumulation of defective Lz protein whereas lower levels of MLF allow its degradation . We thus propose that MLF stabilizes Lz and , together with DnaJ-1 , promotes its proper folding/conformation . In humans , DnaJB4 stabilizes wild-type E-cadherin but induces the degradation of mutant E-cadherin variants associated with hereditary diffuse gastric cancer [54] . Thus the fate of DnaJ client proteins is controlled at different levels and MLF might be an important regulator in this process . In this work , we present the first null mutant for a gene of the DnaJB family in metazoans and our results demonstrate that a DnaJ protein is required in vivo to control hematopoiesis . There are 16 DnaJB and in total 49 DnaJ encoding genes in mammals and the expansion of this family has likely played an important role in the diversification of their functions [55 , 56] . DnaJB9 overexpression was found to increase hematopoietic stem cell repopulation capacity [57] and Hsp70 inhibitors have anti-leukemic activity [58] , but the participation of other DnaJ proteins in hematopoiesis or leukemia has not been explored . Actually DnaJ’s molecular mechanism of action has been fairly well studied but we have limited insights as to their role in vivo . Interestingly though , both DnaJ-1 and MLF suppress polyglutamine protein aggregation and cytotoxicity in Drosophila models of neurodegenerative diseases [17 , 23 , 24 , 59–63 , 64] , and this function is conserved in mammals [24 , 25 , 65 , 66] . It is tempting to speculate that MLF and DnaJB proteins act together in this process as well as in leukemogenesis . Thus a better characterization of their mechanism of action may help develop new therapeutic approaches for these diseases . As shown here , mlf or dnaj-1 mutant larvae harbor more crystal cells than wild-type larvae . This rise in Lz+ cell number is not due to an increased induction of crystal cell fate as we could rescue this defect by re-expressing DnaJ-1 or Lz with the lz-GAL4 driver , which turns on after crystal cell induction , and it was also observed in lz hypomorph mutants , which again suggests a post-lz / cell fate choice process . Moreover mlf or dnaj-1 mutant larvae display a higher fraction of the largest lz>GFP+ cell population , which could correspond to the more mature crystal cells [31 , 40] . It is thus tempting to speculate that mlf or dnaj-1 loss promotes the survival of fully differentiated crystal cells . Our RNAseq data demonstrate that mlf is critical for expression of crystal cell associated genes , but we observed both up-regulation and down-regulation of crystal cell differentiation markers in mlf or dnaj-1 mutant Lz+ cells . Also these changes did not appear to correlate with crystal cell maturation status since we found alterations in gene expression in the mutants both in small and large Lz+ cells . In addition our transcriptome did not reveal a particular bias toward decreased expression for “plasmatocyte” markers in Lz+ cells from mlf- mutant larvae . Thus , it appears that MLF and DnaJ-1 loss leads to the accumulation of mis-differentiated crystal cells . Our data support a model whereby MLF and DnaJ-1 act together to promote Lz accumulation , which in turn represses Notch transcription and signaling pathway to control crystal cell size and number ( Fig 10 ) . Indeed , we observe an abnormal maintenance of Notch expression in the larger Lz+ cells as well as an over-activation of the Notch pathway in the crystal cell lineage of mlf and dnaj-1 mutants or when we interfere with Lz activity . Moreover our data as well as previously published experiments show that Notch activation promotes crystal cell growth and survival [30 , 31 , 40] . Importantly too the increase in Lz+ cell number and size observed in mlf or dnaJ-1 mutant is suppressed when Notch dosage is decreased . Yet , some of the mis-differentiation phenotypes in the mlf or dnaj-1 mutants might be independent of Notch since changes in crystal cell markers expression seem to appear before alterations in Notch are apparent . At the molecular level , our results suggest that Lz directly represses Notch transcription as we identified a Lz-responsive Notch cis-regulatory element that contains conserved RUNX binding sites . The activation of the Notch pathway in circulating Lz+ cells is ligand-independent and mediated through stabilization of the Notch receptor in endocytic vesicles [30 , 45] . Hence a tight control of Notch expression is of particular importance to keep in check the Notch pathway and prevent the abnormal development of the Lz+ blood cell lineage . Notably , Notch transcription was shown to be directly activated by Notch signaling [67] . Such an auto-activation loop might rapidly go awry in a context in which Notch pathway activation is independent of ligand binding . By promoting the accumulation of Lz during crystal cell maturation , MLF and DnaJ-1 thus provide an effective cell-autonomous mechanism to inhibit Notch signaling . Further experiments will now be required to establish how Lz represses Notch transcription . RUNX factors can act as transcriptional repressors by recruiting co-repressor such as members of the Groucho family [68] . Whether MLF and DnaJ-1 directly contribute to Lz-induced-repression in addition to regulating its stability is an open question . MLF and DnaJ-1 were recently found to bind and regulate a common set of genes in cell culture [20] . They may thus provide a favorable chromatin environment for Lz binding or be recruited with Lz and/or favor a conformation change in Lz that allows its interaction with co-repressors . The scarcity of lz>GFP+ cells precludes a biochemical characterization of Lz , MLF and DnaJ-1 mode of action notably at the chromatin level but further genetic studies should help decipher their mode of action . While the post-translational control of Notch has been extensively studied , its transcriptional regulation seems largely overlooked [69] . Our findings indicate that this is nonetheless an alternative entry point to control the activity of this pathway . Given the importance of RUNX transcription factor and Notch signaling in hematopoiesis and blood cell malignancies [1 , 2] , it will be of particular interest to further study whether RUNX factors can regulate Notch expression and signaling during these processes in mammals . In conclusion , our study shows that MLF and DnaJ-1 act together to regulate RUNX transcription factor activity , which in turn controls Notch signaling during hematopoiesis in vivo . We anticipate that the extraordinary genetic toolbox available in Drosophila will help shed further light on the mechanism of action of these evolutionarily conserved proteins and will bring valuable insights into the control of protein homeostasis by MLF and DnaJ-1 during normal or pathological situations .
The following Drosophila melanogaster lines were used: mlfΔC1 , UAS-mlf [17] , UAS-ds-mlf ( National Institute of Genetics ) , UAS-lz , lzGAL4 , UAS-mCD8-GFP , lzg , lzr1 , N55e11 , UAS-dsSu ( H ) , P{EPgy2}DnaJ-1EY04359 , UAS-dnaj-1 , Def ( 3L ) BSC884 , vas-Cas9 , UAS-GFPnls , NRE-GFP , GMR30C06 , GMR30A01 , UAS-dsSu ( H ) ( Bloomington Drosophila Stock Center ) , Bc-GFP [70] , Klu-mCherry [31] UAS-Bro-SMMHC [48] , UAS-DnaJ-1ΔJ [61] , UAS-dsSu ( H ) , UAS-Su ( H ) -VP16 [46] , UAS-Su ( dx ) [71] . To generate dnaj-1 deficient flies , we designed two guide RNA targeting dnaj-1 locus ( S4 Fig ) and the corresponding DNA oligonucleotides ( g2: GTCGACCACAACGCGCCGGATCAA; g3: GTCGCATCACAGTCACGCTTTCCT ) were cloned in pCFD3 ( Addgene ) . vas-cas9 females were crossed to P{EPgy2}DnaJ-1EY04359 males and the resulting embryos were injected using standard procedures with both pCFD3-g2 and pCFD3-g3 plasmids ( 500ng/ul ) . Deletion of the P{EPgy2}EY04359 transposon , as revealed by loss of the w+ marker , was screened for at the F2 generation , and deletion of dnaj-1 locus was assessed by PCR and sequencing . All crosses were conducted at 25°C on standard food medium as described in [72] . For each sample , four third instar larvae were bled ( or 5 . 103 Kc167 cells were dispensed ) in 1ml of PBS in 24-well-plate containing a glass coverslip . Unless mentioned otherwise , only female larvae were used . The hemocytes were centrifuged for 2 min at 900g , fixed for 20 min with 4% paraformaldehyde in PBS and washed twice in PBS . For immunostainings: cells were permeabilized in PBS-0 . 3% Triton ( PBST ) and blocked in PBST- 1% Bovine Serum Albumin ( BSA ) . The cells were incubated with primary antibodies at 4°C over night in PBST-BSA , washed in PBST , incubated for 2h at room temperature with corresponding Alexa Fluor-labeled secondary antibodies ( Molecular Probes ) , washed in PBST and mounted in Vectashield medium ( Eurobio-Vector ) following incubation with Topro3 ( ThermoFisher ) . The following antibodies were used: anti-Lz , anti-Notch intracellular domain , anti-Notch extracellular domain ( Developmental Studies Hybridoma Bank , DSHB ) , anti-MLF [73] , anti-PPO1 [74] , anti-GFP ( Fisher Scientific ) , anti-HA ( Sigma ) . For in situ hybridizations: after fixation , the cells were washed and permeabilized in PBS- 0 . 1% Tween 20 ( PBSTw ) , pre-incubated for 1h at 65°C in HB buffer ( 50% formamide , 2x SSC , 1 mg/ml Torula RNA , 0 . 05 mg/ml Heparin , 2% Roche blocking reagent , 0 . 1% CHAPS , 5 mM EDTA , 0 . 1% Tween 20 ) and incubated over-night with anti-sense DIG-labeled RNA probes ( against CG6733 , CG7860 , Jafrac , Notch and Oscillin ) diluted in HB . The samples were washed in HB for 1h at 65°C , in 50% HB- 50% PBSTw for 30 min at 65°C and three times in PBSTw for 20 min at room temperature . Then the cells were incubated for 30 min in PBSTw- 1% BSA before being incubated with anti-DIG antibody conjugated to alkaline phosphatase ( Roche , 1/2000 in PBSTw ) for 3h . After 4 washes in PBSTw , in situ hybridization signal was revealed with FastRed ( Roche ) . The cells were then processed for immunostaining against GFP as described above , incubated in Topro3 , washed in PBS and mounted in Vectashield medium for analysis . Experiments were performed using at least biological triplicates . Samples were imaged with laser scanning confocal microscopes ( Leica ) and images were analyzed with ImageJ . Cell size and protein expression levels were measured on maximal intensity projections of Z-sections through the whole cell on a minimum of 25 cells per genotype . The following previously described plasmids were used: pAc-Lz-V5 , 4xPPO2-Firefly luciferase ( originally named 4xPO45-Fluc , [37] ) , pAc-MLF [17] . We generated the following Drosophila expression plasmids for C-terminally tagged or N-terminally tagged proteins using standard cloning techniques: pAc-Lz-EGFP , pAc-MLF-EGFP , pMT-MLF-V5-His , pAc-DnaJ-J1-EGFP , pAc-Hsc70-4-EGFP , pAc-3xHA-DnaJ-1 ( 2–334 ) , pAc-3xHA-DnaJ-1 ( P32S ) , pAc-3xHA-DnaJ-1 ( 58–334 ) , pAc-3xHA-DnaJ-1 ( 2–156 ) , pAc-3xHA-DnaJ-1 ( 2–191 ) , pAc-3xHA-DnaJ-1 ( 2–269 ) , pAc-3xHA-DnaJ-1 ( 157–334 ) , pAc-3xHA-MLF ( 2–309 ) , pAc-3xHA-MLF ( 2–147 ) , pAc-3xHA-MLF ( 2–202 ) , pAc-3xHA-MLF ( 202–309 ) , pAc-3xHA-MLF ( 148–309 ) , pAc-3xHA-MLF ( 96–309 ) , pAc-3xHA-MLF ( 96–202 ) . DnaJ-1 and MLF cDNA were also cloned into pBlueScript II to generate pBS-DnaJ-1 and pBS-MLF and in pGEX-2T to generate pGEX-DnaJ-1 and pGEX-MLF . All constructs were verified by sequencing . Drosophila Kc167 cells were grown at 25°C in Schneider medium ( Invitrogen ) supplemented with 10% fetal bovine serum ( FBS ) and 50 μg/ml of penicillin/streptomycin ( Invitrogen ) . For RNAi experiments , double stranded RNA duplexes ( dsRNA ) corresponding to 400-600bp exonic regions were produced using T7 promoter-containing primers and MEGAscript T7 transcription kit ( Ambion ) . After an annealing step , dsRNA probes were purified using the RNeasy cleanup protocol ( Qiagen ) . Independent dsRNA targeting different regions of dnaj-1 were produced . The sequences of the T7-containing primers used to generate the dsRNA are available on request . Cells were seeded at 2x106/ml on dsRNA ( 16 μg/well for 6-well-plate , 8 μg for 12-well-plate and 1 μg for 96-well-plate ) and incubated in Schneider medium without FBS for 40 min before being transferred to 5% FBS containing medium . 24h later , cells were transfected with the plasmids of interest using Effectene ( Qiagen ) and they were collected 72h later for subsequent analyses . For luciferase assays , 50 ng of 4xPPO2-Firefly luciferase reporter plasmid , were contransfected with 20 ng of pAc-Renilla luciferase plasmid , 10 ng of pAc-Lz-V5 and/or 10 ng of pAc expression plasmid for the protein of interest in 96 well-plate . Firefly and Renilla luciferases activities were measured 72h after transfection using Promega Dual luciferase reporter assay . Three biological replicates were performed for each transfection assay . For RT-qPCR , RNAs were prepared from Kc167 cells using RNeasy kit ( Qiagen ) with an additional on-column DNAse treatment step . 1 μg of total RNA was used for reverse transcription using Superscript II and random primers ( Invitrogen ) . 10 μl of a 1/300 dilution of cDNA was used as template for real time PCR using HOT Pol Evagreen qPCR mix ( Bio-rad ) . The sequences of the primers used to assess the expression of dnaj-1 , mlf , lz , PPO2 , Renilla luciferase and rp49 are available upon request . All experiments were performed using biological triplicates or quadruplicates . pET-3c-Lz , pBS-MLF and pBS-DnaJ-1 plasmids were used as template to produce 35S-methionine-labeled proteins in vitro using Rabbit Reticulocyte Lysate coupled transcription-translation system ( Promega ) . pGEX-2T , pGEX-MLF and pGEX-DnaJ-1 were used to produce GST , GST-MLF and GST-DnaJ-1 in Escherichia coli BL21 . Equivalent amounts of GST purified proteins immobilized on Gluthation-Sepharose beads were used to pull down Lz , MLF or DnaJ-1 . Proteins were incubated for 2h at 4°C in buffer A ( 20 mM Tris–HCl , pH 8 . 0 , 150 mM NaCl , 10 mM KCl , 1 mM EDTA , 0 . 1mg/ml BSA , 1 mM DTT , 0 . 05% NP40 ) . After extensive washing in buffer buffer B ( 20 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 1mM DTT , 0 . 05% NP40 ) , bound proteins were eluted in SDS-loading buffer , separated by SDS–PAGE and visualized by autoradiography . Kc167 cells were collected , washed in PBS and incubated for 30 min in IP buffer ( 150 mM NaCl , 0 . 5% NP40 , 50 mM Tris-HCl , pH8 . 0 , 1mM EGTA ) supplemented with protease inhibitor cocktail ( Roche ) . The extracts were cleared by centrifugation at 13 . 000g for 15 min at 4°C and subjected to SDS-PAGE ( 50 μg of proteins par lane ) or immunoprecipitation ( 1 mg per point ) . For immunoprecipitation , proteins were preadsorbed with 100 μl of sepharose beads slurry for 1h at 4°C before being incubated with 20 μl of anti-GFP ( Chromotek ) , anti-V5 ( Sigma-Aldrich ) or anti-HA ( Covance ) antibody coupled to sepharose beads , or with 10 μl of rabbit anti-MLF [19] or rabbit IgG ( SantaCruz ) in the presence of 20 μl of protein A sepharose beads ( Sigma ) , for 4h at 4°C . The beads were spun down and washed in IP buffer and immunoprecipitated proteins were processed for SDS-PAGE and Western Blot analyses . Western blots were performed using standard techniques and the blots were developed by photoluminescence procedure using Lumi-LightPLUS Western Blotting Substrate ( Roche ) and Amersham HyperfilmTM ECL ( GE Healthcare ) or Chemidoc Touch Imaging System ( BioRad ) . The following antibodies were used for Western blots: anti-V5 ( Invitrogen ) , anti-HA ( BioLegend ) , anti-GFP , anti-tubulin ( Sigma-Aldrich ) , anti-Renilla luciferase ( MBL ) , and anti-MLF [19] . Stable Kc167 cells carrying an inducible expression vector for MLF were obtained by cotransfecting pMT-MLF-V5-His and pCoBlast ( Thermo Fisher Scientific ) expression plasmids and selecting individual clones with 25μg/ml blasticidin . For affinity purification , MLF-inducible or parental Kc167 cells were seeded at 106/ml and cultivated for 24h in the presence of 50 mM CuSO4 to induce MLF expression . 20 mg of proteins extracted in IP buffer were then incubated on 200 μl of anti-V5 coupled sepharose beads ( Sigma-Aldrich ) or 400 μl of anti-V5 coupled magnetic beads ( MBL ) . After several washes in IP buffer , affinity purified proteins were eluted in Laemmli buffer , reduced in 30 mM DTT and alkylated with 90 mM Iodoacetamide before being loaded on 12% SDS-PAGE . The single band of proteins was cut and digested overnight at 37°C with 1 μg of Trypsin ( Promega ) in 50 mM NH4CO3 . Digested peptides were extracted from the gel by incubating 15 min at 37°C in 50 mM NH4CO3 and twice for 15 min at 37°C in 5% formic acid/acetonitrile ( 1:1 ) . The dried peptide extracts were dissolved in 17 μl of 2% acetonitrile , 0 . 05% trifluoroacetic acid and the peptide mixtures were analyzed by nanoLC-MS/MS using an Ultimate3000-RS system ( Dionex ) coupled to an LTQ-Orbitrap Velos mass spectrometer ( Thermo Fisher Scientific ) . 5 μl of each peptide extract were loaded on a 300 μm ID x 5 mm PepMap C18 precolumn ( LC Packings , Dionex , ) at 20 μl/min in 5% acetonitrile , 0 . 05% trifluoroacetic acid . After 5 minutes desalting , peptides were online separated on a 75 μm ID x 50 cm C18 Reprosil C18 column . The flow rate was set at 300 nl/min . Peptides were eluted using a 0 to 50% linear gradient of solvent B ( solvent A: 0 . 2% formic acid in 5% acetonitrile , solvent B: 0 . 2% formic acid in 80% acetonitrile ) for 80 min at 300nl/min . The LTQ Orbitrap was operated in data-dependent acquisition mode with the XCalibur software ( version 2 . 0 SR2 , Thermo Fisher Scientific ) , on the 350–1800 m/z mass range with the resolution set to a value of 60 000 . The twenty most intense ions per survey scan were selected for CID-MS/MS fragmentation and the resulting fragments were analyzed in the linear ion trap ( parallel mode ) . A 60 s dynamic exclusion window was used to prevent repetitive selection of the same peptide . The Mascot Daemon software ( version 2 . 2 . 0 , Matrix Science , London , UK ) was used for protein identification against a non-redundant SwissProt database . Mascot results were parsed with Mascot File Parsing and Quantification ( MFPaQ ) version 4 . 0 [75] . Quantification of proteins was performed using the label-free module of the MFPaQ software , where a protein abundance index based on the average of peak area values for the three most intense tryptic peptides of the protein was calculated [76] . Triplicate injections were performed . RNAseq experiments were performed using independent biological triplicates . For each sample , around 150 third instar larvae of control ( lz-GAL4 , UAS-mCD8GFP/+ ) or mlf mutant ( lz-GAL4 , UAS-mCD8GFP/+ , mlf∂C1/mlf∂C1 ) genotypes were bled in ice-cold PBS . The hemocytes were centrifuged through a 40 μm mesh at 1000 rpm for 1 min and lz>GFP+ cells were collected by FACS ( FacsAria II ) under a pressure of 20 psi . A fraction of the collected cells were used to control GFP+ cell purification specificity by examination under an epifluorescent microscope after fixation and mounting in Vectashield medium with DAPI . RNAs were extracted from sorted cells using Arcturus PicoPure RNA kit ( Applied Biosystems ) . RNA samples were run on Agilent Bioanalyzer to assess RNA integrity and concentration . The NuGEN Ovation RNASeq system with Ribo-SPIA technology was used to prepare the cDNA according to the manufacturer instruction . Library preparation was performed using the Illumina TruSeq RNASeq library preparation kit . The resulting libraries were sequenced using a 1x50-bp on Illumina HiSeq 2500 . Initial sequence data QC was done using FASTQC . Reads were filtered and trimmed to remove adapter-derived or low quality bases using Trimmomatic and checked again with FASTQC . Illumina reads were aligned to Drosophila reference genome ( BDGP R5/dm3 ) with TopHat and Bowtie2 . Read counts were generated for each annotated gene using HTSeq-Count . RPKM ( Reads Per Kilobase of exon per Megabase of library size ) values were calculated using Cufflinks . Read normalization , variance estimation and pair-wise differential expression analysis with multiple testing correction was conducted using the R Bioconductor DESeq2 package . Heatmaps and hierarchical clustering were generated with R Bioconductor . The RNAseq data were deposited on GEO under the accession number GSE93823 . | Tight regulation of proteins level is required for proper development . Notably , the aberrant expression of key transcription factors or signaling pathway components controlling blood cell development contributes to hematological diseases such as leukemia . In this report , we use Drosophila as a model to study the function and mode of action of a family of conserved but poorly characterized proteins implicated in leukemia called Myeloid Leukemia Factors ( MLF ) . By combining proteomic , transcriptomic and genetic approaches , we show that Drosophila MLF acts in concert with an Hsp40 co-chaperone to control the level and activity of a RUNX transcription factor and therefore RUNX+ blood cell number and differentiation . Furthermore , we show that RUNX dosage directly impinges on the activity of the Notch signaling pathway , which is critical for RUNX+ cell survival and differentiation , by regulating the transcription of the Notch receptor . These findings shed light on a new mode of regulation of RUNX level and Notch activity to prevent abnormal blood cell accumulation , which could be involved in leukemogenesis . | [
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| 2017 | Control of RUNX-induced repression of Notch signaling by MLF and its partner DnaJ-1 during Drosophila hematopoiesis |
Fragile X syndrome ( FXS ) , the most common form of inherited mental retardation , is caused by the loss of functional fragile X mental retardation protein ( FMRP ) . FMRP is an RNA–binding protein that can regulate the translation of specific mRNAs . Adult neurogenesis , a process considered important for neuroplasticity and memory , is regulated at multiple molecular levels . In this study , we investigated whether Fmrp deficiency affects adult neurogenesis . We show that in a mouse model of fragile X syndrome , adult neurogenesis is indeed altered . The loss of Fmrp increases the proliferation and alters the fate specification of adult neural progenitor/stem cells ( aNPCs ) . We demonstrate that Fmrp regulates the protein expression of several components critical for aNPC function , including CDK4 and GSK3β . Dysregulation of GSK3β led to reduced Wnt signaling pathway activity , which altered the expression of neurogenin1 and the fate specification of aNPCs . These data unveil a novel regulatory role for Fmrp and translational regulation in adult neurogenesis .
Fragile X syndrome , one of the most common forms of inherited mental retardation , is caused by the functional loss of fragile X mental retardation protein ( FMRP/Fmrp ) [1] . Patients with fragile X syndrome show an array of deficits in motor control , cognition , learning , and memory , although their overall brain morphology is generally normal . Fmrp is a selective RNA-binding protein that forms a messenger ribonucleoprotein ( mRNP ) complex that can associate with polyribosomes . Evidence suggests that Fmrp is involved in the post-transcriptional regulation of protein synthesis [2]–[4] . Studies from both human patient brain tissues and Fmrp mutant mice suggest that Fmrp is involved in synaptic plasticity and dendritic development . Fmrp mutant mice are found to perform poorly in highly challenging learning tests [5] , particularly the hippocampus-dependent trace learning test [6] , [7] , suggesting that Fmrp is necessary especially for complex learning that requires an intact hippocampus . However , how the functional deficiency of Fmrp results in learning and memory deficits remains unclear . Neurogenesis persists throughout life in two germinal zones , the subgranular zone ( SGZ ) in the dentate gyrus ( DG ) of the hippocampus and the subventricular zone ( SVZ ) of the lateral ventricles . The neurons produced in the DG during adulthood are known to integrate into the existing circuitry of the hippocampus , and young neurons show greater synaptic plasticity than mature neurons under identical conditions [8] , [9] . Although the specific purpose of adult neurogenesis is still being debated , mounting evidence points to an important role in adult neuroplasticity [9]–[11] . It has been suggested that new neurons in the DG are critical for hippocampus-dependent learning [10] , [12] , [13] . Indeed , blocking of adult neurogenesis using generic anti-proliferative drugs or radiation can lead to deficits in learning and memory [14]–[16] . More recent direct evidence has come from inducing the death of new neurons in the hippocampus [17]–[19] and from inhibiting the Wnt signaling pathway in the hippocampus using retrovirus [20] . Adult neurogenesis is regulated at many levels by both extrinsic factors , such as physiological and pathological conditions , and intrinsic factors , such as genetic and epigenetic programs [21] . Although both adult hippocampal neurogenesis and learning are altered in several pathological conditions , such as stress , diabetes , neurological diseases , strokes , and traumatic injuries , the link between adult neurogenesis and mental retardation , a deficiency in learning and memory , remains elusive [9]–[11] . The cellular basis of adult neurogenesis is adult neural progenitor/stem cells ( aNPCs ) . The maintenance and differentiation of aNPCs are tightly controlled by intricate molecular networks [22] . Despite exhaustive efforts devoted to understanding transcriptional regulation in adult neurogenesis , the role of translational control by RNA-binding proteins , such as Fmrp , has gone largely unexplored . Recently , Fmrp was found to be required for the maintenance of Drosophila germline stem cells [23]; however , its function in mammalian embryonic neurogenesis is controversial [24] , [25] . Whether and how Fmrp regulates neural stem cells in the adult mammalian brain and the implications for learning and memory have not been established . Here we show that loss of Fmrp in vitro and in vivo led to altered adult neurogenesis and impaired learning . Fmrp-deficient aNPCs displayed increased proliferation and decreased neuronal differentiation , but increased glial differentiation . We identified specific mRNAs regulated by Fmrp in stem cell proliferation and differentiation , including glycogen synthase kinase 3β ( GSK3ß ) , a negative regulator of ß-catenin and the canonical Wnt signaling pathway that has been implicated in adult neurogenesis [26] , [27] . The loss of Fmrp resulted in reduced ß-catenin levels and a defective Wnt signaling pathway , which in turn led to the downregulation of neurogenin1 ( Neurog1 ) , which is an early initiator of neuronal differentiation and an inhibitor of astrocyte differentiation [28] , [29] . These data not only reveal a novel regulatory role for Fmrp in adult neurogenesis , but also provide direct evidence that adult neurogenesis could be a factor in the pathogenesis of fragile X mental retardation .
To investigate the role of Fmrp in adult neurogenesis , we determined the expression pattern of Fmrp in the dentate gyrus ( DG ) of the adult hippocampus using cell type-specific markers . Consistent with published literature [30] , [31] , Fmrp was enriched in a majority of the granule neurons in the DG ( Figure S1A ) , but was undetectable in either GFAP-positive or S100β-positive astrocytes ( Figure S1B and S1C ) . Using markers specific to immature neural progenitors ( NPCs ) and young neurons , we discovered that Fmrp was also expressed in Sox2 and Nestin double-positive NPCs ( Figure 1A ) , as well as in either NeuroD1-postive or doublecortin ( DCX ) -positive newly generated neurons ( Figure 1B and 1C ) . The presence of Fmrp in these immature cells supports a potential function of this protein in adult neurogenesis . To determine the functions of Fmrp in aNPCs , we isolated aNPCs from both the forebrain and the dentate gyrus ( DG ) of adult Fmr1 knockout ( KO ) mice and wild-type ( WT ) controls . Due to the difficulty of obtaining large numbers of the DG aNPCs , we performed all functional assays first using forebrain aNPCs , and then confirmed our findings using the DG aNPCs . As shown below , we found that both the forebrain aNPCs and the DG aNPCs yielded similar results . Nearly all cultured aNPCs were positive for the progenitor markers Nestin and Sox2 ( Figure 1D ) , suggesting a relative homogeneity of these primary aNPCs . Fmrp was expressed in WT aNPCs , but not in Fmr1 KO aNPCs ( Figure 1E ) . We pulsed the cells with BrdU for eight hours to assess the proliferation of these aNPCs ( Figure 1F ) and found that Fmr1 KO aNPCs exhibited twice as much BrdU incorporation as WT aNPCs ( Figure 1G ) . We further analyzed the cell cycle profiles of aNPCs and found that more Fmr1 KO cells were in mitotic ( G2/M ) phase compared with WT controls ( Figure S2 , 11% higher; n = 3 , p<0 . 02 ) . Hence a lack of functional Fmrp led to a rise in the proliferative capability of aNPCs . To assess the effect of Fmrp on aNPC differentiation , both WT and Fmr1 KO forebrain aNPCs were differentiated for three days , and the phenotypes of differentiated cells were determined using several independent assays . First , differentiated cells were stained using cell lineage-specific antibodies , β-III tubulin ( Tuj1 ) for neurons and glial fibrillary acidic protein ( GFAP ) for astroglia [32] , [33] . Both WT and Fmr1 KO aNPCs could be induced to differentiate into neurons and astrocytes ( Figure 2A and 2B ) ; however , Fmr1 KO aNPCs exhibited a 60 . 4% decrease in neuronal differentiation ( Figure 2C ) and a 74 . 9% increase in astrocyte differentiation ( Figure 2D ) compared with WT aNPCs . Under our culture conditions , only differentiated astrocytes , not proliferating aNPCs , expressed GFAP ( data not shown ) . To validate our immunocytochemical data , we then assessed the neuronal differentiation of aNPCs by measuring the promoter activity of a pan-neuronal transcription factor , neurogenic differentiation 1 ( NeuroD1 ) , and astrocyte differentiation by measuring the promoter activity of GFAP using two well-characterized promoter constructs [34]–[37] . We found that in Fmr1 KO aNPCs , NeuroD1 promoter activity decreased by 31 . 4% ( Figure 2E ) , whereas GFAP promoter activity increased by 73 . 4% ( Figure 2F ) , which is consistent with our immunocytochemistry results . Finally , using real-time quantitative PCR , we further demonstrated that differentiating Fmr1 KO aNPCs had 17 . 8% reduced NeuroD1 mRNA ( Figure 2G , n = 3 , p<0 . 05 ) , but 1 . 5×-fold increased GFAP mRNA ( Figure 2H; n = 3 , p<0 . 05 ) levels . Since the above three methods , immunostaining , promoter activity assay , and real-time PCR , yielded consistent results , we used these assays as interchangeable methods for assessing aNPC differentiation in subsequent experiments . The increased proportion of astrocytes in differentiating Fmr1 KO aNPCs was not due to an increased proliferation of newly differentiated astrocytes , because GFAP+ astrocytes differentiated from Fmr1 KO aNPCs did not incorporate more BrdU compared with those from WT aNPCs ( data not shown ) . The differentiation to oligodendrocytes was no different between Fmr1 KO and WT aNPCs ( data not shown ) . To confirm that the altered fate specification of Fmr1 KO aNPCs was due to the loss of functional Fmrp , we used siRNA ( Fmr1-siRNA , Figure S3 ) to knock down Fmrp expression in WT aNPCs . We found that acute knockdown of Fmrp expression in WT aNPCs led to both reduced NeuroD1 ( Figure 2I , left , n = 4 , p<0 . 05 ) and Tuj1 ( Figure 2I , middle , n = 4 , p<0 . 001 ) mRNA levels , as well as diminished NeuroD1 promoter activity ( Figure 2I , right , n = 6 , p<0 . 05 ) compared with aNPCs transfected with a nonsilencing control siRNA ( NC-siRNA ) . On the other hand , acute knockdown of Fmrp resulted in increased mRNA levels of both GFAP ( Figure 2J , left; n = 4 p<0 . 01 ) and another astrocyte marker aquaporin4 [38] , [39] ( Figure 2J , middle , n = 4 , p<0 . 001 ) , as well as enhanced GFAP promoter activity ( Figure 2J , right , n = 6 , p<0 . 05 ) . Furthermore , exogenously expressed WT Fmrp , but not mutant ( I304N ) Fmrp , which is unable to bind polyribosomes [40] , rescued both the neuronal ( Figure 2K ) and the astrocyte ( Figure 2L ) differentiation deficits associated with Fmr1 KO cells . We then confirmed that aNPCs isolated from Fmr1 KO DG had similar reductions in neuronal differentiation and increases in astrocyte differentiation ( Figure S4A , S4B , S4C , S4D ) as Fmr1 KO aNPCs derived from forebrain . In addition , acute knockdown of Fmrp in the WT DG aNPCs resulted in phenotypes in neuronal and astrocyte differentiation ( Figure S4E , S4F , S4G , S4H ) similar to those we observed in forebrain aNPCs . Together , these results suggest that the loss of Fmrp alters both the proliferation and fate specification of aNPCs . To investigate the role of Fmrp in adult neurogenesis in vivo , we assessed the proliferation , survival , and differentiation of endogenous aNPCs in both WT and Fmr1 KO mice . Newborn cells were distinguished by the incorporation of BrdU administered through intraperitoneal injections into adult mice using two cohorts of mice ( Figure 3A ) . Cohort 1 animals ( Figure 3C ) had the same injection paradigm as those mice used for the differentiation assay ( Figure 4 ) ; therefore , they were used to assess new cell survival . Cohort 2 animals were used to evaluate cell proliferation in the DG . Quantitative histological analysis at one day following a seven-day regimen of daily BrdU injection ( Cohort 1 ) showed that Fmr1 KO mice had 52 . 0% more BrdU-positive cells compared with WT mice ( Figure 3C ) . To further assess the proliferation of aNPCs without the confound of cell survival in Fmr1 KO mice , we gave mice six doses of BrdU injection within 24 hours to label the entire proliferating population in the DG based on a published paradigm [41] and analyzed the mice at four hours after the last BrdU injection ( Figure 3D , Cohort 2 ) . We found that Fmr1 KO mice had 53 . 2% more BrdU-positive cells compared with WT mice ( Figure 3D , p<0 . 001 ) . Since the volume of the DG is also increased in Fmr1 KO mice ( Figure 3E , p<0 . 05 ) and the above data were normalized to the DG volume , the total number of BrdU-positive cells was even higher in KO mice compared with WT controls . It has been shown that the adult DG contains at least two types of proliferating immature cells that can be labeled by BrdU: one type is GFAP+ and Nestin+ ( Figure 3F lower panel ) and might be stem cells , whereas the other type is GFAP− and Nestin+ ( Figure 3F upper panel ) and more likely to be progenitor cells [10] , [42] . To determine which types of cells exhibited increased BrdU incorporation in Fmr1 KO mice , we stained the brain sections with antibodies against BrdU , GFAP , and Nestin ( Figure 3F ) . We found that the Fmr1 KO DG had increased BrdU incorporation in both the Nestin+/GFAP− cell population ( Figure 3G , 40 . 8% increase , p<0 . 05 ) and the Nestin+/GFAP+ cell population ( Figure 3H , p<0 . 001 , 1 . 2-fold increase ) . The proliferation of astrocytes ( BrdU+ , GFAP+ , Nestin− cells ) was no different between WT and Fmr1 KO mice ( data not shown ) . Cell proliferation in the SVZ was also 1 . 1-fold higher in Fmr1 KO mice ( p<0 . 05 ) . Thus Fmrp deficiency may lead to increased proliferation of both stem and progenitor cells . The long-term survival and differentiation of BrdU-labeled cells was evaluated by analyzing the labeled cells at four weeks after BrdU injections ( Figure 4A–4C ) . The number of BrdU+ cells at four weeks post-injection was no different between WT and Fmr1 KO mice ( Figure 4D ) ; therefore , the percentage of BrdU+ cells that survived from one day to four weeks post-BrdU administration is significantly lower in Fmr1 KO mice compared with WT mice ( Figure 4E , p<0 . 05 ) . Hence Fmrp deficiency may also lead to reduced survival of young neurons . Since we observed altered neuronal and astrocyte differentiation of Fmr1 KO aNPCs in vitro ( Figure 2 ) , we then used triple fluorescence immunostaining with antibodies for mature neurons ( NeuN ) and astrocytes ( S100β ) to further determine the fate of differentiated aNPCs in vivo ( Figure 4B and 4C ) . Consistent with our in vitro observation , we found that in Fmr1 KO mice , the percentage of BrdU+ cells that are NeuN+ neurons was 10 . 4% lower ( Figure 3F , p<0 . 05 ) , whereas the percentage of BrdU-positive cells that are S100β+ astrocytes was 75 . 7% higher compared with WT mice ( Figure 4G , p<0 . 05 ) . In addition , the expression levels of NeuroD1 and Neurog1 , two transcription factors expressed in new neurons , were also reduced in the hippocampus of Fmr1 KO mice ( Figure S5 , n = 3 , p<0 . 05 ) . Therefore , the loss of Fmrp leads to reduced neuronal differentiation but greater glial differentiation in aNPCs residing in the DG . These in vivo data along with our in vitro results suggest that Fmrp indeed plays important roles in regulating the differentiation and proliferation of aNPCs . As an RNA-binding protein , Fmrp is known to bind to a subset of specific mRNAs and suppress their translation [43] . To identify the mRNAs that are regulated by Fmrp in aNPCs , we employed the strategy of specifically immunoprecipitating Fmrp-containing mRNP particles and identifying the copurified mRNAs by probing expression microarrays , which we established previously [44] . Due to the large quantity of cells needed , we only used forebrain aNPCs derived from WT and Fmr1 KO mice for immunoprecipitation with an antibody that could specifically precipitate Fmrp ( Figure 5A ) . Both immunoprecipitated and input RNAs were used to probe Affymetrix arrays ( data not shown ) . The mRNAs of interest were further confirmed to be associated with Fmrp by independent IP and real-time PCR ( Figure 5B ) . Among these mRNAs , we found several already known to be regulated by Fmrp , such as MAP1B [2] and EF1α [3] , confirming the specificity of our assay ( Figure 5B and 5C ) . Also among the identified mRNAs , we found two key factors well established as enhancers of cell cycle progression , cyclin-dependent kinase 4 ( CDK4 ) and cyclin D1 . Their specific association with Fmrp was further confirmed by additional IP and RT-PCR ( Figure 5B ) . We therefore examined the expression levels of CDK4 and cyclin D1 in both WT and Fmr1 KO aNPCs . Though there was no significant change in the mRNA levels ( Figure S6B ) , the loss of Fmrp led to higher protein levels of both genes ( Figure 5C , Figure S6 ) . Both CDK4 and cyclin D1 expression levels are important for the proliferation of neural progenitors [45] , [46] , [47] , [48] . We found that a chemical inhibitor of CDK4 could partially rescue the proliferation phenotype of Fmr1 KO aNPCs ( Figure S6C ) . Hence increased expression of CDK4 and cyclin D1 as a result of Fmrp deficiency could be responsible for the increased proliferation of Fmr1 KO aNPCs . We also noticed that the mRNA of GSK3ß , known to be involved in the Wnt signaling pathway , could be coimmunoprecipitated with Fmrp from aNPCs . We confirmed the specific association between Fmrp and the mRNA of GSK3ß using additional Fmrp IP coupled to real-time PCR ( Figure 5B ) . Furthermore , we confirmed that the loss of Fmrp led to increased protein levels of GSK3β ( Figure 5C ) and reduced protein levels of ß-catenin ( Figure S7 ) , a downstream target of GSK3β in proliferating Fmr1 KO aNPCs . To determine whether Fmrp could regulate the translation of GSK3β protein , we cloned the 3′ untranslated region ( 3′UTR ) of GSK3β and inserted it into the 3′ region of the Renilla luciferase coding sequence , such that the translation of Renilla luciferase could be regulated by the 3′UTR of GSK3β . Upon transfection of this construct into Fmr1 KO and WT aNPCs , we observed significantly higher Renilla luciferase activity in Fmr1 KO aNPCs compared with WT aNPCs , suggesting that the 3′UTR of GSK3ß leads to increased translational activity in Fmr1 KO cells ( Figure S7A ) . To further ensure that this increased protein level was due to increased translation rather than reduced protein stability of GSK3β in Fmr1 KO cells , we treated Fmr1 KO and WT aNPCs with the protein synthesis inhibitor cycloheximide over a 24-hour period . We found that , even though the GSK3β protein level was higher in the KO cells ( time 0 h ) , there was no significant difference in the rate of GSK3β protein degradation between WT and KO aNPCs ( Figure S7B ) . Therefore , these data suggest that Fmrp regulates the protein translation of GSK3β . The canonical Wnt pathway is known to be critical for adult neurogenesis , but the downstream effectors have been a mystery [20] , [26] . Since Fmrp was able to regulate the translation of GSK3β , we further investigated whether the activity of the Wnt pathway was altered in Fmr1 KO aNPCs . GSK3β is known to phosphorylate and promote the proteasome degradation of ß-catenin , a central player in the Wnt signaling pathway . We therefore chose to examine the expression of ß-catenin in aNPCs . In both proliferating and differentiating aNPCs , we observed increased GSK3β protein levels ( Figure 5C ) and decreased expression of ß-catenin ( Figure 6A and Figure S7C ) . Hence Fmrp may promote adult neurogenesis by regulating the expression of GSK3β and subsequently ß-catenin . In the absence of Wnt , ß-catenin is known to be held in cytosol and degraded by a collection of regulatory factors , such as GSK3β [27] . The activation of Frizzled by Wnt leads to stabilization and nuclear translocation of ß-catenin , which forms a complex with TCF/LEF transcription factors and induces the expression of downstream target genes [27] . To confirm that loss of Fmrp led to the deficit in the Wnt signaling pathway , we used a well-characterized luciferase reporter system for monitoring the activity of the Wnt signaling pathway [26] , [49] . Upon growth factor withdrawal and activation by cotransfected Wnt3a expression vector , Fmr1 KO aNPCs exhibited significantly reduced luciferase activity compared with WT aNPCs ( Figure 6B ) . In addition , expression of Axin2 , a downstream effector of the Wnt signaling pathway , was reduced in the hippocampus of Fmr1 KO mice ( Figure S8 ) . Therefore , the Wnt signaling pathway is indeed defective in the absence of Fmrp . In addition , treatment of Fmr1 KO aNPCs with a well-established GSK3β inhibitor SB216763 [50] could enhance the Wnt signaling pathway ( ) and partially rescue the neuronal ( Figure 6C and 6D ) and astrocyte ( Figure 6E and 6F ) differentiation deficits in aNPCs . Similar results were also obtained using the DG aNPCs ( Figure S9B and S9C ) . Interestingly , SB216763 also repressed aNPC proliferation without affecting cyclin D1 expression levels ( Figure S10 ) . Therefore , Fmrp deficiency leads to reduced Wnt signaling , which could be responsible for altered aNPC differentiation . The basic helix-loop-helix family transcription factor neurogenin1 ( Neurog1 ) can be regulated by Wnt signaling , and its promoter contains one single classic TCF/LEF binding element [51] . We therefore assessed the mRNA levels of Neurog1 in Fmr1 KO proliferating and differentiating aNPCs . Neurog1 was transiently expressed in differentiating WT aNPCs ( Figure 6G ) , as shown previously [51] . We found that Neurog1 mRNA levels indeed decreased in Fmr1 KO differentiating aNPCs ( Figure 6G ) . To determine whether the altered Neurog1 expression resulted from a Wnt signaling deficit in Fmr1 KO aNPCs , we created a reporter construct that has a mouse native Neurog1 promoter driving the expression of luciferase . When transfected into Fmr1 WT and KO aNPCs that were subjected to differentiation , the Neurog1-luciferase reporter yielded detectable luciferase activity only in the presence of Wnt3a ( Figure 6H ) , indicating that this promoter is activated by Wnt signaling . As expected , we found that Neurog1 promoter activity was significantly reduced in differentiating Fmr1 KO aNPCs compared with WT cells ( Figure 6H ) . Furthermore , we could rescue the Neurog1 promoter activity by expressing the wild-type but not the mutant Fmr1 in Fmr1 KO aNPCs ( Figure 6I ) . Taken together , these data suggest that the expression of Neurog1 is controlled by Fmrp through the Wnt signaling pathway in aNPCs . Since Neurog1 is an early initiator of neuronal differentiation and an inhibitor of glial differentiation [28] , its downregulation could be responsible for the reduced neuronal differentiation and increased glial differentiation seen in Fmr1 KO aNPCs . To test this possibility , we expressed exogenous Neurog1 in Fmr1 KO forebrain aNPCs and found that exogenously expressed Neurog1 could rescue the altered fate specification of Fmr1 KO aNPCs , as assessed by the mRNA levels of neuronal genes ( Figure 7A , NeuroD1 and Tuj1 ) and astrocytic genes ( Figure 7B , GFAP and aquqporin4 ) , as well as the promoter activity of NeuroD1 and GFAP ( data not shown ) in differentiating cells . To further validate the role of Neurog1 in aNPC differentiation , we acutely knocked down Neurog1 expression in aNPCs using siRNA ( Figure 7C ) and found that acute knockdown of Neurog1 in aNPCs led to decreased neuronal differentiation ( Figure 7D ) , but increased astrocyte differentiation ( Figure 7E ) , reminiscent of what we found in Fmr1 KO aNPCs . Similar results were also obtained using the DG aNPCs ( Figure S11 ) . Therefore , our findings suggest that Fmrp regulates aNPC fate specification by modulating the activity of the Wnt/β-catenin signaling pathway and subsequently its downstream effector , Neurog1 ( Figure 7F ) .
In this study we demonstrate that the loss of functional Fmrp in aNPCs leads to reduced neurogenesis both in vitro and in vivo . We show that Fmrp regulates the translation of several factors involved in stem cell proliferation and differentiation , including CDK4 , cyclin D1 , and GSK3β . As a result of dysregulation of GSK3β and the Wnt signaling pathway , the expression level of Neurog1 , one of the Wnt-regulated genes , is reduced , which is likely responsible for the reduced neuronal differentiation and increased astrocyte differentiation seen in Fmr1 KO aNPCs . Our data demonstrate that Fmrp plays profound regulatory roles in adult neurogenesis . Despite exhaustive efforts devoted to understanding transcriptional regulation in adult neurogenesis , the role of translational control in adult neurogenesis has gone largely unexplored; yet our results indicate that translational control is just as important , if not more so , in the regulation of aNPC functions . We have identified the molecular pathways by which Fmrp regulates aNPC proliferation and fate specification . Both a previous study from another group [52] and our current study found that the mRNAs of both CDK4 and cyclin D1 could be bound by Fmrp . CDK4 and cyclin D1 are well-characterized cell-cycle regulators in many cell types [53] . In mammalian neural progenitor cells , increased cyclin D1 expression is positively correlated with their proliferation [54] , and reduced cyclin D1 levels result in decreased proliferation [48] . CDK4 has been shown to regulate the proliferation of neural progenitors in adult brains [47] , and inhibition of CDK4 activity leads to growth arrest in neural progenitors [46] . The fact that we could rescue the proliferation deficits of Fmr1 KO aNPCs using a chemical inhibitor of CDK4 supports our model that Fmrp regulates aNPC proliferation in part through CDK4 . We also found here that Fmrp could bind and regulate the translation of GSK3β mRNA . As a negative regulator of the Wnt signaling pathway , GSK3β promotes the degradation of β-catenin and inhibits the activity of the canonical Wnt signaling pathway [27] . The Wnt signaling pathway has been shown to promote the proliferation of a number of cell types , including hematopoietic stem cells [55] . Although one study suggests that Wnt signaling can also promote cell proliferation in the DG [56] , other publications clearly point out the function of the Wnt signaling pathway in activating neuronal differentiation during adult neurogenesis , and inhibiting this pathway results in hippocampus-dependent learning deficits [20] , [26] , [57] . Our data show Fmr1 KO aNPCs had reduced Wnt signaling , and we identified Neurog1 as one of the downstream targets of Fmrp and Wnt . Neurog1 is a transcription factor expressed only at the early stage of differentiation , and it promotes neuronal differentiation while inhibiting astrocyte differentiation [28] , [29] . Neurog1 contains a conserved Tcf/Lef binging site in its promoter , allowing it to sense the levels of Wnt signaling . Although the Wnt signaling pathway has been found to enhance cyclin D1 transcription in HeLa cells and several other cell types [58] , we saw no such activation in aNPCs . Interestingly , enhancing Wnt signaling via a Gsk3β inhibitor repressed proliferation of Fmr1 KO aNPCs , possibly due to the neuronal differentiation effect of Wnt signaling . It is likely that in aNPCs , Wnt signaling and cyclin D1 act independently on cell proliferation , and they are both also regulated by Fmrp . Several studies have examined embryonic and early postnatal neurogenesis in mice [25] and humans [24] . One study found that the loss of Fmrp led to increased neuronal differentiation and reduced glial differentiation in mice [25] . Due to the large scale of embryonic neurogenesis , factors affecting aNPCs would also be expected to affect both the overall number of neurons , as well as brain size . However , neither adult fragile X patients nor adult Fmr1 KO mice show any differences in the number of neurons and glia compared with controls [59] , raising questions about the potential significance of increased early neurogenesis to the pathogenesis of fragile X syndrome . Another study found no alteration in the differentiation of embryonic NPCs ( eNPCs ) isolated from one human embryo diagnosed with a fragile X mutation [24] . While the discrepancies between human and mouse studies require further confirmation using additional human tissues , the different phenotypes observed in Fmrp-deficient eNPCs versus aNPCs support the idea that adult neurogenesis is subjected to regulatory mechanisms distinct from those in embryonic neurogenesis [22] . First , during adult neurogenesis , multipotent aNPCs are in intimate contact with the surrounding mature neurons and glia , and the fate of aNPCs can be affected by their microenvironment [8] , [9] , [22] , [60] . Mice that lack Sonic hedgehog [61] , Tlx [62] , Bmi1 [63] , [64] , and Mbd1 [33] have all exhibited profound deficits in postnatal neurogenesis , but not in their embryonic neural development . In fact , in prenatal and early postnatal developing brains , Fmrp is widely expressed in neural cells , including glia and glial precursors , with the levels of Fmrp decreasing during oligodendrocyte differentiation [65] , [66] , whereas in adult brains , Fmrp is expressed predominantly in neurons , with negligible expression in mature glia [30] , [31] . Further studies into the role of Fmrp in both embryonic and adult neurogenesis would facilitate our understanding of the unique molecular networks that regulate eNPCs and aNPCs at the level of translational control . Hippocampal neurogenesis has been associated with hippocampus-dependent learning [8] , [9] , and blocking neurogenesis using methods nonexclusive to adult NPCs or new neurons has supported this model [14]–[17] , [20] . Altered adult hippocampal neurogenesis and impaired learning have been found in several pathological conditions [9] , [21]; however , the possibility of a link between adult neurogenesis and human mental retardation disorders , though recently put forward [67] , has not been studied well . Although there is a low level of DG neurogenesis in adults , mounting evidence points to its potentially important role in neuroplasticity , emotional behavior , and the higher cognitive functions of adult brains . It has been proposed that adult neurogenesis enables the lifelong adaptation of the hippocampal network to the levels of novelty and complexity a person experiences [11] . Using precise and unbiased stereological methods coupled with confocal microscopy , we observed a mild but significant reduction in the number of new neurons in Fmr1 KO mice , which could easily have been missed by others who employed non-stereology quantification methods [68] . Due to the restricted nature and low level of adult neurogenesis , a lack of Fmrp may not affect the total number of neurons in adult brains , but it can contribute to pathological conditions linked to higher cognitive functions and learning abilities [9] , [67] . The learning deficits of the Fmr1 KO mice may be the result of both reduced neurogenesis and defective neuronal maturation . Consistent with the literature [69] , we have observed that Fmr1 KO aNPC-differentiated neurons had reduced dendritic complexity and length ( data not shown ) , which could also contribute to behavioral deficits . In addition , although Fmr1 KO mice have increased proliferation , at four weeks post-BrdU labeling , both KO and WT mice had similar numbers of surviving new cells , possibly due to the decreased survival of new cells in KO mice . How Fmrp regulates the survival of young neurons is another interesting question that is currently being pursued as an independent study . One mystery that remains to be cleared up is why the size of the DG in the adult Fmr1 KO mice is bigger than in controls . Since the new cells generated in the adult DG account for only a small portion of the total DG cells , increased proliferation of these new cells may not contribute much to the increased size of the DG . Castren et al . [25] have shown that Fmr1 KO mice exhibit increased cell proliferation in the subventricular zone during embryonic development ( E13 ) . The mammalian DG is formed during the postnatal period , with P7 as the peak of cell genesis . It is possible that increased cell proliferation during DG formation results in an increased DG volume that persists into adulthood . In addition to its function in the initial stage of neurogenesis , Fmrp-deficient neurons are known to have reduced dendritic complexity [25] , [70]; therefore , it is possible that new neurons generated in the adult DG also have reduced dendritic complexity . Hence deficits in several stages of adult neurogenesis could contribute to the higher brain functions , such as the learning and emotional disabilities associated with fragile X patients , without significantly affecting the gross brain structure of human patients . Our results suggest that translational regulation by Fmrp in aNPCs and young neurons is essential for learning and memory , and the reduced number of new neurons together with defective maturation of these new neurons may contribute to the cognitive deficiency seen in fragile X patients . This is a facet of the etiology of fragile X syndrome that has not been recognized before .
All animal procedures were performed according to protocols approved by the University of New Mexico Animal Care and Use Committee . The Fmr1 KO mice bred onto the C57B/L6 genetic background were as described previously [71] . Adult aNPCs used in this study were isolated from 8- to 10-week-old male Fmr1 KO mice and wild-type ( WT ) controls based on published methods: for the forebrain aNPCs [33] and for the DG aNPCs [72] . ( See Text S1 for details . ) These analyses were carried out using our established method [32] , [34] . ( See Text S1 for details . ) In vivo neurogenesis analyses were performed essentially as described previously [32] , [33] . These experiments have been performed using 3 different batches of animals , with n = 4–6/genotype each batch . For the first two batches , BrdU ( 50mg/kg ) was injected into 8-week-old mice daily for 7 consecutive days to increase the amount of labeling . Mice were then euthanized 1 day post-injection to assess the in vivo proliferation ( and early survival ) of labeled cells . For cell survival analysis , another group of mice was injected with BrdU at 8 weeks of age and euthanized 4 weeks post-injection . The third batch of mice , on the other hand , were given 6 injections of BrdU ( 50 mg/kg ) within 24 hours to label all dividing cells in the DG within this time period and sacrificed at 4 hours post-last injection based on a published protocol [41] . Mice were euthanized by intraperitoneal injection of sodium pentobarbital , and then transcardially perfused with saline followed by 4% PFA . Brains were dissected out , post-fixed overnight in 4% PFA , and then equilibrated in 30% sucrose . Forty-µm brain sections were generated using a sliding microtone and stored in a −20°C freezer as floating sections in 96-well plates filled with cryoprotectant solution ( glycerol , ethylene glycol , and 0 . 1 M phosphate buffer , pH 7 . 4 , 1∶1∶2 by volume ) . We performed immunohistological analysis on 1-in-6 serial floating brain sections ( 240 µm apart ) based on the published method [33] . ( Please see Text S1 for more details . ) The DNA plasmids carrying 2 . 5 kb of glial fibrillary acidic protein ( GFAP ) promoter-firefly luciferase reporter gene ( GF1L-pGL3 ) or its mutant version , with the STAT3 binding site mutated ( GF1L-S-pGL3 ) , and an internal control plasmid containing sea pansy luciferase driven by human elongation factor 1α promoter ( EF1α-Luc ) were as described previously [34] , [73] . NeuroD1-luciferase , a gift from Dr . F . H . Gage , was then cloned into pGL3 plasmid . Fmr1-siRNA , control-siRNA , and mouse Neurog1 expression vector were purchased from Open Biosystems ( www . openbiosystems . com ) . Neurog1 siRNA was purchased from SABiosciences ( Frederick , MD ) . Wild-type Wnt reporter construct pTOPFLASH containing 8 TCF/LEF binding sites and mutant reporter construct pFOPFLASH were gifts from R . T . Moon ( University of Washington ) as described [26] . Wnt3a expression plasmid was a gift from Dr . D . C . Lie ( Institute of Developmental Genetics , Germany ) as described [26] . Wild-type FLAG-Fmrp was cloned into pDEST-27 vector , and mutant FLAG-I304N was generated by site-directed mutagenesis ( Stratagene ) [40] . All the constructs were verified by DNA sequencing . Myelin basic protein ( MBP ) promoter was cloned from mouse genomic DNA based on published information [74] and cloned into pGL3 plasmid . The mouse Neurog1 promoter , containing its native TCF/LEF binding site “cctttgaa , ” was cloned by PCR based on the GenBank sequence ( GenBank ID #18014 ) using the following primers: 5′-GTCTGACTCTGAAGCCATCTCTGA-3′ ( forward ) and 5′ -ACGCGCCGGGCTGGTCTCCT-3′ ( reverse ) . The PCR product was then subcloned into the pCRII-TOPO plasmid , sequenced , and inserted into the KpnI-XhoI site of the pGL2 basic vector to yield Neurog1-luciferase reporter construct . The full-length 3′-UTR of GSK-3ß mRNA was PCR-amplified directly from proliferating aNPC first-strand cDNA generated from 5 µg TRIzol-isolated total RNA using oligo-dT SuperScript III reverse transcription according to the manufacturer's protocol ( Invitrogen , Cat . #1808-093 ) . It was cloned into pIS2 Renilla luciferase vector , and pIS0 firefly luciferase was used as a transfection control [75] . Electroporation of plasmid DNA into aNPCs and the luciferase assay were carried out using an Amaxa Nucleofector electroporator based on the manufacturer's protocol ( Amaxa , #VPG-1004 ) with modifications [34] . Briefly , 2×106 cells were trypsinized , resuspended in Nucleofector solution , mixed with DNA , and electroporated using a preset program for mouse NPCs ( #A033 ) . The cells were then plated onto polyornithin/laminin-coated 24-well plates in proliferation medium . After 24 h , cells were changed into differentiation medium for 48 h . Transfection of aNPCs was carried out using Stemfect ( Stemgent , San Diego , CA ) based on the manufacturer's protocol with modifications . Briefly , aNPCs were plated into 24-well P/L-coated plate for 24 hours . Then 3 µg DNA and 0 . 9 µl Stemgent reagent were mixed , incubated for 10 minutes , and then added to the cells . Sixteen hours later , the transfected cells were changed into differentiation medium for 48 hours . The cells were then collected and luciferase activity was detected using the Dual-Luciferase Reporter 1000 System ( Promega , Cat# E1980 ) based on the manufacturer's protocol . Briefly , collected cells were lysed in 100 µl of 1× passive lysis buffer at room temperature for 15 minutes . Then 20 µL of the lysate was added to 100 µl of Luciferase Assay Buffer II and mixed briefly . Firefly luciferase ( F-luc ) activity was immediately read using a SpectraMax M2E plate reader ( Molecular Devices Corp . ) . Next , 100 µl of Stop & Glo Buffer with Stop & Glo substrate was added and mixed briefly . Renilla luciferase ( R-luc ) activity was immediately read . F-luc activity was normalized to R-luc activity to account for variation in transfection efficiencies . Each experiment was independently repeated 3 times . For each electroporation , 3 µg ( NeuroD1− or GFAP− ) luciferase DNA , 5 µg Neurog1-luciferase DNA , 0 . 2 µg R-Luc , and 0 . 004–2 µg Fmr1 , Neurog1 , or control expression plasmids were used . These procedures were carried out as described [43] . ( Please see Text S1 for details . ) Twenty-mg protein samples were separated on SDS-PAGE gels and then transferred to PVDF membranes ( Millipore ) . Membranes were processed following the ECL western blotting protocol ( GE Healthcare ) . anti-MAP1B ( a gift from I . Fischer , Drexel University , Philadelphia ) , anti-Nestin ( Millipore ) , anti-Fmrp ( 7G1-1 ) , anti-Fmrp ( John Louis ) , anti-β-catenin ( Millipore ) , anti-CDK4 ( Millipore ) , anti-Cyclin D1 ( Upstate ) , anti-TCF4 ( Abcam ) , GSK3ß ( Abcam ) , anti-EF1α ( ATCC ) , anti-Neurog1 ( Millipore ) , anti-NeuroD1 ( Santa Cruz ) , anti-Axin2 ( Cell Signaling ) and anti-β-Actin ( Abcam ) were used as primary antibodies at the concentrations recommended by the manufacturers . HRP-conjugated secondary antibodies were obtained from Sigma . For loading controls , membranes were stripped and reprobed with the antibody against eIF5α ( Santa Cruz Biotechnology ) , anti-GAPDH ( Ambion ) , or eIF4E ( Transduction Laboratories ) . To test the efficiency of Fmr1-siRNA , Fmrp expression plasmid and siRNA expression plasmid were cotransfected into HEK293 cells , and the mRNA and protein expression levels of Fmrp were analyzed using PCR and western blot , respectively . Statistical analysis was performed using ANOVA and Student's t-test , unless specified with the aid of SPSS v . 17 . All data were shown as mean with standard error of mean ( mean ± SEM ) . Probabilities of P<0 . 05 were considered significant . | Fragile X syndrome , the most common cause of inherited mental retardation , results from the loss of functional Fragile X mental retardation protein ( FMRP ) . FMRP is an RNA–binding protein and is known to bind to specific mRNAs and to regulate their translation both in vitro and in vivo . Adult neurogenesis , a process considered important for neuroplasticity and memory , is regulated at multiple molecular levels . Here we show that Fmrp could regulate the proliferation and fate specification of adult neural progenitor/stem cells ( aNPCs ) . These data unveil a novel regulatory role for Fmrp in adult neurogenesis . | [
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| 2010 | Fragile X Mental Retardation Protein Regulates Proliferation and Differentiation of Adult Neural Stem/Progenitor Cells |
Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations . This complex structure can introduce bias in genetic association studies . Using Principal Components Analysis ( PCA ) , we analyze the structure of two independent European American datasets ( 1 , 521 individuals–307 , 315 autosomal SNPs ) . Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations , as demonstrated through joint analysis with HapMap data . The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied . We interpret the first eigenvector of this data as correlated with ancestry , and we apply an algorithm that we have previously described to select PCA-informative markers ( PCAIMs ) that can reproduce this structure . Importantly , we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers , thus increasing genotyping savings . Only 150–200 PCAIMs suffice to accurately predict fine structure in European American datasets , as identified by PCA . Simulating association studies , we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power . The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans . Furthermore , our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs , and significantly decreases genotyping costs .
The first Europeans from the Old World to land in what is now US territory were Columbus' men in 1493 . The initial colonization of the region by the Spanish , English , Scots and Irish , French , Dutch , Swedes , Germans , Italians and Portuguese during the 16th and 17th centuries was followed in the 19th and early 20th century by waves of millions of newcomers originating from the northwestern to the southeastern corners of Europe [1] . Thus , the present day European American population is a mosaic of people that represent different levels of admixture between diverse European populations and , to some degree , also with Native American and African American populations . The identification of population genetic structure has been discussed at length in recent literature , due to the potential bias it can introduce in association studies , searching for susceptibility genes for common complex disorders [2]–[5] . Population stratification is a source of confounding in case-control studies , when allele-frequency heterogeneity that is unrelated with the studied phenotype is coupled with disease-risk heterogeneity and biased sampling in cases and controls . Although European populations were initially considered genetically quite homogeneous , it has recently been shown that significant patterns of structure within Europe along a north to south axis do exist and that unidentified population stratification in European derived populations ( European Americans ) can lead to spurious associations with disease [5]–[8] . As genotyping of thousands of individuals for hundreds of thousands of markers becomes feasible [9]–[14] , and genome wide association studies in large samples of European American populations become increasingly common [15] , identifying and correcting for population stratification will undoubtedly play a central part in the quest to unravel the genetic basis of complex traits . The uniform adjustment proposed by the method of genomic control could be too conservative [16] , [17] , while structured association testing is computationally impractical for very large datasets [18] . Price et al . [19] have shown that Principal Components Analysis ( PCA ) , a powerful linear dimensionality reduction technique can be used as a computationally efficient tool to correct for stratification in the setting of genome wide association studies without loss in power . Identifying a small set of markers that could be used for inference of population structure and adjustment for stratification is of particular importance in order to reduce genotyping costs in studies seeking to replicate the findings of large-scale genome-wide projects or when pursuing specific loci as candidate susceptibility genes . Most existing metrics to select ancestry informative markers ( AIMs ) are allele frequency based and demand prior knowledge of the ancestry of the studied individuals . Consequently , measures like Fst , δ and informativeness for assignment [20]–[25] , require prior assumptions about individual ancestry and cannot be directly applied to admixed populations , like European Americans , in order to identify a panel of genetic markers that can reproduce the structure of the dataset . European American AIMs had so far been proposed in two recent large studies targeting distinct European populations , used as proxies for European American ancestry [7] , [8] . Our work here as well as two studies parallel to ours described in [26] , [27] are the first to attempt the identification of structure informative SNPs through the direct analysis of genomewide datasets of European Americans . All three of these studies are PCA-based . However , here , we directly leverage the power of PCA for the selection of AIMs [28] , without the need for any intermediate steps , such as assigning individuals to clusters , in order to use allele frequency based metrics . We have recently introduced an unsupervised method for the selection of ancestry and structure informative SNPs ( PCA-correlated SNPs or PCA-informative SNPs-PCAIMs ) [28] . Our method does not require prior hypotheses or knowledge of individual ancestry and thus is well-suited for selecting AIMs in admixed populations . In this paper , we employ it to analyze a dense , genome-wide dataset ( approx . 307 , 000 SNPs ) of more than 1 , 500 European Americans from two different studies [29] , [30] . Our main goal is the identification of a small panel of structure informative SNPs in the European American population . The contributions of this paper are three-fold . First , from a statistical perspective , we propose a methodology to remove redundancy from any set of genetic markers , an issue that arises with all existing methods ( supervised or unsupervised ) for the selection of ancestry or structure informative markers , since the “scoring” of the SNPs in all of these methods does not take into account any correlation between them . We reduce the redundancy removal problem to a well-known problem in numerical linear algebra , the so-called Column Subset Selection Problem [31] and we propose an efficient and accurate algorithm that filters out redundant SNPs . Second , we demonstrate that as few as 200 SNPs selected with our methodology can be used to very accurately predict the fine structure of European Americans as identified by PCA , and we employ cross-validation experiments to verify the accuracy of our approach . Third , we show that our method can be coupled with PCA-based stratification correction tools ( such as EIGENSTRAT [19] ) for accurate stratification correction with significant genotyping savings . Using simulated data we experimentally demonstrate that 100–200 PCAIMs can be used to correct for stratification while maintaining power in association studies .
We studied two independent European American datasets . The first dataset ( CHORI dataset-Children's Hospital Oakland Research Institute ) , consists of 980 individuals , that were collected as part of two community-based clinical trials evaluating the anti-inflammatory effects of statins . 305 of these samples ( part of the CAP study [32] ) were collected from the San Francisco Bay Area and Los Angeles . These individuals all had to report at least 3 grandparents of European or Caucasian background . Another 675 individuals were part of a clinical trial that included a large number of sites across the U . S . ( PRINCE study [33] ) . These individuals were self-reported white or Caucasian but no additional information was collected about their parents . All 980 individuals were genotyped using the Illumina Infinium 310K array in one laboratory under the same conditions . The second dataset that we studied here ( CORIELL dataset ) , is a publicly available dataset that has been previously described [29] , and consists of the same SNPs genotyped for 541 samples ( data available from the SNP Resource at the NINDS Human Genetics Resource Center DNA and Cell Line Repository ( http://ccr . coriell . org/ninds/ ) . These are samples from patients with Parkinson's disease and neurologically normal controls , curated at the Coriell institute . Again , genotyping was performed using the Illumina platform ( in the laboratory of Drs . Singleton and Hardy ( NIA , LNG ) , Bethesda , MD USA ) . For all datasets we only considered genotypes for SNPs on autosomal chromosomes in our analysis . Finally , as a third dataset , we also studied the same SNPs using data available from the HapMap database on the HapMap Yoruba ( YRI ) , CEPH European ( CEU ) , Chinese ( CHB ) , and Japanese ( JPT ) samples [34] , [35] . The proportion of missing entries in the above datasets was very small ( on average less than 0 . 1% ) . As a quality control step , we excluded all SNPs with more than 5% missing entries ( separately on each of the three datasets ) . This step further reduced the number of missing entries to less that 0 . 07% on average . We also excluded from the analysis a small number of SNPs that were not in Hardy-Weinberg equilibrium ( HWE ) . After these preprocessing steps we were left with a total of 307 , 315 autosomal SNPs that all three datasets had in common . In order to simplify and speed up our computations , we filled in the ( very small ) number of missing entries randomly so that HWE is satisfied for each SNP . The probabilistic filling in was performed separately for each dataset , and separately in each population of the HapMap data . We then transformed the raw data to numeric values , without any loss of information , in order to apply our linear algebraic methods . Consider a dataset of a population X consisting of m subjects and assume that for each subject n biallelic SNPs have been assayed . Thus , we are given a table Tx , consisting of m rows and n columns . Each entry in the table is a pair of bases , ordered alphabetically . We transform this initial data table to an integer matrix Ax which consists of m rows ( one for each subject ) , and n columns ( one for each SNP ) . Each entry of Ax will be −1 , 0 , +1 , or empty . Let B1 and B2 be the bases that appear in the j-th SNP ( in alphabetical order ) . If the genotypic information for the j-th SNP of the i-th individual is B1B1 the ( i , j ) -th entry of Ax is set to +1; else if it is B1B2 the ( i , j ) -th entry of Ax is set to 0; else if it is B2B2 the ( i , j ) -th entry of Ax is set to −1 [28] , [36] . We carefully studied the two European American datasets for outlier individuals . In the CHORI dataset , we identified five pairs of individuals that showed a very high degree of allele sharing and removed these ten subjects from all further analysis . In particular , we determined the proportion of allele sharing between all pairs of individuals for 1000 randomly selected markers , approximately equally spaced throughout the genome , and subtracted it from the proportion of allele sharing expected under a randomly mating population with the same allele frequencies . These five pairs included one pair that had 100% sharing for all 1000 markers ( indicating either an identical twin or a duplicate sample ) and four others that had significantly higher than expected excess allele sharing , suggesting that they were related . We subsequently used Principal Components Analysis and the Singular Value Decomposition to detect outliers . In particular , given m subjects and n SNPs , let the m×n matrix A denote the subject-SNP matrix encoded as described above . After mean-centering the columns ( SNP genotypes ) of A , the SVD of the matrix returns m pairwise orthonormal vectors ui , n pairwise orthonormal vectors vi , and m non-negative singular values σi such that σ1≥σ2≥…≥σm≥0 . The matrix A may be written as a sum of outer products as ( 1 ) Each triplet ( σi , ui , νi ) may be used to form a principal component of A . Formally , the i-th most significant principal component of a matrix A is the rank-one matrix that is equal to . In our setting , the left singular vectors ( the ui 's ) are linear combinations of the columns ( SNPs ) of A and will be called eigenSNPs [37] . Notice that a principal component is a matrix , whereas an eigenSNP is just a column vector . PCA is a well-known dimensionality reduction technique that , in this case , represents all subjects with respect to a small number of eigenSNPs , corresponding to the top few principal components . All further analysis is then performed on this low-dimensional representation . Figure S1 shows the plot of the 970 CHORI individuals , the 541 CORIELL individuals , and the HapMap European , African and Asian samples , projected on their top three eigenSNPs ( as we shall argue in Results the top eigenSNP is the most informative ) . This plot illustrates how a few subjects from our European American datasets are “pulled” towards the African and Asian HapMap populations . Based on this analysis , we discarded 12 individuals from the CHORI dataset and 2 individuals from the CORIELL dataset that were far from the vast majority of the European American subjects and seem to have a higher degree of non-European ancestry ( Figure S1 ) . Overall , out of the 1521 subjects in the CHORI and CORIELL datasets , we discarded a total of 24 subjects ( ten suspiciously similar subjects and 14 outliers ) . Thus , we were left with 1497 subjects of European American ancestry , genotyped for 307 , 315 SNPs . In order to select ancestry informative markers , we used the procedure that we described previously [28] , [38]–[40] . This procedure is based on the well-documented fact that Principal Components Analysis reveals population structure . More specifically , a number of studies have verified that retaining the top few eigenSNPs in datasets that contain individuals from a number of different populations , or even admixed populations , efficiently reveals the ancestry of the individuals [19] , [28] , [41]–[44] . The PCAIM selection algorithm first determines the number of significant principal components ( and thus the number of informative eigenSNPs ) in the data , and then assigns a score to each SNP . Higher scores correspond to SNPs that correlate well with all informative eigenSNPs . The algorithm returns the top scoring SNPs , and we have demonstrated that these PCAIMs are very efficient for ancestry prediction [28] . This algorithm does not take any special measures in order to avoid redundancy in the set of identified markers . As we will also discuss later here , redundancy may arise in sets of AIMs selected with any of the existing methods ( eg . δ , Fst , informativeness , PCAIMs ) . Redundancy in the case of dense sets of SNP markers is due to tight linkage disequilibrium . Given the increased marker density in the genomewide datasets that are becoming available today , this may lead to significant loss in efficiency by selecting highly correlated markers . It is therefore important to add a redundancy removal step after the initial selection of structure informative markers . We propose a simple , efficient methodology to deal with this issue . Our methodology is based on reducing the redundancy removal problem to the so-called Column Subset Selection Problem . The latter problem is well studied in the Numerical Linear Algebra literature , and many algorithms , with various accuracy vs speed tradeoffs , have been proposed [45] . More specifically , assume that the top r≪n highest scoring SNPs are retained as PCAIMs . Thus , we are given a matrix à that has m rows ( one for each subject ) but only r columns ( one for each PCAIM ) . Recall that n is the total number of SNPs , and could be in the order of hundreds of thousands , whereas we expect r to be in the order of thousands . Our goal is to only retain a small number ( say k ) of columns of à that are as uncorrelated as possible . A naive way of solving this problem would be to examine all possible choices of sets of k SNPs and keep a set that has no pairs of highly correlated SNPs . This is computationally infeasible even for very small values of k ( say ten ) if r is even a thousand . Consider the following definition for the Column Subset Selection Problem ( CSSP ) : Definition 1: Given an m×r matrix à and a positive integer k , pick k columns ( SNPs ) of à such that the maximal Pearson correlation coefficient between all pairs of the selected columns ( SNPs ) is minimized . In words , recall that a large ( close to one ) Pearson correlation coefficient between a pair of SNPs would imply that one of the two SNPs in redundant . Thus , the above problem formulation seeks to minimize the maximal correlation between any pair of selected SNPs , and thus ensure that limited or no redundancy exists . Even though solving the above optimization problem exactly is hard , efficient approximation algorithms exist . For the purposes of this paper , we chose to use an algorithm called greedy QR , that was proposed by Golub in [31] and was subsequently analyzed by Gu and Eisenstat in [46] . The algorithm essentially works in k iterations; in the first iteration , the first column of à ( the top PCAIM ) is picked; in the second iteration , a column of à is picked that is as uncorrelated with the first column as possible; in the third iteration the chosen column has to be as uncorrelated as possible with the first two columns , etc . When expressed in linear algebraic notation , this iterative procedure boils down to a permuted QR decomposition of a matrix , and can be performed efficiently . In particular , an efficient implementation of this algorithm is available in MatLab , and runs in less than one minute when r is in the order of thousands and any value of k less than r . In order to illustrate the potential of the proposed PCAIMs for the correction of stratification in association studies , we run a large simulated association study that closely followed the simulated association study in Price et al . [19] . More specifically , Price et al . [19] demonstrated how EIGENSTRAT ( a PCA-based procedure ) could efficiently identify population structure and remove stratification from association studies on populations with similar structural characteristics with the European American population . To demonstrate the performance of PCAIMs to correct for stratification in association studies on admixed populations with similar characteristics with European American populations , we followed the methods of [19] to generate an admixed population of 1 , 000 individuals genotyped on 100 , 000 SNPs ( see Text S1 for details ) . Thus , we created a 1 , 000×100 , 000 matrix A of genotypes . We then estimated the number of significant principal components , both by looking at the singular values , as well as by the permutation test of [28] . As we will discuss in the Results section , one eigenSNP was deemed significant and was interpreted as ancestry . We then picked panels of PCAIMs from the 100 , 000 SNPs in order to predict the ancestry of the 1 , 000 subjects . We created large sets of random , stratified , and causal SNPs ( 100 , 000 SNPs in each case ) following the methods described by Price et al . [19] ( see Text S1 ) . We performed ten repetitions , and generated sets of 100 , 000 , since we did not observe any change in the fourth decimal digit of the reported results by increasing the set size to 1 , 000 , 000 . Affection status for individuals in the admixed population was determined randomly according to an “ancestry risk” parameter r as defined previously [19] . Results are reported for both r = 2 and 3 . Correlation with affection status was determined by taking the Armitage trend statistic of each SNP with the affection status , with the significance threshold set to 10−4 . For comparison purposes we chose the same threshold as in [19] . Correction for ancestry was first performed using the algorithm of EIGENSTRAT and looking at the top ten eigenSNPs of the full SNP-subject matrix ( mean centering was performed ) . We then performed correction for stratification by looking at the first eigenSNP of the matrix consisting of the panel of selected PCAIMs . Adjustment of genotypes essentially corresponds to “projecting out” the component of each SNP that lies in the subspace spanned by the ancestry prediction . After performing this simple linear algebraic operation on every SNP , the Armitage trend statistic was re-run on the residual of each SNP .
We first examined the number of significant principal components in the two European American datasets that we studied ( CHORI and CORIELL ) . Figure 1 ( panel A ) shows the top few singular values of the CORIELL subject-SNP matrix , and Figure 1 ( panel D ) shows the top few singular values of the CHORI subject-SNP matrix . Clearly , there is a significant gap between the first singular value and the remaining ones in both cases . This is a strong indication that the top principal component is the most informative in both datasets and suggests that subsequent principal components may not be of interest . To further validate this finding , we ran the permutation test that we have recently described [28] . This permutation test essentially measures the ratio of “information” that the i-th principal component contains when compared to the amount of structure in a random matrix . When this ratio is sufficiently high , the principal component is deemed as informative . Again , Figure 1 ( panels B and E ) , shows that , for both datasets , the first principal component has significantly more structure than a random matrix , whereas the remaining principal components are much less informative and contain less than 20% more information than a purely random matrix . The analysis described above suggests that both in the CORIELL and CHORI datasets , individuals of European American ancestry lie along a line , and all the variation is concentrated across the first eigenSNP , which corresponds to the first principal component . Although no information about self-reported ancestry was available for the individuals we studied , we can speculate that this axis of variation corresponds to the well-documented axis of northern to southeastern genetic variation in Europe [7] , [8] , [26] , [27] , [41] , [47]–[50] . Hence we only retained the top principal component for our European American datasets for all further analysis and we interpreted this principal component as the European American ancestry axis . Figure 1 ( panels C and F ) , shows the histogram of the top eigenSNP for individuals in the CORIELL and the CHORI datasets respectively . We would also like to add here a note on the computational efficiency of our methods: our computations are quite efficient and , for example , running PCA on the joint CHORI and CORIELL datasets takes 21 minutes on a standard laptop computer . We then compared the structure of the two European American datasets to the structure of the HapMap Yoruba from Ibadan ( YRI ) , CEPH European ( CEU ) , and East Asian populations ( CHB and JPT ) of the HapMap project . To this end we extracted from the HapMap database genotypes for all SNPs that were also genotyped on our European American samples and computed the top few eigenSNPs of all five populations . Figure 2 shows all 1767 individuals ( 1497 CHORI and CORIELL plus 270 from HapMap ) projected on the first , second , and third eigenSNP of the overall subject-SNP matrix . Adding the HapMap data adds two more axes of variation , one for the African subjects , and one for the Asian subjects . The two large European American samples have similar structure with most individual variation lying across one axis . As expected , they overlap with the CEPH European data . Since outliers were removed as part of a preprocessing step ( see Methods ) , no individuals seem to demonstrate high levels of admixture with non-Europeans . CEPH Europeans form a very tight cluster , which does not seem to encompass the full range of variation observed in European Americans . This also becomes apparent in Figure S2 , which focuses on the CHORI , the CORIELL , and the CEPH European datasets only . The fact that the CEPH European samples essentially represent US residents from Utah with Northern European ancestry , corroborates with this picture . Thus , the position of the CEPH European samples in this analysis seems to mark the end of the axis of variation in our European American datasets , which corresponds to Northern European ancestry . We next tested the feasibility of identifying a small subset of SNPs that could be used to reproduce the structure of the European Americans that we analyzed . Using our algorithm [28] with the number of significant principal components set to one , we selected 100 to 3000 PCAIMs in each dataset in order to predict the ancestry of the European American subjects . As described earlier here , in both European American datasets that we studied , variation lies almost exclusively along the first eigenSNP , which was interpreted as ancestry of the studied individuals . In order to evaluate the performance of the PCAIMs that we select , and show that they can be used to preserve the properties of the complete dataset , we computed the first eigenSNP using all available 307 , 315 SNPs , and compared it to the first eigenSNP using only the selected subset of SNPs . Thus , we essentially predicted the ancestry of each individual by looking at a small subset of SNPs and computing the first eigenSNP of the resulting subject-SNP matrix . Figure 3 shows the Pearson correlation coefficients between “true” and predicted ancestry . In the CHORI dataset , about 1 , 200 PCAIMs are needed in order to reach a correlation coefficient of above 0 . 9 and 700 are needed in the CORIELL . Random SNPs perform much worse in the CORIELL and as many as 3 , 000 random SNPs are needed for the correlation coefficient between true and predicted coordinates of the individuals to reach 0 . 9 . On the other hand , in the CHORI dataset , random SNPs perform worse but overall have comparable performance to PCAIMs ( correlation coefficient between “true” and predicted ancestry of individuals is approximately 0 . 9 with 2 , 000 SNPs ) . As we will show in the following section this is due to the redundancy in the markers selected as informative and great savings are indeed possible , after application of our redundancy removal algorithm . Even though less than 1% ( approx . 1 , 500–2 , 000 ) of the total SNPs suffice to predict ancestry in the studied European American datasets with very high accuracy , we still considered this number to be unnecessarily high . This is reinforced by the fact that 2 , 000–3 , 000 random SNPs start performing quite well in predicting ancestry . This led us to suspect that the sets of PCAIMs that we were selecting included significant amounts of redundancy . Indeed , we computed all r2 values between all pairs of selected PCAIMs for the CHORI dataset , the CORIELL dataset , as well as the joint CHORI-CORIELL dataset . The results are shown in Table 1 . Obviously , a large number of pairs are in high LD , and thus a lot of the selected SNPs are redundant . In an effort to further reduce the number of SNPs that are necessary for ancestry prediction in European Americans and increase genotyping savings , we developed an algorithm that minimizes redundancy from the panels of SNPs that are selected with our scoring algorithm . Applying the redundancy removal procedure described in Methods , we extracted panels of non-redundant SNPs from the top 3 , 000 PCA-correlated SNPs . We varied the size of these panels from 100 to 500 PCA-correlated non-redundant SNPs . As is shown in Figures 3 and 4 , removing redundancy from the selected PCAIMs results in significant savings with as few as 200 SNPs sufficing to accurately predict individual ancestry ( with a correlation coefficient above 0 . 9 ) . Additionally , when we computed all pairwise r2 values in the top 500 non-redundant PCA-correlated SNPs for the CORIELL dataset , the CHORI dataset , and the joint dataset , we observed that there was not a single pair of SNPs with an r2 value above 0 . 2 and only three pairs in the CORIELL dataset with an r2 value between 0 . 1 and 0 . 2 . Thus , our algorithm effectively removed redundant SNPs . In order to generate a potentially more comprehensive list of structure informative SNPs for European Americans , we also analyzed the two datasets jointly ( Figure 4 and Figure S3 ) and tested the efficiency of selected subsets of PCAIMs . Again PCAIMs , after redundancy removal , prove to be quite powerful and as few as 200 can be used to accurately predict the structure of 1497 individuals . Figure S4 shows the scores of selected PCAIMs plotted along each autosome . In order to further evaluate our results , we split the CHORI dataset in 50% training set and 50% test set , selected PCAIMs in the training set ( with and without redundancy removal ) and used these SNPs to predict the ancestry of the individuals in the test set ( Figure 5 ) . The PCAIMs selected in the training set achieve comparable performance in the test set . We repeated the same experiment in the Coriell dataset , as well as with different split sizes for both datasets ( e . g . , 80% training , 20% testing ) and obtained similar results ( data not shown ) . We then cross-validated our results by using the PCAIMs selected in one European American dataset for prediction of structure in the other European American dataset ( Figure 5 ) . We found that as few as 500 of the top PCAIMs selected in each dataset suffice for the accurate prediction of structure in the other dataset . The actual overlap between the top 500 PCAIMs selected in each sample is relatively small ( 6 . 8% or 34 SNPs ) . Of course this is still highly significant compared to the overlap between two random sets of 500 SNPs selected from approximately 307 , 315 SNPs , which is 0 . 16% with a standard deviation of 0 . 07% . Additionally , some amount of linkage disequilibrium can be observed between the top 500 PCAIMs selected in each of the two datasets . We computed r2 values for all possible pairs and found 44 pairs of SNPs that had r2 of at least 0 . 1 , with an average value of 0 . 43 . These pairs are in addition to the 34 pairs of overlapping SNPs between the two sets . So , it seems that there exist different sets of SNPs that are mildly correlated and yet provide similar information about the structure of the European American population . Finally , we examined the extent to which small subsets of PCAIMs can be used for correction of stratification in the setting of an association study . Following the model and parameters used by Price et al . [19] , we first simulated an admixed population with 1000 members genotyped on 100 , 000 SNPs , originating from two ancestral populations that are relatively closely related . In particular , the average Fst between SNPs in the ancestral populations was set to 10−2 ( see Methods and Text S1 for details ) . This gave us the advantage of knowing the “true” ancestry of each simulated individual , while at the same time constructing a simulated population whose structure is quite similar to the structure of our European American datasets . By looking at the singular values associated with the top eigenSNPs of the subject-SNP matrix , as well as by applying our permutation test , one principal component was deemed significant . Thus , in this simulated dataset , again individual variation lies across the first eigenSNP ( Figure S5 ) . In fact , if this eigenSNP is used as a predictor for ancestry , the Pearson correlation coefficient between true and predicted ancestry coefficient over all individuals is 0 . 9967 . As expected , PCAIMs work extremely well for the prediction of ancestry in the simulated data and as few as 100 to 400 PCAIMs are enough to accurately predict the ancestry of each individual ( Table 2 ) . In fact the Pearson correlation coefficient between true ( recall that in this case we know the actual ancestry ) and predicted ancestry , calculated by looking at the first eigenSNP of a matrix containing 100 , 200 , and 400 PCAIMs is 0 . 9102 , 0 . 9478 , and 0 . 9690 respectively . Using this particular method of constructing simulated SNPs results in mostly uncorrelated SNPs . Consequently , in this synthetic dataset , our redundancy removal algorithm did not improve our results . In order to test if small subsets of PCAIMs could be used for correction for stratification , we simulated association studies with sets of 100 , 000 random , extremely stratified , and truly causal SNPs ( see Methods for details ) for 10 different datasets . We first replicated the results of Price et al . [19] in order to correct for stratification using the top 10 principal components computed on all 100 , 000 SNPs without significant loss in power ( Table 2 ) . We then selected subsets of 100 to 400 PCAIMs in order to predict the ancestry of all 1 , 000 individuals . We proceeded to correct for stratification by removing ( projecting out ) our ancestry prediction from each SNP and then ran the Armitage trend test to the resulting SNPs . ( This is essentially the algorithm implemented in EIGENSTRAT . ) We measured the percentage of correlations found using the Armitage-trend test in each scenario and report the results before and after stratification correction in Table 2 . According to our findings , as few as 100 PCAIMs ( instead of 100 , 000 SNPs ) efficiently remove false correlations with disease , while largely maintaining the power of the study .
We have identified small sets of structure informative markers for the European American population through the direct investigation of European American samples and without depending on any assumptions about the ancestry or admixture proportions of the studied individuals . We have analyzed two independent datasets of European Americans , representing a total of almost 1500 individuals genotyped for more than 300 , 000 SNPs spanning the entire autosomal genome , and we have demonstrated that as few as 200 SNPs ( PCAIMs ) , carefully selected with our methodology , can be used to very accurately predict the genetic structure of European Americans as identified by PCA . The cross-validation experiments that we have performed verify the validity of our approach . Investigating the European American population directly for the selection of structure informative genetic markers results in SNP panels that provide a direct reflection of the complex patterns of sub-structure and admixture in European Americans . The analysis of the admixed European American population for the selection of structure informative markers was made possible through the application of the unsupervised method that we have recently introduced for the selection of PCA-correlated SNPs or PCAIMs [28] . As we have previously described , PCAIMs selection can be carried out without any need for prior knowledge of individual ancestry , and is thus feasible in admixed populations without having to trace the origin of the studied individuals or hypothesize about admixture proportions [28] . This is not possible when using allele-frequency based methods for the selection of AIMs like δ , Fst or informativeness for assignment [20]–[25] . An additional important contribution of the present study is the novel algorithm that we developed for the removal of redundancy from a given set of structure informative markers . All existing algorithms for AIM selection ( e . g . , δ , Fst , informativeness , as well as PCAIMs ) , could potentially suffer from selecting a large number of redundant SNPs . For example , consider the simple scenario where a SNP is assigned a high score , and many SNPs are in very high LD with this SNP . Then , they will also be assigned very high scores , and thus will be chosen as AIMs , even though they are clearly redundant . Thus , if the task at hand is to select a minimal set of AIMs ( as is the case in our work ) , a second step is necessary in order to remove redundant AIMs . Given the large number of SNPs ( many of which are in LD ) in genome-wide scans over the last year , this is certainly a significant concern . Notice for example the fact that , in the datasets we studied , fewer than 10 , 000 such pairs exist ( Table 1 ) , and even though this is a proportionally small percentage out of the possible pairs it still significantly increases the number of PCAIMs needed to perfectly capture the structure of the data . In order to address this deficiency , we propose an efficient and accurate algorithm that filters out redundant SNPs from the set of PCA-correlated SNPs . The proposed algorithm emerges by reducing the redundancy removal problem to a well-known problem in the numerical linear algebra community , the so-called Column Subset Selection Problem , as defined earlier here . As we have shown here , applying this algorithm significantly increases genotyping savings , reducing the number of SNPs needed for structure identification almost by six-fold . This method for redundancy removal can be applied to any set of SNPs in order to select a minimally correlated subset . We should note that the proposed algorithm does not necessarily return the absolutely optimal solution to the Column Subset Selection problem . Formal mathematical bounds regarding the accuracy of the algorithm do exist , arguing that the selected subset of columns ( i . e . SNPs ) provides an almost optimal solution [46] . Further discussion on this is perhaps beyond the scope of this paper . Alternatives that take into account LD estimation and physical distance could also be considered . Notice however , that our method is parameter free and achieves effective redundancy removal in a single step . The two independent samples of European Americans that we studied show comparable structure , while the CEPH European Americans represent only a small fraction of the entire breadth of variation that we encountered in these large datasets . We are able to faithfully reproduce this fine structure using as few as 200 PCAIMs . We found that the SNPs selected in the first European American dataset we studied could be successfully applied in the second dataset and vice versa; however , the absolute actual overlap was relatively small ( although significantly higher than what expected by chance alone ) suggesting the possibility that many different such subsets of informative SNPs exist . Several other studies have explored intra-European and European American genetic variation . Classic gene frequency [41] , [47] , Y-chromosome [48] or mitochondrial variation [49] , [50] as well as whole-genome studies [7] , [8] generally agree on a coarse separation of European populations along a northern to southeastern axis . Seldin et al . [7] analyzed 5705 SNPs from the ILLUMINA Linkage IV panel to calculate informativeness for assignment , and identified 400 SNPs that could be used in order to broadly cluster the populations they studied to northern and southern Europeans . Bauchet et al . [8] studied 10 , 000 SNPs ( Affymetrix 10K panel ) and about 100 individuals from 12 European populations and concluded that at least 1 , 200 high Fst SNPs were needed in order to achieve a similar clustering of northern versus southern Europeans . Our results build on these papers , using large datasets of genomewide markers , and an algorithm that can explicitly identify informative markers from admixed populations without knowledge of the ancestral populations . Finally , we demonstrate that our markers are valid across large European American studies . We found almost no overlap between the markers that we identify as ancestry informative and those reported in the above mentioned studies of European populations [7] , [8] ( data not shown ) . This was to be expected since all three studies analyze different datasets and different populations . Notice , that even between these two previous studies , there is very little overlap between the panels of SNPs reported as ancestry informative . Very recently , two studies parallel to ours , used several genomewide sets of markers in European Americans to derive small subsets of European American AIMs [26] , [27] ( see also Tables S1 and S2 ) . An important difference between these studies and ours is the fact that we employed a previously validated algorithm for the selection of AIMs [28] , that operates directly on raw data without the need for intermediate steps ( i . e . , artificial assignment of individuals to clusters , depending on candidate genes for local natural selection , etc . ) . As we have seen here , and as others have also discussed [26] , [51] , individual variation in the European American population seems to lie along a continuum rather than in distinct clusters . Thus , the method we have used here would be easier to generalize to diverse datasets without access to ancestral populations . Another important difference of our study , is the fact that , as we have also discussed previously here , we have employed a novel , linear algebra based algorithm in order to select the least correlated SNPs as part of our structure informative panel thus increasing the efficiency of our informative SNP sets . In comparison , Price et al . [26] and Tian et al . [27] reduced redundancy by applying measures based on physical distance . Our results are consistent with the findings of Price et al . [26] and Tian et al . [27] , who also demonstrated that the vast amount of inter-individual variation in European Americans lies across a single axis . In concordance with what we have also described here , Tian et al . [27] mention that the first principal component in their study accounted for greater than five-fold the variance of the second principal component ( percentage of total variance according to their analysis is 42 . 42% for the first principal component , and 8 . 32% and 6 . 66% for the second and third respectively [27] ) . Both [26] , [27] analyzed individuals of known ancestry and they could distinguish a cluster comprising of individuals of known Ashkenazi Jewish origin . Price et al . [26] argue that an additional principal component is needed in order to discern this line of ancestry . However , both of these studies included large subpopulations with known Ashkenazi Jewish ancestry . For example in Price et al . [26] , in the inflammatory bowel disease ( IBD ) study , 43% of included individuals self-reported as Ashkenazi Jewish ( 78% among individuals of known ancestry in this sample ) . In Tian et al . [27] 28% of the population analyzed was of known Ashkenazi heritage ( for comparison , 2% of the general US population self-reports as Ashkenazi Jewish [26] , [52] , [53] ) . Thus , the larger Ashkenazi Jewish population in the Price et al . [26] study likely helped to bring out an additional principal component for this population . It is likely that there were Ashkenazi individuals in the datasets that we studied; however , they probably constituted a smaller fraction of the overall population . Analyzing the top two eigenSNPs , corresponding to the top two principal components in our datasets ( data not shown ) , a small cluster of individuals becomes visually apparent . ( A similar figure is shown in [26] for the PD dataset which corresponds to our CORIELL dataset . ) As we have no information on individual ancestry , we cannot infer the origin of the individuals in this small cluster . Interestingly , this very small cluster ( which might correspond to Ashkenazi individuals in our population ) , is already reasonably separated from the remaining European Americans along the top eigenSNP , at least in the datasets that we studied . This observation is consistent with Tian et al . 's report [27]; in the sample they studied , the mean score of the top eigenSNP for individuals of known Ashkenazi Jewish ancestry , lay at one end of the distribution ( 0 . 045 for Ashkenazi Jewish individuals , followed by 0 . 022 for Greeks and 0 . 015 for Italians ) . This explains why our permutation test only detects the first principal component as statistically significant: our test removes from the data the amount of information that has already been captured by principal components that were deemed significant . The SNP panels proposed by Price et al . [26] and Tian et al . [27] perform very well when tested on our samples of European Americans ( Table S1 ) . The Tian et al . [27] panel of ancestry informative SNPs was selected by calculating In [25] for two discrete clusters; Ashkenazi Jewish individuals ( as representatives of southeastern or rather mediterranean European ancestry ) and northern Europeans . The SNPs selected by this method perform exceptionally well ( comparably to our SNP panel ) to recreate the individual ancestry in our analysis ( see Table S1 ) . This suggests the fact that most of the variation between southeastern and northern European ancestry is captured by the difference between Ashkenazi versus northwestern European ancestry . On the other hand , we could not fully test the SNPs proposed in the second study [26] , since they had not all been genotyped in our datasets . Price et al . [26] proposed 300 SNPs as informative for European American ancestry ( 100 discerning the northern European versus southeastern cluster and 200 differentiating the southeatern versus Ashkenazi Jewish clusters ) . Out of these SNPs , 141 had also been genotyped in the datasets we studied . However , using these 141 SNPs [26] , results in a correlation coefficient of 0 . 75 between true and predicted individual variation in our combined CORIELL and CHORI datasets ( Table S1 ) . There is generally little ( although far greater than chance ) overlap between the lists of structure informative SNPs identified by each of these three studies ( see Table S2 ) . The greatest overlap is found between the panel we propose here and the 1 , 441 SNPs proposed by Tian et al . [27] as distinguishing between northern European and Ashkenazi Jewish ancestry; out of the 1 , 419 SNPs that were also included in our analysis , 36 were among the 500 top informative SNPs that we selected in the analysis of our combined European American datasets . The overlap between the informative SNPs proposed by Price et al . [26] and the other two studies is even smaller , partly due to the fact that we could only test 141 out of the 300 proposed SNPs ( see Table S2 ) . In any case , as we have also suggested earlier here , it is probably not surprising that there exist more than one subsets of SNPs describing European American population structure . It is now clear that European derived populations are not homogeneous and recent studies have emphasized the problem of population stratification in genetic association studies which may lead to false positive associations with disease or mask true correlations [5] , [19] . As association studies of thousands of individuals are starting to become increasingly common [9]–[14] , population stratification will undoubtedly pose a serious challenge . Various methods have been proposed to tackle the problem [16] , [18] , [19] , [54]–[60] . Among them , PCA-based stratification correction tools seem particularly attractive , since they are computationally efficient and are not overly conservative . Moreover , such methods do not demand the use of discrete clusters , which as we have discussed earlier here may be an over-simplification , especially in the case of admixed populations . We have replicated the analysis of simulated data in [19] and experimentally demonstrated how our method can complement PCA-based stratification correction methods . Using as few as 100 to 200 PCAIMs , we achieved almost perfect stratification correction with virtually no loss in power . In comparison previous simulation studies [19] have shown that as many as 5 , 000 randomly selected SNPs would be needed to reach similar performance , while 20 , 000 random SNPs were needed in a real dataset [19] . Comparing the accuracy of ancestry prediction in the simulated and real data we have studied we can extrapolate that as few as 200 SNPs could be enough for stratification correction in real data ( reaching a Pearson correlation coefficient above 0 . 9 between “true” and predicted ancestry across the second eigenvector ) . While the selection of AIMs for stratification correction may be unnecessary for teams of investigators that undertake an initial genome-wide association study and can afford genotyping of very dense maps of markers , the use of AIMs for stratification correction becomes of critical importance in two-stage study designs , ( where replication of initial findings is sought in large independent samples ) , or studies following the candidate gene approach . In such cases , our methods can greatly facilitate association studies in admixed populations , reducing significantly the genotyping costs needed to ensure correction for stratification . We would like to point out that , the sets of European American AIMs that we and others [7] , [8] , [26] , [27] have identified , are representative of the full genetic structure in the European American population , only to the extent that the samples analyzed in each of these studies are deemed truly representative of the entire European American population . It will be important to study European American population structure with even larger datasets of carefully sampled individuals . Interestingly , in Tian et al . [27] , the effect of stratification on the case-control study of rheumatoid arthritis was mostly due to a difference in Irish ancestry . This suggests that different European American studies will have to exercise caution in detecting and adjusting for ancestry , since the components/axes that affect ancestry are likely to vary from study to study depending on the phenotype and the region sampled . In summary , we are proposing a small set of SNPs that can successfully capture the structure of the European American population samples we studied , as identified by PCA . We identified this minimal set of structure informative SNPs ( PCAIMs ) by applying a novel redundancy removal algorithm that will undoubtedly increase genotyping savings in many different research scenarios . Lists of the sets of markers that we have identified as well as an implementation of our algorithms are available online at http://www . cs . rpi . edu/~drinep/EUROAIMs/ . These panels of SNPs will serve as useful tools in the discovery of susceptibility genes for common complex disorders and can spark interesting questions in population genetics regarding the possible role of natural selection in the regions of the genome harboring these polymorphic sites . | Genetic association studies search to identify disease susceptibility genes through the analysis of genetic markers such as single nucleotide polymorphisms ( SNPs ) in large numbers of cases and controls . In such settings , the existence of sub-structure in the population under study ( i . e . differences in ancestry among cases and controls ) may lead to spurious results . It is therefore imperative to control for this possible bias . Such biases may arise for example when studying the European American population , which consists of individuals of diverse ancestry proportions from different European countries and to some degree also from African and Native American populations . Here , we study the genetic sub-structure of the European American population , analyzing 1 , 521 individuals for over 300 , 000 SNPs across the entire genome . Applying a powerful method that is based on dimensionality reduction ( Principal Components Analysis ) , we are able to identify 200 SNPs that successfully represent the complete dataset . Importantly , we introduce a novel method that effectively removes redundancy from any set of genetic markers , and may prove extremely useful in a variety of different research scenarios , in order to significantly reduce the cost of a study . | [
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| 2008 | Tracing Sub-Structure in the European American Population with PCA-Informative Markers |
Genetic recombination contributes to the diversity of human immunodeficiency virus ( HIV-1 ) . Productive HIV-1 recombination is , however , dependent on both the number of HIV-1 genomes per infected cell and the genetic relationship between these viral genomes . A detailed analysis of the number of proviruses and their genetic relationship in infected cells isolated from peripheral blood and tissue compartments is therefore important for understanding HIV-1 recombination , genetic diversity and the dynamics of HIV-1 infection . To address these issues , we used a previously developed single-cell sequencing technique to quantify and genetically characterize individual HIV-1 DNA molecules from single cells in lymph node tissue and peripheral blood . Analysis of memory and naïve CD4+ T cells from paired lymph node and peripheral blood samples from five untreated chronically infected patients revealed that the majority of these HIV-1-infected cells ( >90% ) contain only one copy of HIV-1 DNA , implying a limited potential for productive recombination in virus produced by these cells in these two compartments . Phylogenetic analysis revealed genetic similarity of HIV-1 DNA in memory and naïve CD4+ T-cells from lymph node , peripheral blood and HIV-1 RNA from plasma , implying exchange of virus and/or infected cells between these compartments in untreated chronic infection .
The genetic diversity of human immunodeficiency virus ( HIV-1 ) allows the virus to develop resistance to antiviral therapy and escape immune pressure . Several different mechanisms contribute to genetic diversity including rapid , high-level virus turnover ( ca . 108–109 cells are infected and die every day ) , nucleotide misincorporation during replication of the HIV-1 genome , and recombination [1]–[3] . HIV-1 recombination , which generates new viral variants through a process of genetic exchange , is initiated when a cell is infected by genetically distinct HIV-1 variants and two RNAs transcribed from the different proviruses are co-packaged into a virion . Subsequent infection of new host cells proceeds with reverse transcription , template switching of reverse transcriptase ( RT ) between the two genetically different genomic RNAs , leading to a recombinant genome that is genetically different from either of the two parental variants . Therefore , an essential and rate limiting step in the process of productive HIV-1 recombination is the co-infection of cells by two or more genetically distinct HIV-1 variants [4] , [5] . To investigate the numbers of cells co-infected by different HIV-1 variants in peripheral blood , we developed the single-cell sequencing ( SCS ) assay , which allows for the analysis of HIV-1 DNA molecules at a single cell level . Using this assay , we found that the majority of CD4+ T-cells ( >90% ) from the peripheral blood of untreated HIV-1-infected patients contain a single HIV-1 DNA molecule [6] . In contrast , other studies reported that CD4+ cells from the spleen are multiply infected by HIV-1 in vivo [7]–[9] . These isolated spleen cells were found to harbor one to eight ( with a mean of 3 . 2 ) genetically diverse proviruses per cell . The discrepancy between our study and these studies of cells isolated from the spleen may be attributable to the source of cells; peripheral blood versus lymphoid tissue . Only 2% of the total amount of lymphocytes are found in the peripheral circulation [10] , with the remainder distributed throughout the body , especially in lymphoid organs — such as lymph nodes and the gut associated lymphoid tissue ( GALT ) where most HIV-1 infection takes place [11]–[14] and where cells are tightly packed , facilitating cell to cell HIV transmission . Therefore , increased frequency of HIV-1 infection and cell exposure to HIV-1 virions may lead to more multiply infected cells in lymphoid tissue than in peripheral blood , which may have substantial clinical consequences . Recent data from Sigal et al . suggest that cell to cell transmission of individual drug resistant mutants in the setting of suboptimal antiretroviral therapy can yield virions capable of generating recombinant viruses encoding multiple drug resistance mutations , thereby rapidly limiting the effectiveness of antiretroviral drugs [15] . An increased frequency of HIV-1 infection has been shown in different tissue compartments and in different cellular subsets . CD4+ T-cells are the major target for HIV-1 infection and both naïve and memory CD4+ T-cells have been shown to be permissive , with most viral DNA detected in CD4+ T-cells that express the memory marker CD45RO [16]–[19] . To investigate the viral genetic composition , frequency of infection and number of multiply infected cells from different tissue compartments and cellular populations , we applied the recently developed single-cell sequencing assay to analyze single infected naïve and memory CD4+ T-cells sorted from paired lymph node tissue and peripheral blood samples . We determined that the majority of infected CD4+ T-cells ( >90% ) located in lymph node tissue and peripheral blood contain only one HIV-1 DNA molecule , implying limited potential for productive recombination in viruses produced by these cells . Furthermore , we show that viral sequences isolated from memory and naïve T-cells from lymph node tissue and peripheral blood are similar to each other and to HIV-1 RNA from contemporaneous plasma from these patients , indicating an exchange of genetic material between these compartments in untreated chronic HIV-1 infection .
The rate of HIV-1 recombination in infected individuals is dependent on the number of cells infected with two or more genetically distinct variants . To investigate the number and genetic makeup of proviruses in CD4+ T-cells residing within lymph node tissue and peripheral blood , we analyzed memory and naïve T-cells from these two compartments in five HIV-1 infected individuals using single-cell sequencing . All five patients were chronically infected with HIV-1 ( Fiebig stage VI ) and were not receiving antiretroviral therapy . All were male , relatively young ( median age 30 y ) and had a broad range of CD4+ T-cell concentrations and viral RNA levels ( Table 1 ) . All of the lymph nodes had evidence of typical HIV-1 infection that included florid follicular hyperplasia . The single-cell sequencing assay was recently published by our group and allows us to both quantify and genetically characterize the virus found in single infected cells [6] . Pools of cells , each containing <<1 infected cell , are lysed and distributed across 10 wells per row in a total of 8 rows per 96 well PCR plate ( for detailed information how cell concentrations with << 1 infected cell were obtained please see [6] and the Material and Methods section below ) . During this analysis of cells from peripheral blood and lymph node tissue the number of infected cells per lane of 10 wells ranged from 0 . 03 to 0 . 4 . PCR amplification and sequencing of the DNA in each well allows enumeration and analysis of the genetic relationship of viral DNA molecules in each infected cell . Using Poisson statistical methods , we also determined the predicted number of multiply infected cells ( Table 2 , parentheses ) , and whether the observed number of multiply infected cells exceeded predicted . Using this assay we isolated and analyzed memory CD4+ T-cells from both lymph node tissue and peripheral blood and found that the majority ( >90% ) of the infected cells contained only one HIV-1 DNA molecule ( Table 2 , column 9 ) . Naïve T-cells isolated from lymph node tissue had a similarly high proportion of singly infected cells . In four of the five patients ( 2–5 ) we found evidence for multiple infection ( multiple HIV-1 DNA molecules in at least one row of 10 wells ) of memory CD4+ T-cells from both lymph node tissue and peripheral blood and naïve T- cells from lymph node tissue . Also , patient 5 showed evidence of multiple infection in naïve T-cells isolated from peripheral blood . In each case , however , the number of rows containing more than one HIV-1 DNA molecule corresponded closely to Poisson predicted values under the assumption that no multiple infection was present ( Table 2 , columns 2–6 , values in parenthesis are Poisson predicted values ) . This result indicates that the detection of multiple HIV-1 DNA molecules in the same row could be due to several singly infected cells being analyzed in the same row rather than presence of one cell containing multiple HIV-1 DNA molecules . In addition , for each patient individually , there was insufficient statistical support to reject the hypothesis that there was no multiple infection present ( p values ranging from 0 . 06 to 1 , Table 2 , column 7 ) . Combining all the data for all the patients , we did , however , find statistical support for multiple infection in memory CD4+ T-cells isolated from lymph node tissue ( p = 0 . 01 ) . The estimated frequencies of multiple infection in naïve CD4+ T-cells isolated from the same cellular compartment ( p = 0 . 08 ) or memory CD4+ T-cells from peripheral blood ( p = 0 . 1 ) did not reach statistical significance , although the low p-values suggest that some double infection may have occurred as well . Even though we cannot definitively determine the frequency of multiple infection in the different cell types from the data in Table 2 , we can use these data to establish confidence limits for the frequency of multiple infection . If we conservatively assume that all rows with multiple HIV-1 DNA molecules are the result of multiply infected cells , we conclude with an upper confidence level of 95% that no more than 26 . 3% ( 26 . 8–40 . 5% ) and 25 . 8% ( 23 . 8–81 . 1% ) of the memory and naïve CD4+ T-cells , respectively , isolated from lymph node tissue are multiply infected . Similarly , the percent of multiply infected memory CD4+ T-cells from peripheral blood is no greater than 24 . 3% ( 27 . 2–84 . 7% ) ( Table 2 , column 8 ) . However , when simulations were conducted to estimate the rate of multiple infection most consistent with the observed distribution of HIV-1 DNA molecules , we found that the percent of multiply infected cells is no greater than 7% in any of the cellular subsets analyzed ( Table 2 , column 9 ) . Interestingly , frequencies of multiply infected cells were similar across most patients despite 100-fold differences in plasma viremia . These results are in agreement with our recent data [6] and demonstrate that the majority of CD4+ T-cells isolated from both lymph node tissue and peripheral blood contain only one HIV-1 DNA molecule . Using the single-cell sequencing assay we analyzed the frequency of infection in memory and naïve CD4+ T-cells from both lymph node tissue and peripheral blood . The frequency of infection for each cell type was based on the total number of cells analyzed divided by the number of infected cells , as determined by the number of HIV-1 DNA molecules detected in each row of 10 wells . This approach yields precise measurements of infection frequency , permitting direct comparisons of frequency of infection in various cell subsets and anatomic compartments . Due to the design of the single-cell sequencing assay , where multiple DNA molecules in one row of 10 wells could have been derived from either singly or multiply infected cells , we used two methods to calculate the frequency of infection . For method 1 , all multiple HIV-1 DNA molecules found in one row were assumed to derive from singly infected cells ( no occurrence of multiple infection ) , yielding a higher frequency of infection than method 2 where all multiple HIV-1 DNA molecules found in one row were assumed to derive from multiply infected cells ( maximal occurrence of multiple infection ) , yielding a lower frequency of infection ( Table 2 , columns 10–13 ) . These calculations yielded a geometric mean of 214–271 cells/HIV-1 DNA molecule in memory CD4+ T-cells from lymph node tissue and a geometric mean of 657–796 cells/HIV-1 DNA molecule in memory CD4+ T-cells isolated from peripheral blood ( patients 1–5 , Table 2 , “patients combined” columns 10–13 ) . In four of the five patients ( patients 1 , 2 , 3 and 5 ) the HIV-1 infection frequency of memory CD4+ T-cells from lymph node tissue was 2–17 times higher than the infection frequency of memory CD4+ T-cells from peripheral blood . When memory CD4+ T-cells sorted from lymph node tissue and peripheral blood for all five patients were compared , however , the 3-fold difference in infection frequencies of the cells from these two separate compartments was not statistically significant ( p = 0 . 12 and 0 . 11 ( Method 1 and 2 respectively , paired t test , Fig . 1a ) . The same result was also obtained when data from patient 4 ( who had similar infection frequencies in CD4+ T-cells from lymph node tissue and peripheral blood ) were removed from the analysis . Moreover , higher infection frequencies in memory T-cells from peripheral blood were positively correlated with plasma RNA levels , consistent with previous observations [16] . We also detected a strong positive correlation between frequencies of infected memory cells in lymphoid tissue and plasma viral RNA level ( r2 = 0 . 77 and 0 . 79 , method 1 and 2 , respectively ) . We also sorted naïve CD4+ T-cells from lymph node tissue from all five patients and the infection frequency of these cells ranged from 2376–2857 cells/HIV-1 DNA molecule ( geometric mean of patients 1–5 , Table 2 , columns 10–13 ) . The infection frequency of naïve CD4+ T-cells was significantly lower ( 12-fold ) than the infection frequency of memory CD4+ T-cells sorted from lymph node tissue ( p = 0 . 01 and 0 . 01 , method 1 and 2 respectively , paired t test , Fig . 1b ) . Naïve CD4+ T-cells could only be isolated from peripheral blood from two of the five patients ( patients 2 and 5 ) and those cells from patient 2 did not contain any detectable HIV-1 DNA ( 0 copies per 10000 naïve T-cells analyzed ) indicating an infection frequency of >10000 cells/HIV-1 DNA molecule . The infection frequency of naïve CD4+ T-cells isolated from peripheral blood of patient 5 ranged from 1600–1846 cells/HIV-1 DNA molecule , similar to the overall frequency of infection of naïve CD4+ T-cells in lymph node tissue ( 2376–2857 cells/HIV-1 DNA molecule ) . In addition , we estimated the contribution that each of the infected cells makes to the total HIV-1 reservoir in CD4+ T-cells in peripheral blood and lymph node tissue . In samples from lymph node tissue , we found that the mean % contribution was 93 . 8 and 6 . 2% in memory and naïve T-cells , respectively . Also in peripheral blood memory T-cells were the major component of the HIV-1 reservoir ( mean % contribution was 70 . 9 and 29 . 1% in memory and naïve CD4+ T-cells respectively , patients 3 and 5 ) . To directly compare the distribution of HIV-1 infected cells in the combined peripheral blood and lymph node compartments , we calculated the total levels of HIV-1 infected cells in peripheral blood and in the lymphoid tissue ( extrapolating from our lymph node data and excluding spleen and GALT ) . In general , total levels of HIV-1 infected cells in lymphoid tissue exceeded peripheral blood although the proportions varied; 80–97% of all infected memory cells and 92–100% of all infected naïve cells are present in lymph nodes To evaluate the genetic relationship between the HIV-1 DNA sequences that reside within infected cells in lymph node tissue and peripheral blood and viral RNA sequences isolated from contemporaneous plasma we conducted phylogenetic analyses . Maximum likelihood phylogenetic trees of the single gag , pro and pol ( p6 through nt 900 of RT ) sequences isolated from memory and naïve CD4+ T-cells from the paired lymph node tissue , peripheral blood and plasma RNA samples from each of the five individuals were performed . Sequences from all five patient samples as well as standard laboratory viruses formed independent populations that were at least 5% different from one another , with no intermingling , demonstrating that the patient-specific viral populations were genetically distinct ( data not shown ) . Sequence analysis revealed that intracellular HIV-1 DNA in memory and naive CD4+ T-cells from lymph node tissue and peripheral blood samples and plasma RNA were phylogenetically similar in each of the patients ( examples from patients 2 , 4 , and 5 are shown in Fig . 2a–c ) . In fact , we identified sequences in PBMC , lymphoid cells and plasma that were identical to each other ( Fig . 2 red circles , red squares indicate identical sequences from the same row ) . The genetic similarity of HIV-1 DNA populations in CD4+ T-cells from lymph node tissue , peripheral blood and HIV-1 RNA from plasma implies exchange of virus and/or infected cells between these compartments . Genetic characterization of viral sequences within multiply infected cells ( detected by two or more DNA molecules amplified from the same row of 10 wells ) revealed both identical and genetically different HIV-1 variants within the same cell ( Table 3 , columns 3–8 ) . For example , in memory CD4+ T-cells isolated from lymph node tissue we identified a total of 38 rows with doublet DNA molecules where 10 of the rows contained identical and 28 genetically different viral sequences . Identical HIV-1 DNA molecules detected in the same row could result from one cell being infected by two genetically identical virions , a cell in the process of S-phase DNA synthesis , or from multiple singly infected cells detected in the same row . It is highly unlikely that this finding was the result of two independently infected cells adhering to each other as 1 ) all the cells had been subjected to single cell sorting and formed a monodisperse population and 2 ) significantly greater frequencies of identical sequences were present in rows with more than one provirus ( p<0 . 04 for all cell types , Fisher's exact test ) . Also , the frequency of identical sequences in memory cells in lymph nodes was about 3 fold higher than in blood , consistent with greater rates of either cell division or multiple infection , but the difference was not statistically significant . Although multiply infected cells were infrequent , sequence alignments from all patients showed clear presence of recombinant sequences which were identified by using a genetic algorithm for recombination detection ( GARD , data not shown ) . The degree of phylogenetic interspersion of sequences from different compartments in the trees ( Fig . 2a–c ) indicates genetic flow of HIV-1 between cells in lymph node tissue , peripheral blood and plasma during untreated chronic HIV-1 infection . To better understand the genetic relationship among the isolated sequences and assess whether the HIV-1 populations are structured by the different compartments from where they were isolated , we conducted compartmentalization tests using both distances-based ( Wright's measure of population subdivision ( Fst ) [20] ) and tree-based ( the Slatkin Madison test [21] and Simmonds Association index [22] ( AI ) ) methods ( Table 4 ) . We first conducted compartmentalization analysis using the Wright's measure of population subdivision ( Fst ) . As shown in Table 4 , ( column 7 ) , nine of the 15 Fst analyses showed no statistical support for a genetic subdivision between sequences isolated from lymph node , peripheral blood or plasma . Statistical support for viral subdivision was , however , found in 6 of the Fst analyses , indicating a higher degree of structure than expected by chance . When conducting the Slatkin-Madison test , we found similar results with no statistical support for compartmentalization in 15 of 20 analyses and statistical evidence for compartmentalization in 5 of the 20 analyses ( Table 4 , column 8 ) . The number of analyses that showed statistically significant evidence for compartmentalization did however drop to only 3 analyses for both the Fst and SM analyses after Bonferroni correction for multiple comparisons . This conclusion is consistent with limited evidence of compartmentalization as assessed by Simmonds Association Index ( AI ranging from 0 . 45 to 1 with bootstrap values ranging from 0 . 30 to 1 ) . An AI value of 1 implies that the clustering of HIV sequences does not deviate from that expected from random compartment association ( randomness between sequences indicating no compartmentalization ) while an AI value closer to 0 implies a structured compartmentalization between sequences . No patient except patient 2 showed evidence of compartmentalization in all three analyses conducted . However , the corresponding AI values indicate that the extent of compartmentalization in the samples from this patient is limited ( Table 4 , column 10 ) . Identical sequences in the dataset can influence the results of the compartmentalization analyses . To evaluate this possibility , we collapsed all the identical sequences in each compartment into one sequence and repeated the analyses . As shown in Table S1 , the amount of evidence for compartmentalization was clearly reduced when identical sequences were collapsed and we only found evidence for compartmentalization in all of the analyses in one of the datasets ( peripheral blood vs plasma ) for patient 2 . Also , differences in the number of sequences in each compartment can influence the result of the compartmentalization analyses [23] , therefore we conducted the same analyses based on an equal number of sequences in each compartment ( Table S2 ) , which did not lead to substantially different results . Taken together , these data suggest compartmentalization is infrequent during chronic HIV infection , and provide strong support for free HIV flow between lymphoid tissue and peripheral blood compartments . To further evaluate the genetic relationship among sequences from the different cell types , cellular compartments and plasma , we measured average pairwise differences ( APD ) within each subset of sequences from each patient . In four of the five patients ( patients 2–5 ) the genetic diversity was above one percent in each of the different compartments . However , the genetic diversity of the HIV population in patient 1 was lower , around 0 . 5% in the different compartments ( Table 5 ) . This difference probably reflects the shorter duration of infection in this patient compared to the other four [6] , [24] . The genetic diversity did not vary greatly within each cell type for all of the patients , averaging 1 . 3% ( range = 0 . 6–2 . 0% ) within the memory population from lymph node tissue and 1 . 1% ( range 0 . 2–1 . 7% ) in naïve T-cells from the same anatomical compartment ( Table 5 , columns 2–3 ) . Similar diversity was found in sequences isolated from memory and naïve T-cells from peripheral blood , 1 . 3% ( range = 0 . 5–1 . 7% ) and 1 . 4% ( data from patient 5 only ) respectively ( Table 5 , columns 4–5 ) . The APD of the sequences isolated from extracellular RNA was 1 . 2% ( range = 0 . 4–1 . 7% , Table 5 , column 6 ) which is similar to the cells analyzed from each compartment . Additional analysis did not indicate a statistically significant difference in average pairwise distance across the cellular subsets and plasma ( p = 0 . 08 , one-way repeated measures ANOVA ) . These results further indicate similar genetic composition of viral sequences isolated from memory and naïve T-cells from lymph node tissue , peripheral blood and viral sequences from plasma RNA in untreated chronic HIV-1 infection .
The rate of productive HIV-1 recombination is dependent on the frequency of cells infected with two or more genetically different HIV-1 variants . Multiple proviruses per infected cell have been reported in the spleen of some HIV-infected individuals [7]–[9] . We have recently shown , however , that the majority of CD4+ T-cells ( >90% ) in peripheral blood are not multiply infected [6] . The differences between our recent study and earlier studies may be attributable to the source of the cells ( peripheral blood versus solid lymphoid tissue ) , since earlier studies have shown that most HIV-1 infection and viral replication takes place in lymphoid tissue such as lymph nodes and GALT [11]–[14] . The increased rate of HIV-1 infection and cell exposure to HIV-1 virions in lymphoid tissue could facilitate the transmission of multiple HIV-1 genomes to single cells , as reported for in vitro infections [25] . To investigate the difference in the frequency of multiply infected cells isolated from lymph node tissue and peripheral blood , we used the single-cell sequencing assay recently developed by our group which allowed us to quantify and genetically characterize the virus isolated from single-infected cells [6] . When we analyzed individual HIV-1 DNA molecules from memory and naïve CD4+ T-cells from paired lymph node and peripheral blood samples from five chronically untreated HIV-1-infected patients , we found that the majority ( >90% ) of both memory and naïve HIV-infected CD4+ T-cells in lymph nodes contained only one HIV-1 DNA molecule , and that the observed number of samples with multiple HIV-1 DNA molecules was close to that expected from the Poisson distribution ( Table 2 ) . In individual patient analyses , we did not detect evidence of multiple infection . Increasing the sample size by pooling the data from all of the patients did , however , reveal statistical support for multiple infection in memory CD4+ T-cells isolated from lymph node tissue ( Table 2 ) . Single cell sequencing revealed that some multiply infected cells had identical HIV-1 DNA sequences , which may arise from true multiple infection with HIV-1 with low genetic diversity ( patient 1 , low viral diversity ) or from cells infected with a single provirus undergoing DNA replication . Restricting statistical analysis to data from patient 1 and only genetically distinct viruses of patients 2–5 was still suggestive of multiple infection , although the limited sample size and reduced statistical power yielded a more marginal p-value ( 0 . 066 ) to rule out 0% multiple infection . Overall , these findings suggest that levels of multiply infected cells with distinct proviruses are infrequent and our simulations predict that the percent of multiply infected cells is no greater than 7% in any of the cellular subsets analyzed ( Table 2 , column 9 ) . Despite the low level of multiply infected cells , we did detect phylogenetic evidence of for recombination in all patient samples ( data not shown ) implying that only few multiply infected cells in peripheral blood and lymph node tissue are needed to generate recombinant viral variants . These results agree with our earlier studies of CD4+ T-cells from peripheral blood and with the results of Neher et al [26] and Batorsky et al [27] , who used modelling based on the amount of viral recombination found during chronic HIV-1 infection , to show that only about 10% of HIV-1-infected cells are multiply infected . The role of low frequency multiply infected cells on the rate of HIV-1 spread or emergence of variants encoding multiple resistance mutations in vivo is unknown . Previous in vitro data indicates cell to cell transmission may facilitate HIV spread despite antiretroviral therapy [15] , which can serve to generate new recombinant viruses . Quantifying the role of multiply infected cells in vivo will require additional analysis that models frequency of infected cells , replication rates , as well as the size of replicating populations . Our estimates of the frequency of multiply infected cells are substantially lower than that reported previously [7] , [8] . The reasons for the discrepancy between our and previously reported results are uncertain; it is possible that the source of cells isolated for analysis ( spleen [7] , [8] vs . PBMC , inguinal , and axillary lymph node [6 , present ms . ] ) are responsible , in part , for the differences . Functional differences between these lymphoid organs may also play a role; immune cells , such as dendritic cells , which concentrate HIV-1 on their cell surface and facilitate infection of T-cells , may be essential participants in presenting genetically distinct HIV-1 to susceptible cells but differentially distributed in spleen and lymph node of HIV-1 infected individuals . Hence , one possible explanation for differences in numbers of infected cells may be higher DC – T-cell interactions in the spleen compared to other lymphoid node structures . It is unlikely that differences in clinical status of source patients was responsible for differences . Gratton et al . [7] and Jung et al . [8] studied patient with AIDS; two of our patients also met AIDS criteria ( Table 1 ) and we did not detect substantial differences in numbers of multiply infected cells in AIDS and non-AIDS patients . In addition , the frequency of multiply infected cells was fairly constant across the majority of the patients studied , and did not correlate with frequency of infected cells , or with level of plasma RNA; similar levels of multiply infected cells were present in patients with viral RNA levels of 103 or 105 copies/ml . These data suggest that , in vivo , multiple infection events of cells from lymph node tissue or peripheral blood are not determined by levels of circulating HIV-1 and other mechanisms may facilitate multiple infection . Our studies revealed that the average infection frequency of lymph node derived memory CD4+ T-cells ( 250 cells per HIV DNA molecule , 0 . 4% ) was 2–17 times higher but not significantly different from peripheral blood derived memory CD4+ T-cells ( 700 cells per HIV DNA molecule , 0 . 14% ) . The higher HIV-1 infection frequency of cells from lymph node tissue compared to peripheral blood is consistent with the higher concentration of HIV-1 in lymphoid tissue [11] , [28] , [29] . In both peripheral blood and lymph node we found that the infection frequency of memory CD4+ T-cells was substantially higher than that of naïve CD4+ T-cells . This difference in infection frequency has been explained by the expression of the CCR5 co-receptor in memory T-cells compared to naïve T-cells [30] , [31] . In addition , the frequency of infection of one cell type compared to another has been related to the number of proviruses per infected cell [16] . Since the majority ( >90% ) of memory and naïve T-cells from peripheral blood and lymph node tissue contain only one HIV-1 DNA molecule , the higher infection frequency of memory compared to naïve T-cells is not related to differences in the number of HIV-1 DNA molecules within each infected cell . We also found that the major component of the viral reservoir in both peripheral blood and lymph node tissue was composed of memory T-cells in peripheral blood and lymph node tissue . The contribution of non-T cells to dual infection is uncertain; previously we did not detect infected monocytes in the periphery , it is not known whether non T-cells in tissues sources ( e . g . , macrophages microglial cells ) may contribute to dual infection . Given the relatively high contribution of memory CD4+ T-cells in lymph node tissue and the fact that most of the CD4+ T-cells are located in lymphoid tissue these data suggest that the major HIV-1 reservoir is located in memory CD4+ T-cells residing in lymphoid tissue consistent with previous data [32] , [33] . Direct comparative analyses revealed that 80–97% of infected memory cells and 82–100% of infected naïve cells were present in lymph nodes . Reasons for variability in distribution are uncertain , but may be related to duration of infection and degree of immunodeficiency . It was unlikely that lymph node “exhaustion” was responsible for variability; all of the lymph nodes were enlarged , had clear histologic follicular hyperplastic architecture without obvious fibrosis , and yielded large numbers ( 1×109–8×1010 ) of cells for analysis . We also found a strong correlation between levels of plasma RNA and frequency of infected memory cells in lymphoid-derived material ( r2 = 0 . 77 and 0 . 79 , methods 1 and 2 , respectively ) . Consistent with previous observations of Brenchley and coworkers [16] we also found a correlation between plasma HIV RNA and frequency of infected memory cells in peripheral blood . The viral genetic makeup of multiple infected cells is important for the production of heterodimeric virions and the development of new recombinant variants . Using single-cell sequencing , in four of the five patients we were able to detect more than one HIV-1 DNA molecule per cell in memory and naïve CD4+ T-cells from lymph node tissue and memory T-cells from peripheral blood . These molecules could be the result of multiply infected cells . When analyzing the genetic makeup of the virus within these infected cells we found both identical and distinct HIV-1 variants in single rows of cellular lysate . In total we found 49 rows ( all patients and cell types combined ) with distinct viral variants , which , if they arose from multiple infection could give rise to heterodimeric virions and new viral recombinants . One drawback of the SCS assay is however that we cannot distinguish whether the HIV-1 DNA that we are analyzing is in an integrated , linear , or circular form or productive or not . The presence of un-integrated HIV-1 DNA has been well documented [34] , [35] hence , even though we detect different proviruses in the same lane we cannot determine if these possible multiply infected cells will produce virions with two different RNA molecules or not . We also detected 10 individual cells in both the memory and naïve cell compartments with at least 2 identical sequences . The presence of identical HIV-1 sequences within a single cell suggests a scenario in which the cell had been infected , and subsequently underwent DNA replication . Alternatively , such cells could have been infected by cell-cell transfer of genetically identical virions from a nearby cell . Both infected memory and naïve CD4 cells have been reported to be capable of proliferation in HIV infected individuals [36] , [37] , and the greater extent of proliferation of these cells in lymph nodes than in blood is consistent with the difference in frequency of identical HIV sequences in the two compartments . Determining the genetic relationship among HIV-1 populations from peripheral blood and tissue compartments is important for understanding the dynamics of HIV-1 infection . Using single-cell sequencing we demonstrated that the intracellular HIV-1 DNA sequences isolated from memory and naïve CD4+ T-cells residing in lymph node tissue are phylogenetically interspersed with intracellular sequences from cells in peripheral blood and plasma-derived RNA sequences . The similarity of sequences from lymph node tissue , peripheral blood , and plasma was confirmed by two phylogenetic approaches , maximum likelihood tree construction ( intermingling of sequences ) and compartmentalization tests ( lack of signal for compartmentalization in 4 of the 5 patients ) . We did find statistical support for compartmentalization by all three compartmentalization tests ( Wrights measurement of subdivision , Simmonds Association Index and the Slatkin-Madison ) in samples from patient 2 . However , the corresponding AI values indicate that the extent of compartmentalization is limited . Also , the statistically significant evidence for compartmentalization was clearly reduced after Bonferroni correction for multiple comparisons and when identical sequences from the same compartment were collapsed . These results indicate that there is considerable flow of virus and/or infected cells between lymph node tissue and peripheral blood and that the peripheral blood reflects the genetic make-up of HIV-1 in lymph node tissue of untreated patients . The fact that we find the same frequency of multiply infected memory CD4+ T-cells in lymph node tissue ( 7% ) as in peripheral blood ( 6% ) also indicates that HIV-1 infected cells are trafficking between these two compartments . In addition , we found genetically identical sequences from plasma-derived RNA and memory CD4+ T-cells from both peripheral blood and lymph node tissue . This observation could be explained by circulation of viral variants or infected cells between the lymph node tissue and peripheral blood compartments . In conclusion , by using the single-cell sequencing assay we found that the majority ( >90% ) of memory and naïve CD4+ T-cells isolated from both lymph node tissue and peripheral blood contain only one HIV-1 DNA molecule . We show a higher infection frequency in memory CD4+ T-cells compared to naïve T-cells from lymph node tissue . The genetic similarity of HIV-1 DNA populations in CD4+ T-cells from lymph node tissue , peripheral blood and HIV-1 RNA from plasma implies ongoing exchange of virus and/or infected cells between these compartments during untreated chronic HIV-1 infection .
Written informed consent was provided by all study participants . The study was approved by the institutional review boards at the NIH and the Karolinska Institutet . Participants in this study were chronically infected with HIV-1 , subtype B ( all Feibig stage VI ) , were not receiving antiretroviral therapy , and were enrolled in clinical studies at National Institute of Allergy and Infectious Disease ( NIAID ) Critical Care Medical Department ( NIAID/CCMD ) Clinic of National Institutes of Health ( NIH ) , Bethesda , Maryland ( Table 1 ) . One patient had received transient antiretroviral therapy but had not undergone any therapy for over two years prior to biopsy; the remaining four patients were antiretroviral naïve . Two patients ( 3 , 4 ) had CD4 percent <14% and therefore have AIDS . Palpable lymph nodes were identified in axilla ( 2 ) or inguinal ( 3 ) regions and entire lymph nodes were excised . In one case ( patient 3 ) , the node was removed because of a suspicion of tuberculosis . Nodal tissue was divided for research and standard histopathology; in all cases , review of histopathology revealed preserved nodal architecture with marked follicular hyperplasia and plasmacytosis characteristic of HIV lymphadenopathy . One participant ( patient 3 ) had necrotizing granulomas present , with no pathogens identified by staining , culture , or PCR amplification . All participants tolerated the procedure well; one participant had persistent seroma , which was treated with antibiotic therapy for possible infection , and which resolved completely . Peripheral blood samples were obtained at the time of , or within 3 weeks of biopsy . The single-cell sequencing assay [6] was used to quantify and genetically analyze the intracellular HIV-1 viral populations found in memory and naïve CD4+ T cells from lymph node tissue and peripheral blood . In brief , pools of cells , each containing <<1 infected cell* , were lysed and distributed across 10 wells per row in a total of 8 rows on a 96 well PCR plate . PCR amplification ( p6 through nt 1–900 of RT ) and sequencing of the DNA in each well allowed enumeration and analysis of the genetic relationship of viral DNA molecules in each infected cell . For a detailed description we refer to the original publication [6] . *To determine the concentration of cells containing less than one infected cell per well , several plates were set up with different amount of cells sorted into each well ranging from 30–1000 cells/well . A test run for each cellular concentration was performed and from the number of HIV-1 DNA molecules amplified by PCR in the different test runs the frequency of infection for each sorted cell concentration was calculated for each patient to determine the concentration level of cells containing far less than one HIV-1-infected cell . On the basis of these calculations , additional HIV-1 DNA amplification PCR plates were set up for the particular sorted cell concentration determined to contain much less than one HIV-1–infected cell . The SCS was used as previously described with minor changes described below . Lymph node biopsy samples were processed directly after excision . Single cell suspensions were obtained by gentle mechanical manipulation of the lymph node tissue [38] , [39] . Blood samples from the five patients were collected and peripheral blood mononuclear cells ( PBMCs ) were separated from the plasma using Ficoll . Cryopreserved cells from lymph node tissue and peripheral blood were thawed in R10 media ( RPMI medium 1640 , 10% fetal bovine serum ( FBS ) , 100 U/mL penicillin , 100 µg/mL streptavidin , 2 mM L-glutamine and 20 mM HEPES ) at 37°C , immediately washed twice with R10 and then rested in R10 containing 20 U of DNase/ml ( Roche ) for 1 hour . Cells were then spun down and washed with Dulbecco' s phosphate buffered saline ( Gibco ) . Cells were spun down once more and resuspended in a minimal volume and then stained with LIVE/DEAD Violet Viability/Vitality stain ( Invitrogen ) . Ten minutes after the addition of LIVE/DEAD Violet Viability/Vitality stain pre-titered amounts of CD3 H7APC , CD4 Cy55PE , CD8 APC , CD45RO TRPE , CD27 Cy5PE and CD14 Pacific Blue and CD19 Pacific Blue were added to the cells , which were subsequently incubated for an additional 20 minutes at room temperature . Cells were washed once with R10 and then immediately sorted into 96 well plates using a modified FACSAria flow cytometer at 70psi . Both peripheral blood cells and cells from lymph nodes were sorted in the same manner . Singlet cells were sorted based on Forward Scatter Height ( FSC-H ) and Forward Scatter Area ( FSC-A ) . Dead cells , B cells , and monocytes were excluded by staining with either LIVE/DEAD fixable violet dead cell stain or CD14 and CD19 staining . Naive ( CD27+CD45RO− ) and memory ( CD27+/CD45RO+ and CD45RO+/−CD27− ) CD3+CD4+CD8− small lymphocytes were then sorted in 96 well PCR plates containing 50 µl of lysis buffer . CD3 H7APC and CD8 APC were purchased from BD Bioscience . CD4 Cy55PE , CD14 and CD19 Pacific Blue were from Invitrogen . CD45 RO TRPE and CD27 Cy5PE were from Beckman Coulter . To compare the intracellular populations identified using the single-cell sequencing assay to HIV-1 RNA populations found in plasma , we performed single-genome sequencing ( SGS ) on the plasma samples from each of the 5 patients as described earlier [24] , [40] , [41] . The viral region amplified from plasma spanned from p6 through RT and was the same region that was amplified from intracellular viral DNA . Alignments of the intracellular and extracellular HIV-1 populations were performed using an in-house computer program written in Perl scripting language ( available upon request ) . Recombination was screened using a genetic algorithm for recombination detection ( GARD ) [42] . For phylogenetic analysis of the HIV-1 populations , maximum likelihood ( ML ) phylogenetic trees were constructed in PhyML version 3 . 0 [43] . We used the general time reversible ( GTR ) nucleotide substitution model incorporating gamma-distributed rate variation among sites and allowing a proportion of invariable sites . Branch support was inferred using 1000 bootstrap replicates . All sequences obtained from the five patients were compared by phylogenetic analysis to each other and standard laboratory viruses to ensure that no contamination between patient samples or lab strains had occurred . Evidence for compartmentalization between sequences from lymph node , peripheral blood and plasma was evaluated using tree-based methods , namely Simmond's Association Index ( AI ) [22] and the Slatkin-Maddion ( SM ) test [21] . AI statistical support was obtained using 1000 bootstrap trees and 10 re-labelings per sample ( only bootstrap values above 0 . 95 were considered significant ) . For the SM method , 10000 permutations were performed ( p-values<0 . 05 were considered significant ) . In addition , we used the nucleotide distance-based statistic known as Wright's measure of population subdivision ( FST ) [20] . Distances were estimated using a ML approach under a GTR nucleotide substitution model , estimating all parameters independently for each branch . To obtain the significance of the statistics , 10000 permutations were computed , with the permutation test randomly allocating sequences into lymph node , peripheral blood or plasma pre-defined clades . All compartmentalization tests were performed using the package Hyphy [44] . Measurements of the HIV-1 genetic diversity ( average pairwise distance , APD ) within each cell type and plasma were calculated using MEGA5 . 0 ( http://www . megasoftware . net/ ) . Diversities are reported as percent differences . Rates of infected CD4+ T-cells , rates of multiple infection , and comparisons thereof were calculated using methods previously described [6] . We estimated total number of infected cells in the blood compartment by determining total blood volume using the Nadler formula [45] . Total infected cell levels in lymphoid node were calculated using proportion of lymph node used for analysis , total yield of cells , and reported estimates of the number of lymph nodes in humans [46] . | One of the greatest challenges facing treatment and vaccine development for human immunodeficiency virus ( HIV-1 ) is the genetic diversity of the virus . One of the main factors contributing to HIV-1 diversity is recombination between two genetically different viral RNA genomes that enter a cell in the same virion . Such heterozygous virions can only arise from cells that contain two or more genetically distinct HIV-1 proviruses . Therefore , the amount of productive HIV-1 recombination in infected individuals is dependent on the number of multiple infected cells and the genetic relationship of the proviruses they contain . In this work we use a recently developed assay , single-cell sequencing , to analyze the number and genetic makeup of HIV-1 DNA molecules in single infected cells . We used this assay to analyze memory and naïve CD4+ T cells from lymph node tissue and peripheral blood sampled from five chronically untreated HIV-1 infected individuals . Our results revealed that <10% of infected memory and naïve T-cells from either the lymph node tissue or peripheral blood are multiply infected , a number far below earlier estimates . In addition , we demonstrate a similar genetic composition of HIV-1 in lymph node tissue , peripheral blood and plasma during untreated chronic HIV-1 infection . | [
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| 2013 | Single Cell Analysis of Lymph Node Tissue from HIV-1 Infected Patients Reveals that the Majority of CD4+ T-cells Contain One HIV-1 DNA Molecule |
Stabilization of neurotransmitter receptors at postsynaptic specializations is a key step in the assembly of functional synapses . Drosophila Neto ( Neuropillin and Tolloid-like protein ) is an essential auxiliary subunit of ionotropic glutamate receptor ( iGluR ) complexes required for the iGluRs clustering at the neuromuscular junction ( NMJ ) . Here we show that optimal levels of Neto are crucial for stabilization of iGluRs at synaptic sites and proper NMJ development . Genetic manipulations of Neto levels shifted iGluRs distribution to extrajunctional locations . Perturbations in Neto levels also produced small NMJs with reduced synaptic transmission , but only Neto-depleted NMJs showed diminished postsynaptic components . Drosophila Neto contains an inhibitory prodomain that is processed by Furin1-mediated limited proteolysis . neto null mutants rescued with a Neto variant that cannot be processed have severely impaired NMJs and reduced iGluRs synaptic clusters . Unprocessed Neto retains the ability to engage iGluRs in vivo and to form complexes with normal synaptic transmission . However , Neto prodomain must be removed to enable iGluRs synaptic stabilization and proper postsynaptic differentiation .
Synapse development is a highly orchestrated process that enables proper establishment of neural circuits and development of the nervous system . Crucial to synapse assembly is the recruitment and stabilization of neurotransmitter receptor complexes at synaptic sites [1] . Receptor complexes can be inserted directly into synaptic membranes via vesicular trafficking from ER-Golgi network , or they can move into the synaptic regions by lateral diffusion from extrasynaptic pools ( reviewed in [2 , 3] ) . Clustering of neurotransmitter receptors at new synapses induces expression of synaptic components and assembly of postsynaptic structures , such as postsynaptic densities ( PSDs ) , which in turn help maintain the local density of receptors [4] . Neural activity and trans-synaptic communication between pre- and postsynaptic specializations together with intracellular signals within the synaptic partners themselves ensure the maturation , refinement and plasticity of the synaptic connections and synapse growth [5–9] . The molecular mechanisms that coordinate the recruitment and stabilization of receptors at synaptic sites and assembly of synaptic structures with synaptic growth remain unclear . The Drosophila NMJ provides an ideal genetic system to examine the mechanisms that couple synapse assembly with synapse growth and development . The fly NMJ is a glutamatergic synapse similar in composition and physiology to vertebrate AMPA/kainate central synapses [10 , 11] . The fly NMJ iGluRs are tetrameric complexes composed of three essential subunits , GluRIIC , GluRIID and GluRIIE , absolutely required for assembling functional channels [12–14] . The fourth subunit can be either GluRIIA ( type-A channels ) or GluRIIB ( type-B ) [15–17] . GluRIIA and GluRIIB compete for the essential subunits , which are limiting for the formation of functional receptors . Before a muscle is innervated , low levels of iGluRs are present diffusely in the muscle membrane . Innervation triggers the clustering of iGluRs at synaptic locations and postsynaptic differentiation [18–20] . Type-A channels are the first to arrive at nascent synapses , while type-B , which desensitize ten times faster than type-A , mark more mature synapses [12 , 20 , 21] . The fly NMJ iGluRs , but not other PSD components , show very little turnover suggesting that the iGluR complexes are stably incorporated at synaptic sites [22] . At the Drosophila NMJ , clustering of iGluRs and formation of postsynaptic specializations requires an additional essential protein , Neto [23] . Neto belongs to a family of highly conserved transmembrane proteins sharing an ancestral role in the formation and modulation of glutamatergic synapses [24–26] . Vertebrate Neto proteins ( Neto1 and 2 ) and C . elegans Neto/Sol-2 have emerged as auxiliary subunits that modulate the gating properties of AMPA/kainate-type channels and their synaptic localization without influencing their delivery to the cell surface [24–29] . Likewise , Drosophila Neto associates with iGluRs in vivo and controls their trafficking and clustering at NMJ synapses without affecting their muscle expression levels [23] . Reduced synaptic iGluRs alter the function of NMJs causing locomotor defects and reduced synaptic transmission [12 , 30] . Lack of junctional iGluRs also induces a cascade of defects in the assembly and maintenance of postsynaptic specializations [23 , 30] . For example , Neto- or iGluRs-deprived synapses have reduced accumulation of PSD components , such as p21-activated kinase ( PAK ) , and sparse subsynaptic reticulum ( SSR ) , a structure comprised of stacks of muscle membranes surrounding and stabilizing synaptic boutons [31] . Intriguingly , synapses developing at suboptimal Neto/iGluR levels share a number of morphological and physiological defects with mutants in the BMP signaling , a pathway that controls the NMJ growth and confers synaptic homeostasis [32] . Similar to neto mutants , BMP mutant NMJs have fewer boutons and reduced excitatory junctional potential ( EJP ) ( reviewed in [32] ) . Furthermore , Neto in complex with type-A receptors promote the phosphorylation and accumulation of the BMP pathway effector Mad at synaptic locations [33] . The BMP-type signaling factors are produced as inactive precursors , with inhibitory prodomains that must be removed by proprotein convertases to generate the active ligands [34] . Furin-type proteases control the limited proteolysis of inactive BMP precursors and directly regulate their activities [35–37] . In many tissues , sequential processing of BMP prodomains modulates the range and signaling activities of BMP ligands [38] . At the Drosophila NMJ additional TGF-β factors regulate the expression of Glass bottom boat ( Gbb ) , a BMP7 homolog required for the BMP retrograde signaling [39 , 40] . Furin-type proteases activate all these TGF-β-type factors as well as the BMP-1/Tolloid enzymes that augment TGF-β signaling indicating that Furins provide an important means for controlling cellular signaling at the Drosophila NMJ . Here we report that Neto protein levels are critical for synaptic trafficking and clustering of iGluRs . Excess or reduced Neto protein in the striated muscle induced formation of NMJs with reduced number of synaptic boutons , decreased synaptic iGluRs and diminished neurotransmission . Neto activities are regulated by Furin-mediated proteolysis and removal of an inhibitory prodomain . In the absence of prodomain cleavage , Neto engages the iGluRs but fails to promote their recruitment and stable incorporation at synaptic sites and to initiate postsynaptic differentiation . Since Furins also cleave and activate signaling molecules , such as TGF-β factors , Furins may synchronize the processing of Neto and TGF-β to control synaptic growth .
Similar to neto hypomorphs , RNAi-mediated knockdown of Neto in the striated muscle altered NMJ development ( Fig . 1A , B and [23 , 33] ) . Interestingly , neto overexpression in the muscle also induced abnormal synapse development . We rescued neto null mutants ( neto36 ) with neto transgenes with various expression levels and found that excess Neto accumulated at NMJ synapses and extrajunctional locations in a dose-dependent manner ( Fig . 1A , C ) . Low to moderate levels of Neto clustered at synaptic sites ( i . e . using neto-A9 transgene ) , but excess Neto ( neto-A3 , or neto-A1 for the highest level ) had predominantly diffuse distribution with fewer individual synaptic puncta and abundant extrasynaptic signals . Similar patterns were found in animals with overexpressed neto transgene ( where neto-A1 induced the strongest phenotypes ) . Excess Neto had detrimental effects on the viability of rescued animals at all stages of development ( S1 Fig . ) . Neto levels also affected NMJ growth . In larvae with either reduced or excess Neto , the number of boutons was decreased although the branching patterns differed: longer branches at reduced Neto and shorter branches at excess Neto ( Figs 1A , S1 ) . This suggests that independent signaling pathways control NMJ growth and bouton formation . To examine the effects of Neto levels on synapse function we recorded excitatory junction potentials ( EJPs ) and spontaneous miniature potentials ( mEJPs , or minis ) from muscle 6 of third instar larvae ( Fig . 1D-F ) . In control larvae ( Dicer/+: 24B-Gal4/+ ) minis occurred two times per second on average . This was reduced to 0 . 3 events per second at Neto-depleted synapses ( dicer; 24B>netoRNAi ) similar to that observed in neto hypomorphs [23] . Excess Neto showed a reduction in mini frequency , and to a lesser extent in mini amplitude , but only when Neto was expressed at very high levels; larvae with moderate levels of additional Neto had normal mEJPs . The mini frequency appeared particularly sensitive to Neto levels and was significantly reduced in both Neto-depleted and Neto-excess conditions . The reduction in mini frequency and amplitude occurred in muscles with no change in both resting potential and input resistance . The EJP amplitude was similarly sensitive to Neto levels: mild/moderate increase in Neto levels showed no significant change in EJP amplitude , while strong perturbations of the Neto levels ( depletion or excess ) induced significant reduction in the EJP amplitude . Although GluRIIC muscle levels were constant in larvae with increased Neto expression ( Fig . 1B ) , the similarities between the NMJ physiological properties at reduced or excess Neto suggest that excess Neto could affect the number and density of postsynaptic iGluRs . Indeed , excess Neto produced a significant decrease of GluRIIC synaptic clusters: the number of synaptic contacts per bouton did not change , but the intensity of the GluRIIC synaptic signals was reduced to 58% ± 12% of the control ( Fig . 2A-E ) . The anti-GluRIIC also labeled extrasynaptic puncta that occasionally accompanied small Neto clusters , but did not co-localize with the large extrajunctional Neto-positive puncta , presumably associated with secretory vesicles ( Fig . 2C ) . In contrast , the synaptic distribution of Bruchpilot ( Brp ) , an active zone scaffold [41] , remained unaffected by excess Neto , indicating that Neto specifically regulates the distribution of postsynaptic receptors . The decrease of synaptic iGluRs showed no subtype specificities when Neto was overexpressed in a wild-type background ( G14>neto-A1 ) ; both GluRIIA and GluRIIB synaptic levels were similarly decreased ( to 60% and respectively 54% from control ) ( Fig . 2E-F ) . This is consistent with the normal quantal size ( or mini amplitude ) , observed at these NMJs ( Fig . 1E ) [15 , 16] . However , when excess Neto was introduced in the neto null background ( neto36; G14>neto-A3 ) , the GluRIIA synaptic levels were reduced slightly more than the GluRIIB , to 48% and respectively 62% from control . Loss of synaptic pMad , the BMP pathway effector , correlated with small NMJs with reduced synaptic release in neto and importin-β11 mutants [33 , 42] . We found that Neto overexpression also caused attenuation of the synaptic pMad levels likely by decreasing the levels of synaptic type-A receptors ( Fig . 3 ) . Reduced synaptic iGluRs together with diminished retrograde BMP signaling could explain the small size of NMJs with excess or reduced Neto levels . However , there were several differences between these NMJs . Unlike neto hypomorph larvae , which showed diminished synaptic localization of multiple synaptic components , such as p21-activated kinase ( PAK ) , Discs large ( Dlg ) , and α-Spectrin [23] , excess Neto did not affect the synaptic accumulation of any of these proteins ( S2 Fig . ) . In line with normal Brp , excess Neto did not affect the presynaptic localization of cysteine-string protein ( CSP ) [43] . Thus , the neto gain-of-function NMJ phenotypes cannot result from insufficient trafficking and recruitment of postsynaptic components . Normal recruitment of Dlg at synaptic locations was also observed when V5- or GFP-tagged Neto variants replaced the endogenous Neto protein ( S3 Fig . ) . Similar to untagged Neto , excess Neto-V5 or Neto-GFP induced smaller NMJs with normal synaptic transmission ( Neto-GFP-rescued NMJ shown in S3 Fig . ) , indicating that the addition of tags did not affect Neto activities and gain-of-function phenotypes ( Figs 1 , S3 and [23] ) . How could excess Neto diminish the synaptic iGluR levels without affecting any other synaptic components tested here ? Stable synaptic receptors are thought to be part of large aggregates organized by proteins secreted from the presynaptic compartment [44 , 45] and further stabilized by postsynaptic scaffolds [46] . Neto may interact with neuron-secreted proteins that trigger iGluRs synaptic clustering and/or with intracellular motors and scaffolds that promote iGluRs trafficking and stabilization at synaptic sites . Excess Neto may engage in unproductive interactions and overwhelm the cellular machineries involved in the trafficking and clustering of iGluRs at synaptic locations . Since Neto does not affect the net levels of receptor subunits in the postsynaptic muscle ( Fig . 1B and [23] ) , then iGluRs are predicted to accumulate at extrajunctional locations at suboptimal Neto levels . Indeed , genetic manipulation of Neto levels triggered a redistribution of iGluR-positive signals from junctional to extrajunctional locations ( Fig . 2C and [23] ) . Moreover , Neto proteins appear to have no roles in the surface delivery of the iGluRs in vertebrate and in C . elegans [25 , 26] suggesting that reduced or excess Neto levels should induce accumulation of extrajunctional iGluRs at the muscle surface . We tested this prediction by staining the larval fillets in detergent-free protocols with antibodies raised against the extracellular domain of GluRIIC . Under these conditions , extrajunctional GluRIIC staining was barely visible in control , but was very prominent on the muscle of larvae with reduced or excess Neto ( Fig . 4A-B ) . Surface accumulation at extrajunctional locations of GluRIIA was also observed in neto109 hypomorphs [23] . Similar results were obtained in both rescue and overexpression experiments with either neto-A3 or neto-A1 transgenes even though neto-A1 appears to induce a higher Neto expression level ( Figs 4 , 1 ) . Together , our data indicate that optimal Neto levels are crucial for the recruitment and stabilization of iGluRs at synaptic sites . Similar to vertebrate or C . elegans , perturbations of the Drosophila Neto levels do not appear to affect the surface delivery of iGluRs and instead influence the iGluRs distribution between synaptic and extrasynaptic locations . Unlike vertebrate or C . elegans Neto , Drosophila Neto contains a long sequence preceding the first CUB domain ( CUB1 ) . Full-length Neto is predicted to be a 78 kD protein , yet when expressed in S2 insect cell , Neto runs as two bands: a minor band with relative mobility ~100 kD , and a major band of ~85 kD ( Fig . 5A ) . Truncated Neto variants containing only the extracellular part ( Neto-extra ) showed bands of ~60 and ~45 kD . Similar pattern was also detected in neto36 null embryos rescued with a neto-V5 transgene . To examine whether Neto is cleaved , we generated a binary-tagged CUB1 fragment ( Myc-CUB1-V5/His , Fig . 5B ) . This secreted fragment produced three distinct bands corresponding to full length , N-terminal and C-terminal fragments . The C-terminal fragment was purified and analyzed by Edman degradation and mass spectrometry . Three cleavage sites within a region containing tandem repeats of RXXR dibasic motifs , upstream of the CUB1 domain , were identified . The major cleavage site appears to be the R129-Q bond , but R126-S and R123-A bonds could also be cleaved ( Fig . 5C ) . Interestingly , this region is highly conserved in all Drosophila species but not in vertebrate or C . elegans Neto , suggesting that this processing has functional implications for Neto functions in flies . The cleavage sites match the consensus processing sequence for Furin-like proprotein convertases ( PC ) , also known as PACE ( Paired basic Amino acid Cleaving Enzyme ) , which process latent precursor proteins into their biologically active forms [47] . Drosophila genome codes for three Furin-type enzymes: Furin1 ( Fur1 ) , Furin2 ( Fur2 ) , and Amontillado ( Amon ) . Fur1 and Fur2 were expressed and analyzed in vitro , but their mutants have not been described yet [48 , 49] . Mutants in amon , encoding the Drosophila homolog of the neuropeptide precursor processing protease PC2 , display partial embryonic lethality , defective larval growth , and arrest during the first to second instar larval molt [50 , 51] . To confirm that Furins are responsible for cleaving Neto we used an RNAi approach [36] . We generated double strand RNA ( dsRNA ) for each of the three Furin-like coding genes , co-transfected them with Neto expression constructs in S2 cells , and examined the protein products . The efficiency of RNAi treatments was verified by RT-PCR ( Fig . 5D ) . We found that knockdown of Fur1 activities reduced the production of the small , cleaved bands and increased the level of unprocessed form ( Fig . 5E , lanes 1 and 2 ) . However , we did not find any difference by knocking down Fur2 or Amon ( Fig . 5E , lanes 3 and 4 ) . Combination of all 3 different dsRNAs did not further reduce the proportion of uncleaved Neto forms compared to Fur1 RNAi ( Fig . 5E , lanes 1 and 5 ) , indicating that Fur1 is the primary enzyme for cleaving Drosophila Neto in S2 cells . In flies , fur1 is expressed throughout development in multiple tissues including larval central nervous system and carcass [52] . RNAi-mediated fur1 knockdown in the striated muscle produced NMJs with fewer and smaller boutons , normal Brp synaptic clusters , but significantly diminished levels of synaptic iGluRs ( S4 Fig . ) . While these phenotypes are reminiscent of NMJs with suboptimal Neto , they cannot be solely attributed to reduced Neto activities due to lack of processing . Fur1 , like all Furin-type proteases , cleaves and activates multiple developmentally important substrates , including extracellular matrix components and signaling molecules such as TGFβ-type ligands [53] . In fact , stronger RNAi treatments ( in the presence of Dicer or at higher rearing temperature ) distorted the muscle fibers and induced early larval lethality . A pulse of high temperature ( one day at 30°C ) also disrupted the muscle structures . Fur1 knockdown also induced significant reduction in GluRIIA and pMad synaptic signals , likely because inefficient activation of precursor TGFβ-type factors , including Gbb ( S4 Fig . ) . Interestingly , down-regulation of fur1 in motor neurons elicited similar NMJ phenotypes , underscoring the complexity of Fur1-dependent activities . To study the biological relevance of Neto processing by Furin-type proteases we generated a constitutively active Neto variant ( CA-Neto ) , without the prodomain , and a processing mutant Neto ( PM-Neto ) , with an uncleavable prodomain ( Fig . 6A ) . When expressed in S2 cells , Neto-GFP was detected as double bands of expected sizes , mostly processed form . CA-Neto-GFP was found as a single , processed protein , while PM-Neto-GFP was predominantly unprocessed . We noticed that a small fraction of PM-Neto ( <15% ) was processed presumably by promiscuous proteolysis , which may partly remove the prodomain; however , such cleavage usually occurs at ectopic locations , adding or removing additional residues from the processed product . Further mutations in this conserved region did not completely abolish Neto processing , but could impact the proper function of the adjacent CUB domain . To examine the subcellular distribution of Neto variants we took advantage of the apical localization of Neto in epithelial tissues . G14-Gal4 drives the expression of UAS transgenes in muscles but also in salivary glands . We found that all Neto variants localized to the luminal side of the salivary gland ( apical surface ) , indicating that prodomain processing does not affect membrane targeting and apical localization of Neto proteins ( Fig . 6B ) . Nor did prodomain processing impact the ability of Neto variants to form complexes with iGluRs in the striated muscle . Similar to Neto , CA-Neto and PM-Neto retained the capacity to pull-down iGluRs from muscle extracts ( Fig . 6C ) . However , PM-Neto was severely impaired in its ability to rescue the neto null mutants , while CA-Neto generally resembled the Neto control ( Fig . 6D , E ) . Very few PM-Neto rescued animals reached the adult stages: these flies did not fly and had locomotor defects . Similar to the wild-type neto transgenes , moderate levels of CA-Neto rescued the NMJ morphology and iGluRs clustering defects of neto null mutants , while excess CA-Neto generated smaller NMJs with reduced iGluRs synaptic signals ( Fig . 7A , B ) . In contrast , PM-Neto rescued NMJs developed abnormally irrespective of the expression levels . At moderate levels , PM-Neto distributed diffusely and disrupted the synaptic localization of iGluRs , in particular the type-A receptors ( Fig . 7A-D , quantified in 7E , F ) . Animals rescued with high PM-Neto levels died during the early larval stages; the rare third instar escapers did not move and had severely altered NMJs with sparse boutons decorated by irregular Brp-positive aggregates and almost undetectable synaptic GluRIIC puncta ( Fig . 7A , B , G ) . These data suggest that PM-Neto is inadequate for the proper recruitment and stabilization of iGluRs at postsynaptic locations even though PM-Neto appears to bind to GluRIIC in vivo and to enable embryos to hatch into larval stages ( Fig . 6C , D ) . The severity of phenotypes at PM-Neto rescued synapses indicates that prodomain removal is required for iGluRs synaptic clustering , for development of postsynaptic structures , or both . Perisynaptic Dlg signals flank but do not co-localize with PSD components [54] . At control NMJ , Dlg appeared to surround the Neto-positive puncta ( Fig . 8A ) . The synaptic accumulation of Dlg was severely reduced at PM-Neto rescued NMJs , without any detectable change in the level of Dlg protein in larval muscle . These mutant NMJs were hardly recognizable since both Dlg and Neto synaptic signals were diminished and distributed diffusely among very few boutons . Similar to iGluRs and Dlg , PAK did not accumulate at PM-Neto rescued NMJs ( Fig . 8B ) . In contrast , the assembly of presynaptic components was not affected in PM-Neto rescued synapses: Brp and CSP showed discrete synaptic distributions ( Figs 7 , 8C ) . The severe postsynaptic defects at PM-Neto rescued NMJs were not accompanied by cytoskeletal disruption as indicated by normal α-Spectrin distribution ( Fig . 8D ) . Thus , postsynaptic differentiation and organization of PSD structures appear to be specifically affected by Neto processing . The aberrant postsynaptic differentiation at PM-Neto rescued NMJs was also captured by electron micrographs of larval NMJs . These NMJs had rare boutons with no postsynaptic electron dense structures and no detectable SSR , and surrounded instead by dense ribosome fields or myofibrils ( Figs 9A , B-F , and S5 ) . The T-bar structures were often misshaped , collapsed or floating at PM-Neto boutons , suggesting that lack of Neto/iGluRs clustering affects proper assembly and organization of presynaptic structures . Larger T-Bars and synaptic vesicles at PM-Neto-rescued NMJs may reflect a homeostatic compensatory response to reduced postsynaptic receptors . Similar structures were reported in mutants with enhanced presynaptic release [10] . Physiological recordings indicated that the mini frequency was severely reduced at PM-Neto rescued NMJs consistent with drastically reduced synaptic iGluRs ( Fig . 9G , H ) . The mini amplitude was also decreased , likely due to the preferential loss of type-A receptors at these synapses ( Figs 7D , 9I ) . Consistent with the large vesicle seen in electron micrographs we occasionally observed very large minis at PM-Neto rescued NMJs . However , the evoked potentials were normal suggesting a presynaptic compensatory response ( Fig . 9J-L ) . Thus , Neto processing is required for the normal density of postsynaptic iGluRs , but is not essential for triggering a compensatory increase in presynaptic release . PM-Neto not only failed to cluster and stabilize the iGluRs at postsynaptic locations but it was also unable to support the recruitment of postsynaptic components , formation of PSDs , and stabilization of postsynaptic structures . The postsynaptic differentiation program was simply not initiated at PM-Neto rescued NMJs . Our data are consistent with a model in which Fur1-dependent processing activates Neto and allows it to function to stabilize iGluR complexes at synaptic sites . The prodomain may prevent the formation and/or maintenance of stable Neto/iGluR synaptic aggregates by obstructing Neto-mediated protein interactions . Lack of iGluRs clustering precludes the initiation of postsynaptic differentiation .
The increase as well as the decrease of Neto levels affects the NMJ development , albeit with different consequences . Neto-deprived NMJs have diminished postsynaptic specializations and long branches , spanning over large muscle areas , suggesting that lack of postsynaptic receptors maintains the motor neurons in a growing , exploratory state . By contrast , NMJs with excess Neto are short and have normal accumulation of postsynaptic components . In fact , PAK and Dlg signals are slightly elevated at NMJs with excess Neto compared with control ( S2 Fig . ) . Early accumulation of synaptic Dlg may restrict expansion of these NMJs and produce hypo-innervation . Interestingly , overexpression of Neto in the wild-type background ( G14>neto-A1 ) induced gain-of-function phenotypes slightly milder than when the same transgene replaced the endogenous neto in rescue experiments ( compare the last two columns in Fig . 1A ) . This could be due to the different genetic backgrounds or may indicate additional Neto functions that are missing at neto-A1-rescued NMJs . Physiological studies also captured the differences between postsynaptic iGluR receptor fields at different Neto levels . Neto-deprived NMJs in neto hypomorphs or RNAi experiments have severely reduced mini frequency consistent with their reduced postsynaptic iGluRs density ( Fig . 1C-F and [23 , 33] ) . Strong reduction of postsynaptic Neto levels induced a reduction of EJP amplitudes , suggesting that Neto deprivation interferes with the normal homeostatic mechanisms . Similar to iGluRs-deprived synapses , lack of Neto may render these synapses “beyond repair” [12 , 14] . In contrast , the NMJ physiological parameters appeared more to be resilient to excess Neto since addition of moderate levels of Neto did not affect the mEJP and EJP amplitude . However , high levels of excess Neto ( G14>neto-A1 ) induced a significant decrease of mEJP frequency , consistent with the reduced synaptic and increased extrasynaptic iGluRs observed at these NMJs ( Figs 1 , 2 , 4 ) . At central glutamatergic synapses in vertebrates , synaptic receptors are cycling into and out of the synapses indicating that synapses behave as donors or acceptors for receptors , and the extrasynaptic receptors function as a reserve pool [2] . At the Drosophila NMJ , the iGluRs are recruited to the nascent synapses from extrajunctional receptor pools , but are stably integrated in synaptic aggregates with very low turnover [22] . In the absence of Neto , or any essential iGluR subunit , the iGluRs are not recruited at synaptic locations [55] . Conversely , excess Neto induces accumulation of iGluR-positive puncta at extrajunctional locations ( Figs 2 , 4 ) . This is different than overexpression of any of the essential iGluR subunits , which don’t show gain-of-function phenotypes , presumably because other subunits are limiting [11] . Furthermore , the iGluR complexes appear to be on the muscle membrane at suboptimal Neto levels since they are accessible by antibodies in the absence of detergents ( Fig . 4 and [23] ) . Likewise , Neto proteins from worms and mammals appear to have no roles ( or very modest ones ) in the surface delivery of the iGluRs [25 , 26] . We speculate that Neto binds iGluRs on the cell surface and engages in extracellular and/or intracellular interactions that enable the recruitment and clustering of iGluRs at synaptic sites . In this scenario , reduced Neto levels are inefficient for the iGluRs synaptic trafficking and clustering , whereas excess Neto may engage in protein interactions that sequester iGluRs at ectopic locations . At the Drosophila NMJ , Neto activities are regulated by Fur1-dependent limited proteolysis . The removal of Neto prodomain appears to be essential for the stabilization of iGluRs at PSDs . Lack of iGluRs stabilization precludes postsynaptic differentiation although the receptors are functional ( Figs 8 , 9 ) . Thus , synapse activity does not trigger iGluRs clustering or postsynaptic differentiation; instead , stabilization of iGluRs at synaptic sites initiates the recruitment of PSD components and assembly of postsynaptic structures . It has been proposed that a neuron secreted molecule triggers clustering of iGluRs at Drosophila NMJ [18 , 20 , 56] . Secreted molecule ( s ) may mediate iGluRs clustering by binding and trapping Neto/iGluR complexes at new synapses . Mind the gap ( Mtg ) is a neuronal protein reported to organize the synaptic cleft [57] . In mtg null mutant embryos , Neto and iGluRs form aggregates comparable in size with control clusters , but which fail to concentrate at nascent synapses [55] . Unfortunately , we could not detect in vitro interactions between Neto and Mtg . But while the molecular nature of the “trapping” mechanism remains to be determined , our study demonstrates that this process requires the removal of Neto prodomain . The Neto prodomain does not interfere with targeting and apical localization of Neto , nor does it affect its ability to bind iGluRs and form complexes , but it appears to preclude Neto engagement in protein interactions required for the formation of iGluR synaptic clusters . Neto CUB1 domain interacts with itself , but self-association is not enough to explain the formation of large iGluR aggregates . Prodomains could mediate binding to extracellular factors , such as heparan proteoglycans , fibrillin and perlecan that protect the active molecules and modulate their extracellular distribution [58 , 59] . Our study does not address a role for Neto prodomain in binding to extracellular molecules that modulate Neto distribution . The prodomains could also function as chaperones that allow proper folding of biologically active molecules , such as TGF-β-type ligands [34] . However , Neto prodomain is unlikely to play a role in the folding and secretion of Neto because CA-Neto is functional and induces NMJ gain-of-function phenotypes similar to excess Neto . Alternatively , the prodomain could maintain Neto in an inactive form , thus limiting clustering and stable incorporation of Neto/iGluR complexes at PSDs . Similar regulation has been described for the Tolloid/BMP-1 family of enzymes: their prodomains must be removed before the catalytic domains could assume active conformations [60] . It is tempting to speculate that the prodomain masks Neto extracellular domain ( s ) and prevents interactions required for iGluR clustering at PSDs . Is Neto processing a general step in Neto passage through the secretory pathway or could it actively modulate Neto activity/ availability ? To test if processing plays an active role in regulating Neto function we compared the changes in Neto processing in larvae with hunger-induced increase of locomotion [61] . The proportion of processed Neto increased in starved larvae and decreased in fed animals ( Fig . 10 ) , indicating that Neto processing indeed changes in response to an increase in locomotion and/or due to starvation . While this analysis cannot distinguish between the two possibilities , Neto processing emerges as an active mechanism to control the level of Neto available for effective iGluRs recruitment at PSDs . Neto processing/activation phenomenon appears to be highly conserved in insects . Most insects have glutamatergic NMJs , and their genomes encode for Neto proteins with prodomains and Furin minimal sites ( R-X-X-R ) preceding the first CUB domain . For example , Neto proteins in Apis florea and Apis mellifera share an R-Q-M-R motif at positions equivalent to the Furin site in Drosophila Neto . In all cases , the Furin consensus sites are suboptimal suggesting that processing of insect Neto proteins will be slow and restricted by Furin activities . Furins cleave their substrates mainly in late Golgi , though recent data indicate that Furins also function at the cell surface and in the extracellular space [62] . Interestingly , Fur1 also cleaves and activates TGF-β-type ligands , including Gbb , Maverick and Dawdle , which are secreted from muscle and glia and control NMJ development [37 , 39 , 40] . This raises the possibility that Fur1 synchronizes the activation of Neto and TGF-β factors and may serve as a means to coordinate synapse assembly with NMJ growth . This study does not exclude other mechanisms that may regulate the density of synaptic iGluR , such as local insertion of iGluRs from intracellular vesicles [63] . Nonetheless , our study demonstrates that Neto activation by prodomain processing plays an important role in the regulation of iGluR trafficking and clustering at synapses . Trafficking of Neto itself or Neto/iGluR complexes on the muscle membrane may be further controlled by cellular signals that modulate the intracellular domain of Neto and regulate its coupling with scaffold and motor complexes . In fact , Drosophila neto locus codes for two isoforms generated by alternative splicing that differ in their intracellular domains . Both intracellular domains contain multiple putative phosphorylation sites , raising the possibility of rich modulation of Neto/iGluRs distribution in the striated muscle .
Fly lines were generated by standard germline transformation of pUAST-based plasmids containing various neto constructs ( BestGene , Inc ) . Other stocks used in this study were as follows: neto null and hypomorph alleles , neto36 and respectively neto109 [23] , netoRNAi [33] , G14-Gal4 and MHC-Gal4 ( obtained from C . Goodman , University of California at Berkeley ) , da-Gal4 ( BL-5460 ) , 24B-Gal4 ( BL-1716 ) , and elav-Gal4 ( BL-8760 ) . For RNAi-mediated knockout we used the following TRiP lines generated by the Transgenic RNAi Project: GluRIIC ( P[TRiP . JF01854}attP2 ) , and fur1 ( P[TRiP . GL01340] attP40 ) . The control is y1w1118 unless otherwise specified . For rescue analyses , neto transgenes were introduced into neto36 null mutant background using tissue-specific promoters . Since neto is on the X-chromosome we used only FM7-GFP balanced stocks to eliminate any meiotic non-disjunction event . The F1 progenies were genotyped during late embryogenesis and reared at the indicated temperatures . After 24 hours , crawling larvae were removed , counted , and kept at the same temperatures for further analyses or adult viability testing . Neto variants were generated using QuikChange site-directed mutagenesis kit ( Stratagene ) as described previously [23] . CA-Neto has a deletion that joins A51-Q130 and loops out the Neto prodomain . PM-Neto has two point mutations: R123I and R126I . Double-tagged Neto constructs were generated by QuikChange loop-in of various Neto fragments in a previously described AcPA-SP-Myc-V5/His plasmid [64] . This actin promoter/terminator plasmid contains the sequences coding for the Tolloid-related signal peptide , the 5xMyc cassette , a multiple cloning site , followed by the V5 and RGS-6xHis epitopes . All constructs were verified by DNA sequencing . For RNA interference , PCR primers for Furins that carry the T7 promoter sequence at the 5’ end were designed as previously described [36] . The primers were as follows: dFur1-F 5’-TAATACGACTCACTATAGGGACGCAAAGATCCTCTGTGGCA; dFur1-R 5’- TAATACGACTCACTATAGGGACATTGCTCCCGGAACTGC; dFur2-F 5’- TAATACGACTCACTATAGGGACGCTAGAGGCCAATCCGGAA; dFur2-R 5’- TAATACGACTCACTATAGGGACCCTTCTCGCCCCAAAAGTG; Amon-F 5’- TAATACGACTCACTATAGGGACCCACATGGAGCTGGCTGT; Amon-R 5’- TAATACGACTCACTATAGGGACCCTGACTTTGCCGCCATT . PCR products were amplified from genomic DNA or S2 cells cDNA . In vitro transcribed dsRNA was produced using the MEGAscript kit ( Ambion ) . RNAi treatment was carried out by transfections of 5 mg/ml of dsRNA into S2 cells . S2 cells were transfected with indicated constructs and harvested after five days incubation . Total RNA was extracted using TRIZOL reagent ( Invitrogen ) according to manufacturer's instructions . AccuScript High Fidelity First-Strand cDNA Synthesis Kit ( Agilent ) was used to generate cDNAs from the extracted total RNAs according to manufacturer’s instructions . PCR reaction for each target gene was executed using the cDNAs as templates with specific primer pairs ( above ) and β-Actin as a reaction standard ( Actin-Forward: 5’-CTGGCACCACACCTTCTACAATG-3’ , Actin-Reverse: 5’-GCTTCTCCTTGATGTCACGGAC-3’ ) . Wandering third instar larvae were dissected as described previously in ice-cooled Ca2+-free HL-3 solution [65 , 66] . Dissecting larval tissues were fixed in either 4% formaldehyde or Bouin's fixative ( Polysciences , Inc . ) for 20 min or 5 min respectively . PBS containing 0 . 5% Triton X-100 was used for washing and antibody reaction . For detergent-free staining , 1X PBS was used . Primary antibodies from Developmental Studies Hybridoma Bank were used at the following dilutions: mouse anti-GluRIIA ( MH2B ) , 1:100; mouse anti-Dlg ( 4F3 ) , 1:1000; mouse anti-Brp ( Nc82 ) , 1:100; mouse anti-CSP ( 6D6 ) , 1:100; mouse anti-α-spectrin ( 3A9 ) , 1:100 . Other primary antibodies were as follows: rat anti-Neto , 1:1000 [23] , rabbit anti-GluRIIB , 1:2000 ( a gift from David Featherstone ) [67]; rabbit anti-GluRIIC , 1:2000 [33]; rabbit anti-PAK , 1:2000 ( a gift from Nicholas Harden ) [68]; FITC- , rhodamine- , and Cy5-conjugated goat anti-HRP , 1:1000 ( Jackson ImmunoResearch Laboratories , Inc . ) . Alexa Fluor 488- , Alexa Fluor 568- , and Alexa Fluor 647-conjugated secondary antibodies ( Molecular Probes ) were used at 1:400 . All samples were mounted with ProLong Gold reagent ( Invitrogen ) and incubated for 24 hours at RT . Confocal images were acquired using Carl Zeiss LSM 780 or 510 laser scanning microscopic system with Plan-Apochromat 63X/1 . 4 oil DIC objective using ZEN software . Z-stacked images were collected , processed , and analyzed using Imaris X64 ( 7 . 6 . 0 , Bitplane ) or ImageJ ( NIH ) software . In each experiment , samples of different genotypes were processed simultaneously and imaged under identical confocal settings . To quantify fluorescence intensities , confocal regions of interest ( ROIs ) surrounding anti-HRP immunoreactivities were selected and the signals measured individually at NMJs from ten or more different larvae for each genotype ( number of samples is indicated in the graph bar ) . The signal intensities were calculated relative to HRP volume and subsequently normalized to control . For the extrajunctional , cell surface GluRIIC staining , where the GluRIIC positive signals are predominantly in the form of puncta at both Neto-depleted and Neto-excess NMJs , intensities from several size-matched areas of the muscles were collected and averaged using Image J software . The numbers of muscles analyzed per genotype are indicated inside the bars . Quantification of NMJ morphological features was performed at muscle 4 of abdominal segment 4 using the filament tracing function of Imaris software . Boutons were counted manually , while blind to the genotype , using anti-HRP and anti-Dlg staining . Statistical analyses were performed using the Student’s t-test with a two-tailed distribution and a two-sample unequal variance . All graphs represent mean value of all samples of the given genotype ± SEM . Transiently transfected Drosophila S2 cells were used for producing recombinant proteins as previously described [69] . The S2 cells were maintained in M3 ( Shields and Sang M3 insect medium , Sigma ) with 1x insect medium supplement ( Sigma ) and Penicillin/Streptomycin ( Sigma ) , and sub-cultured every 7 days at 2 X 106 cells/ml . For the transfection , dimethyldioctadecyl-ammonium bromide ( DDAB ) solution ( 250 μg/ml ) was mixed with M3 media at 1:2 ratio and incubated 5 min at RT , then the DNA was added to the DDAB-M3 mixture ( 1μg of plasmid DNA to 100 μl suspension ) . The mixture was incubated for 20 min and transfected into S2 cells ( 100 μl mixture to 2 X 106 cells/ml culture ) . After five days , the secreted proteins were collected for analysis , and membrane proteins were extracted by homogenizing cells in lysis buffer ( 50 mM Hepes-NaOH , 150 mM NaCl , 0 . 2 mM EDTA , 0 . 5% NP-40 , 0 . 1% SDS , 2mM AEBSF [MP BIO] , and protease inhibitor cocktail [Roche] ) for 30 min on ice . The lysates were collected by centrifugation at 13 , 000rpm for 30 min at 4°C , separated by SDS-PAGE on 4%–12% NuPAGE gels ( Invitrogen ) and transferred onto PVDF membranes ( Millipore ) . Primary antibodies were used at the following dilutions: rat anti-Neto , 1:1000; chicken anti-GFP ( Abcam ) , 1:2000; anti-GluRIIC , 1:1000; anti-tubulin ( Sigma ) , 1:1000 . Immune complexes were visualized using secondary antibodies coupled with IR-Dye 700 or IR-Dye 800 followed by scanning with the Odyssey infrared imaging system ( Li-Cor Biosciences ) . To analyze muscle proteins , wandering third instar larvae were dissected , and the body walls were mechanically homogenized in lysis buffer for 30 min on ice . The lysates were analyzed by Western blotting . For co-immunoprecipitation , the lysates were incubated with rabbit anti-GFP antibody ( Invitrogen ) for 1 hr at 4°C . Protein A/G UltraLink Resin ( 50% slurry , Thermo Scientific ) was added and incubated overnight at 4°C . The beads were washed with lysis buffer . Proteins were eluted with 1x SDS sample buffer and analyzed by Western blotting . Secreted and processed Neto fragment ( CUB1-V5/His ) was purified using His-Trap affinity column equipped with AKTA FPLC system ( Pharmacia ) and separated by SDS-PAGE . A specific gel band was isolated and analyzed at Microchemistry and Proteomics Analysis Facility , Harvard University . Wandering third instar larvae were dissected in Jan's saline containing 0 . 1 mM Ca2+ and processed as previously described [70] . Dissected larvae were fixed in EM fixative ( 4% p-formaldehyde , 1% glutaraldehyde , 0 . 1 M sodium cacodylate , and 2 mM MgCl2 , pH 7 . 2 ) for 20 min at room temperature followed by incubation overnight at 4°C , then washed extensively ( 0 . 1 M sodium cacodylate , and 132 mM sucrose , pH 7 . 2 ) . The samples were processed and analyzed at the Microscopy and Imaging Core Facility , NICHD . The standard larval body wall muscle preparation first developed by Jan and Jan ( 1976 ) was used for electrophysiological recordings [71 , 72] . Wandering third instar larvae were dissected in physiological saline HL-3 [65] , washed , and immersed in HL-3 containing 0 . 8 mM Ca2+ using a custom microscope stage system [73] . The nerve roots were cut near the exiting site of the ventral nerve cord so that a suction electrode could pick up the motor nerve later . Intracellular recordings were made from muscle 6 . Data were used when the input resistance of the muscle was >5 MΩ and the resting membrane potential was between −60 mV and −80 mV for the entire duration of the experiment . The input resistance of the recording microelectrode ( backfilled with 3 M KCl ) ranged from 20 to 25 MΩ . Muscle synaptic potentials were recorded using Axon Clamp 2B amplifier ( Axon Instruments ) and pClamp software . Following motor nerve stimulation with a suction electrode ( 100 μsec , 5 V ) , evoked EJPs were recorded . Three to five EJPs evoked by low frequency of stimulation ( 0 . 1 Hz ) were averaged . For mini recordings , TTX ( 1 μM ) was added to prevent evoked release [65] . To calculate mEJP mean amplitudes , 50–100 events from each muscle were measured and averaged using the Mini Analysis program ( Synaptosoft ) . Minis with a slow rise and falling time arising from neighboring electrically coupled muscle cells were excluded from analysis [72 , 74] . In addition , when comparing mini sizes between preparations , the Kolmogorov-Smirnov test was administrated . Quantal content was calculated by dividing the mean EJP by the mean mEJP after correction of EJP amplitude for nonlinear summation according to the methods described [75 , 76] . Corrected EJP amplitude = E[Ln[E/ ( E − recorded EJP ) ]] , where E is the difference between reversal potential and resting potential . The reversal potential used in this correction was 0 mV [75 , 77] . Data are presented as mean ± SEM , unless otherwise specified; EJP amplitudes and quantal contents after the nonlinear correction are shown . A one-way analysis of variance followed by Tukey's HSD test was used to assess statistically significant differences among the genotypes . Differences were considered significant at p < 0 . 05 . | Synapse development is initiated by genetic programs , but is coordinated by neuronal activity , by communication between the pre- and postsynaptic compartments , and by cellular signals that integrate the status of the whole organisms and its developmental progression . The molecular mechanisms underlining these processes are poorly understood . In particular , how neurotransmitter receptors are recruited and stabilized at central synapses remain the subject of intense research . The Drosophila NMJ is a glutamatergic synapse similar in composition and physiology with mammalian central excitatory synapses . Like mammals , Drosophila utilizes auxiliary subunit ( s ) to modulate the formation and function of glutamatergic synapses . We have previously reported that Neto is an auxiliary protein essential for functional glutamate receptors and for organization of postsynaptic specializations . Here we report that synapse assembly and NMJ development are exquisitely sensitive to postsynaptic Neto levels . Furthermore , we show that Neto activity is controlled by Furin-type proteases , which regulate the processing and maturation of many developmentally important proteins , from growth factors and neuropeptides to extracellular matrix components . Such concerted control may serve to coordinate synapse assembly with synapse growth and developmental progression . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
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| []
| 2015 | Prodomain Removal Enables Neto to Stabilize Glutamate Receptors at the Drosophila Neuromuscular Junction |
Mass treatment with ivermectin is a proven strategy for controlling onchocerciasis as a public health problem , but it is not known if it can also interrupt transmission and eliminate the parasite in endemic foci in Africa where vectors are highly efficient . A longitudinal study was undertaken in three hyperendemic foci in Mali and Senegal with 15 to 17 years of annual or six-monthly ivermectin treatment in order to assess residual levels of infection and transmission and test whether ivermectin treatment could be safely stopped in the study areas . Skin snip surveys were undertaken in 126 villages , and 17 , 801 people were examined . The prevalence of microfilaridermia was <1% in all three foci . A total of 157 , 500 blackflies were collected and analyzed for the presence of Onchocerca volvulus larvae using a specific DNA probe , and vector infectivity rates were all below 0 . 5 infective flies per 1 , 000 flies . Except for a subsection of one focus , all infection and transmission indicators were below postulated thresholds for elimination . Treatment was therefore stopped in test areas of 5 to 8 villages in each focus . Evaluations 16 to 22 months after the last treatment in the test areas involved examination of 2 , 283 people using the skin snip method and a DEC patch test , and analysis of 123 , 000 black flies . No infected persons and no infected blackflies were detected in the test areas , and vector infectivity rates in other catching points were <0 . 2 infective flies per 1 , 000 . This study has provided the first empirical evidence that elimination of onchocerciasis with ivermectin treatment is feasible in some endemic foci in Africa . Although further studies are needed to determine to what extent these findings can be extrapolated to other endemic areas in Africa , the principle of elimination has been established . The African Programme for Onchocerciasis Control has adopted an additional objective to assess progress towards elimination endpoints in all onchocerciasis control projects and to guide countries on cessation of treatment where feasible .
Onchocerciasis control strategies have evolved significantly over the last three decades . The Onchocerciasis Control Programme in West Africa ( OCP ) [1] , launched in 1975 , used aerial larviciding of vector breeding sites in river rapids . This strategy was very successful in interrupting onchocerciasis transmission and ultimately eliminating the disease as a public health problem in the savanna areas of 10 West African countries [2] . However , aerial larviciding was not considered feasible or cost-effective elsewhere in Africa and in the absence of a drug that could be safely used in mass treatment , nothing was done to fight this debilitating disease in the rest of the continent where over 85% of the 37 million infected persons lived [3] . This situation changed dramatically in 1987 with the registration of ivermectin for the treatment of human onchocerciasis , and its donation free of charge for as long as needed by the manufacturer of the drug [4] . This revolutionized the fight against the disease , and led to the creation of the African Programme for Onchocerciasis Control ( APOC ) [5] that covered all the remaining onchocerciasis endemic areas in Africa , and the Onchocerciasis Elimination Programme for the Americas ( OEPA ) [6] . Currently , onchocerciasis control is nearly exclusively based on annual or six-monthly ivermectin treatment of all eligible members of communities at risk . By the time APOC was launched in 1995 , it was known from clinical and community trials that ivermectin was highly effective against the microfilariae that cause the severe manifestations of the disease , and hence that mass treatment with ivermectin was an effective strategy for controlling the disease as a public health problem [7]–[9] . But research had also shown that the drug had limited effect on the viability and productivity of the adult onchocercal worms which resumed production of microfilariae a few months after treatment [10] , making it necessary to repeat treatment at intervals of no longer than one year to maintain microfilarial loads below levels of public health concern . Community trials had shown that mass treatment with ivermectin significantly reduced but did not interrupt onchocerciasis transmission during the first years of treatment , and given the adult worm life expectancy of about 10 years on average , it was concluded that annual treatment needed to be continued for a very long period of time [11] . Hence APOC's principal aim was to establish and sustain high treatment coverage in all areas where onchocerciasis was a public health problem [12] . To achieve this , APOC supported the establishment of community-directed treatment with ivermectin ( CDTi ) in all APOC countries [13] , [14] . However , the question of whether , and if so when , the parasite could ultimately be eliminated with ivermectin treatment , and treatment safely stopped , remained unanswered at that time . Initial computer simulations with the model ONCHOSIM that were based on the results of the first community trials of ivermectin and the assumption that ivermectin is only a microfilaricide , predicted that annual treatment may needed to be continued for more than 25 years [11] . When subsequent studies after five years of treatment indicated that ivermectin treatment also reduced the fertility of the adult worms by some 30% after each treatment , these predictions were revised downward [15] , [16] . However , this cumulative reduction in adult worm reproductivity was not seen in another study [17] and the predictions remained untested . Although it was generally believed that elimination would be possible in most of the Americas where onchocerciasis foci are often small and circumscribed , and several ( though not all ) vector species are relatively inefficient , there remained considerable uncertainty as to whether ivermectin treatment could ever achieve sustained interruption of transmission in Africa where onchocerciasis is endemic over vast areas and where all vectors are highly efficient [18]–[20] . Among the areas where large-scale ivermectin treatment was first introduced in Africa were onchocerciasis foci in Mali and Senegal in the Western Extension area of the OCP where treatment started in 1988 and 1989 , shortly after the registration of ivermectin for the treatment of human onchocerciasis in 1987 . Although part of the OCP , vector control was never used in this section of the Western Extension area and ivermectin has been the sole intervention tool since the start of control . A detailed review in 2001 of the available evidence on the impact of ivermectin treatment on onchocerciasis transmission in West and Central Africa showed that the prevalence of infection had fallen to very low levels after 12 years of treatment in onchocerciasis foci in Mali and Senegal in the Western extension area of the OCP [21] . The long period of treatment and the observed decline in prevalence of infection suggested that these foci would be among the first areas where the hypothesis of whether onchocerciasis can be eliminated with ivermectin from endemic foci in Africa could be tested . A longitudinal study was therefore started in 2005 in three initially hyperendemic onchocerciasis foci in Mali and Senegal to undertake a detailed assessment of the residual levels of infection and transmission , and , if sufficiently low , test whether ivermectin treatment could be safely stopped . The first results of this study are reported here .
The three study areas are located along the River Bakoye in Mali , the River Gambia in Senegal , and the River Faleme on the border of the two countries ( figure 1 ) . The study areas were selected on the basis of the following criteria: ( i ) they were part of the Western Extension area of the OCP where onchocerciasis control has been exclusively based on ivermectin treatment; ( ii ) ivermectin treatment started in 1988–1989 and the area was part of the first large-scale ivermectin treatment programs launched after registration of the drug in 1987; ( iii ) there existed good epidemiological baseline data for at least 10 villages where pre-control skin snip surveys had been undertaken by the OCP using standard onchocerciasis survey methods; ( iv ) the area contained hyperendemic villages , i . e . villages with a prevalence of microfilaridermia ≥60% or a Community Microfilarial Load ( CMFL , the geometric mean number of microfilariae per skin snip among adults aged 20 years and above ) >10 microfilariae per skin snip ( mf/s ) [22]–[24]; ( v ) the area was located along a river with known breeding sites of Simulium damnosum s . l . , and has a length of at least 100 kilometers along the river and a width of at least 15 km at each side of the river . All three selected study areas met these criteria . An additional reason for including the River Gambia area was that it was the only area in Africa where six-monthly treatment with ivermectin had been given for more than 10 years . Demographically , the three study areas were similar with a rural population in 2006 of 20 , 000 to 30 , 000 people living in 75 to 94 villages per site ( table 1 ) . In the R . Gambia focus there is also one town with a population of about 18 , 000 but there are no urban settlements in the other two study areas . De Sole et al . [25] , [26] have mapped the pre-control distribution and severity of onchocerciasis in the Western Extension of the OCP , including all of Senegal and western Mali . According to their results , the selected study areas along the River Gambia and the River Bakoye were the two areas with the highest level of onchocerciasis endemicity in Senegal and western Mali where there was a high risk of onchocercal blindness . Along the River Faleme there was also an appreciable risk of onchocercal blindness along the southern part of the river where the study site is located . All three study sites were mapped in detail by the OCP and figures 2a , 3a and 4a show for each of the sites the spatial distribution of the prevalence of infection before the start of control . Onchocerciasis was endemic throughout the study areas , and in each area there were several hyperendemic villages . In the River Gambia focus , 8 out of 22 surveyed villages had a CMFL>10 mf/s ( range 12 . 0 to 48 . 1 mf/s ) [26] . In the River Bakoye focus 5 out of 11 surveyed villages had a CMFL>10 mf/s ( range 10 . 2 to 21 . 6 mf/s ) and in the River Faleme focus this was the case for 3 out of 27 surveyed villages , which had CMFL's of 13 . 3 , 16 . 0 and 21 . 0 mf/s respectively . All three onchocerciasis foci are isolated with respect to long-distance migration of the Simulium vectors except for the first few weeks of the rainy season . During the dry season , the rivers do not flow and there are no blackflies . At the beginning of the rainy season , when the Inter-tropical-conversion-zone ( ITCZ ) moves to the north , the breeding sites are reinvaded by simuliids from the south ( mainly S . sirbanum ) that migrate with the prevailing winds and start the repopulation of the breeding sites [27]–[29] . After a few weeks , when the winds change , this long distance migration stops and the vector population becomes purely local with virtually no migration from outside or from neighboring river basins . At the end of the rainy season , reverse migration takes place with blackflies from the study sites moving with the winds to perennial rivers in the south . All river basins involved in this migration pattern are either free from onchocerciasis or under large-scale ivermectin treatment since 1990 . For the R . Bakoye , S . dieguerense has also been reported but this is a non-migratory Simulium species that only plays a local role in onchocerciasis transmission [30] . The three study areas are not completely isolated from neighboring endemic areas . Along all three rivers there are onchocerciasis endemic villages downstream of the study areas but their endemicity levels are generally lower and they are all covered by the same national ivermectin treatment programs of Mali and Senegal . The neighboring river basins are also endemic for onchocerciasis and undergoing ivermectin treatment . Although there is little vector migration between the river basins , human migration cannot be excluded . Upstream in Guinea there are some endemic areas that are also reported to be under ivermectin treatment . Hence , the three study areas cannot be considered completely isolated areas , but rather as the most endemic sections of onchocerciasis zones along three rivers that are fully covered by the national ivermectin treatment programs . Ivermectin treatment started first in 1988 in the R . Gambia focus as part of the community trials of ivermectin undertaken by the OCP to confirm the safety of large-scale ivermectin treatment [31] , and in 1989 in the other two foci ( table 1 ) . Treatment was not immediately introduced in all villages in the three areas but first targeted at the most affected villages . During the next 5 years the treatment program was gradually expanded until it covered all villages . As a result of this stepwise introduction of treatment , the number of years that each village had received treatment by the time of the study ranged from 14 to 19 years . From an epidemiological point of view , the most significant period was when all first-line villages , located near the river and the vector breeding sites and which play a dominant role in onchocerciasis transmission [32] , were treated . This was achieved for the R . Gambia from 1990 onwards , for the R . Bakoye from 1992 onwards and the R . Faleme from 1991 onwards . Hence , by the end of 2006 , all first-line villages in the R . Gambia area had been under treatment for 17 years , in the R . Bakoye area for 15 years , and in the R . Faleme area for 16 years . We will use those numbers when referring to the number of years of ivermectin treatment in each study area . In the R . Gambia focus , treatment was given at six-monthly intervals from 1990 onwards , and the number of treatments per village ranged from 30 to 36 , with all first-line villages receiving at least 34 treatments . The urban area was excluded from treatment in accordance with national treatment policy . In the other two basins treatment was given annually in all villages . Initially , ivermectin treatment was ensured by mobile teams of the Ministry of Health . The reported treatment coverage during the first three years was not very high but subsequently improved and reached between 75 to 81% of the total population between 1992 and 1996 . In 1997 , there was a change in policy and treatment was changed from the costly mobile-team approach to Community-directed Treatment with ivermectin ( CDTi ) [2] . The new policy was introduced rather abruptly while there was some resistance from health workers who would no longer benefit from the financial support that OCP provided for mobile teams . As a result , there was a fall in treatment coverage during the transition year of 1997 . In 1998 , the situation was corrected and following proper social mobilization efforts , CDTi took off effectively . A second implication of the change from mobile teams to CDTi was the integration of treatment reporting into the national health information systems . This was initially problematic and for several years the available records were incomplete ( and largely missing for 1997 ) until the new system was properly functioning . The change to CDTi resulted in a further improvement of treatment coverage which in several years even exceeded 80% of the total population ( about 95% of eligibles ) . Overall , the reported treatment coverage has been high since 1992 with the exception of the year 1997 . Onchocerciasis elimination is here defined as the reduction of local onchocerciasis infection and transmission to such low levels that transmission can no longer sustain itself and treatment can be safely stopped without risk of recrudescence of infection and transmission . Surveillance would still be needed to detect possible reintroduction of the parasite through human or vector migration from other endemic areas where elimination has not yet been achieved . To assess whether elimination has been achieved in the three study areas , the study was designed in three phases ( figure 5 ) . The aim of the first phase was to undertake a detailed assessment of onchocerciasis infection and transmission levels after 14 to 17 years of treatment . Skin snip surveys were to be undertaken in a stratified random sample of some 40 villages in each study site , and transmission would be monitored for a full transmission season through entomological evaluations in 4 to 6 fly-catching points per study site . If the observed infection and transmission levels in a study site were below predefined , provisional thresholds ( see section on indicators below ) , phase 2 would start in which treatment would be stopped in a test area of 5–8 villages located around one of the catching points in the study site . The effect of stopping treatment on infection and transmission would be evaluated by epidemiological surveys 20 to 22 months after the last treatment in the test villages , and by entomological evaluation in all catching points during another full transmission season . If there was no recrudescence of infection and transmission in the test area , phase 3 would start in which treatment would be stopped throughout the study site and infection and transmission monitored for another two years in all sample villages and catching points . The first two phases of the study have been completed in all three study sites . At the beginning of the study , all villages located in the study area were visited to obtain exact geographic coordinates using a geographic positioning system ( GPS ) . These coordinates were used to generate exact maps of the study areas , and using these maps a spatial sample of at least 40 villages were selected to be surveyed during the first phase of the study . Of these 40 villages , 20 were selected from the first-line villages along the river , while ensuring a good spatial coverage along the length of the river basin , and the remaining 20 villages selected randomly from the second line or further away from the breeding sites . Skin snip surveys were done in all selected villages 11–12 months after the last treatment round . A few selected villages proved to be very small ( <50 people ) , and for those the nearest village was also included in the surveys . In each village , all persons above the age of 1 year who agreed to participate ( or whose parent agreed for them to participate in the case of children ) were examined for onchocerciasis infection . The surveys used established skin snip examination methods in which the national onchocerciasis teams have been trained in the past by the OCP . Two skin snips were taken from the iliac crests with a 2 mm Holth corneoscleral punch and microscopically examined after incubation for 30 minutes in distilled water ( and a further 24 hours in saline for negative skin snips ) for the presence and number of O . volvulus microfilariae [33] . The numbers of microfilariae were counted and the results recorded for each person examined . Basic information on the migration history for each person during the last 10 years before the survey was also collected . During phase 2 , treatment was stopped in test areas of 5–8 villages located around one of the catching points . Skin snip surveys were done in all test villages 20–22 months after the last treatment in 2006 . During these surveys , an additional diagnostic test was also used . This was an improved version of the traditional diethylcarbamazine-citrate ( DEC ) patch test [34] that had recently been developed by LTS Lohmann Therapie-Systeme AG and undergone successful clinical testing at the Onchocerciasis Chemotherapy Research Centre in Hohoe ( OCRC ) , Ghana ( K . Awadzi , personal communication ) . The new test uses transdermal technology for the application of a low dose of 5 . 4 mg of DEC-citrate on the skin which produces within 24 hours a characteristic skin reaction in persons infected with O . volvulus . The new patch test was applied at the same time as the skin snip examination . Patients were requested to return 24 hours later when the patch was removed and the skin examined . A positive skin reading was defined as the presence of a characteristic skin lesion consisting of mild edema of the area covered by the patch , studded by fine pinpoint papules . Before the surveys , all examiners were trained by a senior technician from OCRC in the application of the DEC patch test , and in standardized reading of skin reactions . During each phase , a detailed entomological evaluation was done throughout the full transmission season in order to determine the levels of O . volvulus transmission . Four vector catching points were selected per study area ( six for the river Faleme which covers a larger area and in two countries ) . Every week , 3 days of capture were carried out at each catching point during the transmission period which generally covers 4 to 5 months per year ( June–October or July–October ) . Flies were collected using the method of bulk catches with a team of 3 to 4 fly catchers working from 7 AM to 6 PM . Each daily catch was preserved in 80% alcohol and sent to the DNA laboratory of the Multi-Disease Surveillance Centre ( MDSC ) in Ouagadougou , Burkina Faso [35] . In the laboratory , the flies were rinsed with distilled water , the heads separated from the bodies and sorted in lots for DNA extraction . The purified DNA was used as a substrate in a O-150 ( an Onchocerca-specific DNA sequence ) PCR , and the resulting product classified by hybridization to the O . volvulus-specific oligonucleotide probe OVS-2 [36] , [37] . A computer program ( Poolscreen™ ) was used to translate the molecular biology data obtained from screening pools into an estimate of the infectivity rate in the vector population [37] . The two main indicators of onchocerciasis infection and transmission used in the present study are the vector infectivity rate as measured by the number of flies with O . volvulus L3 ( infective ) larvae in the head per 1 , 000 flies ( FLH/1 , 000 ) and the prevalence of microfilariae in the skin in the human population . Model predictions as well as large-scale experience in the OCP had shown that these indicators do not have to be equal to zero to ensure elimination , but that there are thresholds below which infection and transmission will die out [38]–[40] . Computer simulations with the model ONCHOSIM predicted that the risk of recrudescence was negligible if the vector infectivity rate was below 0 . 9 to 1 . 3 FLH per 1 , 000 parous flies and the OCP adopted therefore a threshold of 1 FLH per 1 , 000 parous flies [41] . When after 14 years of vector control , onchocerciasis elimination appeared to have been achieved in the original OCP area , it was decided to stop vector control operations in nine river basins . To ensure that the decision to stop had been correct , a large scale entomological evaluation was undertaken during the first two years after stopping vector control [39] . The results showed that there were still infective flies in each river basin but at levels below the threshold of 1 FLH per 1 , 000 parous flies . Definite evidence that the decision to stop vector control had been correct was provided by epidemiological surveys undertaken 10 years after the cessation of control which showed that there had been no recrudescence of infection [38] , [42] . The entomological evaluation methods used by the OCP involved dissection of hundreds of thousands of flies , which was technically and financially highly demanding and difficult to sustain by the countries alone after the closure of the OCP in 2002 . When pool screening became operational in 1998 , it was made the standard method for entomological surveillance of onchocerciasis transmission by national onchocerciasis control programs in the OCP countries , supported by the MDSC [43] . In this approach , black flies are collected by village members for the full transmission season and subsequently forwarded through the national onchocerciasis control programs to the MDSC molecular biology laboratory in Ouagadougou for analysis [44] . As no fly dissections are done in the field , the proportions of parous flies are not known . The threshold of 1 FLH per 1000 parous flies was therefore converted by the OCP to a threshold of 0 . 5 FLH per 1 , 000 flies , assuming an average parous rate of about 50% over the transmission season [45] . The pool screen method and the corresponding threshold appear to have worked well for entomological surveillance in West Africa since 1998 , confirming that transmission levels remained insignificant in most river basins but having detected residual transmission in a few areas where control was known to have been unsatisfactory . The same standard pool screening method with a pool size of 300 flies and threshold of 0 . 5 FLH per 1 , 000 flies were used in the current study . To ensure that a sample with 0 FLH would imply that the infectivity rate was with 95% confidence below the threshold of 0 . 5 FLH per 1000 flies , a minimum of 3900 flies was to be analyzed per catching point [37] . The provisional thresholds for the prevalence of infection in the current study were also defined on the basis of the experience with successful cessation of vector control in the OCP . Just before stopping control , the OCP had undertaken skin snip surveys in eleven initially hyperendemic villages from the nine river basins . Four of the villages had become skin snip negative but seven villages still had a prevalence of infection between 1 . 0% and 4 . 8% [39] . Guided by these data , the provisional thresholds for elimination with ivermectin treatment in the present study were conservatively defined as a microfilarial prevalence <1% in 90% of sample villages , and a prevalence <5% in 100% of sample villages . The above thresholds were provisional thresholds to guide decision making and analysis in the current study . One of the objectives of the study is to review these thresholds , and revise them as required , in a detailed model-based analysis of the final study results . Ethical review and clearance of the research protocol , research instruments and informed consent procedures were obtained from the national ethical review boards of the ministries of health in Mali and Senegal , as well as from the World Health Organization ( WHO ) ethical review committee . Community meetings were held in all villages to explain the research objectives and procedures , and the right of each individual to decide whether to participate in the examinations or not . Before each examination , each individual who had voluntarily come to the examination point and agreed to participate signed , or put a thumb print if not literate , on the examination form to indicate consent . For children , one of the parents or the responsible guardian would sign the examination form . The use of community meetings to discuss the research project and the right of individuals to refuse participation in the examination was considered the most culturally appropriate and effective method for providing the necessary information to community members , and this approach was approved by both the national ethical review boards and the WHO ethical review committee .
During phase 1 , epidemiological evaluations were done in 126 villages between mid March to mid May 2006 , just before the last full treatment round in April and May 2006 ( table 2 ) . A total of 17 , 890 persons ( 71 . 1% of the census population ) voluntarily came to the examination points and agreed to participate in the skin snip examination . Those who did not participate included 11 . 3% of the census population who were absent from the village for up to one year , and 17 . 6% who were in the village but did not come to the examination for reasons of non-eligibility ( age<1 year ) , advanced age or illness , or who refused to participate . Information on refusal was obtained indirectly from family or other community members , indicating that some 9% of the census population refused to participate in the skin snip examination . The results of the evaluations showed that 14 to 16 years of ivermectin treatment had fundamentally changed the epidemiological situation in all three study areas ( figures 2 to 4 ) . While onchocerciasis was highly endemic during the pre-control period in the R . Gambia area , after 16 years of treatment only 3 out of 5 , 271 persons examined were skin snip positive and 98% of villages had a microfilarial prevalence <1% . A similar change was seen in the R . Bakoye where the prevalence in this previously hyperendemic focus had dropped to 0 . 26% and 95% of villages had a microfilarial prevalence <1% . It is noteworthy that 13 of the 18 skin snip positives in the R . Bakoye focus came from one third-line village . Further investigation revealed that the families concerned lived most of the year on their farms on the river banks , far away from their village but close to the vector breeding sites . Because of the distance to the village , most of them had never or only once been treated with ivermectin . Their skin microfilarial loads were generally low except for two persons , one male of 32 years who was never treated and one boy of 10 years who was treated once , and who had microfilarial loads of 87 mf/s and 96 mf/s respectively . Along the R . Faleme the epidemiological results were equally good in the north and in the center of the study area , with only 11 infected persons in 31 villages examined . However , in the southern third of the focus there were still seven villages with a microfilarial prevalence between 1% and 13%: 4 villages in Mali with a total of 15 infected persons and 3 villages in Senegal with 22 infected persons . Following the last full treatment round of early 2006 ( and thus after 15 to 17 years of treatment ) , entomological evaluations of onchocerciasis transmission were undertaken during the rainy season from July to November 2006 . The results are summarized in table 3 . A total of 157 , 500 black flies were collected through the bulk catches method and examined in the molecular biology laboratory in Ouagadougou using the pool screening technique [37] , [44] . For all catching sites the number examined exceeded the target of 3 , 900 . The results showed that onchocerciasis transmission levels were extremely low in all three river basins . In seven of the catching points , not a single infective larva was detected . In the remaining five catching points , the vector infectivity rate was below the threshold of 0 . 5‰ . The location of the catching points is shown in figure 6 . Two catching points in Senegal , Yamoussa along the R . Gambia , and Bambadji along the R . Faleme , were for logistic reasons not yet operational in phase 1 . For the others , figure 6 also shows the vector infectivity rate . Both in the R . Gambia and the R . Bakoye areas all epidemiological and entomological indicators were below the provisional thresholds for elimination . In R . Faleme area , the epidemiological results for the center and north of the area were below the threshold , as were the infectivity rates for all catching points . Based on these results , it was decided to proceed with phase 2 of the study and stop treatment in test areas in each of the three study foci . Following the decision to proceed with the cessation of treatment , test areas were identified in each of the study areas ( figures 7 to 9 ) . Each test area consisted of 5 to 8 villages located around a catching point , and included at least one village that had a skin snip positive person in the phase 1 surveys . Treatment was stopped in all villages in the test areas and during the next treatment round in 2007 , ivermectin treatment was only given in the study villages outside the test areas . The impact of stopping ivermectin treatment on infection and transmission was evaluated by epidemiological surveys that were undertaken in January and February 2008 , i . e . 20 to 22 months after the last treatment in the test villages , and entomological evaluation in all catching points during the transmission season of 2007 . The results of the epidemiological evaluation are summarized in table 4 . This time only 55% of the census population came voluntarily to be examined , 22% were absent from the village , 5% were not eligible or could not come because of advanced age or illness , and 28% of the population refused to be examined . A total of 2 , 283 people were examined in 21 test villages , and all of them were skin snip negative . The same result was obtained with the DEC patch test for which also everybody was negative in all three study sites . The numbers examined with the DEC patch test are lower than those with the skin snip method in two of the study areas because of people not returning after 24 hours for the follow-up examination . Furthermore , up to one third of the patches had partly or completely detached during the 24-hour follow-up period . The few persons who were skin snip positive during phase 1 in these tests villages had become skin snip negative or could not be examined because of their absence from the village . The entomological evaluation was done from mid August 2007 to mid December 2007 , i . e . 16 to 20 months after the last treatment in the test areas . Again , a very large number of 123 , 000 black flies was collected through bulk catches and examined in the molecular laboratory in Ouagadougou . For all but one catching points the number examined largely exceeded the target of 3 , 900 flies ( table 5 ) . The results showed that overall the vector infectivity rate was even lower than in phase 1 . Figures 7 to 9 show the location of the catching points and the surrounding villages in the test areas . The vector infectivity rates at the catching points in the test areas were zero in all three study sites , as well as in most other catching points . Only in two catching points in the R . Faleme focus were infective larvae detected but the infectivity rate was again below the threshold of 0 . 5 FLH/1 , 000 . In phase 2 all epidemiological and entomological indicators were below the provisional elimination thresholds , and it has therefore been decided to proceed with phase 3 . Treatment has now been stopped in all villages in the R . Gambia and R . Bakoye study areas . Because of the less satisfactory epidemiological results in the southern part of the R . Faleme , it was decided to proceed more cautiously and create two new test areas in the southern section of the focus where treatment has been stopped first and will be evaluated for one year before a final decision is taken to stop treatment in all villages throughout the focus .
Ever since ivermectin became the principal tool for onchocerciasis control , it has been debated whether , in addition to controlling the disease as a public health problem , it could also be used to interrupt transmission and eliminate the parasite [11] , [18] . As the drug does not kill or permanently sterilize the adult worms , elimination was clearly not possible in the short term . However , it was not unreasonable to assume that sustained interruption of transmission could be achieved after a long period of mass treatment . The first community trials had shown that mass treatment with ivermectin significantly reduces transmission and thus the incidence of infection with new worms [46]–[49] . It was likely , therefore , that repeated mass treatment would result in a progressive reduction in transmission of the parasite , probably accelerated by an additional effect of ivermectin treatment on the fertility of the adult worm [15] . Model predictions had indicated that elimination might be possible [16] , but empirical longitudinal data were not yet available to test this prediction and there remained considerable uncertainty as to whether elimination could be achieved , especially in Africa where the disease is endemic over large areas and where the vectors are highly efficient [18] . The current study has provided the first evidence that elimination of onchocerciasis with ivermectin treatment is feasible in some endemic foci in Africa . After 15 to 17 years of annual or six-monthly ivermectin treatment in three foci in Mali and Senegal , only few infections remained in the human population , infective O . volvulus larva were extremely rare in hundreds of thousands black flies examined , and vector infectivity rates were everywhere below the postulated threshold for interruption of transmission . The evidence generated after stopping treatment in test areas of each focus was even more convincing . Evaluations conducted 16 to 22 months after the last treatment showed no recrudescence of infection in the human population and no recrudescence of transmission . In fact , not a single skin snip positive person or infected black fly was detected in the test areas themselves . This is a significant finding as one of the main uncertainties was whether the residual adult parasite population was still sufficiently viable to restart microfilarial production after the withdrawal of ivermectin . The fact that no skin microfilariae were found up to 22 months after the last ivermectin treatment indicates that even if there still were adult worms in the human population , they were no longer productive or produced too few microfilariae , to be detected by the skin snip method , and posed therefore no significant risk for onchocerciasis transmission . A difficulty in the present study was to define the total treatment period in each study area . The treatment programs were introduced in a stepwise manner , covering during the first years the most infected villages and gradually expanding coverage during subsequent years to villages with lower levels of endemicity . We have defined the effective treatment period as the number of years that all first-line villages were included in the treatment program . These villages are ‘first line’ towards the river with no other human populations between them and the vector breeding sites , and they play a dominant role in onchocerciasis transmission [32] . The implication of this definition is that some first-line villages received treatment for one or two more years than the overall treatment period reported , and other villages that were deemed less important for transmission received less years of treatment . A unique feature of the current study is that it allowed a comparison of the long-term impact of two different treatment strategies: annual and six-monthly treatment . The final results in the R . Gambia focus , where ivermectin treatment was given at six-monthly intervals , and in the R . Bakoye , where treatment was annual , were virtually identical . The prevalence of infection had fallen to very low levels in both areas , the vector infectivity rates were close to zero and , most importantly , there had been no recrudescence in infection and transmission after stopping treatment in the test areas . In the R . Faleme focus , where treatment was annual , the evaluation results were equally good in the centre and north of the focus , but in the south there were still seven villages with a microfilarial prevalence between 1% and 13% . A higher prevalence in the south was seen both on the right bank of the river in Mali and on the left bank in Senegal , suggesting that the reason was of a spatial nature rather than related to treatment coverage or strategy . The R . Faleme borders on Guinea in the south and the results could be explained by some limited reinfection originating from across the border . The critical question for elimination , however , is whether the residual levels of infection in the R . Faleme constitute a risk for recrudescence of transmission . Vector infectivity rates were below the postulated threshold for elimination in all catching points in the R . Faleme focus , including in the south , and following cessation of treatment in the test area there was no evidence of recrudescence . It appears that elimination has also been achieved in the north and centre of the R . Faleme focus , and possibly in the south but this will be further investigated in 2009 . From the perspective of elimination , therefore , the impact of 15–17 years of treatment was not very different between the three river basins and the six-monthly treatment regimen did not show a clear advantage over annual treatment . However , historical epidemiological evaluation data of the OCP have shown that infection levels in the R . Gambia initially fell much faster than in other river basins [21] and it is quite possible that elimination was achieved several years earlier in the area with six-monthly treatment . Although the current study has provided the first evidence of elimination with ivermectin treatment in onchocerciasis endemic areas in Mali and Senegal , the results do not imply that elimination is feasible in all other endemic areas in Africa . The feasibility of elimination depends on several factors that may vary significantly between onchocerciasis endemic areas , e . g . pre-control endemicity levels , vector competence , human and vector migration , and treatment factors of coverage , frequency , duration , and efficacy . Previous modeling studies have indicated that the probability of elimination of onchocerciasis infection and transmission depends strongly on the pre-control level of endemicity [16] . The endemicity level reflects the density and competence of the local vector population and the intensity of human-vector contact during the pre-control period , and it is therefore an important predictor of the local potential for transmission after cessation of treatment . In all three river basins there were initially hyperendemic villages and their maximum intensity of infection , as reflected by the CMFL , ranged from 22 to 48 mf/s . Although these fall within the hyperendemic range , there are many onchocerciasis foci in Africa where the level of endemicity is significantly higher , and where elimination will probably be more difficult to achieve . Simulium species differ considerably in vector competence and elimination is predicted to be more difficult when vector competence is high [1] , [50] . The importance of vector competence was already obvious during the first community trials of ivermectin which showed a much greater reduction in onchocerciasis transmission after ivermectin treatment in an onchocerciasis focus in Guatemala , where the vector S . ochraceum s . l . has a relatively low vector competence , than in community trials in Africa where the vectors belonged to the S . damnosum complex [46]–[48] . In the study areas in Mali and Senegal the main vector is S . sirbanum , which is the most widely distributed vector in West Africa and the predominant vector in the dry savanna . [1] , [30] , [51] . In the wet savanna the distribution of S . sirbanum overlaps with that of S . damnosum s . s . These two savanna species cannot be differentiated morphologically and there exist only few studies that have analyzed elements of vector competence for these two species separately , showing no consistent difference between S . sirbanum and S . damnosum s . s . [52] , [53] Transmission is seasonal in the study sites in Mali and Senegal and only takes place during the rainy season when the vectors have repopulated the breeding sites . Seasonal transmission does not necessarily imply less transmission than in areas where the vectors are present throughout the year . In fact , the reverse is often true in West Africa where the highest endemicity levels are found in areas with seasonal rather than perennial transmission . But seasonal transmission allows for a treatment strategy that optimizes the impact of annual treatment on transmission by distributing ivermectin just before the start of the rainy season . This ensures that microfilarial loads are at their lowest during the transmission season and that when they rise again there are no vectors around to ingest such microfilariae . The above characteristics of the study sites , i . e . seasonal transmission by S . sirbanum and endemicity levels in the lower range of hyperendemicity with CMFL's between 10 and 20 mf/s , and occasionally up to 40–50 mf/s , are typical for the dry savanna belt in West Africa which runs from Senegal and Mali , through northern Nigeria to Chad and Sudan [1] , [30] , [51] , [54] . This is a vast area with millions of people infected with onchocerciasis , for whom the study findings are directly relevant . However , more to the south the vectors are different and there are many areas where pre-control endemicity levels are higher and where elimination may be more difficult . There is therefore an urgent need for further investigations to determine to what extent the findings of the current study can be extrapolated to other onchocerciasis endemic areas in Africa . Experience with vector control in the OCP has shown that long-distance migration by infected simuliids from outside a control program area can result in significant transmission within the area under control [27] . The study areas in Mali and Senegal were not subject to long-distance vector migration except for a short period at the beginning of the rainy season when a new wave of simuliids repopulated the breeding sites . However , the study foci were not completely isolated from neighboring endemic areas . Along the rivers there were other endemic villages beyond the boundaries of the study areas and all neighboring river basins were endemic for onchocerciasis . It is likely that there was some vector dispersal along the rivers across the study boundaries as well as some human movement from other endemic areas , but , with the possible exception of south Faleme , this did not result in any significant infection or transmission in the study areas . The reason is probably that all onchocerciasis endemic areas in Senegal and western Mali have been treated by the national ivermectin treatment programs of the two countries since the early 1990s , irrespective of whether they fell within or outside the boundaries of the current study , and that the epidemiological situation was equally good ( if not better because of lower pre-control endemicity levels ) outside the study areas . It is quite possible therefore that onchocerciasis is near elimination in all of Senegal and western Mali , and that nationwide elimination of onchocerciasis may be a realistic target for these countries for the coming years . With the exception of the year 1997 , annual treatment coverage was good throughout the control period and this is an important reason for the results obtained . Onchocerciasis foci where treatment coverage has been less good or where the geographic coverage has been patchy , may require considerably more years of treatment to achieve elimination . On the other hand , in this first experimental cessation of ivermectin treatment ever , we have proceeded very prudently and it is possible that equally satisfactory results might have been obtained if treatment had been stopped a few years earlier . The study provided a unique opportunity to evaluate a new diagnostic test in the field . The improved DEC patch test was easy to use and was shown to be highly specific in these onchocerciasis foci in West Africa . A high specificity is critically important for the potential use of the test as an epidemiological tool in low prevalence situations . The DEC patch test had some operational shortcomings , i . e . the requirements to return for the examination 24 hours after application of the patch ( due to the test measuring a delayed hypersensitivity reaction to microfilarial antigens ) which led to the failure of some people to do so , and a considerable proportion of patches having become partly or completely detached during the 24-hour period . But these limitations do not outweigh the great advantage that the DEC patch test , as a noninvasive test , has over the skin snip examination in which the populations of endemic areas are increasingly reluctant to participate . This was also evident in the current study where a quarter of the population refused to participate in the skin snip examination during the second phase of the study . This high rate of refusals might have introduced some bias in the epidemiological evaluation , and it was therefore important to have a second , independent source of evidence on interruption of transmission from the entomological evaluation in the same locations . The results of the study , indicating elimination after 15 to 17 years of annual or six-monthly ivermectin treatment , are quite consistent with previous ONCHOSIM predictions on the feasibility of elimination for comparable levels of endemicity , treatment coverage and treatment frequency [16] . These predictions were based on data from the first five years of ivermectin treatment only , and a more detailed model-based analysis of the data of the current study is being undertaken to develop improved predictions of where and when ivermectin treatment can be safely stopped . Furthermore , the thresholds for elimination used in the current study are provisional and based on previous model predictions and large-scale evaluations after cessation of vector control . A second objective for the ongoing modeling research therefore is to review and revise the elimination thresholds for ivermectin treatment on the basis of the data from the current study . The study in Mali and Senegal still continues . Following the excellent results of the second phase , the third phase of the study has now been started and will generate additional data on onchocerciasis infection and transmission two to three years after stopping treatment in all villages in the three onchocerciasis foci . If the follow-up findings confirm the current results , they would provide the definite evidence that it was safe to stop ivermectin treatment and that onchocerciasis infection and transmission has been eliminated from the three foci in Mali and Senegal . In the meantime , the study has provided the first evidence that onchocerciasis elimination with ivermectin treatment is feasible in some endemic foci in Africa , and this has already introduced a new paradigm for onchocerciasis control in the continent . Although this first evidence does not yet imply that elimination with ivermectin will be possible everywhere in Africa , the principle of elimination has been established . It now becomes a priority to evaluate in all onchocerciasis control programs in Africa their impact on onchocerciasis infection and transmission , and their progress towards elimination endpoints . The board of APOC has already acted upon the preliminary results of this study and adopted an additional objective for APOC to “develop the evidence base on when and where ivermectin treatment can be stopped , and provide guidance to countries on how to prepare for and evaluate cessation of treatment where feasible” [55] . APOC has already started to systematically collect epidemiological data on the impact of large-scale ivermectin treatment on onchocerciasis infection in different countries , focusing first on areas with the highest pre-control endemicity levels and different vector species . When large scale ivermectin treatment started in 1987 , it was not known if it would ever be possible to stop treatment . The present study has provided the first evidence that this is possible and that onchocerciasis elimination can be a realistic target , also in endemic areas in Africa . | The control of onchocerciasis , or river blindness , is based on annual or six-monthly ivermectin treatment of populations at risk . This has been effective in controlling the disease as a public health problem , but it is not known whether it can also eliminate infection and transmission to the extent that treatment can be safely stopped . Many doubt that this is feasible in Africa . A study was undertaken in three hyperendemic onchocerciasis foci in Mali and Senegal where treatment has been given for 15 to 17 years . The results showed that only few infections remained in the human population and that transmission levels were everywhere below postulated thresholds for elimination . Treatment was subsequently stopped in test areas in each focus , and follow-up evaluations did not detect any recrudescence of infection or transmission . Hence , the study has provided the first evidence that onchocerciasis elimination is feasible with ivermectin treatment in some endemic foci in Africa . Although further studies are needed to determine to what extent these findings can be extrapolated to other areas in Africa , the principle of onchocerciasis elimination with ivermectin treatment has been established . | [
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| 2009 | Feasibility of Onchocerciasis Elimination with Ivermectin Treatment in Endemic Foci in Africa: First Evidence from Studies in Mali and Senegal |
Many aspects of behavior and physiology are under circadian control . In Drosophila , the molecular clock that regulates rhythmic patterns of behavior has been extensively characterized . In contrast , genetic loci involved in linking the clock to alterations in motor activity have remained elusive . In a forward-genetic screen , we uncovered a new component of the circadian output pathway , which we have termed dyschronic ( dysc ) . dysc mutants exhibit arrhythmic locomotor behavior , yet their eclosion rhythms are normal and clock protein cycling remains intact . Intriguingly , dysc is the closest Drosophila homolog of whirlin , a gene linked to type II Usher syndrome , the leading cause of deaf-blindness in humans . Whirlin and other Usher proteins are expressed in the mammalian central nervous system , yet their function in the CNS has not been investigated . We show that DYSC is expressed in major neuronal tracts and regulates expression of the calcium-activated potassium channel SLOWPOKE ( SLO ) , an ion channel also required in the circadian output pathway . SLO and DYSC are co-localized in the brain and control each other's expression post-transcriptionally . Co-immunoprecipitation experiments demonstrate they form a complex , suggesting they regulate each other through protein–protein interaction . Furthermore , electrophysiological recordings of neurons in the adult brain show that SLO-dependent currents are greatly reduced in dysc mutants . Our work identifies a Drosophila homolog of a deaf-blindness gene as a new component of the circadian output pathway and an important regulator of ion channel expression , and suggests novel roles for Usher proteins in the mammalian nervous system .
In diverse phyla , circadian systems act to synchronize changes in arousal and internal physiology to optimal time periods for feeding , courtship , and other ethologically relevant behaviors . In Drosophila , the molecular basis of the internal clock that drives such rhythmic alterations in behavior has been extensively characterized [1] . Molecular and genetic approaches have demonstrated that a transcriptional negative-feedback loop lies at the heart of the clock , in which the transcription factors CLOCK and CYCLE activate expression of their own repressors , PERIOD ( PER ) and TIMELESS [1] . In combination with additional modulatory feedback loops and post-translational regulatory mechanisms , oscillatory activation of CLOCK/CYCLE leads to temporally controlled expression of a wide range of clock-controlled genes , thus altering the functional properties of clock neurons in a time-dependent manner [2]–[6] . In contrast to the core clock mechanism , only a small number of genes that act downstream of the clock have been identified , including pigment dispersing factor ( pdf ) , pdf receptor ( pdfr ) , neurofibromatosis-1 ( nf1 ) , slowpoke ( slo ) , narrow abdomen ( na ) , and ebony [7]–[14] . Two of these output genes encode voltage-gated ion channels , SLO and NA , suggesting that modulation of neuronal excitability is an essential component of circadian output . Of the two channels , the electrophysiological properties and cellular consequences of SLO channels have been defined in much greater detail . SLO is a member of the BK ( big K+ ) family of voltage-gated Ca2+-activated potassium channels , and generates non-inactivating K+ currents with high single-channel conductance [15] , [16] . SLO channels act to repolarize the membrane potential during action potentials , and Drosophila slo mutants thus exhibit broader action potentials in flight muscles and cultured neurons [17]–[19] . Intriguingly , BK channel function is critical for circadian behavior in both Drosophila and mammals . Drosophila slo mutants are arrhythmic , yet restoring SLO expression in clock neurons does not robustly rescue rhythmic behavior , suggesting that SLO acts downstream of clock cells [2] , [7] . Mammalian BK channels are also required for clock output from the suprachiasmatic nucleus ( SCN ) , and contribute to the silencing of SCN neurons during the night [20] . Consistent with the key role of ion channels in the control of neuronal physiology and behavior , regulators of ion channel function have also been found to modulate behavioral outputs . For example , SLEEPLESS , a positive regulator of Shaker potassium channels , strongly affects sleep in Drosophila [21] . Here we identify a novel SLO-binding protein , which we have termed DYSCHRONIC ( DYSC ) . dysc mutants exhibit arrhythmic locomotor activity but normal eclosion rhythms and wild-type molecular oscillations in clock neurons , suggesting dysc is specifically required for circadian locomotor output . Intriguingly , DYSC is the closest Drosophila homolog of Whirlin , a PDZ ( PSD-95/DLG/ZO-1 ) domain-containing protein mutated in Type II Usher syndrome , a human deaf-blindness disease [22] , [23] . Through targeted rescue experiments , we demonstrate that DYSC acts downstream of clock cells to control locomotor output . We show that DYSC co-localizes with SLO in major neuronal tracts in the brain , and that the two proteins form a complex to regulate each other's expression post-transcriptionally . Furthermore , SLO-dependent potassium currents are significantly reduced in dysc neurons in the adult brain . Our results define a novel channel regulator required for rhythmic alterations in behavior and suggest new roles for Whirlin in the mammalian brain .
In an ongoing forward-genetic screen for sleep and circadian mutants , we identified an arrhythmic mutant line resulting from a P-element insertion , which we named dyschronics168 ( dyscs168 ) . Most dyscs168 homozygotes were arrhythmic in constant-dark ( DD ) conditions , with a minority showing weak rhythmicity ( Figure 1A and Table 1 ) . To assess whether dysc regulates circadian behavior in general or locomotor behavior specifically , we examined circadian patterns of eclosion from the pupal case , a behavior that is dependent on correct output from small ventral lateral neurons ( s-LNvs ) , a subset of clock neurons required for rhythmic locomotion in DD [12] , [24] . Interestingly , despite clear arrhythmic locomotor patterns in dysc mutants , we observed an eclosion rhythm that closely mirrored wild-type controls ( Figure 1B ) , suggesting that s-LNv output is unimpaired in dysc mutants . To determine whether DYSC functions as a component of the clock , we next assessed the integrity of molecular oscillations in clock neurons in dysc mutants . We examined PER cycling in two sets of clock neurons: s-LNvs and a cluster of dorsal neurons ( DN1s ) , which has been proposed to be a direct target of output from the s-LNvs [25] . In dysc mutants , we found that daily cycling of PER expression was indistinguishable from wild-type controls in both sets of clock neurons ( Figure 1C , 1D ) . Similarly , PER levels exhibited wild-type oscillatory patterns in head extracts of dysc mutants ( Figure S1 ) . Since the bulk of PER protein in head extracts derives from eye tissue , this suggests that the molecular clock is unimpaired in the periphery as well . These findings establish that core clock function is normal in dysc mutants , and thus identify DYSC as a constituent of the circadian locomotor output pathway . We mapped the s168 P-element insertion to an intron in a previously uncharacterized locus , CG34400 ( Figure 2A ) . The dysc transcription unit generates two predominant classes of mRNAs via alternative splicing: multiple long isoforms and a short isoform ( Figure 2A ) . Full-length dysc transcripts encode proteins containing three PDZ domains ( Figure 2B ) , a motif commonly associated with scaffolding proteins [26] , while the short DYSC isoform lacks the C-terminal PDZ domain . Intriguingly , comparative genomics identified dysc as the closest Drosophila homolog of whirlin ( USH2D , DFNB31 ) , a gene implicated in type II Usher syndrome ( USH2 ) in humans [22] . Like dysc , the mammalian whirlin locus encodes several distinct splice-forms , including those corresponding to long dysc isoforms as well as one containing the C-terminal PDZ domain alone [22] , [27] ( www . ensembl . org; Figure 2B ) . USH2 is characterized by early-age hearing loss due to alterations in ear cell stereocilia formation followed by progressive blindness resulting from photoreceptor degeneration [23] . Genetic ablation of whirlin in mice also leads to hearing loss coupled with abnormal photoreceptor development [27] , [28] . In addition to sensory tissues , Whirlin is also expressed in the mammalian central nervous system [29] , [30] , yet the function of Whirlin in the brain is unclear . The identification of dysc thus provides a platform in a genetically amenable model organism to investigate novel functions of a Whirlin homolog . We performed several independent genetic experiments to establish that specific disruption of the dysc locus is indeed causative of the arrhythmic phenotype in dysc mutants . We obtained two additional P-element insertions in dysc ( c03838 and c05107 ) ( Figure 2A ) . Like dyscs168 , most dyscc03838 mutants were arrhythmic in DD ( Figure 2C and Table 1 ) . On the other hand , dyscc05107 mutants exhibited a milder circadian phenotype , with some showing robust rhythmicity ( Figure 2C and Table 1 ) . To analyze the effects of the three separate P-element insertions on DYSC expression , we generated a polyclonal antibody to DYSC . Part of the DYSC antigen is common to all isoforms , and thus the antibody is expected to recognize all isoforms . As expected , in wild-type adult head extracts , we observed multiple bands corresponding to the predicted long isoforms , and a single band at the expected size of the short isoform ( Figure 2D ) . Western blotting further revealed differential effects of the three P-element insertions on the expression of DYSC isoforms ( Figure 2D ) . The dyscc05107 insertion acts as a hypomorphic allele , leaving expression of all DYSC isoforms reduced but still detectable . In contrast , dyscc03838 renders expression of all DYSC isoforms undetectable , and is therefore a null or a strong hypomorphic allele . The remaining DYSC expression in dyscc05107 homozygotes is thus likely to be sufficient to partially rescue rhythmic behavior . While dyscs168 also renders the long isoforms undetectable , it leaves expression of the short isoform intact . Our finding that the s168 mutation causes as strong a circadian phenotype as c03838 suggests that the short isoform is not sufficient for rhythmic behavior and that the C-terminal PDZ domain plays an important role in circadian rhythms . In addition to assessing rhythmicity in DD conditions , we also examined locomotor patterns of dysc mutants in 12 h light∶dark ( LD ) conditions ( Figure S2A ) . In contrast to wild-type flies , dyscs168 and dyscc03838 homozygotes did not exhibit anticipation of lights-on , further suggesting that output of the morning oscillator ( which drives rhythmic behavior in DD ) is impaired by loss of DYSC . Hypomorphic dyscc05107 flies showed normal morning anticipation , while anticipation of lights-off was maintained in all dysc allelic backgrounds ( Figure S2A ) . Overall daytime and nighttime activity in LD conditions was greater in dysc mutants relative to wild type controls ( Figure S2B ) . This is in contrast to climbing defects observed in dysc mutants ( Figure S2C ) , and suggests that although dysc flies have some motor problems , overall inactivity is not a contributing factor for arrhythmicity . All dysc alleles were recessive; trans-heterozygotic combinations of the three alleles were largely arrhythmic; and heterozygosity for both dyscs168 and dyscc03838 in combination with a deficiency removing the dysc locus also resulted in arrhythmia ( Table 1 ) . In addition , precise excision of the dyscs168 P-element restored wild-type patterns of locomotion ( Table 1 ) , indicating that the P-element insertion is responsible for arrhythmicity . Finally , to test whether transgenic expression of dysc could restore rhythmic behavior , we generated flies carrying a UAS-dysc transgene encoding a long isoform of DYSC . Pan-neuronal expression of the UAS-dysc transgene in dysc mutants was sufficient to rescue rhythmic behavior ( Figure 2E and Table 2 ) . Consistent with the fact that dyscs168 homozygotes , which express normal levels of the short isoform , are arrhythmic , transgenic expression of a short DYSC isoform did not restore rhythmicity in dyscc03838 mutants ( Table 2; see Figure S3 for expression levels of long and short dysc transgenes ) . Over-expression of either the long or short isoforms of DYSC in a wild-type background did not affect circadian rhythmicity ( Table 2 ) . These results comprehensively demonstrate that DYSC is required for circadian alterations in locomotor activity , and furthermore indicate that correct circadian output requires DYSC expression in the nervous system . We next examined DYSC expression in the adult Drosophila nervous system by performing whole-mount immuno-staining of the adult brain . DYSC-specific immuno-reactivity was enriched in major neuronal tracts , i . e . , dense bundles of neuronal processes , throughout the central brain ( Figure 3A ) . Interestingly , in the mushroom bodies , ellipsoid body and antennal lobes , DYSC expression was broader than in other regions . For example , DYSC expression was observed throughout the mushroom body including the lobes , peduncle , and calyx , although not in the cell bodies ( Figure 3A ) . In fact , no cell-body expression of DYSC was detected in any brain region . To define the cell-body locations of DYSC-expressing neurons , we generated transgenic flies carrying Gal4 under the control of the dysc promoter . Consistent with the widespread expression of DYSC , GFP expression driven by dysc-Gal4 was detected in many cell bodies in the brain ( Figure S4A ) , including subsets of clock neurons ( Figure S4B ) , and expression of DYSC using the dysc-Gal4 driver restored rhythmic behavior in dysc mutants ( Figure 3B ) . To examine whether DYSC expression was under circadian control , as has previously been documented for certain output genes [2] , [13] , we examined DYSC expression and localization at various circadian time-points . These experiments revealed that DYSC protein levels in head extracts were not subject to circadian cycling , nor was any obvious temporal alteration in DYSC expression and localization in the adult brain observed ( Figure S5 ) . However , we cannot rule out the possibility that DYSC undergoes circadian regulation in a subset of cells . We attempted to narrow down the key DYSC-expressing cells required for circadian locomotor behavior using a targeted rescue strategy ( Figure 3B ) . Complementing our pan-neuronal rescue data , transgenic expression of DYSC in muscle or glial cells did not restore rhythmic behavior ( Figure 3B ) . Expression of DYSC in PDF- or TIM-expressing clock neurons also failed to rescue the circadian phenotype ( Figure 3B ) . We next attempted to rescue dysc mutant arrhythmicity via targeted expression of UAS-dysc to major centers in the Drosophila nervous system . Expression of DYSC in the central complex , pars intercerebralis or the mushroom bodies , regions of the Drosophila brain implicated in motor control and complex behaviors [31]–[33] , was insufficient to restore rhythmicity . Whereas c164-Gal4 , a driver widely used for expression in motor neurons , robustly rescued circadian behavior , OK371-Gal4 , which drives expression in glutamatergic neurons , including motor neurons , did not ( Figure 3B ) . c164-Gal4 drives expression in several brain regions in addition to motor neurons [34] ( Figure S6A ) , but not in the ellipsoid body , a region important for locomotor behavior [32] . Co-staining with PER shows that it also drives expression in a few clock cells ( Figure S6B ) . Given that dysc-Gal4 drives expression in many clock cells ( Figure S4B ) and that a recent study identified dysc as a potential direct target of CLK [35] , DYSC may function in both clock and non-clock cells . However , our results clearly show that DYSC expression in clock cells alone is not sufficient to restore rhythmicity . Combined with our data indicating that DYSC does not affect clock protein oscillations ( Figure 1 ) , this suggests that DYSC is required downstream of clock neurons . Collectively , these results indicate a role for DYSC in an intermediary circuit between the central clock neurons and motor neurons , and further suggest that the cellular requirements for DYSC in the circadian output circuit are likely to be structurally complex and not easily recapitulated using restricted driver lines . In mammalian photoreceptors and cochlear stereocilia , Whirlin , the mammalian DYSC homolog , forms a scaffolding complex to properly localize the transmembrane proteins Usherin and Very large G-protein-coupled receptor 1 ( VLGR1 ) [27] , [29] , [36] , [37] . We hypothesized that DYSC might also be required for appropriate transmembrane protein localization in the Drosophila nervous system . Since Usher proteins have previously been shown to interact with ion channels [38] , we focused on SLO , a Ca2+-activated potassium channel , which is required for clock output in flies and mammals [2] , [7] , [20] . Using a new anti-SLO antibody , we observed clear enrichment of SLO in major neuronal tracts throughout the central brain ( Figure 4A ) . SLO staining in neuronal tracts was not detected in slo4 mutants ( Figure 4A ) , confirming the specificity of the antibody . Intriguingly , DYSC and SLO exhibited a high degree of co-localization in neuronal tracts ( Figure 4B ) . Unlike DYSC , however , we did not observe strong SLO staining in the mushroom body lobes , calyx or the ellipsoid body ( Figure S7 ) . Given the degree of overlapping expression , we examined whether SLO expression was altered in dysc mutants . Remarkably , SLO staining in neuronal tracts was undetectable in dysc mutants ( Figure 4A ) , and the only remaining SLO signal within the brain was localized to the mushroom body peduncle . To test whether voltage-gated potassium channels in general were affected in dysc mutants , we examined the expression and localization of the A-type potassium channel , Shaker . In contrast to SLO , Shaker protein levels and localization within the brain were unaffected in dysc flies ( Figure 4C–4D ) . Thus , DYSC specifically regulates the expression of a potassium channel subtype . To assess the cellular consequences of the regulation of SLO by DYSC , we performed in vivo whole-cell patch-clamp electrophysiology on adult dilp2-positive neurons in wild-type and dyscs168 adult brains . These neurons are located in the pars intercerebralis ( PI ) and have previously been shown to express a SLO-dependent Ca2+-activated non-inactivating potassium current [39] . We chose these neurons for their easy accessibility , and because neurons in the PI were positively labeled by the dysc-Gal4 driver ( Figure S4A ) . Voltage-dependent outward potassium currents were evoked by depolarizing voltage steps in the whole-cell recording mode ( Figure 5A ) . To examine the proportion of non-inactivating potassium component ( which includes SLO channels ) in the total outward current , we initially applied voltage pulses to pulse potentials ranging from −60 mV to +50 mV from a holding potential of −70 mV . Subsequently , outward currents from the same neuron were evoked via a similar protocol but from a holding potential of −30 mV . Inactivating channels are predominantly inactivated when the membrane potential is held at −30 mV , and non-inactivating channels ( including SLO ) can thus be isolated from the total outward current . We observed that the outward current at +50 mV in wild-type neurons showed a moderate reduction when the membrane potential was held at −30 mV relative to −70 mV ( Figure 5A–5B ) ; in dysc mutants , the outward current showed a much greater reduction when the membrane potential was held at −30 mV ( Figure 5A–5B ) , indicating a loss of non-inactivating currents in dysc mutants . To determine what proportion of the non-inactivating outward current is carried by SLO potassium currents , we examined the effect of extracellular Ca2+ on outward currents in dilp2-neurons , since the SLO channel is highly activated by intracellular Ca2+ that enters through Ca2+ channels . In wild-type neurons , adding 2 mM CaCl2 significantly potentiated the non-inactivating component of the current ( Figure 5C–5D ) . In contrast , the non-inactivating currents in dysc dilp2-neurons exhibited only a slight increase upon addition of CaCl2 . Furthermore , while the application of 1 mM tetraethylammonium ( TEA ) , a blocker of SLO channels [40] , reduced the total outward current by 63% in wild-type neurons , it produced only a 17% reduction in dysc neurons ( Figure 5C and 5E ) . Thus , the non-inactivating outward current in dilp2-neurons is predominantly carried by SLO channels , and is markedly reduced in dysc mutants . These results are in accord with our data showing greatly reduced SLO channel expression in dysc mutants ( Figure 4 ) . Finally , we asked if DYSC and SLO exhibit a mutually dependent relationship , since such co-dependence has been demonstrated between Whirlin and its binding partners VLGR1 and Usherin [27] , [36] , [37] . Interestingly , DYSC in neuronal tracts was largely undetectable in slo4 mutants , yet DYSC expression in the mushroom body , ellipsoid body and antennal lobes , areas that do not robustly express SLO , remained intact ( Figure 6A–6B ) . In fact , we noted a significant increase in DYSC levels in the mushroom body lobes in slo4 mutants relative to controls ( α/β-lobes: increase = 33 . 1±8% , p<0 . 05 , Mann-Whitney U-test; γ-lobes: increase = 41 . 9±8% , p<0 . 001; controls: n = 13 brains , slo4: n = 10 ) . The mechanistic basis for this increase in DYSC levels is unclear . One possibility is that in the mushroom bodies , DYSC has a mutually dependent relationship with an unidentified protein that is upregulated in the absence of SLO , which leads to an increase in DYSC . Loss of SLO also resulted in a substantial reduction in total head DYSC protein levels ( Figure 6C ) . SLO and DYSC thus exhibit a reciprocal requirement for expression in neuronal tracts . We did not observe any reduction of dysc mRNA levels in slo4 mutants , nor any change in slo mRNA levels in dysc mutants ( Figure S8 ) , indicating that the mutual regulation of DYSC and SLO is a post-transcriptional effect . The reciprocal requirement of SLO and DYSC suggests formation of a stable complex . To examine whether the two proteins can physically interact , we performed co-immunoprecipitation experiments in human embryonic kidney ( HEK-tsA ) cells . When co-expressed with SLO , DYSC was co-immunoprecipitated with SLO , but not when expressed without SLO ( Figure 6D ) . These data suggest that the two proteins regulate each other's expression through direct protein-protein interaction . In summary , our data identify DYSC as a novel binding partner and regulator of SLO , and suggests that the arrhythmic phenotype exhibited by dysc mutants is in part due to a loss of SLO channels .
Here we describe a novel mutant , dysc , which exhibits arrhythmic locomotor patterns in DD conditions . Analysis of clock protein oscillations and eclosion rhythms in dysc mutants , as well as targeted rescue of the arrhythmic dysc phenotype , all indicate that dysc is a crucial constituent of the circadian output pathway and acts downstream of the core clock . One intriguing aspect of dysc function is its ontology . Comparative genomics identifies dysc as the closest Drosophila homolog of whirlin , a loci associated with nonsyndromic deafness and Type II Usher syndrome ( USH2 ) in humans [22] , [41] . USH is a genetically heterogeneous disorder associated with alterations in cochlear stereocilia structure , vestibular dysfunction and retinitis pigmentosa , resulting in deaf-blindness with varying ages of onset [23] . Our study demonstrates that Usher proteins can also play a crucial role in complex behaviors . This is intriguing given the broad expression of many Usher proteins in the mammalian nervous system , and the lack of functional roles ascribed to the Usher interactome in the brain [29] , [30] . Our data also point to a plausible mechanism by which an Usher protein homolog regulates a behavioral output: the control of ion channel expression . This parallels the role of several other PDZ domain-containing proteins in both the mammalian and Drosophila nervous systems , such as members of the PSD-95 family , which serve to cluster potassium channels at axons and synapses [42]–[44] . Recent data indicate that the Usher interactome also includes ion channels . Harmonin , a PDZ-containing protein linked to USH1 , co-localizes with and negatively regulates the Cav1 . 3 voltage-gated calcium channel in inner hair cells [38] . We identify a novel interaction between an Usher protein homolog and the SLO Ca2+-activated potassium channel . DYSC physically interacts with SLO , and in the absence of DYSC , SLO expression in neuronal tracts as well as SLO currents in dilp2-neurons in vivo are markedly reduced . DYSC's influence on other potassium channels appears to be limited , since Shaker expression was unaffected in dysc mutants , and in dysc dilp2-neurons robust outward potassium currents were still detected , albeit with a reduced non-inactivating component caused by the loss of SLO expression . Thus , in contrast to the relationship between Harmonin and Cav1 . 3 [38] , DYSC positively regulates SLO expression in the Drosophila brain . Usher proteins and their homologs can therefore both promote and inhibit ion channel function in a subtype-specific manner . It is also noteworthy that both Harmonin's and DYSC's effect on cellular physiology via control of Cav1 . 3 and SLO respectively is to reduce the excitability of the cell and synaptic output . Thus , one question arising from these studies is whether Usher proteins generally act to negatively tune neuronal excitability . Further studies investigating potential interactions between Usher proteins and other ion channels will help to shed light on this intriguing issue . We also demonstrate a mutually dependent relationship between DYSC and SLO . In mammals , this finding is paralleled by similar relationships between several Usher proteins and their binding partners [27] , [37] , [38] , and between potassium channels and their associated proteins [45] , [46] . Interestingly , whereas loss of SLO greatly reduces DYSC levels in major neuronal tracts in most brain regions , it has an opposite effect in the mushroom body , ellipsoid body , and antennal lobes . In addition , SLO expression is detectable only in the peduncle of the mushroom bodies in dysc mutants . These findings raise the possibility that DYSC has a mutually dependent relationship with different proteins depending on the cell type . Given the broad expression in the brain and its interaction with SLO , DYSC is likely to have pleiotropic effects on behavior . SLO is involved in multiple complex behaviors , including the production of courtship songs and ethanol sensitivity [47] , [48] . It will be interesting to investigate whether DYSC is also involved in these behaviors . SLO channels have previously been implicated in the circadian output circuit [7] , suggesting a mechanism by which DYSC affects rhythmic behavior . Previous work has demonstrated that loss of SLO de-synchronizes clock protein oscillations in DN clusters [7] . In contrast , in dysc mutants the molecular clock is unaffected in these neurons . It is possible that a sufficient level of SLO remains in dysc mutants to maintain clock protein cycling . Given our results and the previous finding that restoring SLO in clock cells is not sufficient for a full rescue of the arrhythmic phenotype of slo mutants [7] , it is likely that SLO performs an important role in the intermediate circuit between the clock and motor neurons where DYSC is required , as well as in clock cells . We propose that DYSC links the central clock output to locomotor activity by regulating membrane excitability , in part through its control of SLO expression . Thus , precise control of neuronal excitability is required not only for correct clock neuron function [49] , [50] , but also in downstream circuits that connect clock cells to motor neuron targets . In conclusion , we have identified a novel Drosophila ion channel regulator and human disease gene homolog that impacts complex behavior . In addition to shedding new light on genetic components of the circadian output pathway , our results suggest new roles for Whirlin in the mammalian nervous system . It will be interesting to determine if whirlin mutants are also arrhythmic , and whether Whirlin similarly regulates SLO expression in the mammalian brain .
Flies were reared on standard food containing cornmeal , yeast , and molasses . The s168 mutant strain was isolated from an ongoing screen for sleep and circadian mutants . Novel strains carrying random insertions of the P[XP] transposable element in a white ( iso31 ) background were generated using the Δ2–3 transposase . Sleep and circadian behavior was assayed as previously described [51] . Inverse PCR revealed that the s168 line carries a P-element insertion in the dysc locus . Two additional P-element insertion alleles of dysc , c05107 and c03838 , were obtained from the Exelixis collection at the Harvard Medical School . All three alleles were backcrossed to the iso31 strain at least 5 times , and balanced mutant and sibling control lines were established . The BSC614 deficiency line that removes the dysc locus and OK371- , c819- , c107- , and c061-Gal4 lines were obtained from the Bloomington Stock Center . c164-Gal4 was obtained from L . Griffith ( Brandeis University ) . Other drivers and the Shaker deficiency line were obtained as previously described [46] , [52] . Precise excision lines were derived from the s168 line by a transposase-mediated mobilization of the P element . We identified three precise excision lines by PCR amplification and sequencing . Preliminary results indicated that they had similar circadian behavior , and data from one of them are presented . We screened ∼150 excision lines , but were unable to obtain imprecise excision lines that remove coding regions . To monitor circadian locomotor behavior , 2- to 5-day old male flies , entrained to a 12 h∶12 h LD cycle for at least 3 days , were put into glass tubes containing 5% sucrose and 2% agar , and their activity was monitored using the Drosophila Activity Monitoring System ( Trikinetics ) at 25°C . For quantification of circadian behavior , activity counts were collected in 30-min bins over a 6-day period in DD . Actograms were generated using ClockLab ( Actimetrics ) , and circadian period and power of rhythmicity were determined using Fly Activity Analysis Suite for Mac OSX ( FaasX , M . Boudinot ) . The power of rhythmicity is defined as the difference between the χ2 value and the significance value at p = 0 . 05 . Flies with power of less than 25 were considered arrhythmic , between 25 and 50 , weakly rhythmic , and over 50 , rhythmic . Circadian period was determined for rhythmic flies only , whereas power of rhythmicity was determined for all flies , including arrhythmic and weakly rhythmic ones . Locomotor patterns in 12 h∶12 h LD conditions were calculated as follows: single-fly activity was monitored over a three day period and averaged to generate a mean 24 h activity plot . This activity plot was then further averaged across the experimental population . For analysis of eclosion behavior , pupae entrained to a 12 h∶12 h LD cycle throughout development were taped to eclosion monitors ( Trikinetics ) using double-sided tape . Data were collected in 1-h bins over a 4-day period in DD at 25°C . Climbing assays were performed as described previously [46] . Fly head mRNA was extracted using the Ultraspec RNA Isolation System ( Biotecx ) and reverse transcribed using High Capacity cDNA Reverse Transcriptase Kit ( Applied Biosystems ) . To generate the UAS-dysc construct , dysc cDNA was PCR-amplified in two pieces using two sets of primers: for N-terminus: 5′-CTG AAT TCC CAG CAG TGT AAT GC-3′ and 5′-CGA GAA AGG ATT GCC CAT T-3′; for C-terminus: 5′-CGA TCC GGA CTG ATG ATT G-3′ and 5′-CTG GTA CCG GCA GGG CAA GC-3′ . The N- and C-terminus fragments were subcloned into the TOPO TA-cloning vector ( Invitrogen ) , sequenced , and subsequently inserted into the pUAST vector . The cloned cDNA represents a novel isoform , differing slightly from other long isoforms in FlyBase ( www . flybase . org ) through alternative splicing , and we have designated it Isoform G . The C-terminus of the short isoform ( F ) was amplified using the following primers: 5′-CGA TCC GGA CTG ATG ATT G-3′ and 5′-CTG GTA CCT AAG TGT ATA TAG TGT CTG-3′ . To generate the dysc-Gal4 construct , approximately 4 . 5 Kb upstream of the transcriptional start site was PCR-amplified from genomic DNA using the following primers: 5′-TCC TGC CTC TGG ATC CCG CCA CGT TG-3′ and 5′-ACG CGG CCG CGG CTT CAA ACC AAA TCA GC-3′ . The PCR fragment was inserted into the pPT-Gal vector . Transgenic fly lines carrying the UAS-dysc or dysc-Gal4 construct were generated by standard germline transformation in the iso31 background ( Rainbow Transgenics ) . The rat polyclonal antibody to DYSC ( TJR43 ) was raised against a portion of the DYSC protein fused to N- and C-terminal 6× HIS tags . A PCR fragment amplified using the primers 5′-CCG AAT TCT GCA CCT CCA TCG A-3′ and 5′-CCT TCG ATA GCA ATA CCT CGA GTT-3′ was inserted into the pET-28a vector . Protein expression and purification was performed at the Protein Expression Facility of Wistar Institute . The antibody recognizes both long and short isoforms of DYSC . The rabbit polyclonal antibody to SLO ( 763 ) was a generous gift from I . Levitan , and will be described elsewhere . While the antibody detected SLO-specific signal in immuno-staining assays , it could not detect SLO on Western blots due to masking by a non-specific band present in slo4 mutants . Western blot experiments were carried out essentially as described [46] except that fly heads were homogenized and lysed in 2× SDS sample buffer containing 5% β-mercaptoethanol . Antibodies to PER ( PA1139 ) , DYSC ( TJR43 ) , SLO ( 763 ) , and Shaker ( UPR55 ) [46] were used at 1∶1000 . Antibodies to MAPK ( Sigma ) and β-ACTIN ( Abcam ) were used at 1∶10 , 000 . Western blot experiments were repeated at least three times except as noted , and representative blots are shown . To examine cycling of PER and PDF in the central clock cells , young female flies ( 1–4 days old ) were entrained to a 12 h∶12 h LD cycle for at least 3 days , and were collected at indicated times during the second day in DD . Dissected brains were fixed in 4% paraformaldehyde , and incubated overnight in antibodies to PER ( UPR34 ) and PDF ( HH74 ) [24] diluted 1∶1000 . PER and PDF levels were judged through visual inspection . For DYSC and SLO staining , male flies were used . Rat anti-DYSC and rabbit anti-SLO antibodies were used at 1∶400 and 1∶1000 , respectively . Fluorophore-conjugated secondary antibodies were obtained from Invitrogen . Brains stained with PER and PDF antibodies were imaged with a Leica TCS-SP5 confocal microscope , and those stained with DYSC and SLO antibodies were imaged with an Olympus Fluoview confocal microscope . Samples for comparison were processed at the same time and imaged with the same settings at sub-saturation intensities . At least five brains were examined per condition . To quantify DYSC levels in the mushroom body lobes , average pixel intensities in the α/β- and γ-lobes of the mushroom bodies were determined in each brain hemisphere using Image J . For each pair of lobes , a mean value was calculated , yielding a single value for α/β- and γ-lobes for each brain . Data from paired batches of control and slo4 brains were normalized to the mean of the controls for each batch . For co-immunoprecipitation ( co-IP ) experiments , the coding region of the G isoform of dysc was inserted into the pcDNA3 expression vector using standard molecular biology techniques . The O isoform of SLO ( www . flybase . org ) was tagged with the 3×FLAG epitope via PCR-driven overlap extension , and was cloned into the pcDNA3 vector . HEK-tsA cells were transfected with various combinations of dysc and Flag-slo constructs ( 330 ng each ) in 60 mm Petri dishes using Effectene ( Qiagen ) . pCDNA3 vector DNA was included in some conditions to make the total amount of DNA equal in all conditions , and pIRES-GFP ( 330 ng ) was included in all conditions to monitor transfection efficiency . Co-IP was performed essentially as described [46] except that cells were lysed in extraction buffer containing 50 mM KCl , 10 mM HEPES , 2 mM EDTA , 5 mM Tris at pH 7 . 5 , 1% Triton X-100 , 10% glycerol , 10 µg/mL leupeptin , 10 µg/mL aprotinin , 2 µg/mL pepstatin A , 0 . 5 mM PMSF , 1 mM Na3VO4 , 10 mM r-nitrophenyl phosphate , pH 7 . 5 , and an antibody to FLAG ( Sigma ) was used . cDNAs from fly heads were generated as described above . Real-time RT-PCR was performed using SYBR green ( Applied Biosystems ) with the following primers: 5′-CGG CAT TTG CGT TAA AGG AG-3′ and 5′-GAG ATG TAG ACG CCT AAG CCT GAG-3′ for dysc , and 5′-GTC GTA CGG AAT GCT GTG CA-3′ and 5′-GAG CTG GTG TCC CTG AAT CG-3′ for slo . Both sets of primers recognize regions common to all isoforms . Dilp2-positive neurons were labeled by driving a membrane-tagged GFP ( CD8::GFP ) using the dilp2-Gal4 driver , expressed in either a wild-type or dyscs168 background . For in vivo patch recording from PI neurons [33] , [39] flies were anesthetized with CO2 and glued ventral side down to a glass coverslip . The coverslip was placed in a chamber containing extracellular solution ( 101 mM NaCl , 3 mM KCl , 4 mM MgCl2 , 1 . 25 mM NaH2PO4 , 20 . 7 mM NaHCO3 , 5 mM glucose [pH 7 . 2] ) and then the cuticle was peeled off using fine forceps to expose the surface of the brain . The chamber was placed on the stage of an Olympus BX51 fluorescent microscope , and PI neurons were identified by their location and fluorescence . Patch-recording electrodes ( WPI ) were fire polished , and had resistances from 3 to 4 MΩ when filled with intracellular solution ( 102 mM K-gluconate , 17 mM NaCl , 2 mM CaCl2 , 0 . 5 mM MgCl2 , 5 mM EGTA , 10 mM HEPES , pH 7 . 2 ) . Standard techniques were used to record macroscopic currents in the whole-cell voltage-clamp mode with an Axopatch 200A amplifier ( Molecular Devices ) . Data were digitized with a Digidata 1322A interface ( Molecular Devices ) and stored on a PC hard drive for further analysis with pClamp9 software ( Molecular Devices ) . For comparison of rhythm strength between pairs of conditions , Student's t-tests ( unpaired , two-tailed ) were performed with Bonferroni correction for multiple comparisons . When comparing multiple experimental genotypes to controls , one-way ANOVA with Dunnett post-hoc tests were used . For electrophysiology data , Mann-Whitney U-tests were performed . Significance values were calculated using Kaleidograph ( Synergy Software ) or Excel ( Microsoft ) . | In most organisms , endogenous circadian clocks help to restrict adaptive activities such as foraging and mating to ecologically appropriate periods of the day–night cycle . The fruit fly Drosophila melanogaster has been a crucial genetic model system for understanding the molecular underpinnings of the clock . Here , using a forward-genetic screen for mutant flies that lack circadian patterns of locomotion , we identify a novel gene critical to circadian behavior , which we have termed dyschronic ( dysc ) . Interestingly , DYSC is not part of the molecular clock itself , but acts in an intermediate circuit between clock cells and motor neurons to regulate temporal alterations in locomotion . DYSC contains several protein-binding domains , suggesting a role as a scaffolding protein . Indeed , we show that DYSC forms a mutually dependent complex with the SLOWPOKE Ca2+–activated potassium channel , an ion channel required for circadian output . DYSC regulates SLOWPOKE expression and SLOWPOKE-dependent currents in the fly brain . Furthermore , dysc is the closest Drosophila homolog of whirlin , a locus mutated in the human deaf-blindness disease Type II Usher syndrome . Our results identify a novel ion channel regulator that impacts neuronal physiology and complex behavior , and suggest new roles for Whirlin in the human nervous system . | [
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| 2012 | dyschronic, a Drosophila Homolog of a Deaf-Blindness Gene, Regulates Circadian Output and Slowpoke Channels |
Stable low pre-control prevalences of helminth infection are not uncommon in field settings , yet it is poorly understood how such low levels can be sustained , thereby challenging efforts to model them . Disentangling possible facilitating mechanisms is important , since these may differently affect intervention impact . Here we explore the role of assortative ( i . e . non-homogenous ) mixing and exposure heterogeneity in helminth transmission , using onchocerciasis as an example . We extended the established individual-based model ONCHOSIM to allow for assortative mixing , assuming that individuals who are relatively more exposed to fly bites are more connected to each other than other individuals in the population as a result of differential exposure to a sub-population of blackflies . We used the model to investigate how transmission stability , equilibrium microfilarial ( mf ) prevalence and intensity , and impact of mass drug administration depend on the assumed degree of assortative mixing and exposure heterogeneity , for a typical rural population of about 400 individuals . The model clearly demonstrated that with homogeneous mixing and moderate levels of exposure heterogeneity , onchocerciasis could not be sustained below 35% mf prevalence . In contrast , assortative mixing stabilised onchocerciasis prevalence at levels as low as 8% mf prevalence . Increasing levels of assortative mixing significantly reduced the probability of interrupting transmission , given the same duration and coverage of mass drug administration . Assortative mixing patterns are an important factor to explain stable low prevalence situations and are highly relevant for prospects of elimination . Their effect on the pre-control distribution of mf intensities in human populations is only detectable in settings with mf prevalences <30% , where high skin mf density in mf-positive people may be an indication of assortative mixing . Local spatial variation in larval infection intensity in the blackfly intermediate host may also be an indicator of assortative mixing .
Onchocerciasis prevalence varies widely between geographical locations , with nodule and microfiladermia ( mf ) prevalence levels in adults ranging from just above 0% to over 80% [1 , 2] . Onchocerciasis control programmes historically aimed for morbidity control and focussed interventions on so-called meso and hyperendemic areas , i . e . areas with mf prevalence levels above 40% . Many hypoendemic areas ( mf prevalence <40% ) were left untreated [3] . Now the target has shifted to elimination the question has arisen whether such hypoendemic areas can maintain themselves and may act as a source of infection for areas that have achieved elimination . If so , hypoendemic areas should be covered by elimination campaigns . Answering these questions is not straightforward , as the transmission dynamics in hypoendemic settings are not fully understood . This also applies to other helminthic diseases that are currently the subject of large-scale control and elimination programmes , such as lymphatic filariasis ( LF ) , schistosomiasis and soil-transmitted helminthiasis . Mathematical models can be useful tools to understand how various processes can help to stabilize helminth transmission in low endemic areas . Population dynamics of helminth infections are unique given the need for male and female worms to be present in the same host for reproduction , leading to a so-called breakpoint prevalence below which transmission cannot maintain itself [4 , 5] . Most models for helminth transmission explain sustained low pre-control prevalences by assuming high degrees of exposure heterogeneity among human hosts [6–10] , meaning that some people are heavily exposed while the majority experience much lower exposure levels . The resulting concentration of worms in few heavily exposed individuals allows female and male worms to mate , even if overall worm numbers in the host population are low . In addition , existing models for helminth transmission typically assume homogeneous mixing . This assumption implies that every person can infect any other person in the community with probability directly proportional to the product of one person’s contribution and another person’s exposure to transmission , as if all transmission takes place in a singular point in space . However , in reality mixing patterns in helminth transmission are assortative ( i . e . non-homogeneous ) as sub-groups of human hosts mix preferentially and transmit infection amongst themselves because they spend different amounts of time in different shared locations such as e . g . schools , water collection sites , and/or household locations . In summary , assortative mixing in helminth transmission implies the existence of multiple vector or environmental reservoirs and differential exposure of individuals to such reservoirs with a sub-group of high-risk individuals concentrating around at least one of those reservoirs , which is very well conceivable . Here , we consider for the first time to which extent assortative mixing may play a role in sustaining low levels of helminth transmission . Assortative mixing has been shown to play an important role in the transmission of many infections [11–15] . Especially for sexually transmitted or drug-use related infections , individuals often infect those of similar risk level to their own , as they meet at specific venues or parties [13 , 14] . In onchocerciasis transmission , which we consider here , there may be specific sub-groups of humans spending relatively much time where fly densities are highest; for example , fisherman will be often near the water where fly breeding sites are found [1] . It is very well conceivable that these high-risk individuals would not only be bitten more often ( as assumed by current models ) , but also more often by flies that previously bit another ( or the same ) high-risk individual . Under this assumption , the probability of infections spilling over from the highly exposed fishermen to the rest of the community is relatively lower , which means that in very low endemic situations transmission events are not “wasted” on transmission from fishermen to the rest to the population , but more efficiently used to sustain a high concentration of worms in the fishermen , sustaining transmission at relatively low prevalence . In this paper , we explore how adding assortative mixing to the individual-based model ONCHOSIM impacts onchocerciasis equilibrium prevalence levels and can explain stable low prevalence levels . Furthermore , we show how the ( combination of ) mechanisms for sustaining low prevalence will be relevant for the impact of control measures , especially when pushing for elimination . Having shown its potential importance , we consider what field data might enable us to identify and quantify assortative mixing in field situations . The findings of our study are also of relevance for other helminth infections that require mating of male and female worms .
We use the model ONCHOSIM , an established individual-based model for transmission and control of onchocerciasis [16–21] . ONCHOSIM simulates the individual life histories of humans and the male and female worms living within them . Patent female worms produce microfilariae ( mf ) as long as there is at least one patent male worm present in the same host . Flies biting on hosts take up mf , but their uptake capacity is limited resulting in diminishing returns with increasing mf levels in hosts ( i . e . negative density dependence ) . Individual human exposure to fly bites is assumed to vary with age and sex , and to vary randomly between individuals as a consequence of other factors ( e . g . attractiveness , occupation ) , leading to a highly overdispersed worm population within the human population . The model further simulates the impact of treatment with ivermectin in context of a mass drug administration , accounting for variation in participation by age and sex and presence of potential systematic non-participation by a subset of individuals . Ivermectin is assumed to kill all microfilariae in treated individuals and to permanently reduce the reproductive capacity of adult female worms by 35% , allowing for cumulative effects of repeated treatments . In addition , after treatment female worms temporarily stop producing mf but gradually recover to their new maximum reproductive capacity in a period of 11 months on average . The model provides output in terms of simulated skin snip surveys ( two snips per person ) , assuming that all individuals in the population are sampled . More technical details and quantification of the “default”model ( i . e . with homogeneous mixing ) can be found elsewhere [20] . To investigate the effect of assortative mixing on pre-control equilibrium prevalence and intervention impact , the default model was reprogrammed in R and extended as follows . In the default model , the fly vector population is represented as a single fly population that transmits infectious material ( larvae ) from human to human . To simulate assortative mixing we have divided this fly population into two sub-populations , which we name fly population L and H that are relatively more connected with low and high risk groups of the human population , respectively . As in the default model , an individual’s exposure to fly bites is determined by his or her age , gender , and a lifelong relative exposure factor γi that represents variation due to random factors such as occupation and attractiveness for flies; γi is drawn from a gamma distribution with shape and rate equal to k ( i . e . mean = 1 . 0 ) . S1 Fig illustrates the assumed distribution of individual relative exposure under the default assumption of k = 3 . 5 ( used in previous ONCHOSIM modelling studies ) and an alternative scenario with a higher level of exposure heterogeneity of k = 1 . 0 , which we consider to be still realistic and relevant for low endemic situations [19] . For each human i we define that his or her vector contacts are divided between the two fly sub-populations as a function of γi such that those who are bitten less often are bitten mostly by flies from population L , and vice versa those with high exposure to fly bites are bitten most often by flies from population H . This leads to assortative mixing , i . e . greater connectedness of individuals with similar risk levels . We define the fraction of an individual’s total fly contacts that are with fly population H ( rather than with fly population L ) as a function of an individual’s relative exposure in terms of his or her percentile r ( γi ) relative to the rest of the population: Β-iCDF ( x = r ( γi ) |α , β ) . Here B-iCDF is the inverse-cumulative beta distribution function ( naturally bounded between 0 and 1 ) with shape parameters α and β and r ( . ) is the cumulative gamma distribution function with shape and rate equal to k , the model parameter for exposure heterogeneity . We further set α = ( 1 − s ) / s and β = ( ( 1 − s ) /s ) ∙S , where s ( range 0–1 ) scales the strength of segregation between the two groups ( steepness of the population connection distributional curve in S2 Fig ) and S is solved numerically such that Β-iCDF ( x = fH │α , β ) = 0 . 5 , where fH is the parameter for the proportion of the population that is relatively more exposed to fly population H ( i . e . more than 50% of these individuals’ contacts with flies are with flies from fly population H ) . S2 Fig illustrates the association between individual relative exposure and different fractions of fly contacts with fly population H considered in this paper ( fH = 0 . 5 , 0 . 25 and 0 . 1 ) . When s = 1 we have two fully separate pairs of human and fly populations . When s <1 , the association between individual relative exposure and fraction of bites received from fly population H follows an s-curve ( S2 Fig ) , with higher steepness in the middle for higher values of s . When s = 0 , the fraction of fly contacts that an individual has with flies from fly population H is the same ( i . e . fH ) for all individuals , resulting in homogenous mixing . For illustrative purposes , we only consider relatively strong assortative mixing ( s = 0 . 8 ) . For the homogenous mixing scenario , we compare medium ( k = 3 . 5 ) with high ( k = 1 ) heterogeneity in individual exposure to fly bites . Note that the fraction of all fly bites that are from fly population H will be substantially larger than the fraction of humans fH connected mostly to fly population H: when k = 3 . 5 , s = 0 . 8 , and fH respectively 0 . 5 , 0 . 25 and 0 . 1 , the fraction of all bites by flies from population H is 69% , 44% and 26% ( see also S3 Fig ) . The model concepts for assortative mixing described above were implemented in a new version of the original model [20] which we programmed in R ( S1 File ) . We simplified the R version of the model for a limited number of factors that we consider to be of minor relevance to the research question investigated here . First , the model does not distinguish between male and female humans and therefore assumes no difference in exposure to fly bites between the sexes . Second , survival of microfilariae is assumed to be exponential instead of having a fixed duration , which is of limited importance when comparing the impact of MDA ( which kills microfilariae ) under different assumptions about mixing patterns . Third , we do not consider a fraction of individuals that are permanently excluded from MDA due to pre-existing conditions , nor do we consider non-participation due to e . g . pregnancy ( i . e . everybody is eligible for treatment ) . We do however only allow individuals of age five and above to be treated in MDA , as before . Fourth , all worms and humans are always born at the start of each monthly time step in the model , instead of spread out over the month . Finally , to explore the potential impact of random vs . systematic MDA participation , we included the model concept recently developed by Irvine et al . [9] , which is more parsimonious compared to that in ONCHOSIM . With these simplifications , the R version of the ONCHOSIM could very closely reproduce predictions in terms of prevalence and intensity of infection by the original model .
Fig 1 shows how the mean annual fly biting rate ( ABR ) determines the dynamic equilibrium mf prevalence level at which onchocerciasis transmission is sustained in the absence of interventions . At a moderate level of heterogeneity in individual exposure to fly bites ( scenario “k = 3 . 5 ( one fly population ) ” , i . e . the default assumption in previous ONCHOSIM modelling studies ) , we see a very steep decline in equilibrium skin microfilarial ( mf ) prevalence with decreased ABR , especially at ABR below 12 , 000 . At around ABR = 10 , 000 we find a boundary in transmission stability ( defined as <50% probability of extinction during 200 years of simulation time ) , which is due to a relative low worm mating probability at lower prevalence combined with the assumed transmission conditions . With greater heterogeneity in individual exposure to fly bites ( scenario “k = 1 . 0 ( one fly population ) ” ) , at a high ABR of 20 , 000 the achieved mf prevalence decreases from about 88% to 79% ( compared to “k = 3 . 5 ( one fly population ) ” ) . Stronger heterogeneity implies that there is more variation in biting rates experienced by people , resulting in a larger proportion of people with very high number of bites , but also a larger proportion of people experiencing very low number of bites . The latter group has a relatively low risk of infection , which limits the maximum achievable prevalence in the simulation . However , in this more heterogeneous setting the prevalence declines far less steeply with decreasing ABR; that is , transmission remains efficient since those bitten often both carry high worm burdens and they transmit to more flies . As this concentration of worms within fewer individuals allows for continued mating , transmission is now sustained ( i . e . probability of extinction <50% ) down to mf prevalence of 30% , at an ABR as low as about 7000 . Assortative mixing has less of a dampening impact on prevalence at high biting rates , compared to increasing heterogeneity ( i . e . lower values of k ) . Further , it somewhat lowers the threshold ABR below which extinction occurs , but not as much as lower values of k . However , it does allow for sustained transmission at much lower biting rates , especially if there is a relatively small higher risk sub-group , whose members are connected through a shared population of vectors . When the high-risk group constitutes 50% , 25% or 10% of the general human population , the model can maintain stable mf prevalences as low as 28% , 16% or even 8% , respectively . The predicted effect of mass drug administration ( MDA ) strongly depends on the assumed exposure heterogeneity as well as the mixing pattern within a population ( Fig 2 ) . The probability of elimination decreases with higher levels of exposure heterogeneity ( purple vs . red lines ) and when transmission is concentrated in a smaller part of the population ( blue vs . red lines ) . In case of recrudescence of infection after stopping MDA , the slope of the rebound over time varies highly between simulations in the scenario with homogeneous mixing and high exposure heterogeneity ( purple lines ) , while this variation is much smaller in case of assortative mixing driven by a small fraction of the human population ( blue ) . Also , the speed of bounce-back is slower in the scenario where transmission is concentrated in a smaller subgroup of the general population ( blue ) . These patterns are also seen for other endemicity levels and patterns in MDA participation ( S4 Fig ) . Table 1 summarises the outcome of simulated scenarios in terms of the probability of elimination ( defined as the proportion of repeated simulations with zero worm prevalence 50 years after stopping MDA ) , confirming the patterns in Fig 2 . Finally we consider what real-world data might help us identify whether low pre-control prevalences are the result of stable low transmission facilitated by either assortative mixing or high exposure heterogeneity , or are the result of a transient decline due to stochastic fade-out . Hypothesising that assortative mixing and high exposure heterogeneity impact the distribution of intensity of infection in different ways , we explore the association between prevalence of skin mf and the arithmetic mean skin mf density in mf positives ( Fig 3 ) . At low mf prevalences ( <30% ) the arithmic mean density of mf in mf-positive individuals is considerably higher in settings with strong assortative mixing ( fH = 0 . 25 and 0 . 1 ) compared to in settings with homogeneous mixing with moderate ( k = 3 . 5 ) to high exposure heterogeneity ( k = 1 . 0 , which we consider a plausible extreme value ) . As such , relatively high arithmic mean skin mf loads in mf positive persons in settings with mf prevalence <30% may be an indication of stable transmission facilitated by assortative mixing . For settings with pre-control mf prevalences of 40% to 60% , different mixing conditions and levels of exposure heterogeneity result in very similar associations between arithmic mean skin mf density in mf-positives and the mf prevalence ( Fig 3 ) as well as very similar mf intensity distributions ( Fig 4 ) . For settings with mf prevalence >60% , arithmic mean skin mf densities are almost identical for different mixing conditions , but are relatively higher in settings with higher exposure heterogeneity ( purple line ) . Another indication for assortative mixing may be found by considering local level fly data , as assortative mixing can only play a role if the mean larval intensity is not equally distributed across fly sub-populations that humans are exposed to . Fig 5 illustrates how the ratio of intensity of infection in the high and low risk fly populations might change with pre-control mf prevalence in humans , assuming perfect measurements from locations with minimal overlap of the two fly populations . A ratio of 1 . 0 ( dashed horizontal black line ) represents settings where infection intensity is uniformly distributed across the fly sub-populations ( i . e . homogeneous mixing ) . This ratio increases strongly with lower mf prevalence in humans , with a difference of factor 10 to 50 for settings with mf prevalences under 20% . However , the ratio provides little information about the extent to which transmission is concentrated in a human sub-population ( similar curves for different values of fH ) .
Our study shows that stable low prevalences of onchocerciasis can be explained by both high exposure heterogeneity and assortative mixing . In contrast , if assortative mixing is the main driver of sustained low prevalences , the probability of elimination declines when transmission is sustained by a smaller human sub-population . Also , recrudescence of infection after stopping MDA is slower and less variable in terms of speed when assortative mixing is driven by a smaller human sub-population . Pre-control skin mf density distributions provide little information to distinguish exposure heterogeneity and assortative mixing , or to quantify the degree of assortative mixing . Only in situations with mf prevalence <30% , high arithmic mean skin mf densities ( >20 mf/ss ) in mf positives may be an indication of assortative mixing . Entomological data may also provide evidence for presence of assortative mixing , but unfortunately not the size of the human sub-population by which it is driven . Our findings about the role of assortative mixing also apply to the transmission of other human helminth infections . Especially for LF , which is transmitted by mosquitoes and also targeted for elimination , the relatively low mobility of mosquitoes ( compared to blackflies ) means that people in the same household are likely to be bitten by the same mosquito sub-population near their household [15 , 22] . In this context , differences between LF vector species mobility and biting behaviour will also be relevant for degree of and patterns in assortative mixing . Similarly , transmission of soil-transmitted helminths and schistosomiasis most likely takes place through multiple reservoirs that are situated near households and/or schools , instead of one central reservoir [23] . Although schistosomiasis and soil-transmitted helminth are not ( yet ) officially targeted for elimination , there has been increasing interest in the potential of interrupting transmission [10 , 24–26] , which means that also here assortative mixing will become an important factor to consider . Our study clearly demonstrates that low prevalence of onchocerciasis could be sustained by assortative mixing . Another suggested mechanism to explain low prevalences is that infection spills over from nearby higher endemic areas through movement of infected humans and/or flies [27] . This is undoubtedly true for many of such settings , and can in fact be considered a form of assortative mixing at a wider geographical scale , as it simply constitutes flow of infections between two or more populations with each their own local transmission conditions . As such , we expect that the impact of migration is qualitatively similar to the impact of assortative mixing that we predict here . Another logical alternative explanation of ( seemingly stable ) low endemic levels is that these are the result of high transmission in the past that has stopped due to changes in human behaviour , demography , the environment , and/or the impact of ( undocumented ) interventions . However , such situations are obviously not stable in the long run . Our study also shows that assortative mixing substantially influences the impact of interventions . Its importance may be even greater if mixing is correlated with MDA uptake , especially if high-risk groups are less likely to participate in MDA . If missed , such high-risk groups may reintroduce infection into the general population . As such , if assortative mixing occurs at a very local scale , e . g . at household level , high coverage of treatment within households may be even more important than overall population treatment coverage . Further , bounce-back of infection levels is relatively slower under assortative mixing than with homogeneous mixing and may therefore occur later than expected , a pattern similar to relatively slower outbreaks of malaria in populations where mixing is more assortative [15] . Therefore , identifying , treating , and monitoring of high-risk groups is highly important . Similarly , if vector control is considered , locating and targeting those breeding sites that are most important for transmission is pivotal . The same applies if low prevalences are sustained by movement of infected humans and/or flies over larger distances; uniform intervention coverage and in particular coverage of high risk groups/areas is pivotal to minimise the risk of recrudescence of infection after stopping interventions . Unfortunately , proving existence and quantifying the degree of assortative mixing with data may not be easy . If assortative mixing plays a relevant role in helminth transmission , it is most likely related to patchy distribution of vectors or environmental reservoirs of infection . For example , onchocerciasis transmission in forest areas is sometimes driven by multiple smaller fly breeding sites . Because in savanna areas the number of fly breeding sites that a village is exposed to is typically limited , assortative mixing ( if any ) may be more likely to be driven by a sub-group of individuals ( e . g . fishermen ) that frequent a breeding site further away from the community . In both cases , local fly data from such areas may be informative . More specifically , locally high prevalence among flies and/or annual transmission potential ( i . e . the number of fly bites times the average number of L3 larvae per fly bite ) could perhaps be linked to a specific sub-group of humans that spend more time near certain fly breeding sites . In addition , data on the intensity distribution of infection in a community may provide some information in communities where prevalence of infection is under 30% , although subtle patterns may easily be masked by measurement and sampling error . Eventually , genetic studies may provide an answer to the question who infects whom . Although such studies have not yet been attempted , genome-wide analyses of Onchocerca volvulus populations have been performed in Cameroon and Ghana , demonstrating that this technique is able to genetically distinguish geographically separate worm populations ( i . e . populations that mix in a limited fashion ) [28] . To what extent such analyses can be used to quantify the degree of past and ongoing mixing remains to be investigated . For soil-transmitted helminths and schistosomiasis , quantitative studies of human open defaecation may help inform the degree and importance of assortative mixing for transmission and impact . Although challenging to reliably quantify , questionnaires about or direct observations of where uniquely identified people defaecate exactly ( preferably repeated over a period of time ) could help quantify the spatial patchiness of transmission sites and how often they are frequented by whom , allowing construction of more realistic transmission models that account for assortative mixing . We realise that our implementation of assortative mixing is a simplification of reality . In real-world situations more than two risk groups may well exist , and the degree of assortative mixing between such groups may differ from what we assume here . Still , a related modelling study on hepatitis C transmission in and between the general populations and high-risk groups demonstrated that simply adding the process of assortative mixing itself captures much of the qualitative behaviour of a system , and adding more risk groups to the system does not change its behaviour much [29] . In conclusion , assortative mixing could play an important role in helminth transmission dynamics , but is difficult to measure in real-world situations . The presence of assortative mixing will reduce the chance of achieving interruption of transmission . More detailed data on infection intensity distribution in human and vector populations ( or environmental reservoirs ) , and actual contact rates between humans and vectors or environmental reservoirs are needed to answer to which extent assortative mixing plays a role in reality . For modelling studies , introducing the phenomenon of assortative mixing will help to explain low stable endemic situations . | Most mathematical models for parasitic worm infections predict that at low prevalences transmission will fade out spontaneously because of the low mating probability of male and female worms . However , sustained low prevalence situations do exist in reality . Low prevalence areas have become of particular interest now that several worm infections are being targeted for elimination and the question arises whether transmission in such areas is driven locally and should be targeted with interventions . We hypothesise that an explanation for the existence of low prevalence areas is assortative mixing , which is the preferential mixing of high-risk groups among themselves and which has been shown to play an important role in transmission of other infectious diseases . For onchocerciasis , assortative mixing would mean that transmission is sustained by a sub-group of people and a connected sub-population of the blackfly intermediate host that mix preferentially with each other . Using a mathematical model , we study how assortative mixing allows for sustained low prevalences and show that it decreases the probability of interrupting transmission by means of mass drug administration . We further identify data sources that may be used to quantify the degree of assortative mixing in field settings . | [
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| 2018 | The effect of assortative mixing on stability of low helminth transmission levels and on the impact of mass drug administration: Model explorations for onchocerciasis |
Current treatments available for African sleeping sickness or human African trypanosomiasis ( HAT ) are limited , with poor efficacy and unacceptable safety profiles . Here , we report a new approach to address treatment of this disease based on the use of compounds that bind to parasite surface glycans leading to rapid killing of trypanosomes . Pradimicin and its derivatives are non-peptidic carbohydrate-binding agents that adhere to the carbohydrate moiety of the parasite surface glycoproteins inducing parasite lysis in vitro . Notably , pradimicin S has good pharmaceutical properties and enables cure of an acute form of the disease in mice . By inducing resistance in vitro we have established that the composition of the sugars attached to the variant surface glycoproteins are critical to the mode of action of pradimicins and play an important role in infectivity . The compounds identified represent a novel approach to develop drugs to treat HAT .
Human African trypanosomiasis or sleeping sickness is a neglected disease caused by the protozoan parasite Trypanosoma brucei . Treatments are largely insufficient and unsatisfactory and new approaches for drug design are highly necessary . T . brucei parasites living in the mammalian host rely on antigenic variation to evade the immune system of the host . They are mainly covered by only one kind of a variant surface glycoprotein ( VSG ) that constitutes an effective barrier that protects from effectors of the host immune system . In the formation of this protective barrier the N-glycosylation of VSGs is of major importance [1] . The VSGs are covered by mannose-rich and complex glycans [2–4] . During antigenic variation this shield is changed by expressing new VSGs in a stochastic process known as VSG switching . Recently , we have reported a series of carbohydrate-binding agents ( CBAs ) that bind to parasite surface glycoproteins and exhibit a strong trypanocidal activity against the clinically relevant bloodstream form , presenting activity in the nanomolar range [5] . Analysis of the mode of action showed a rapid internalization of glycoprotein-CBA complexes and accumulation in the lysosome leading to perturbation of endocytosis and progression of the cell cycle . Selection for CBA resistance resulted in modification of the N-glycan composition of VSGs by changes in the expression of oligosacharyltransferases . Thus , CBAs appear to exert their mode of action against Trypanosoma by specifically binding to surface glycans [5 , 6] . However , the previously reported antitrypanosomal CBAs were proteins with molecular masses ranging between 8 , 700 Da ( i . e . , UDA ) and 50 , 000 Da ( i . e . , HHA , GNA ) or even higher . Proteins present a series of disadvantages to become potential drugs , including efficient scale-up , poor , if any , oral bioavailability and/or potential generation of an immune response . Non-peptidic , low-molecular-weight antibiotics designated PRM-A and benanomycin A have been discovered in the culture fluid of Actinomadura hibisca [7] and Actinomadura sp [8] , respectively . PRM-A inhibits the growth of fungi ( such as Aspergillus ) [9] and viral infections [10] . It has been shown that PRM-A acts as a lectin in terms of glycan recognition , antiviral activity , and drug resistance patterns [11] . Members of the pradimicin family are unique among natural products in their ability to specifically bind sugars in a Ca2+-dependent manner [9] . PRM-S is a highly water-soluble , negatively charged derivative of the antibiotic PRM-A in which the terminal xylose moiety has been replaced by 3-sulfated glucose . The antibiotic is nontoxic against a variety of cell lines , is not mitogenic , and does not induce cytokines or chemokines in peripheral blood mononuclear cell cultures [12] . In addition , pradimicins can be modified chemically , such as BMY28864 which is a derivative of PRM-A . PRM-A and PRM-S exhibit activity against fungi , yeasts and several viruses including HIV and HCV [9 , 11–13] , while the derivatives PRM-FS [14] , PRM-FA-1 [15] , BMS181184 [16] and BMY28864 [17] were reported to exhibit efficient antifungal activity . Here we report that pradimicins inhibit the growth of T . brucei bloodstream forms at low micromolar concentrations by perturbing cytokinesis and endocytosis and consequently inducing parasite cell lysis . We provide information on their mode of action by generating mutant parasite cells resistant to the drug and examining binding efficiency of the pradimicins to parental and resistant parasitic VSGs and the glycan composition . Furthermore , we found that treatment at 50 mg/kg with PRM-S cures T . brucei brucei and T . brucei rhodesiense infection in mice . We propose that pradimicins and carbohydrate-binding agents in general may provide a unique and highly novel avenue for the development of an efficient treatment of parasitic diseases .
The in vitro trypanocidal activities of PRM-A , PRM-S and the derivatives BMY28864 , PRM-FS , PRM-FA-1 and BMS181184 were evaluated against the bloodstream forms of T . brucei . All of them exhibited 50% effective concentration ( EC50 ) values in the low micromolar range ( Figs 1 and S1 , Table 1 ) . PRM-A and PRM-S that preferentially exhibit binding specificity for α ( 1 , 2 ) mannose residues were the most active . A more detailed study of the effect of pradimicins on growth and morphology was accomplished . Thus , a time course of the consequences of exposure to 1- , 5- , 10- and 20-fold the EC50 was performed . Parasite viability was severely compromised at the different concentrations tested and total lysis was observed at 10- and 20-fold the EC50 after 4 to 8 h of treatment ( Fig 2A and 2B ) . To determine cytocidal activity , PRM-S was removed after 8 h of exposure at different concentrations and growth was monitored thereafter . PRM-S behaved as a trypanocidal agent at concentrations 10-fold the EC50 since complete abolishment of growth was achieved at this and higher concentrations ( Fig 2C ) . Furthermore , after 1 h of incubation with PRM-S at 53 . 0 μM , cells exhibited a rounded shape and detachment of the flagellum ( Fig 2D ) . In order to identify cell cycle alterations , the distribution of nuclei and kinetoplasts by DAPI staining was examined after PRM-S exposure at 5 . 3 μM ( EC50 ) for 48 h ( Fig 2E and 2F ) . The microscopic analysis revealed a slight increase of cells which have completed mitosis ( 2N2K ) ( 17% ) , as well as the emergence of a population with multiple nuclei and kinetoplasts ( XNXK ) ( 11 . 5% ) suggesting that PRM-S impairs cytokinesis by binding to the variant surface glycans . As a first approach for assessing effective pradimicin binding to glycans of the surface glycoproteins , competition assays were performed between PRM-S and HHA , a lectin that has been previously reported to bind VSGs [5] , and PRM-S and CV-N , a lectin with a similar α ( 1 , 2 ) Man specificity as PRM-S . Accordingly , different PRM-S concentrations were examined first in the presence of HHA-FITC conjugates and fluorescence was analysed at 0 min and 60 min of incubation by flow cytometry and microscopy . HHA binding to the surface coat ( determined at 0 min ) was significantly decreased by 1 . 7 and 3-fold after incubation with 25 μg/ml and 50 μg/ml PRM-S , respectively ( Fig 3A ) and uptake was drastically reduced at the highest concentration tested compared to the control without PRM-S ( Fig 3A and 3B ) . We also monitored fluid-phase endocytosis ( dextran uptake ) in the presence and absence of PRM-S and HHA in order to discard possible effects of HHA on endocytosis that could be interpreted as a decrease in binding/uptake of PRM-S . Fig 3C shows that while PRM-S produces a significant reduction in dextran internalization at 25 to 50 μg/ml in the absence of additional HHA , HHA at 1 μg/ml does not affect endocytosis at the PRM-S concentrations tested ( Fig 3D ) . In the case of competition assays with CV-N-FITC conjugates , fluorescence was analysed at 0 , 10 and 60 min of incubation by flow cytometry and microscopy . The presence of 25 μg/ml PRM-S reduced CV-N binding to the surface coat at 0 and 60 min of incubation to 59% and 64% of the control values respectively ( Fig 3E and 3F ) . Therefore , we conclude by these observations that PRM-S binds to glycans of the trypanosome surface glycoproteins , such as the VSGs , in a similar fashion to HHA and CV-N and subsequently , the PRM-S-VSGs complex triggers endocytosis defects and parasite death . PRM-A-resistant parasites were generated in order to provide an insight into the mode of action . Adaption of the parasites to this CBA was achieved by exposure to stepwise increasing concentrations of PRM-A and cell lines named PRM-A25 , PRM-A50 and PRM-A100 were generated that grew and were isolated at concentrations of 25 , 50 and 100 μg/ml PRM-A , respectively ( corresponding to 8- , 16- and 32-fold the EC50 value for the parental cell line ) . The resistance selectivity index ( ratio EC50 resistant parasite/EC50 parental parasite ) was determined as an estimate of the degree of drug resistance . A value of 25 was obtained for PRM-A25 and PRM-A50 , and of 40 for PRM-A100 ( Table 2 ) . The drug-resistance phenotype was retained after 3 months in the absence of PRM-A pressure suggesting that we are dealing with a stable and genetically-encoded phenotype . Cross-resistance was analysed for the PRM-A100 cell line using a variety of CBAs with predominant different specificities; HHA ( α ( 1 , 3 ) -α ( 1 , 6 ) Man ) , EHA ( Man ) , GNA ( α ( 1 , 3 ) Man ) , NPA ( α ( 1 , 6 ) Man ) , UDA ( GlcNAc oligomers ) and PRM-S ( α ( 1 , 2 ) Man ) were tested . Cross-resistance indices ( R-index ) were calculated as ratios of EC50 PRM-A100 /EC50 parental cell line ( BSF ) . PRM-A100 cells exhibited significant resistance to PRM-S , HHA and EHA and low resistance to UDA , NPA and GNA ( Table 3 ) . We have reported previously that prolonged exposure to peptidic CBAs lead to induction of CBA resistance caused by genotypic changes in the N-glycosylation profile [5 , 6] . In order to evaluate whether the non-peptidic CBAs would produce a similar phenotype , the expression of VSGs and their N-glycosylation nature were analysed in parasites resistant to PRM-A . First , indirect immunofluorescence using an anti-TbVSG221 polyclonal antibody [18] revealed that VSG221 expression was maintained in all PRM-A-resistant cells ( Fig 4A ) . Secondly , the soluble form of VSG ( sVSG ) was isolated and resolved using SDS/PAGE and Coomassie Blue staining . Unlike parental cells , sVSGs of all PRM-A-resistant cells appeared as doublets , which were positively identified as VSG221 by tryptic peptide mass fingerprinting using MALDI-TOF analysis ( Voyager DE PRO , AB Sciex ) . The existence of two forms of sVSG with different migration properties ( Fig 4B and 4C ) , in addition to differences in endoglycosidase digestion ( Fig 4D ) , clearly indicated the induction of modifications in the N-glycan nature of the resistant parasite strains . Endo H or PNGase F , which remove conventional triantennary oligomannose and hybrid N-glycans or all types of N-glycans respectively , were used to confirm this . Parental sVSG harbours an Endo H-resistant ( Asn263 ) and an Endo H-sensitive ( Asn428 ) N-glycosylation site , coming from the action of different oligosaccharyltransferase activities , such as TbSTT3A and TbSTT3B which transfer Man5GlcNAc2 or mainly Man9GlcNAc2 structures , respectively [19] . In the case of PRM-A-resistant strains , after Endo H treatment no molecular mass shift was observed , while PNGase F digestion converted both bands into a fully deglycosylated form ( Fig 4D ) . Therefore , two glycoforms of VSG221 seem to coexist in the resistant population . The VSG221 sequences of PRM-A50 and PRM-A100 strains were identical to that of the parental T . brucei BSF strain used in this study ruling out the possibility that changes in glycosylation are due to mutations in the N-glycosylation sites ( S2 Fig ) . Blotting with lectins of different specificities was performed to further characterize the nature of the N-glycans . We used the TL lectin that recognizes poly-N-acetyl lactosamine [21] or the Manβ1-4GlcNAcβ1-4GlcNAc trisaccharide core of paucimannose glycans [22] , ECL displaying preference for single LacNAc units [23] but also with any poly-N-acetyl lactosamine-containing glycoproteins , and ConA with oligomannose- and hybrid glycans-containing Manα1-3 ( Manα1–6 ) Manα1-specificity [24–26] . In addition , chitin hydrolysate , D-lactose , and α-methylmannose , which are inhibitors of TL , ECL and ConA , respectively , were included in the study as specificity controls . Fig 5A shows that the binding of TL or ECL to sVSGs was significantly increased in PRM-A100 , which further suggests changes in the glycosylation status from oligomannose to paucimannose N-glycans containing mainly one or several N-acetyl lactosamine structures . However , the additional presence of any abbreviated core structures derived from paucimannose N-glycans cannot be discarded . In contrast , the binding of ConA to sVSG was unaltered in the PRM-A-resistant cells . When whole cell extracts were probed with TL , ECL and ConA , moderate changes in the glycosylation patterns were observed . Changes in N-glycosylation were also assessed by labelling with lectin-FITC conjugates with different glycan binding specificities: CV-N-FITC ( α ( 1 , 2 ) Man ) , HHA-FITC ( α ( 1 , 3 ) - α ( 1 , 6 ) Man ) and UDA-FITC ( GlcNAc oligomers ) . Thus , resistant cells had a strongly reduced ability to bind to all of the lectins tested , CV-N ( Fig 5B and 5E ) , HHA ( Fig 5C and 5F ) and UDA ( Fig 5D and 5G ) , even after culture for 3 months in the absence of PRM-A . For a more detailed view of N-glycosylation of VSG221 from PRM-A100-resistant parasites , free and procainamide glycans after enzymatic cleavage with PNGase F were analysed by both MALDI-TOF MS and ( ultra ) -high performance liquid chromatography-fluorescence coupled to mass spectrometry ( UPLC-FLD/MS ) . Assignment of peaks was based on exact mass and previous biochemical knowledge of the trypanosome glycome or via diagnostic fragment ions [19] . The two major peaks of MALDI-TOF spectra obtained for Tb BSF free glycans at m/z 932 . 877 and 1905 . 687 , were assigned to paucimannose Man3GlcNAc2 ( H3N2 with m/z 932 . 877 ) and triantennary oligomannose Man9GlcNAc2 ( H9N2 , m/z 1905 . 687 ) , respectively . In line with previously published trypanosoma VSG glycosylation profiles , further peaks were tentatively assigned to the oligomannoses structures such as the biantennary H4N2 ( Man4GlcNAc2 , m/z 1095 . 025 ) or the triantennary H5N2 ( Man5GlcNAc2 , m/z 1257 . 171 ) , H6N2 ( Man6GlcNAc2 , m/z 1419 . 308 ) , H7N2 ( Man7GlcNAc2 , m/z 1581 . 433 ) and H8N2 ( Man8GlcNAc2 , m/z 1743 . 564 ) . In addition , we observed peaks corresponding to the biantennary hybrid N-glycans H3N3 ( Man3GlcNAc3 , m/z 1136 . 077 ) , H4N3 ( Man4GlcNAc3 , m/z 1298 . 221 ) , H5N3 ( Man5GlcNAc3 , m/z 1460 . 358 ) and H6N3 ( GlcMan5GlcNAc3 , m/z 1622 . 476 ) . Further peaks in the glycan profile were assigned to the poly-N-acetyl lactosamine complex N-glycans Gal2Man3GlcNAc4 ( H5N4 , m/z 1663 . 538 ) and Gal4Man3GlcNAc6 ( H7N6 , m/z 2394 . 116 ) ( S3 Fig ) . The analysis of glycan composition mostly agrees with the results previously obtained by Manthri et al for VSG221 [19] . In the PRM-A100 resistant cell line , the oligomannose structures H3N2 , H4N2 , H5N2 , H6N2 and H9N2 were found whereas H7N2 and H8N2 were absent . In addition , peaks corresponding to hybrid N-glycans ( H3N3 , H4N3 , H5N5 and H6N3 ) and complex N-glycans ( H5N4 and H7N6 ) were identified . The two new H4N4 ( m/z 1501 . 401 ) and H6N5 ( m/z 2028 . 827 ) species present in the profile were assigned to GalMan3GlcNAc4 and Gal3Man3GlcNAc5 structures , respectively ( S4 Fig ) . In-source fragmentation of ions at m/z 1663 . 538 , 2028 . 827 and 2394 . 116 produced diagnostic fragments indicating the presence of poly-N-acetyl lactosamine structures in the H6N5 and H7N6 species and terminal N-acetyl lactosamine in the H5N4 species ( S5–S7 Figs ) . UPLC-FLD/MS analysis provided a more quantitative view of the glycan distribution present on Tb BSF VSG221 highlighting H9N2 triantennary oligomannose and H4N2 and H3N2 paucimannose structures as major compounds together with minor amounts of other triantennary oligomannose , hybrid and complex N-glycans . In contrast , UPLC-FLD/MS analysis of procainamide labelled glycans from sVSG221 of PRM-A100-resistant parasites presented a ~8-fold decrease in oligomannose structures ( H9N2 and H8N2 ) and a significant increase in the hybrid ( H4N3 , H5N3 and H6N3 ) and complex N-glycans with poly-N-acetyl lactosamines ( H5N4 , H6N5 and H7N6 ) , whereas paucimannoside levels ( H3N2 , H4N2 and H3N3 ) remained unaltered ( Fig 6 and Table 4 ) . Therefore , these results firmly demonstrate that alterations in N-glycosylation occur in response to PRM-A pressure , leading to an N-glycan profile with a lower content of oligomannose structures containing α ( 1 , 2 ) or α ( 1 , 3 ) -α ( 1 , 6 ) bonds and the emergence of complex glycans with terminal poly-N-acetyl lactosamine motifs , which would be responsible for the decreased binding affinity of pradimicins . To confirm that indeed pradimicin interacts with parasite-encoded VSGs , surface plasmon resonance studies ( SPR ) were performed using VSG221 derived from parental ( BSF ) and drug ( PRM-A100 ) -resistant T . brucei cell lines . Both VSGs were immobilized on a sensorchip . Parental Tb BSF VSG221 was bound at low and high density ( 826 and 6 , 500 RU , respectively ) and PRM-A100 VSG221 derived from the drug-resistant parasites at high density ( 6 , 070 RU ) ( Fig 7 ) . PRM-A binding to the low-density wild-type Tb BSF VSG221-based sensorchip was hardly visible in the sensorgrams . Only 50 μM PRM-A provided a poor binding amplitude ( Fig 7A ) . Instead , using the high-density parental Tb BSF VSG221 sensorchip , concentration-dependent binding of PRM-A could be observed ( Fig 7B ) . Interestingly , binding of PRM-A to the high-density PRM-A100-resistant VSG221-bound sensorchip caused also a concentration-dependent binding amplitude , but at a ~ 10-fold lower efficiency than to the parental Tb BSF VSG221 that was immobilised at comparable densities ( 6 , 070 and 6 , 500 RU , respectively ) ( Fig 7C ) . A similar phenomenon was observed for the more-soluble PRM-S derivative ( Fig 7D–7F ) . A significant concentration-dependent binding to the parental Tb BSF VSG221-bound sensorchip was observed and this binding was much more pronounced for the high-density compared to the low-density Tb BSF VSG221 sensorchip . As demonstrated for PRM-A , binding of PRM-S to PRM-A-resistant VSG221 was 6- to 7-fold less pronounced than to the parental VSG221 . Attempts to calculate the binding affinities ( KD ) of the pradimicins , as previously reported for HIV gp120 [12] , to parental and PRM-A-resistant VSG221 , including determination of the kon and koff rates failed , mainly due to the lack of 1:1 stoichiometric kinetics of the binding . Indeed , given the small size of the pradimicins , it might be assumed that several pradimicin molecules can bind on one single VSG molecule given the high amount of glycans present on VSG . In addition , the pradimicin antibiotics are known to internally staple ( associate ) at higher ( micromolar ) concentrations , further compromising relevant calculations of the KD values for the parental and resistant VSGs . Nevertheless , the SPR-based binding study of the pradimicins to parental and PRM-A-resistant VSG convincingly revealed that PRM-A and PRM-S concentration-dependently bind to VSG and that both PRM-A and PRM-S showed a compromised binding efficiency for the PRM-A100-resistant versus the parental Tb BSF VSG . These findings confirm the specific binding of the pradimicins to parasitic VSGs , and the poorer binding of PRM-A and PRM-S to PRM-A-resistant VSG than parental VSG . In addition , they support our view and provide further evidence that resistance against pradimicins is due to the glycan changes in the parasitic VSG . Obvious candidates potentially involved in the changes in carbohydrate composition are the oligosaccharyltransferase ( OST ) activities coded by three genes: STT3A , STT3B and STT3C [5 , 6] . OSTs mediate N-glycosylation of VSGs in a site specific manner [19 , 27 , 28] . Accordingly , TbSTT3A , TbSTT3B and TbSTT3C mRNA levels were examined by RT-qPCR in the PRM-A100 cell line . Specific primers designed against the variable region of each gene were used in the analysis [5] . The occurrence of recombination events between these genes was also examined using a combination of primers . S7 Fig shows that mutant parasites harbour only canonical genes . However a reduction in the expression levels of TbSTT3A ( 2 . 0-fold ) and TbSTT3B ( 2 . 9-fold ) in PRM-A-resistant cells compared to the parental line was observed , even when PRM-A pressure was removed for up to 3 months ( Fig 8 ) . In order to identify modifications in the nucleotide sequence that could be responsible of the changes in mRNA levels , the sequences of the TbSTT3 gene open reading frames as well as their corresponding 5’UTRs and 3’UTRs were determined as described in supporting information ( S8 Fig and S1 Text ) . Three nucleotide changes were found in the TbSTT3A coding sequence with regard to the database sequence resulting in the amino acid changes E510G , K513E and L705P ( S1 Table ) . In the case of TbSTT3B , a G248S replacement was identified while no differences were found in TbSTT3C . Further analysis is currently underway to ascertain the functional significance of the mutations affecting the TbSTT3 genes . No differences were observed in the sequences of the STT3 UTRs . Parasites from the parental , PRM-A25 , PRM-A50 and PRM-A100-resistant cell lines were used to infect mice and survival was monitored . We observed a strong parasite fitness cost for the three resistant cell lines that resulted in reduced infectivity . Whereas the parental line exhibited a median survival days ( MSD ) of 6 . 0 ± 0 . 0 days , the four mice infected with PRM-A25-resistant parasites and two mice out of the seven infected with the PRM-A100-resistant parasites died showing an MSD of 38 ± 7 days and 34 . 5 ± 0 . 7 days , respectively . All the mice infected with PRM-A50 were alive after 50 days ( MSD >50 days ) ( Fig 9 ) . Thus , the PRM-A-resistant parasites invariably showed a pronounced compromised infectivity potential in mice . With the aim of establishing if defective expression of OSTs is the major factor involved in resistance to pradimicins , overexpression of TbSTT3A and TbSTT3B in the PRM-A100 cell line and conversely RNAi mediated depletion of TbSTT3A , TbSTT3B and TbSTT3C in the parental line were accomplished ( S1 Text ) . Firstly , PRM-A100 cells were transfected individually with constructs that allowed for the expression of TbSTT3A or TbSTT3B , yielding the PRM-A100 STT3A-OE and PRM-A100 STT3B-OE cell lines , respectively ( Fig 10A ) . The mRNA levels of the STT3 genes were evaluated by RT-qPCR . TbSTT3B mRNA increased significantly after induction in the PRM-A100 STT3B-OE cell line , while TbSTT3A mRNA levels were maximally 1 . 3-fold enhanced upon induction of PRM-A100 STT3A-OE ( Fig 10B ) . The determination of EC50 values established that sensitivity to PRM-S in the PRM-A100 STT3B-OE parasites increased upon induction 12 . 1-fold with regard to the parent PRM-A100 strain ( EC50 77 . 7 ± 0 . 8 μM ) thus pointing towards a major role for this OST in the resistance mechanism . Moderate overexpression of TbSTT3A did not result in sensitization to PRM-S , thus curtailing its role in the resistance phenotype ( Table 5 ) . On the other hand , RNAi-mediated depletion of TbSTT3 genes was evaluated individually or simultaneously in the wild-type strain . Thus , the cell lines Tb BSF STT3A-RNAi , Tb BSF STT3B-RNAi , Tb BSF STT3A/B-RNAi and Tb BSF STT3A/B/C-RNAi were generated . While the knockdown of TbSTT3A or TbSTT3B [5] had no effect on growth , simultaneous knockdown of TbSTT3A and TbSTT3B or TbSTT3A , TbSTT3B and TbSTT3C resulted in severe growth defects ( Fig 10C and 10E ) , in agreement with previous studies showing that N-glycosylation is essential [28] ( Fig 10D and 10F ) . Depletion of TbSTT3B gave rise to high resistance to PRM-S ( 14 . 3-fold ) whereas RNAi-mediated reduction of TbSTT3A had no notable consequences and even slightly sensitizes parasites to the drug ( 0 . 6-fold ) compared to the parental line ( EC50 5 . 3 ± 0 . 2 μM ) ( Table 5 ) . These results confirm the central role of OSTs in defining the VSG glycosylation profile and PRM-S binding capacity . Specifically TbSTT3B , which transfers Man9GlcNAc2 rendering oligomannose N-glycans , appears as the main player in the molecular mechanism responsible for resistance to pradimicins . To evaluate whether PRM-A resistance involved modifications in endocytosis , PRM-A100-resistant and parental parasite strains were probed with ConA as a marker for membrane-bound endocytic activity [29] , transferrin as a receptor-mediated endocytosis marker , and dextran as a fluid-phase endocytosis marker . PRM-A-resistant parasites exhibit a slightly reduced capacity to internalize both ConA and transferrin , although internalization was restored when trypanosomes were cultured in the absence of PRM-A and remain resistant ( Fig 11A and 11B ) . On the other hand no differences were found in dextran uptake between resistant and parental parasites ( Fig 11C ) . Whereas ConA interiorization is dependent on the interaction with surface glycans , transferrin and dextran uptake ( fluid phase endocytosis ) are mostly independent of protein glycosylation . Indeed mutant non-glycosylated ESAG6 and ESAG7 ( the two subunits forming the transferrin receptor ) are capable of forming a heterodimer and of binding transferrin [30] . Nonetheless , changes in the glycan nature of the transferrin receptor could interfere with transferring binding due to steric hindrance . We conclude that the minor reversible defects observed in ConA and transferrin uptake do not have a major role in the resistance phenotype . Given the limited solubility and availability of PRM-A , the trypanocidal effect of several PRM-A derivatives , including PRM-S , BMS181184 and BMY28864 , was examined in mice using an acute model of African trypanosomiasis . Mice were infected with the T . brucei rhodesiense EATRO3 ETat1 . 2 TREU164 or T . brucei brucei single-marker 427 strains . PRM-S exhibited a dosage-related efficacy at intraperitoneal dosages of 25 mg/kg and 50 mg/kg per day administered on four consecutive days . At 25 mg/kg PRM-S , two of five mice infected with T . brucei rhodesiense were cured ( Fig 12A ) , and the survival markedly improved for the treated animals since the MSD was 14 . 0 ± 3 . 0 days and the mean relapse days ( MRD ) 10 . 7 ± 0 . 6 days , while controls treated only with the drug vehicle formulation exhibited a MSD of 7 . 5 ± 1 . 7 days ( Table 6 ) . In the case of T . brucei brucei-infected mice , at 25 mg/kg all the mice died although the MSD was extended to 18 . 0 ± 7 . 0 days with a MRD of 10 . 7 ± 0 . 6 days with regard to an MSD of 6 . 2 ± 0 . 5 days in the control ( non-treated ) group ( Fig 12B and Table 6 ) . Notably , at 50 mg/kg , PRM-S produced parasitological cure of all the mice infected with either T . brucei rhodesiense or T . brucei brucei ( Fig 12A and 12B ) . Parasites from T . brucei brucei-infected mice treated with 25 mg/kg PRM-S were isolated just before animal sacrifice , inoculated into HMI-9 medium and exposed to increasing concentration of PRM-S for determination of the EC50 value . The EC50 obtained ( 5 . 0 ± 0 . 2 μM ) was similar to the control , showing that short term PRM-S exposure in vivo did not generate resistance to PRM-S . The parasitaemia and morphology were determined in mice infected with T . brucei brucei at 30 min , 1 h and 2 h after drug treatment ( 50 mg/kg ) in order to provide an insight into the mechanism of action in vivo . PRM-S provokes a rapid parasite clearance ( Fig 12C ) and a pronounced increase in the population of cells with a rounded shape ( 90% ) ( Fig 12D and 12E ) suggesting that direct interaction with the parasite surface glycans is the mode of action in the mouse model . Other pradimicin derivatives somewhat less active in vitro than PRM-A/PRM-S were also investigated in vivo . Specifically , BMS181184 and BMY28864 at a single dosage of 50 mg/kg were used to treat mice infected with T . brucei rhodesiense . BMS181184 produced parasite clearance after the first dosage yet further relapsed ( MRD of 9 . 0 ± 2 . 2 days ) , while the MSD was extended to 12 . 8 ± 3 . 1 days , thereby doubling the survival of the control group ( MSD of 6 . 25 ± 0 . 5 days ) . BMY28864-treated animals died at the same time as the control group , and therefore , proved not to be active in vivo ( S9 Fig and Table 6 ) .
In this study we have explored the trypanocidal activity of pradimicins and the mode of action of these non-peptidic CBAs in order to provide an insight into the potential of these highly novel antiparasitics . Pradimicins are low-molecular-weight antibiotics ( ~ 900 Da ) that exhibit antiviral and antifungal properties mediated by lectin-mimic binding to surface glycans [11 , 12 , 14 , 17 , 31] . We show that these CBAs exhibit a remarkable trypanocidal activity in vitro in the low micromolar range , in particular PRM-A and its highly water-soluble derivative PRM-S proved most active . Extraordinarily , PRM-S also exhibits a potent trypanocidal effect in vivo , resulting in a parasitological cure in acute models of African trypanosomiasis using both the T . brucei rhodesiense and T . brucei brucei species . These findings are a continuation of previous work conducted in our laboratory where we identified a series of plant lectins such as HHA , UDA , GNA , NPA and EHA , that exhibit strong inhibitory activity against T . brucei [5] . Our observations were in contrast to the general belief that most lectins are not toxic for T . brucei bloodstream forms since rapid internalization and degradation of the surface glycoprotein-lectin complex would result in a lack of toxicity [32] . Although the dissociation constant of the PRM-A-VSG221 or PRM-S-VSG221 complexes could not be determined in detail due to the existence of multiple binding sites in the VSG molecule , we provide multiple evidence that the mode of action of pradimicins is indeed due to tight binding to surface VSGs and perturbation of the endocytic pathway resulting in a rapid parasite death . Defects in endocytosis of a similar fashion have been observed earlier upon formation of VSG-specific nanobody complexes ( Nsbs ) and have been reported to play an essential role in the nanobody’s cytotoxic action [33] . Studies on the molecular mechanisms of resistance to the pradimicins were designed in order to shed light on the mode of action of these compounds . Thus resistance was generated by a step-wise selection to PRM-A and a high resistance index was achieved for mutant cells that also exhibited cross-resistance to PRM-S and to other mannose-binding lectins . The resulting resistance phenotype was characterized by defects in the N-glycosylation pathway that resulted in an altered N-glycosylation of VSGs and other glycoproteins which presumably lead to a reduced binding of the CBA . Indeed in the resistant mutant parasites , lectin blotting analysis together with the observation that CV-N , HHA and UDA uptake and binding are impaired suggested profound modifications in surface glycans . Given that this phenotype remained after withdrawal of drug pressure , we concluded that the resistance phenotype was genetically encoded and stable . On the other hand , defects in endocytosis in the resistant mutants were minor and reversible upon drug withdrawal . Transferrin and ConA uptake reduction was reversed after culture in the absence of PRM-A while no defects were observed in fluid-phase endocytosis thereby establishing that this process is not relevant to the resistance phenotype . Pradimicins and benanomicins comprise a unique family of antibiotics with a lectin-like ability to bind D-mannose ( D-Man ) in the presence of Ca2+ [8 , 9 , 34] . Lately they have been attracting attention as the only class of non-peptidic small molecules that can capture D-Man under physiologically relevant conditions [35] . The evidence available suggests that PRM-A recognizes the 2- , 3- , and 4-hydroxyl groups of D-Man although binding to pyranosides of l-Fuc and l-Gal when the Ca2+ concentration is not excessive has also been reported [35] . Both PRM-A and PRM-S bind HIV-1 gp120 with a dissociation constant ( KD ) of ~ 0 . 4 μM and hence exhibit promising antiviral properties [12] . Here , we demonstrate that pradimicins bind primarily to N-glycans of the trypanosome surface glycoproteins . Moreover while PRM-A/S is able to bind VSGs , affinity is strongly dependent on N-glycan structures and was markedly reduced in PRM-A-resistant parasites . Moreover , binding competition experiments with HHA and CV-N indicate clearly that pradimicins compete with these lectins in the interaction with VSGs . On the other hand , direct evidence for efficient pradimicin binding was provided by SPR analysis . Indeed , VSGs from resistant parasites exhibit a lower capacity to bind pradimicin than parental VSGs further confirming changes in glycan composition in order to overcome the anti-parasitic CBA suppressive effects . Definitive evidence for changes in VSG glycosylation was obtained by analysis of free and procainamide labelled glycans by mass spectrometry and liquid chromatography . Mutant resistant cells exhibited a significant reduction in the proportion of oligomannose type glycans , namely H8N2 and H9N2 , while hybrid and complex species accounted for 47% of total glycans versus 23% in the parental cell line . In summary , studies on VSG endoglycosidase treatment , lectin binding , lectin blotting and glycan composition show that parasites overcome PRM-A pressure by an altered N-glycan processing leading to an enrichment in hybrid and complex N-glycan structures presenting lower numbers of α ( 1 , 2 ) -mannose residues prone to bind this CBA . We sought to establish how changes in glycan composition occur in pradimicin-resistant parasites . It is well-known that VSG glycosylation is accomplished in a site-specific manner by the action of two catalytic OSTs: STT3A activity transfers Man5GlcNAc2-PP-Dol to asparagines flanked by an acidic sequence yielding paucimannose structures , and STT3B relocates Man9GlcNAc2-PP-Dol to any remaining asparagine rendering oligomannose N-glycans , respectively [19 , 27] . These two enzymes were obvious candidates to be responsible for the resistance phenotype however there was a possibility that increased trimming of glycans by α ( 1 , 2 ) -mannosidases is involved . In resistant cells we identified a down-regulation of TbSTT3A and TbSTT3B mRNA levels that prompts hypoglycosylation and changes in the N-glycan nature directed towards a reduction of oligomannose and an increase in paucimannose structures . These modifications clearly minimize their accessibility and the ability for binding pradimicins , and consequently are responsible for the appearance of resistance . In this process TbSTT3B appears to be a major player since overexpression of the enzyme in resistant parasites reversed resistance to pradimicins while conversely RNAi mediated depletion in wild type parasites resulted in high levels of resistance . Thus down-regulation of TbSTTB appears to be the main mechanism involved in pradimicin resistance . A question that remains to be addressed is how long term down-regulation of TbSTT3A and TbSTT3B mRNA is achieved . It is well-established that trypanosomatids lack the ability to regulate RNA-polymerase II transcription initiation , and the control of mRNA abundance and protein profiles depend largely on RNA-binding proteins [36] . For example , depletion of DRBD3 , an RNA binding protein involved in mRNA stability , leads to destabilization of several transcripts and splicing defects , binding preferentially within the 3′-UTR of its target genes , although binding sites within the ORFs and the 5′-UTR are possible [37] . We have explored changes in the sequences of 3’ and 5’-UTRs as well as in the ORFs of STT3 genes as potentially responsible of STT3 mRNA down-regulation . No modifications were identified in the UTRs yet a series of mutations were found in the ORFs of STT3A and STT3B . The possibility that mutations within the coding region are related with the modification of mRNA levels has not been established . RNA-binding proteins that bind within the coding region of mRNAs have been described although their major role is modulation of translation [38] . In addition , the impact of these mutations on the catalytic properties of OSTs was not examined . Hence , how the expression and function of STT3 genes are regulated in mutant cells remains to be understood . The striking efficacy in vivo further demonstrates that we can obtain highly potent and efficient trypanocidal agents by designing compounds that interact with surface glycans . This is an entirely novel concept that warrants further investigation . Indeed , our data revealed that small-size non-peptidic CBA molecules are emerging as a promising strategy for parasite suppression although further studies will be required to improve the pharmacokinetic properties of this kind of compounds and to achieve sufficient central nervous system penetration and efficacy in the late stage of the disease . Pharmacokinetic data with pradimicin derivatives obtained in previous studies have shown that drug levels in brain tissue and cerebrospinal fluid were lower than those measured in other tissues but detectable at concentrations exceeding 1 μg/g after multiple dosing [39] . Here the trypanocidal activity in vivo appears to result from direct interaction of the CBA with bloodstream forms since after treatment the morphology of parasites isolated from the blood would suggest a similar mechanism of action to that observed in vitro . Interestingly , an important consequence of glycosylation changes was the strong fitness cost observed in mice models . Resistant parasites were either not infective or exhibited a highly attenuated virulence . These findings are in agreement with previous work that has shown that a correct glycosylation of VSG is critical for optimal and efficient host-parasite interaction [5 , 6 , 28] . In conclusion , pradimicins exhibit a highly cytotoxic activity against bloodstream forms of T . brucei and render parasitological cure in vivo using an acute model of sleeping sickness . By binding to surface glycans , pradimicins lead to defects in cytokinesis resulting in cell lysis . While specific binding to surface VSGs has been demonstrated , interaction with other glycoproteins cannot be ruled out . All this evidence allows us to propose the development of lectin-mimetic agents , such as the non-peptidic pradimicins , as a novel approach for the design of antitrypanosomal agents .
Trypanosoma brucei brucei single-marker bloodstream forms ( BSF ) ( antigenic type 1 . 2 , MITat 1 . 2 , clone 221a ) strain 427 , harbouring T7 RNA polymerase and the tetracycline repressor [40] and Trypanosoma brucei rhodesiense EATRO3 ETat1 . 2 TREU164 [41] were used in this study . The parasites were cultured at 37°C and 5% CO2 in HMI-9 with 10% ( v/v ) or 20% fetal bovine serum , respectively . The following non-peptidic mannose-specific CBAs of prokaryotic origin have been used: pradimicin A ( PRM-A , Actinomadura hibisca ) [7]; pradimicin S ( PRM-S , Actinomadura spinosa strain A A08 51 ) [42]; pradimicin Fs ( PRM-Fs , Actinomadura spinosa strain A A08 51 grown in presence of D-serine ) [14]; pradimicin FA-1 mono sugar ( PRM-FA-1 mono sugar , Actinomadura hibisca P157-2 grown in presence of D-serine ) [15]; BMS181184 ( synthesized by a semisynthetic process or by direct production from D-serine supplemented fermentation of Actinomadura sp ) [43]; and BMY28864 ( synthesized chemically from PRM-A ) [17] . The Amaryllis lectin Hippeastrum hybrid agglutinin ( HHA ) [44] , stinging nettle lectin ( UDA , Urtica dioica ) [45] , broad-leaved helleborine lectin ( EHA , Epipactis helleborine ) [46] , snowdrop lectin ( GNA , Galanthus nivalis ) [47] , daffodil lectin ( NPA , Narcissus pseudonarcissus ) [48] and cyanovirin-N ( CV-N , Nostoc ellisporum ) [49] . Tomato lectin ( TL ) and Erythrina cristagally lectin ( ECL ) were obtained from Vector Laboratories , Inc and ConA from Sigma . PRM-A , a non-peptidic CBA with α ( 1 , 2 ) mannose specificity , was used to generate resistant cell lines of T . brucei bloodstream forms by exposure to increasing concentrations of compound . The process started with a pradimicin concentration equal to the EC50 ( 3 . 20 ± 0 . 04 μg/ml ) and then stepwise selection was performed , obtaining several strains at escalating PRM-A concentrations of 3 . 2 , 3 . 6 , 10 , 25 , 50 and 100 μg/ml . Parasites were exposed to a higher drug concentration when the generation time ( 6–8 hours ) had equalled that of the parental line , a process which took around 15–25 days . Resistance stability was checked at 1 , 2 or 3 months on PRM-A50 and PRM-A100 strains after removal of the drug pressure . The coding sequences for PRM-A50 and PRM-A100 VSG221 were amplified by PCR using cDNA as template , cloned in the pGEM-T vector ( Promega ) and finally sequenced . VSG221 expression was evaluated by immunofluorescence using an anti-VSG221 polyclonal antibody on PRM-A-resistant cells as described [5] . Briefly , parasites were fixed for 20 min on poly-L-lysine-coated slides with 4% p-formaldehyde , washed twice ( PBS and 0 . 2% Tween 20 ) and blocked during 30 min with Blocking Reagent 1% ( Roche ) . Subsequently , samples were incubated with anti-VSG221 polyclonal antibody for 1 h , labelled with FITC-conjugated anti-rabbit secondary antibody for 1 h , washing before and after labelling . Slides were then dehydrated in methanol for 1 min and finally stained and mounted with Vectashield-DAPI ( Vector Laboratories , Inc . ) . The microscopy and digital image acquisition were performed using a Zeiss Axiophot microscope ( Carl Zeiss , Inc . ) Total RNA of parental and PRM-A-resistant cell lines was extracted using TRIzol reagent ( Invitrogen ) , and treated with DNase to avoid a genomic DNA contamination using the RNeasy Micro kit ( Qiagen ) . cDNA was obtained by reverse transcription using iScript cDNA synthesis kit ( Bio-Rad ) . Quantitative PCR assays were carried out in an iCycler IQ real-time PCR detection system ( Bio-Rad ) using SsoFast EvaGreen Supermix ( Bio-Rad ) . All procedures were performed according to the manufacturer’s instructions . Relative expression of the TbSTT3A , TbSTT3B and TbSTT3C genes was measured as described [5] using the myosin 1B gene ( Tb927 . 11 . 16310 ) as reference , which was kindly provided by Dr . Navarro [50] . Three independent experiments and sample triplicates were performed in all RT-qPCR assays . The sVSG isolation of PRM-A-resistant strains was performed following the protocol described by Cross et al [20 , 51] with slight modifications . Pellets from 2 x 108 cells were lysed in 300 μl of hypotonic lysis buffer ( 10 mM sodium phosphate buffer , pH 8 . 0 plus protease inhibitor cocktail ( Roche ) ) for 5 min at 37°C . The supernatant containing VSG was collected by centrifugation at 14 , 000 x g for 5 min , loaded onto 0 . 2 ml of a DE52 ( Whatman ) and eluated with 10 mM sodium phosphate buffer , pH 8 . 0 . Finally VSG was diluted in water after concentrating and diafiltering on a Nanosep 10K Omega ( Pall Corporation ) . sVSGs isolated of parental and PRM-A-resistant cell lines were subjected to endoglycosidase digestion . For each enzyme digestion , 1 μg of sVSG was denatured in 10 μl of 0 . 5% SDS and 0 . 1 M dithiothreitol for 10 min at 100°C , followed by overnight treatment at 37°C with 500 units of Endo H or PNGase F ( New England Biolabs ) in the corresponding buffer supplied by the manufacturer . To assess the VSG nature in PRM-A-resistant cells , an immunofluorescence analysis using an anti-TbVSG221 polyclonal antibody was carried out . Trypanosomes were fixed in 4% p-formaldehyde on poly-L-lysine-coated slides at RT for 20 min , washed ( PBS and 0 . 2% Tween 20 ) and blocked with Blocking Reagent 1% ( Roche ) for 30 min . Then , samples were incubated with anti-TbVSG221 for 1 h , washed , probed with FITC-conjugated anti-rabbit antibody for 1 h and washed again . Slides were finally stained and mounted with Vectashield-DAPI ( Vector Laboratories , Inc . ) after dehydrating in methanol . The microscopy and digital image acquisition were carried out with a Zeiss Axiophot microscope ( Carl Zeiss , Inc . ) Binding of PRM-A and PRM-S to VSG221 expressed in parental or PRM-A-resistant cell lines was evaluated using SPR on a Biacore T200 instrument ( GE Healthcare , Uppsala , Sweden ) . TbBSF VSG221 was covalently immobilized on a CM5 sensor chip in 10 mM sodium acetate , pH 5 , using standard amine coupling chemistry , resulting in chip densities of 826 ( low-density ) and 6 , 500 ( high-density ) RU . The same coupling chemistry was used to immobilize 6 , 070 RU of PRM-A100-resistant VSG221 to the sensorchip . Interaction studies with PRM-A were performed at 25°C in HBS-P ( 10 mM HEPES , 150 mM NaCl and 0 . 05% surfactant P20 , pH 7 . 4 ) containing 5% DMSO and 10 mM CaCl2 . Interaction studies with PRM-S were performed in the same buffer without DMSO ( due to a markedly higher solubility of PRM-S versus PRM-A ) . A reference flow cell was used as a control for non-specific binding and refractive index changes . Several buffer blanks were used for double referencing . A variety of PRM-A and PRM-S concentrations were injected for 2 min at a flow rate of 30 μl/min and followed by a dissociation phase of 5 min . The CM5 sensor chip surface was regenerated with a single injection of 10 mM NaOH . An HHA-FITC conjugate binds to VSG N-glycans containing α ( 1 , 3 ) and/or α ( 1 , 6 ) mannose forming a VSG-HHA complex which is rapidly endocytosed [5] . A CV-N-FITC conjugate would bind to glycans containing α ( 1 , 2 ) mannose . Competition experiments with HHA-FITC and CV-N-FITC were used to evaluate binding of PRM-S to VSGs . For this purpose , live parental T . brucei cells ( 1 . 5 x 106 parasites ) were washed once with Voorheis PBS ( PBS containing 10 mM glucose and 79 mM sucrose ) , resuspended in 1 ml of serum-free HMI-9 medium containing 1% BSA and preincubated for 20 min at 37°C . Samples were incubated with HHA-FITC ( 1 μg/ml ) or CV-N-FITC ( 0 . 6 μg/ml ) in the presence or absence of PRM-S , washed twice with cold PBS and resuspended finally in PBS . FACS analysis was carried out using a Becton Dickinson FACSCalibur and BD CellQuest Pro version 4 . 0 . 2 software . For microscopy analysis , cells were fixed after labelling with 2% p-formaldehyde for 1 h at 4°C , washed , adhered on poly-L-lysine coated slides , dehydrated in methanol and stained with Vectashield-DAPI ( Vector Laboratories , Inc . ) . Vertical stacks of 10–15 slices ( 0 . 2 μm steps ) were captured using an Olympus microscope and Cell R IX81 software . Deconvolution and pseudo-colouring of images was performed using Huygens Essential software ( version 3 . 3; Scientific Volume Imaging ) and Image J software ( version 1 . 37; National Institutes of Health ) , respectively . UDA-FITC conjugates that bind to N-acetylglucosamine residues of VSG N-glycans [6] , CV-N-FITC and HHA-FITC were used to establish changes in glycan nature . Samples of live parental and resistant T . brucei cells were prepared and labelled using CV-N-FITC ( 0 . 6 μg/ml ) , HHA-FITC ( 1 μg/ml ) and UDA-FITC ( 5 μg/ml ) . Endocytosis dynamics in the parental line upon PRM-S supplementation and in the presence or absence of HHA ( 1 μg/ml ) was determined by uptake of Alexa Fluor 488-dextran 10 , 000 conjugates ( 1 mg/ml ) in 50 μl of final sample volume . To analyse endocytosis in PRM-A-resistant cell lines , AlexaFluor 594-ConA ( 100 μg/ml ) , FITC-transferrin ( 50 μg/ml ) , and Alexa Fluor 594-dextran 10 , 000 ( 1 mg/ml ) conjugates were used as described [5] . All conjugates were purchased from Molecular Probes Inc ( Life technologies , Thermo Fisher Scientific Inc ) . Samples were prepared and analysed as described above for FACS analysis . In order to study the glycosylation profile of the PRM-A-resistant strains , lectin blotting using TL , ECL and ConA was performed . Both , sVSG ( 2 μg ) and cell pellets ( 1 x 10−6 cell equivalents/sample ) coming from hypotonic lysis of PRM-A100 cells were denatured in SDS-sample buffer containing 8 M urea and 50 mM DTT , analysed using MOPS electrophoresis on NuPAGE Bis-Tris 4–12% gradient gel ( Invitrogen ) and transferred to a nitrocellulose membrane . Proteins were stained with Ponceau S ( Sigma ) as loading control and blocked with 3% BSA in PBS , previously probing with either biotinylated TL ( 0 . 33 μg/ml , Vector Laboratories , Inc . ) in a solution containing 50 mM Tris-HCl pH 7 . 4 , 0 . 5 M NaCl , 0 . 05% IGEPAL and 0 . 25% BSA , biotinylated ECL ( 1 μg/ml , Vector Laboratories , Inc . ) in a solution containing 10 mM HEPES pH 7 . 4 , 0 . 15 M NaCl , 1 mM CaCl2 , 0 . 05% IGEPAL and 0 . 25% BSA , or biotinylated ConA ( 0 . 05 μg/ml , Sigma ) in PBS containing 1 mM MgCl2 , 1 mM CaCl2 , 1 mM MnCl2 , 0 . 05% IGEPAL and 0 . 25% BSA . In all cases , specific inhibitors of lectin binding such as chitin hydrolysate ( 1:10 dilution , Vector Laboratories , Inc . ) for TL , D-lactose ( 0 . 2 M , Sigma ) for ECL and methyl α-D- mannopyranoside ( 0 . 5 M , Sigma ) for ConA were used as carbohydrate-specific binding controls . Finally , glycoproteins were detected with Extravidin-peroxidase conjugated ( Sigma ) by chemiluminescent detection ECL Western Blotting Detection Reagents ( GE Healthcare ) . Glycan profiling by mass spectrometry and liquid chromatography was performed to gain a more detailed view of VSG glycosylation including the relative quantification of glycan distribution . T . brucei VSG221 samples ( 50 μg ) of parental ( Tb BSF ) and resistant ( PRM-A100 ) cells were denatured and enzymatically deglycosylated with PNGase F according to the manufacturer´s instructions ( New England Biolabs , PNGaseF glycerol free ) . After deglycosylation , the protein fraction was removed by filtration on a 10 kDa spin filter ( Amicon Ultra-0 . 5 ml Centrifugal Filters , Merck Millipore ) , recovering the free glycans in the filtrate . The released glycans from 50 μg of VSG221 protein were purified over C18 loaded Zip-Tips to remove polymers , the sample was treated with 1 μl of sodium citrate tribasic ( 1 mM ) to favour the exclusive production of sodium adducts and subjected to MALDI-TOF MS analysis . Super-DHB was used as MALDI matrix as a 20 mg/ml solution in ACN . 1 μl of both sample and matrix solution were spotted to the MALDI sample plate into the same spot . The spectra were recorded in reflector positive mode in the 700–3000 Da range , on an UltrafleXtreme III MALDI-TOF , Bruker Daltonics , Germany . Further information on glycan structure was obtained by fragmentation by MALDI-TOF MS selecting the ions 2393 m/z , 2028 m/z and 1663 m/z , and employing in-source fragmentation routine ( LIFT , Bruker ) . Glycan fragments were identified using the Flexcontrol Ultraflex TOF/TOF software application and GlycoWorkBench 2 . 1 free software . A relative quantification of the glycan profile was performed by analysing procainamide labelled glycans by UPLC-FLD-MS . Glycans were enzymatically released from 50 μg of filtered VSG221 , dried and labelled with procainamide ( procainamide/cyanoborohydride in DMSO/AcOH ) for 3h at 60°C . The labelled glycans were dried , redissolved in ACN , and analysed by UPLC-FLD on an Acquity UPLC Glycan BEH amide column ( 1 . 7 μm , 2 . 1 mm x 150 mm ) in ammonium formate/ACN . Fluorescent labelled glycans were detected with a FLD and an ESI-TOF mass analyser . PRM-S , a more soluble analogue of PRM-A , BMS181184 and BMY28864 , were used to evaluate the trypanocidal activity in vivo of CBAs . Groups containing between three to six C57BL/6 or Balb/C mice ( 6–8 weeks old ) ( Jackson Laboratories , Bar Harbor , ME ) were infected intraperitoneally with 5 x 103 monomorphic T . brucei brucei ( PRM-S treatment ) or 1 x 104 T . brucei rhodesiense parasites ( PRM-S , BMS181184 and BMY28864 treatment ) , respectively . Different dosages of 25 mg/kg and 50 mg/kg of PRM-S ( four dosages ) and 50 mg/kg of BMS181184 and BMY28864 ( three dosages ) , were administered once a day intraperitoneally ( 0 . 2 ml ) starting on the third day post-infection . Compounds were initially dissolved in PBS at 12 . 5 mg/ml , followed by dilution in 0 . 5% w/v hydroxypropylmethylcellulose , 0 . 4% v/v Tween 80 and 0 . 5% v/v benzyl alcohol used as dosage formulation . One group of mice in each case was also infected and treated with the vehicle as control . Parasitaemia was monitored daily in a haematocytometer and morphology was examined under a microscope from the third day post-infection and after tail blood extraction . To study the effect of the glycosylation changes observed in PRM-A-resistant parasites on infectivity , 600 monomorphic T . brucei brucei parasites of parental , PRM-A25 , PRM-A50 and PRM-A100 strains were used to infect intraperitoneally between four and seven female C57BL/6 mice per group , respectively ( 6–8 weeks old ) . Parasitaemia was monitored daily in a haematocytometer under a microscope from the fourth day post-infection and after tail blood extraction . The animal research described in this manuscript complied with Spanish ( Ley 32/2007 ) and European Union Legislation ( 2010/ 63/UE ) . The protocols used were denoted as 1646/13 ( PRM-A100 infection ) , 511/15 . 2 ( PRM-A25 and PRM-A50 infection ) , 1947/13 . A . 1 , 1947/13 . B . 1 and DGP . 2/2014/CEEA ( PRM-S treatment ) and 2738/13 . A . 1 ( BMS181184 and BMY28864 treatment ) and approved by the Animal Care Committee of the Instituto de Parasitología y Biomedicina “López-Neyra” , CSIC . We expressed the results as the mean ± SD for each group , and comparisons between groups were performed using Student’s t-tests using a commercially available , computer-based statistical package ( GraphPad Software Inc . ) for all calculations . A p value ≤0 . 05 was considered statistically significant . | Trypanosoma brucei , the causative agent of African trypanosomiasis , is coated with a dense layer of the variant surface glycoprotein ( VSG ) , which plays an essential role in antigenic variation and the ability of the parasite to evade the immune system . VSGs are N-glycosylated with complex and/or mannose-rich N-glycans by two oligosaccharyltransferase activities in a site-specific manner . Here , we describe that a series of non-peptidic carbohydrate binding agents , previously reported to display antifungal and antiviral properties , exhibit a marked trypanocidal activity . Pradimicin A ( PRM-A ) and pradimicin S ( PRM-S ) show specific binding to VSGs and a pronounced cytocidal effect by inducing defects in endocytosis and cytokinesis . Remarkably , PRM-S produces parasitological cure in two different models of acute sleeping sickness . Information on the mode of action was provided by generating PRM-A-resistant parasites , which suffered from defective glycan composition as a consequence of down-regulation of oligosaccharyltransferase genes . Aberrant glycosylation of VSGs resulted in decreased binding of pradimicins but reduced parasite infectivity underscoring the role of glycosylation in virulence . Our findings identify antibiotics with the ability to effectively bind to glycans of the parasite surface as agents capable of affording parasitological cure , thereby providing a novel avenue for design of highly specific drugs to combat African trypanosomiasis . | [
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| 2016 | Carbohydrate-Binding Non-Peptidic Pradimicins for the Treatment of Acute Sleeping Sickness in Murine Models |
Released by many eukaryotic cells , the exosomes are 40–100 nm vesicles shown to operate over the complex processes of cell-cell communication . Among the metazoan cell lineages known to generate exosomes is the mononuclear phagocyte lineage , a lineage that parasites such as Leishmania are known to subvert as host cells . We previously reported that mouse macrophage signaling and functions are modified once co-incubated with exoproteome of Leishmania promastigotes . Using mass spectrometry analysis , we were curious to further compare the content of purified exosomes released by the J774 mouse macrophage cell line exposed or not to either LPS or to stationary phase Leishmania mexicana promastigotes . Collectively , our analyses resulted in detection of 248 proteins , ∼50–80% of which were shared among the three sources studied . Using exponentially modified protein abundance index ( emPAI ) and network analyses , we found that the macrophage exosomes display unique signatures with respect to composition and abundance of many functional groups of proteins , such as plasma membrane-associated proteins , chaperones and metabolic enzymes . Moreover , for the first time , L . mexicana surface protease GP63 is shown to be present in exosomes released from J774 macrophages exposed to stationary phase promastigotes . We observed that macrophage exosomes are able to induce signaling molecules and transcription factors in naive macrophages . Finally , using qRT-PCR , we monitored modulation of expression of multiple immune-related genes within macrophages exposed to exosomes . We found all three groups of exosomes to induce expression of immune-related genes , the ones collected from macrophages exposed to L . mexicana sharing properties with exosomes collected from macrophage left unexposed to any agonist . Overall , our results allowed depicting that protein sorting into macrophage-derived exosomes depends upon the cell status and how such distinct protein sorting can in turn impact the functions of naive J774 cells .
Exosomes are 40–100 nm vesicles that are released by many eukaryotic cells . These vesicles are formed through invagination of the membrane into the multivesicular endosome ( MVE ) and can be released from the cell upon fusion of the MVE with the plasma membrane [1] . Although exosomes were once believed to be just packed with inert debris , current research suggests that along with other released vesicles , exosomes actually have an important part to play in different forms of long distance cell-cell communications [2] . Studies on exosomes derived from macrophages or dendritic cells ( DCs ) infected with bacteria shows that these exosomes are generally pro-inflammatory to naive macrophages , induce maturation of DCs and activate both CD4+ and CD8+ T cells [3] , [4] . In addition , bacterial antigens such as glycopeptidolipids ( GPLs ) and immunogenic proteins have been found to be present on these exosomes and to be responsible for the pro-inflammatory nature of these exosomes [5] , [6] . Therefore , exosomes introduce a novel class of communication among immune cells for antigen presentation and immune activation . In contrast to bacterial pathogens , the biology of exosomes released from macrophages infected with immunomodulatory parasites such as Leishmania has not been previously studied . Leishmania parasites toggle between the extracellular motile and flagellated promastigotes , dwelling in the Phlebotomine sandfly and the roundshape nonmotile amastigotes residing in the phagolysosome of the mammalian macrophage [7] . These parasites have the ability to successfully parasitize macrophages thanks to their mechanisms for efficient inhibition of the signaling and microbicidal functions of their host . The hallmarks of these modulations are activation of protein tyrosine phosphatases ( PTPs ) , inhibition of proinflammatory transcription factors NF-κB , AP-1 and STAT-1 as well as other critical signaling molecules such as JAK-2 , IRAK-1 and MAP Kinases . Together , modulation of these molecules and pathways results in deactivation of macrophage microbicidal functions such as production of nitric oxide ( NO ) or proinflammatory cytokines such as TNF and IL-12 . In addition to inhibition of macrophage functions , Leishmania infection renders the macrophage unresponsive to external stimulations such as LPS or IFN-γ ( Reviewed in [8] ) . Moreover , we recently showed that GP63 , the major surface protease of Leishmania , is able to gain access to the macrophage cytoplasm and directly cleave many intracellular targets , leading to inactivation of the macrophage [9]–[11] . Considering the modulatory nature of these parasites , studying the exosomes released from Leishmania-infected macrophages is of great interest . Importantly , it can shed light on how protein sorting to exosomes is altered following Leishmania infection and how it could affect targeting and functions of exosomes on other immune cells . Different classes of proteins are now recurrently observed to be sorted into exosomes , such as proteins involved in adhesion ( tetraspanins and integrins ) , vesicular trafficking ( Alix , Tsg101 ) , molecular chaperones ( HSP 70 , HSP 90 ) , metabolic enzymes , and also cytoskeletal proteins [1] . Nevertheless , the content of exosomes is highly dependent on the cell type , its developmental status , as well as external stimulations [12] , [13] . The combined function of those proteins on the recipient cell is still a subject of study . Still , exosomes have also been shown to carry molecules with known function in cell-cell interactions , such as MHC I or II , co-stimulatory molecules ( e . g . CD80 ) , and cytokines ( e . g . TNF-α , TGF-β ) . The specific combination of surface molecules on exosomes could allow for specific targeting of the cytokines to distinct recipient cells . Additionally , infection with intracellular pathogens such as viruses or Mycobacterium species has shown to alter exosome content [3] , [4] , [14] . However , besides looking at specific markers or cytokines , alterations in the total proteome of macrophage exosomes after stimulation or infection have not been studied . Studying the proteome of exosomes is critical for understanding their biology , target selection and possible effects on recipient cells . Here we report the first comparative proteomic analysis of macrophage exosomes after LPS stimulation or infection with Leishmania mexicana . We show that the contents of macrophage exosomes go through dynamic changes following LPS stimulation or Leishmania infection . Furthermore , we show how these exosomes induce signaling and modulate expression of immune-related genes in naive macrophages .
J774A . 1 murine macrophages were cultured in RPMI1640 medium ( Wisent ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , streptomycin ( 100 µg/ml ) , penicillin ( 100 U/ml ) , and 2 mM L-glutamine at 37°C and 5% CO2 . L . mexicana parasites were cultured in Schneider's Drosophila Medium ( SDM ) supplemented with 10% FBS at 25°C . Macrophages were stimulated with 100 ng/ml of LPS ( Sigma ) , infected with stationary L . mexicana parasites at 1∶20 ratio or left untreated for 6 h . Macrophages were then washed with PBS and cultured for 24 h in RPMI medium supplemented with exosome-free FBS . Exosome-free FBS was acquired by overnight ultracentrifugation of FBS at 100 , 000 g for exosome collection . Culture supernatant was then collected and centrifugated at 4 , 000 g for 20 min to clear floating cells and debris . Supernatant was then passed through a 0 . 45 µm filter ( Pall ) to clear debris . Exosomes were pelleted by 1 h centrifugation at 100 , 000 g . Pelleted exosomes were resuspended , passed through a 0 . 22 µm filter ( Pall ) and washed in 20 mM HEPES pH 7 . 5 . Washed exosomes were then resuspended in sterile HEPES 20 mM and kept at −80°C until use . In order to acquire ultrapure exosomes for mass spectrometry , following pelleting , resuspended exosomes were rapidly mixed with a cocktail of protease inhibitors ( Roche ) and washed in HEPES . Exosomes were then overlayed on a 0–2M gradient of sucrose and centrifugated for 12–16 h at 100 , 000 g . Fractions corresponding to 1 . 13–1 . 19M of sucrose were collected , passed through a 0 . 22 µm filter and pelleted at 100 , 000 g for 1 h . Exosomes were then resuspended in HEPES buffer and kept at −80°C until use . Purified exosomes were quantified using MicroBCA protein dosing assay ( Thermo ) . Exosomes were put on Fomvar Carbon grids ( Mecalab , QC , Canada ) , fixed in 1% glutaraldehyde and stained with 1% uranyl acetate . Samples were visualized using FEI Technai-12 120 KV transmission electron microscope and AMT XR80C CCD Camera . Proteins ( 5 µg ) were loaded on an SDS-PAGE polyacrylamide gel containing 10% sucrose and run for 1 cm into the resolving gel . In-gel digestion was performed as described previously [15] . The gel lane was excised into 3 bands and each band was cut into 1 mm3 pieces . Gel pieces were first washed with water for 5 min and then dehydrated with acetonitrile ( ACN ) . Proteins were reduced by adding the reduction buffer ( 10 mM DTT , 100 mM ammonium bicarbonate ) for 30 min at 40°C , and then alkylated by adding the alkylation buffer ( 55 mM iodoacetamide , 100 mM ammonium bicarbonate ) for 20 min at 40°C . Gel pieces were dehydrated and washed at 40°C by adding ACN for 5 min before discarding all the reagents . Gel pieces were dried for 5 min at 40°C and then re-hydrated at 4°C for 40 min with the trypsin solution ( 6 ng/µl of sequencing grade trypsin ( Promega ) , 25 mM ammonium bicarbonate ) . The concentration of trypsin was kept low to reduce signal suppression effects and background originating from autolysis products when performing LC-MS/MS analysis . Protein digestion was performed at 58°C for 1 h and stopped with 15 µl of 1% formic acid/2% ACN . Supernatant was transferred into a 96-well plate and peptides extraction was performed with two 30 min extraction steps at room temperature using the extraction buffer ( 1% formic acid/50% ACN ) . All peptide extracts were pooled into the 96-well plate and then completely dried in vacuum centrifuge . The plate was sealed and stored at −20°C until LC-MS/MS analysis . Prior to LC-MS/MS , protein digests were re-solubilized under agitation for 15 min in 10 µl of 0 . 2% formic acid . Desalting/cleanup of the digests was performed by using C18 ZipTip pipette tips ( Millipore , Billerica , MA ) . Eluates were dried down in vacuum centrifuge and then re-solubilized under agitation for 15 min in 10 µL of 2% ACN/1% formic acid . The LC column was a C18 reversed phase column packed with a high-pressure packing cell . A 75 µm i . d . Self-Pack PicoFrit fused silica capillary column ( New Objective , Woburn , MA ) of 15 cm long was packed with the C18 Jupiter 5 µm 300 Å reverse-phase material ( Phenomenex , Torrance , CA ) . The column was installed on the Easy-nLC II system ( Proxeon Biosystems , Odense , Denmark ) and coupled to the LTQ Orbitrap Velos ( ThermoFisher Scientific , Bremen , Germany ) equipped with a Proxeon nanoelectrospray ion source . The buffers used for chromatography were 0 . 2% formic acid ( buffer A ) and 100% ACN/0 . 2% formic acid ( buffer B ) . During the first 12 min , 5 µl of sample was loaded on column at a flow rate of 600 nl/min and , subsequently , the gradient went from 2–55% buffer B in 100 min at a flow rate of 250 nl/min followed by a rapid increase to 90% buffer B and then came back at 2% buffer B for 10 min at a flowrate of 600 nl/min . LC-MS/MS data acquisition was accomplished using a eleven scan event cycle comprised of a full scan MS for scan event 1 acquired in the Orbitrap . The mass resolution for MS was set to 60 , 000 ( at m/z 400 ) and used to trigger the ten additional MS/MS events acquired in parallel in the linear ion trap for the top ten most intense ions . Mass over charge ratio range was from 380 to 2000 for MS scanning with a target value of 1 , 000 , 000 charges and from ∼1/3 of parent m/z ratio to 2000 for MS/MS scanning with a target value of 10 , 000 charges . The data dependent scan events used a maximum ion fill time of 100 ms and 1 microscan . Target ions already selected for MS/MS were dynamically excluded for 25 s . Nanospray and S-lens voltages were set to 0 . 9–1 . 8 kV and 50 V , respectively . Capillary temperature was set to 225°C . MS/MS conditions were: normalized collision energy , 35 V; activation q , 0 . 25; activation time , 10 ms . Protein database searching was performed with Mascot 2 . 2 ( Matrix Science ) against NCBI Mus musculus and Leishmania protein databases . The mass tolerances for precursor and fragment ions were set to 10 ppm and 0 . 6 Da , respectively . Trypsin was used as the enzyme allowing for up to 2 missed cleavages . Carbamidomethyl and oxidation of methionine were allowed as variable modifications . Duplicates of separately analyzed sets of MS/MS data were used for calculation of the Exponentially modified protein abundance index ( emPAI ) values using emPAICalc web server ( http://empai . iab . keio . ac . jp/ ) [16] . Mascot output files were uploaded to emPAICalc server and hits with minimum 3 peptides and minimum score of 20 were chosen as true hits for further analyses . Gene Ontology ( GO ) annotations of identified proteins were extracted using STRAP software [17] . Protein-protein interaction networks of the identified proteins were created using STRING database with default parameters and visualized using Cytoscape software 2 . 8 [18] , [19] . J774 macrophages were left un-treated , stimulated with 3–5 µg/ml of exosomes , 100 ng/ml of LPS or infected with stationary L . mexicana parasites at the ratio of 1∶20 . Following the mentioned time-courses , cells were washed with PBS ( 3 times for the infected cells ) and then lysed with appropriate lysing reagent as described below . Following in vitro stimulation , cells were lysed in a Western blotting lysis buffer . Proteins were dosed by Bradford reagent ( Biorad ) and run on SDS-PAGE according to standard methods . Proteins were blotted to Hy-bond nylon members ( Amersham ) and were detected by antibodies against actin , tubulin , PGK1 , PABP , ERK , phospho-ERK , JNK , phospho-JNK , p38 and phospho p38 ( all from Cell Signaling ) , or anti-phosphotyrosine clone 4G10 ( Millipore ) . Anti-mouse or anti-rabbit and anti-rat antibodies conjugated to horse-radish peroxidise ( HRP ) ( Amersham ) were used as secondary antibodies . Membranes were then visualized by ECL Western blotting detection system ( Amersham ) . EMSA was performed as described previously [20] . Briefly , nuclear proteins were extracted using an isotonic and then a hypotonic buffer . Extracted nuclear proteins were incubated with radiolabelled consensus sequences of NF-κB ( 5′-AGTTGAGGGGACTTTCCCAGGC-3′ ) , AP-1 ( 5′-AGCTCGCGTGACTCAGCTG-3′ ) and SP-1 ( 5′-ATTCGATCGGGGCGGGGCGAGC-3′ ) ( Santa Cruz , CA , USA ) as non-specific control . Samples were run on a native 4% acrylamide gel . Following electrophoresis , gels were dried and autoradiography was performed using Kodak film . Densitometry was performed using ImageJ software ( NIH ) . Mean grey values of bands ( ratio of phospho-proteins against their respective total proteins in case of MAP Kinases ) were acquired and then normalized against the non-treated sample . J774 macrophages were left untreated , infected with stationary L . mexicana parasites at 1∶20 ratio , stimulated with 100 ng/ml of LPS or 5 µg/ml of exosomes for 8 h . Following stimulation , cells were washed with PBS , ( 3 times for infected cells ) and were lysed in Tryzol reagent ( Invitrogen ) according to the manufacturer's instructions for RNA extraction . Extracted RNA was then cleaned-up using Qiagen clean-up columns . Clearance of possible genomic DNA contamination was performed using DNase I ( Promega ) according to manufacturer's protocol . 1 µg of total RNA was used for cDNA preparation using reverse transcriptase enzyme Superscript III ( Invitrogen ) and random oligo-hexamers ( Invitrogen ) . Samples were then treated with Escherichia coli RNase H ( Invitrogen ) for clearance of RNA-DNA helices . qRT-PCR was performed using Qiagen SABioscience RT2 profiler arrays in a Strategene mx3000 thermocycler according to SABiosciences protocol . Results were analyzed by ΔΔCt method using the Qiagen qRT-PCR data analysis web interface .
To collect exosomes from Leishmania-infected macrophages , we infected J774 macrophages with stationary L . mexicana parasites for 6 h to confidently saturate all macrophages with parasites . We washed away non-internalized parasites by PBS and incubated the macrophages in exosome-free medium for 24 h ( LEISHX ) . Similarly , we stimulated the macrophages with 100 ng/ml of LPS as a strong stimulant for 6 h , to compare its effect with the immunomodulatory properties of Leishmania ( LPSX ) . As a negative control , we incubated non-treated macrophages in exosome-free media for 24 h ( NILX ) . Following incubations , we collected the conditioned medium and extracted the exosomes via multiple centrifugation and filtration processes as detailed in the materials and methods section . Exosomes settle at the density of 1 . 13 to 1 . 19 g/ml as can be seen in Figure 1A that shows a silver staining of fractions following sucrose density gradient centrifugation of exosomes . We further verified presence and purity of exosomes by western blotting against actin , known to be enriched in exosomes ( Figure 1B ) . We did not observe any differences in density of exosomes after density gradient centrifugation of NILX , LPSX and LEISHX samples ( data not shown ) . We recovered consistently but non-significantly less exosomes from LPS macrophages but observed no difference amongst LEISHX and NILX in terms of protein amount . Reduction in exosome release following LPS stimulation has been previously described to occur in DCs [21] . It is important to mention that our purification steps , especially involving filtration and density gradient centrifugation , would minimize contamination of our samples with other types of secreted vesicles . Nevertheless , transmission electron microscopy ( TEM ) of all samples showed only vesicles of 40–100 nm in size and morphology described for exosomes . We did not observe any vesicles of larger size suggesting that there was negligible if any contamination with larger secreted vesicles such as membrane vesicles [22] . No differences were observed among the samples suggesting that morphology and size of exosomes remain unaltered following LPS stimulation or Leishmania infection ( representative TEM image shown in figure 1C ) . We performed mass spectrometry ( LC-MS/MS ) to analyze the content of the purified exosomes . Due to multiple washing steps in the purification , very few hits of contaminated serum proteins were found and were removed from the protein list . Detailed spectrum and peptide report as well as Pearson Coefficients comparing sample replicates against other samples are available in Supplemental File S1 . Because peptide counts are not a reliable quantitative measure for sample comparison , we analyzed our proteomic data using the exponentially modified protein abundance index ( emPAI ) [23] . This method , calculates a ratio of observed to observable peptides , based on factors such as the conditions of the mass spectrometry analyses , protein biochemical properties and previously published empirical data . emPAI values are proposed to be linearly correlated to protein concentration and to give a more accurate estimate of protein abundance compared to simple peptide or spectral count [16] , [23] . Only proteins with minimum 3 peptides and peptide score higher than 20 were considered as true hits . Also some proteins had to be removed because their relevant information was absent in the emPAI database . With these criteria , we ended up with a total of 248 proteins , which we used as the primary list for the rest of our analyses . The selected proteins and their calculated emPAI values are listed in supplemental files S1 and S2 respectively . We found 137 proteins in NILX , 173 proteins in LPSX and 200 proteins in LEISHX ( Figure 2 ) . We compared the list of the identified proteins against previously published exosome data at Exocarta database ( www . exocarta . org , [24] ) and found that the majority of the hits had been previously observed to be present in at least one group of exosomes ( Supplemental File S1 ) . Since it had been previously reported that proteins from bacterial pathogens such as Mycobacterium can enter the macrophage exosomes , we also looked for Leishmania proteins in our MS/MS data . Interestingly , we observed positive hits for Leishmania surface metalloprotease GP63 in LEISHX exosomes and as expected not in NILX or LPSX ( Supplemental File S3 ) . To our knowledge , this is the first report of a protein from a eukaryotic parasite entering macrophage exosomes . Interestingly , we observed that a high percentage of discovered proteins are shared among the 3 samples . 78% of proteins found in NILX , 62% of those found in LPSX and only 53% of proteins found in LEISHX were common among the 3 samples ( 107 proteins ) . While LEISHX had the highest number of unique proteins ( 44 , 22% ) , LPSX and LEISHX had the highest percentage of shared proteins in between pairs ( 37 , ∼20% ) . Therefore , we decided to compare the levels of abundance of the shared proteins among the samples . We compared the levels of abundance of the common 107 proteins by calculating their LEISHX/NILX and LPSX/NILX emPAI ratios ( Supplemental File S2 ) . Log10 of these ratios are plotted in Figure 3A and B , sorted from highest to lowest ratio for LEISHX/NILX and LPSX/NILX , respectively . The plots clearly show that although these proteins are shared among exosomes of naive , LPS-stimulated and Leishmania-infected exosomes , their abundance is greatly altered following these stimulations . In fact , very few proteins appear to have equal abundance , and a significant percentage have been altered more than 2 or 3 fold ( Log10>0 . 3 , Figure 3C ) . Interestingly , it appears that increase or decrease in abundance follows a similar trend in LPSX and LEISHX samples , whereby proteins increased in LEISHX are also increased in LPSX and vice-versa ( Correlation coefficient = 0 . 72 ) . This trend can also be observed when plotting the frequency distributions of LEISHX/NILX and LPSX/NILX emPAI ratios ( Figure 3C ) . However , not all proteins follow the trend . In fact , ∼30% of proteins show opposite and divergent modulations between LPSX and LEISHX . We next compared modulation of proteins between LEISHX and LPSX themselves and also included the 37 proteins common between them to the 107 common proteins ( Figure 4A ) . About half of the identified proteins have higher abundance in LEISHX , while about 35% have higher abundance in LPSX . However , frequency distribution of the emPAI ratios shows that the majority of the modulations lie within 2-fold difference ( −0 . 3>Log10<0 . 3 ) ( Figure 4B ) . Together these results show that Leishmania infection and LPS stimulation induce a similar trend of modulations in protein abundance in macrophage exosomes; although significant differences exist amongst the two types of stimulations . Various groups of proteins are sorted into exosomes . Having seen the modulations in protein abundance , as well as in the presence/absence of proteins among samples ( Figures 2–4 ) , we used Gene Ontology ( GO ) classification of proteins to look at the cellular localization , function and biological processes of groups that were up- or down-regulated . For simplicity , we merged the proteins unique to one sample with the proteins up-regulated in the same sample . We chose proteins with 1 . 5 fold or more difference in their emPAI value ( −0 . 15>Log10<0 . 15 ) as the ones that are modulated between two samples . It is worthy to mention that since we chose proteins with minimum of 3 peptides as our starting criteria for inclusion into analyses , and we used emPAI analyses for correction of MS error , we are confident that 1 . 5 fold differences can have a real biological meaning . These cut-off lines resulted in labelling of 130 and 103 proteins as up-regulated in LEISHX and LPSX respectively . 51 and 60 proteins were also described as down-regulated in LEISHX and LPSX respectively . Finally , comparing LEISHX and LPSX resulted in 108 proteins higher in LEISHX compared to LPSX and 56 in the opposite . Between 20–25% of proteins in each comparison group were labelled as unchanged . Comparative graphs of number of GO terms associated with each group of proteins show modulations in proteins associated with multiple functions , processes and cellular localizations in both LEISHX and LPSX ( Figure 5 ) . The comparative GO graphs of molecular function are presented in Supplemental File S4 . Since more proteins are up-regulated than down-regulated in LEISHX and LPSX , it is not surprising that there appears to be generally more increases in GO terms associated with these samples . Although LPSX and LEISHX show similar patterns in up and down regulations compared to NILX ( Figure 5A and B ) , direct comparison of LPSX and LEISHX reveals many differences in their associated GO terms . This shows that despite similarity , distinct functional groups and cellular processes are enriched in each set of exosomes . Finally , to assess at the functional differences among the three samples at the protein level , we created exosome protein-protein interaction ( PPI ) networks using the STRING database for PPIs . Figure 6A shows the PPI network of the proteins identified in LEISHX ( PPI networks of NILX and LPSX can be seen in Supplemental File S5 ) . Looking closely at the PPI network , different functional groups of proteins known to be enriched in exosomes can be observed as interaction groups . For instance , circle I includes proteins associated with the plasma membrane and cell binding such as Integrin-β1 and β2 ( Itgb1 , Itgb2 ) , CD63 and ICAM1 , circle II includes chaperones such as members of the T-complex proteins , circle III includes proteins important in vesicular trafficking such as TSG101 and Alix ( Pcdc6ip ) and circle IV includes metabolic enzymes such as enolase , phosphoglycerate kinase ( PGK1 ) , lactate dehydrogenase ( Ldha ) and hexokinase ( HK3 ) . Other proteins usually present in exosomes such as signaling proteins , annexins and proteins involved in translation can also be observed in the PPI network . It can also be seen that actin ( actb ) is one of the proteins that connects these interaction groups with each other . Since proteins associated with the plasma membrane generated the largest interaction group , we decided to compare those proteins among NILX , LPSX and LEISHX samples . Figures 6B and C show the proteins in the PPI network bearing the Cellular Component GO term , plasma membrane in LPSX and LEISHX respectively . Interestingly , many modulations occur with proteins involved in cell-cell contact . Levels of surface receptors or co-receptors such as Fc-γ-receptor 1 ( Fcgr1 ) , TLR2 , CD40 and CD14 seem to increase with both stimulations . Integrins seem to be modulated with Integrin-β1 and Integrin-α4 decreasing and Integrin-β5 increasing , while Integrin-β4 remains unchanged . On the other hand , CD9 and CD44 , two proteins important in cell-cell interaction and usually seen in exosomes , only increased in LPSX and not in LEISHX , while LEISHX shows an increase in PTPN6 ( SHP-1 ) , a PTP that we have shown to be modulated and activated after Leishmania infection [9] , [25] . Together , our PPI network analysis of exosomes allows us to closely monitor the alterations in exosome surface that could play a role in exosome targeting and effector functions on recipient cells . We verified a number of the alterations in protein content in exosomes observed in our MS/MS results by western blotting . Firstly , we were able to confirm presence of GP63 in LEISHX ( Figure 7A ) . Then , we looked at tubulin and PGK1 , both of which that had shown higher emPAI values in both LPSX and LEISHX compared to NILX and we were able to observe their increase by western blotting as well ( Figure 7B ) . In fact , we did not detect PGK1 in NILX by MS/MS that is probably due to its low abundance in these exosomes . Lastly , we looked at polyadenylated binding protein ( PABP ) . Although emPAI values from MS/MS data showed reduction of in PABP in LPSX and LEISHX , we detected equal levels of this protein by western blotting . This reiterates the fact that results from MS/MS analyses should always be taken with caution . Although the effect of exosomes on recipient cell function has been previously studied , the signaling pathways triggered leading to those functions have not been explored . To look at signaling pathways induced via exosome stimulation , we stimulated naive J774 macrophages with 3 µg/ml of pelleted and washed NILX , LPSX , LEISHX exosomes for 1 h and looked at patterns of general tyrosine phosphorylation , as well as phosphorylation of MAP Kinases ERK , JNK and P38 . Figure 8A shows that stimulation with LPSX and LEISHX induces early Tyr phosphorylation of multiple proteins within as short as 15 min and increasing up to 1 h . Expectedly , stimulation with NILX does not induce a strong tyrosine phosphorylation compared to LPSX and LEISHX . Comparison of LPSX and LEISHX-stimulated cells identified both similar and unique Tyr phosphorylation patterns . We also looked at phosphorylation of MAP Kinases in naive macrophages within 1 h of stimulation with exosomes ( Figure 8B ) . All 3 groups of exosomes appeared to induce phosphorylation of MAP Kinases , except for JNK which was not induced as strongly by LEISHX . Densitometric quantification of 3 separate experiments also supports weaker phosphorylation of JNK in response to LEISHX stimulation compared to other exosomes . Looking more downstream of protein phosphorylation , we studied activation of prominent pro-inflammatory transcription factors ( TFs ) NF-κB and AP-1 by exosomes . We stimulated naive macrophages with 3 µg/ml of exosomes for 1 h and performed EMSAs on extracted nuclear proteins ( Figure 9 ) . We observed that all 3 exosomes induce nuclear translocation of NF-κB and AP-1 , although translocation of NF-κB is slightly less induced in response to LEISHX stimulation ( Figure 9A ) . Infection with L . mexicana itself results in degradation of AP-1 and alteration of NF-κB as we had previously reported [20] , [26] . We did not detect translocation of STAT-1 following exosome stimulation ( data not shown ) . Overall , we saw that macrophage exosomes are capable of stimulating signaling molecules in naive macrophages , possibly resulting in distinct responses . The observed distinct activation patterns of signaling molecules and transcription factors by exosomes can lead to modulation of expression of many genes and different activation states in the recipient cell . To further scrutinize exosome-induced modulation of gene expression , we prepared cDNA from J774 macrophages stimulated with exosomes for 8 h and performed qRT-PCR using Qiagen SABiosciences RT2 Profiler arrays . Using these arrays , we measured modulation of expression of 90 immune related genes in exosome-stimulated macrophages . LPS ( 100 ng/ml ) and L . mexicana infection were used as controls . The genes found to be at least 2-fold up-regulated or down-regulated after exosome stimulation are listed in Tables 1 and 2 ( for complete data see Supplemental File S6 ) . All 3 groups of exosomes appeared to be more stimulatory than inhibitory , as they induced more gene up-regulation than down-regulation . Especially , we observed up-regulation of pro-inflammatory cytokines such as IL-6 and IL-1 as well as certain interleukin receptors and TLRs . This is concurrent with our observations on activation of signaling molecules by exosomes . Figure 10 shows up-regulations and down-regulations that are shared following stimulation of macrophages with NILX , LPSX and LEISHX exosomes . The 10 genes that are induced by all 3 groups constitute ∼40–60% of the genes upregulated by each ( Figure 10A ) . Interestingly , more than 80% of genes induced by NILX are induced by LEISHX as well , while this percentage for LPSX is only 50% . Additionally , LPSX induces the most unique set of genes compared to the other 2 groups . The same is true for the downregulated genes by LPSX ( Figure 10B ) . This shows that NILX and LEISHX modulate gene expression more similarly compared to LPSX . Therefore , it suggests that exosomes from Leishmania-infected macrophages resemble more those of untreated macrophages than LPS-stimulated and activated macrophages . Infection with L . mexicana did not result in a stimulatory in terms of induction of immune-related genes and macrophage activation , in comparison to LPS and exosomes ( Supplemental File S6 ) . This is not surprising since Leishmania strongly modulates the pro-inflammatory transcription factors to avoid macrophage activation and establish its infection ( Figure 9 and [10] , [26] ) . Concurrently , the exosomes released from the infected macrophages also did not possess strong pro-inflammatory properties compared to naive exosomes . Overall , we compared the modulations that occur in macrophage exosome protein content , as well as their effector function on recipient cells , following L . mexicana infection and LPS stimulation . These data provide a better understanding of biology of exosomes and host-parasite interactions of Leishmania .
Studies on different functions of secreted vesicles , especially exosomes have now established them as yet another route for cell-cell communication , especially among immune cells [1] . Furthermore , it is now clear that sophisticated mechanisms are involved in exosome formation and exosomal protein sorting [27] , [28] . Importantly , different extracellular stimulations or infectious agents such as viruses , intracellular bacteria or protozoa have been shown to alter exosome release from their host cells [14] , [29] . However , modulations in the protein content of exosomes following these stimulations , especially infection , had not been previously studied . Here we report the first comparative proteomic analysis of naive macrophage exosomes against exosomes produced following stimulation with LPS or infection with L . mexicana . We interestingly observed that around 50–80% of proteins are shared among NILX , LPSX and LEISHX exosomes and this includes proteins that are usually reported to be sorted into exosomes , such as proteins involved in cell-cell communication , folding , vesicular trafficking and signaling . Combining the unique hits and also nuances in the levels of the shared proteins , we were able to look closely at the alterations in the sorting of functional groups of proteins into exosomes following these stimulations ( See Figures 5 and 6 ) . We used emPAI values for our proteomic comparisons , which is a method regularly applied to assist in quantitative analysis of label-free mass spectrometric data [30]–[32] . This method allowed us to better quantify the differences among NILX , LPSX and LEISHX and observe many alterations in the levels of common proteins ( Figures 3–6 ) . Choi et al . recently created the PPI network of proteins in exosomes derived from human colorectal cancer cells . They suggested that interacting proteins form complexes and functional modules in exosomes; and that PPIs can be involved in exosomal protein sorting [33] . We observed similar functional groups as those of Choi et al . and we were able to see how changes in the status of the macrophage can alter the composition and also abundance of these functional groups . Unfortunately , to date the underlying reasons for presence or absence of many of these proteins in exosomes are unclear . Therefore , our study can help connect the dots between the status of the macrophage , the contents of the released exosomes and their effector functions . For instance , using PPI networks of exosomes we saw that increases in chaperones occurred in both LPS and LEISHX , although increases in metabolic enzymes was more evident in LEISHX ( Figure 6 ) . Plasma membrane associated proteins showed most differences among the three sample groups , which can suggest that most alterations in exosome content could most importantly affect its targeting of recipient cells [34] . We were able to show that Leishmania GP63 is sorted into exosomes of infected macrophages via both MS/MS analysis and western blotting . GP63 is a key and highly abundant virulence factor of Leishmania promastigotes and our lab has previously shown that this enzyme is able to gain access to the macrophage cytoplasm and nucleus very early following Leishmania infection [9] , [10] to alter many signaling molecules of the macrophage . It is therefore possible that presence of only GP63 and not other Leishmania proteins in the macrophage exosomes is due to its direct entrance through the cytosol and not through the communication of the phagolysosome with the MVE . Another possibility is that GP63 is a contamination from the parasites and not macrophage exosomes . We believe this to be very improbable because firstly , the unphagocytosed parasites were taken out by multiple washes before the exosomes-collection media was put on the macrophages; secondly , we did not detect any other proteins that are enriched in Leishmania exosomes or exoproteome; and thirdly , the collected exosomes were washed multiple times during the purification process to avoid contamination with any CM content . Previous studies on exosomes released from infected cells with viruses and bacteria such as Herpes Simplex virus , Epstein-Barr virus and Mycobacterium sp . also showed that proteins from intracellular pathogens could be sorted into exosomes [6] , [14] . In addition , it was proposed that the pro-inflammatory properties of exosomes released from bacterially infected macrophages are due to presence of these molecules and their triggering of pattern recognition receptors ( PRRs ) on the recipient cells [3] . However , we did not detect any Leishmania proteins other than GP63 to be sorted into exosomes by MS/MS or western blotting of common immunogenic Leishmania proteins such as LACK [35] ( data not shown ) . GP63 is not naturally immunogenic , but is rather an immunomodulatory protein ( see [8] , [36] for reviews ) . Therefore , this could in part explain the absence of strong pro-inflammatory properties in LEISHX compared to NILX exosomes . The reasons for absence of other Leishmania proteins in macrophage exosomes could be the possible modulation of the interactions between the phagolysosome and the MVE . We observed that the macrophage exosomes were able to induce phosphorylation of signaling proteins and translocation of activatory TFs into the nucleus . It was intriguing to observe a reduction in JNK phosphorylation following stimulation with LEISHX but not other exosomes . We speculate that this could be due to transfer of activated PTPs from the infected to the naive macrophage . As we have shown previously , Leishmania infection results in activation of a number of macrophage PTPs in a cleavage-dependent manner [9] , [20] . In addition , our proteomic results show that exosomes contain PTPs , especially SHP-1 levels were shown to be increased in LEISHX ( Figure 6C ) . Therefore , these transferred and active PTPs could possibly be involved in dephosphorylation of JNK . Similar mechanisms might also participate in reduction of NF-κB nuclear translocations . To see how the activation of the signaling molecules is translated into function , we measured regulation of expression of immune-related genes by NILX , LPSX and LEISHX exosomes . Interestingly , all exosomes showed a relatively stimulatory behaviour and induced the expression of cytokines such as IL-6 , members of the IL-1 family as well as certain receptors such as Interleukin receptors and TLRs ( Table 1 and Figure 10 ) . Exosome-induced production of cytokines such as IL-6 , IL-1 and TNF by macrophages and monocytes has been reported elsewhere as well [37] , [38] . Nevertheless , we did not detect secretion of TNF or NO by the exosome-stimulated macrophages ( Data not shown ) . This shows that production and release of these critical immune modulators could be regulated at multiple levels . Exosomes might provide one of the necessary signals for this purpose . The relatively weak immuno-stimulatory properties of LPSX exosomes compared to macrophages infected with bacteria in other studies ( reviewed in [4] ) could be because we stimulated the macrophages with only LPS rather than living bacteria . Although it has been reported for bacterial antigens to be sorted into exosomes , this might not occur as much with LPS . Thus , LPSX exosomes might not be as immuno-stimulatory as exosomes derived from bacterially infected macrophages . Still , it was interesting to observe that the members of the LPS downstream signaling pathway , namely TLR4 , MyD88 and TRAF6 to be up-regulated by LPSX , maybe priming macrophages for more sensitive detection of LPS in the environment . The fact that we observed a pro-inflammatory behavior for NILX exosomes poses a question on its physiological relevance . However , one should bear in mind that there are fundamental shortcomings in the in vitro exosome function studies . First , there is limited knowledge on the physiological concentration and half-life of exosomes in vivo; thus most studies looking at exosomes in vitro usually have to use arbitrary concentrations . Additionally , the concerted effect of exosomes from various sources together with other factors such as cytokines and growth factors might lead to different outcomes from what observed in vitro . Gene-expression regulation patterns of NILX and LEISHX looked alike ( Figure 10A ) . This is in contrary to the exosomes released by bacterially infected macrophages , where they have been shown to have strong pro-inflammatory properties , compared to naive exosomes [3]–[5] , [29] . However , Leishmania-induced exosomes do not show this behavior and appear to be more similar to naive exosomes , especially compared with LPS-induced exosomes . This is despite the fact that the macrophage is infected with an intracellular pathogen and its exosome content is altered . Therefore , Leishmania alters exosome production by the macrophage in a fashion that matches its other immune evasion virulence mechanisms . One of the genes upregulated by NILX and very strongly by LEISHX is the Adenosine receptor 2a ( Adora2a ) . Extracellular adenosine ( usually generated during stress and inflammation via dephosphorylation of extracellular ATP ) has been suggested to be a modulator of the innate immune response [39] . Especially , binding of adenosine to Adora2a in macrophages results in an inhibitory response consisting of increased IL-10 and reduced TNF release [40] . Interestingly , a recent study also showed that Leishmania amazonensis utilizes this receptor to antagonize inflammation and spread infection [41] . Thus , LEISHX upregulates Adora2a in bystander macrophages that could potentially be the next in line to be infected and more readily inhibited . To conclude , we observed that LPS stimulation and Leishmania infection induce modulation of exosomal sorting into macrophages , especially with plasma membrane associated proteins . These modulations in turn altered effector functions and targeting of released exosomes . We were able to see that these modulations result in activation of signaling proteins and differential regulation of expression of immune-related genes . Our study also suggests that Leishmania could modulate the host's exosome machinery in its benefit; a virulence mechanism which needs to be further explored , especially by looking at antigen presentation by Leishmania-induced exosomes . Together our results give a better understanding of exosome biology in the innate immune system , connecting it to Leishmania host-parasite interactions . | Secreted vesicles , such as exosomes , are now considered as an important route of communication among eukaryotic cells . Depending on the donor cell source and protein content , these vesicles are expected to distinctly impact the recipient cell properties . Here , three groups of exosomes released by the mouse macrophage cell line J774 exposed or not - naive exosomes - to either Leishmania mexicana promastigotes or to LPS were compared through proteomic analysis . Also , their biological activities on naive J774 macrophages were tested . Regardless of the source , the three groups of exosomes shared 50–80% of their proteins , although their relative abundances differed , especially those associated with the plasma membrane . Post exposure to one out of the three groups of exosomes , naive J774 recipient macrophages were compared for their profile of immune transcripts . Of note , whether they were exposed to either naive exosomes or to L . mexicana- induced exosomes , the naive J774 macrophages shared similar immune transcriptional signatures , the latter being distinct from the ones displayed by the macrophages exposed to LPS-induced exosomes . These data are discussed within the context of the unique cross talk that accounts for the early establishment of an immunomodulatory parasite such as Leishmania in its mammalian host . | [
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| 2013 | Immunomodulatory Impact of Leishmania-Induced Macrophage Exosomes: A Comparative Proteomic and Functional Analysis |
Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience . The “common input” problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons . Available techniques allow us to simultaneously record , with sufficient temporal resolution , only a small fraction of the network . Consequently , naive connectivity estimators that neglect these common input effects are highly biased . This work proposes a “shotgun” experimental design , in which we observe multiple sub-networks briefly , in a serial manner . Thus , while the full network cannot be observed simultaneously at any given time , we may be able to observe much larger subsets of the network over the course of the entire experiment , thus ameliorating the common input problem . Using a generalized linear model for a spiking recurrent neural network , we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data , in which only a small fraction of the network is observed in each time bin . We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects . Networks with thousands of neurons , in which only a small fraction of the neurons is observed in each time bin , can be quickly and accurately estimated , achieving orders of magnitude speed up over previous approaches .
It is now possible to image hundreds of neurons simultaneously at high spatiotemporal resolution [1] or tens of thousands of neurons at low spatiotemporal resolution [2] . The number of recorded neurons is expected to continue to grow exponentially [3] . This , in principle , provides the opportunity to infer the “functional” ( or “effective” ) connectivity of neuronal networks , i . e . a statistical estimate of how neurons are affected by each other , and by a stimulus . The ability to accurately estimate large , possibly time-varying , neural connectivity diagrams would open up an exciting new range of fundamental research questions in systems and computational neuroscience [4] . Therefore , the task of estimating connectivity from neural activity can be considered one of the central problems in statistical neuroscience . Naturally , such a central problem has attracted much attention in recent years ( see section 8 ) . Perhaps the biggest challenge here involves the proper accounting for the activity of unobserved neurons . Despite rapid progress in simultaneously recording activity in massive populations of neurons , it is still beyond the reach of current technology to simultaneously monitor a complete large network of spiking neurons at high temporal resolution . Since connectivity estimation relies on the analysis of the the activity of neurons in relation to their inputs , the inability to monitor all of these inputs can result in persistent errors in the connectivity estimation due to model miss-specification . More specifically , “common input” errors , in which correlations due to shared inputs from unobserved neurons are mistaken for direct , causal connections , plague most naive approaches to connectivity estimation . Developing a robust approach for incorporating the latent effects of such unobserved neurons remains an area of active research in connectivity analysis ( see section 8 ) . In this paper we propose an experimental design which can greatly ameliorate these common-input problems . The idea is simple: if we cannot observe all neurons in a network simultaneously , perhaps we can instead observe many overlapping sub-networks in a serial manner over the course of a long experiment . Then we can use statistical techniques to patch the full estimated network back together , analogous to “shotgun” genetic sequencing [5] . Obviously , it is not feasible to purposefully sample from many distinct sub-networks at many different overlapping locations using multi-electrode recording arrays , since multiple re-insertions of the array would lead to tissue damage . However , fluorescence-based imaging of neuronal calcium [6 , 7] ( or , perhaps in the not-too-distant future , voltage [8] ) makes this approach experimentally feasible . For example , such a shotgun approach could be highly beneficial and relatively straightforward to implement using a 3D acousto-optical deflector microscope [1] . Using such a microscope , one can scan a volume of 400 × 400 × 500 μm , which contains approximately 8000 cells . In normal use , the microscope’s 50kHz sampling rate allows for a frame rate of about 6Hz when scanning the entire volume . Unfortunately , this frame rate is too low for obtaining reliable connectivity estimates , which requires a frame rate of at least 30Hz [9] . However , we can increase the effective frame rate to 30Hz by using a shotgun approach . We simply divide the experimental duration into segments , where in each segment we scan only 20% of the network . As a side benefit of this shotgun approach , photobleaching and phototoxicity ( two of the most important limitations on the duration of these experiments [10] ) are reduced , since only a subset of the network is illuminated and imaged at any given time . Connectivity estimation with missing observations is particularly challenging ( section 9 ) . Fortunately , as we show here , given the shotgun sampling scheme , we do not have to infer the unobserved spikes . We considerably simplify the network model loglikelihood using the expected loglikelihood approximation [11–13] , and a generalized Central Limit Theorem ( CLT ) [14] argument to approximate the neuronal input as a Gaussian variable when the size of the network is large . This approximate loglikelihood and its gradients depend only on the empiric second order statistics of the spiking process ( mean spike rate and spike correlations ) . Importantly , these approximate sufficient statistics can be calculated , even with partial observations , by simply “ignoring” any unobserved activity ( section 3 . 6 ) . In order to obtain an accurate estimation of the connectivity , posterior distributions involving this simplified loglikelihood ( along with various types of prior information about network connectivity ) can be efficiently maximized . Using a sparsity inducing prior on the weights , we demonstrate numerically the effectiveness of our approach on simulated recurrent networks of spiking neurons . First , we demonstrate that the shotgun experimental design can largely eliminate the biases induced by common input effects ( section 4 ) . Then , we show that we can quickly infer connectivity for large networks , with a low fraction of neurons observed in each time bin ( section 5 ) . For example , our algorithm can be used to infer the connectivity of a sparse network with O ( 103 ) neurons and O ( 105 ) connections , given O ( 106 ) time bins of spike data in which only 10% − 20% of the neurons are observed in each time bin . On a standard laptop , simulating such a network takes about half an hour , while inference takes a few minutes . This is faster than previous approaches by orders of magnitude , even when all spikes are observed ( section 6 . 2 ) . Our parameter scans suggest that our method is robust , and could be used for arbitrarily low observation ratios and an arbitrarily large number of neurons , given long enough experiments . We will discuss the outlook for experimental realizations of the proposed approach below , after presenting the basic methodology and simulated results . The supplementary material S1 Text contains the full details of the mathematical derivations and the numerical simulations .
We use a Bayesian approach to infer the unknown weights . Suppose initially , for simplicity , that all spikes are observed and that there is no external input ( G = 0 ) . In this case , the log-posterior of the weights , given the spiking activity , is ln P ( W | S , b ) = ln P ( S | W , b ) + ln P 0 ( W ) + C , ( 4 ) where ln P ( S∣W , b ) is the loglikelihood , P0 ( W ) is some prior on the weights ( we do not assume a prior on the biases b ) , and C is some unimportant constant which does not depend on W or b . Our aim is to find the Maximum A Posteriori ( MAP ) estimator for W , together with the Maximum Likelihood ( ML ) estimator for b , by solving max W , b ln P ( W | S , b ) . ( 5 ) If S is fully observed , this problem can be straightforwardly optimized without requiring an approximation ( though the optimization procedure can be slow ) . However , our goal is to provide an estimate when only a subset of S is observed . This cannot be easily done using standard method . To see this , we examine the likelihood of a GLM ( recalling Eqs ( 1 ) and ( 2 ) ) , ln P ( S | W , b ) = ∑ i = 1 N ∑ t = 1 T ln [ e S i , t U i , t 1 + e U i , t ] ( 6 ) = ∑ i = 1 N ∑ t = 1 T [ S i , t U i , t - ln ( 1 + e U i , t ) ] . ( 7 ) This likelihood ( Eq 7 ) , and its gradients , both contain a sum over weighted spikes in Ui , t ( the WS⋅ , t−1 term in Eq 2 ) , that cannot be evaluated if some spikes are missing , unless the missing spikes are accurately inferred ( section E in S1 Text ) . However , methods for inferring these missing spikes are typically slow , and do not scale well . To circumvent these issues , we will show the loglikelihood can be approximated with a simple form , under a few reasonable assumptions . Importantly , this simple form can be easily calculated even if there are missing observations ( the full derivation is in section 2 . 1 ) . Using an extension of the techniques in [11–13] , we develop an approximation to the likelihood based on the law of large numbers ( the “expected loglikelihood” approximation ) together with a generalized Central Limit Theorem ( CLT ) argument [14] , in which we approximate the neuronal input to be Gaussian near the limit N → ∞; then we calculate the “profile likelihood” maxbln P ( S∣W , b ) , in which the bias term has been substituted for its maximizing value . The end result is max b ln P ( S | W , b ) ≈ T ∑ i = 1 N [ ∑ j = 1 N [ W i , j Σ i , j ( 1 ) ] - h ( m i ) 1 + π 8 ∑ k , j W i , j Σ k , j ( 0 ) W i , k ] , ( 8 ) where we defined the mean spike probability , spike covariance , and the entropy function , respectively: m i≜⟨ S i , t ⟩ T ( 9 ) Σ i , j ( k ) ≜⟨ S i , t S j , t - k ⟩ T - m i m j ( 10 ) h ( m i ) ≜- m i ln m i - ( 1 - m i ) ln ( 1 - m i ) . ( 11 ) A few comments: Importantly , the profile loglikelihood ( Eq 8 ) depends only on the first and second order moments of the spikes m and Σ ( k ) for k ∈ {0 , 1} . When all of the neurons in the network are observed , these moments can be computed directly , and therefore the empirical moments are approximate sufficient statistics , whose value contains all the information needed to compute any estimate of W . As we explain in section 3 , these empirical moments can be estimated even if only a subset of the spikes is observed . As we show in section A in S1 Text , the profile loglikelihood ( Eq 8 ) is concave , so it is easy to maximize the log-posterior and obtain the MAP estimate of W . This can be done orders of magnitude faster than in the standard MAP estimate ( section 6 . 2 ) , since Eq 8 does not contain a sum over time , as the original loglikelihood ( Eq 7 ) . Moreover , the optimization problem of finding the MAP estimate can be parallelized over the rows of W . max b ln P ( W | S , b ) = ∑ i max b ln P ( W i , · | S , b ) , ( 12 ) because the profile loglikelihood ( Eq 8 ) decomposes over the rows of W , as does the L1 prior we will use here ( Eq 46 ) . As we show in section A in S1 Text , we can straightforwardly differentiate Eq 8 to analytically obtain the gradient , Hessian , and even the maximizer of this profile loglikelihood , which is the maximum likelihood estimate of W . However , due to the nature of the integral approximation we make in Eq 14 , more accurate results are obtained if we first differentiate the original loglikelihood ( Eq 7 ) , and then use the expectation approximation ( together with the generalized CLT argument ) . This results in an adjustment of the loglikelihood gradient ( section D in S1 Text ) . A novel aspect of this work is that we apply the Expected LogLikelihood ( ELL ) approximation to a GLM with a bounded logistic rate function ( Eq 1 ) , which allows us to infer connectivity in recurrent neural networks . In contrast , previous works that used the ELL approximation [11–13] focused on single neuron responses , with an emphasis on either a Poisson neuron model with an exponential rate function , or simpler linear Gaussian models . Such models are less suitable for recurrent neural networks . Exponential rate functions cause instability , as the activity tends to to diverge , unless both the weights and the time bins are small . Linear networks are not a very realistic model for a neural network , and do not perform well in inferring synaptic connectivity [19] . Though we assumed a logistic neuron model ( Eq 1 ) , similar results can be derived for any spiking neuron model for which 1−f ( x ) = f ( −x ) . This is explained in section A . 3 S1 Text . Though we assumed the network does not have a stimulus ( G = 0 ) , one can be incorporated into the inference procedure . To do so , we treat the stimulus X⋅ , t simply as the activity of additional , fully observed , neurons ( albeit Xi , t ∈ ℝ while Si , t ∈ {0 , 1} ) . Specifically , we define a new “spikes” matrix Snew ≜ ( S⊤ , X⊤ ) ⊤ , a new connectivity matrix W new≜ ( W G 0 D × N 0 D × D ) , and a new observation matrix Onew ≜ ( O⊤ , 1T×D ) ⊤ . Repeating the derivations for Snew , Wnew and Onew , we obtain the same profile loglikelihood . Once it is used to infer Wnew , we extract the estimates of W and G from their corresponding blocks in Wnew . For simplicity and efficiency , we chose to focus on MAP estimates . However , other types of estimators and Bayesian approaches ( e . g . , MCMC , variational Bayes ) might be used with this approximate loglikelihood , and should be explored in future work . As we showed in section 2 , in order to infer network connectivity , we just need to estimate the first and second empiric spike statistics , defined in Eqs ( 9 ) – ( 10 ) . These statistics cannot be calculated exactly if some observations are missing; in this case they must be estimated , as we discuss in section 3 . 6 below . First , though , it is useful to discuss a few concrete examples of the partial network observation schemes we are considering ( Fig 1 ) . We discuss the pros and cons of each scheme in terms of both inferential and experimental constraints .
In this section we use a toy network with N = 50 neurons to visualize the common input problem , and its suggested solution—the “shotgun” approach . Errors caused by common inputs are particularly troublesome for connectivity estimation , since they can persist even as T → ∞ . Therefore , for simplicity , we work in a regime where the experiment is long and data is abundant ( T = 5⋅108 timebins ) . In this regime , any prior information we have on the connectivity becomes unimportant so we simply use the Maximum Likelihood ( ML ) estimator . We chose the weight matrix W to illustrate a “worst-case” common input condition ( Fig 2A ) . Note that the upper-left third of W is diagonal ( Fig 2B ) : i . e . , neurons i = 1 , … , 16 share no connections to each other , other than the self-connection terms Wi , i . However , we have seeded this W with many common-input motifs , in which neurons i and j ( with i , j ≤ 16 ) both receive common input from neurons k with k ≥ 17 . If we use a “shotgun” approach and observe the whole network with pobs = 16/50 with a fully random observation scheme , we obtain a good ML estimate of the network connectivity , including the 16 × 16 upper-left submatrix ( Fig 2C ) . Now , suppose instead we concentrate all our observations on these 16 neurons , so that pobs = 1 within that sub-network , but the other neurons are unobserved . If common input was not a problem , our estimation quality should improve on that submatrix ( since we have more measurements per neuron ) . However , if common noise is problematic , then we will “hallucinate” many nonexistent connections ( i . e . , off-diagonal terms ) in this submatrix . Fig 2D illustrates this phenomenon . In contrast to the shotgun case , the resulting estimates are significantly corrupted by the common input effects . Next , we quantitatively test the performance of the Maximum A Posteriori ( MAP ) estimate of the network connectivity matrix W using a detailed network model with biologically plausible parameters from the mouse visual cortex . Details on the network parameters , simulation details and definitions of the quality measures are given in section B in S1 Text . We use the inference method described in section 2 , with a sparsity inducing prior ( section C in S1 Text ) on a simulated network with GLM neurons ( Eqs ( 1 ) – ( 2 ) ) . First , in Fig 3 , we examine a small GLM network with N = 50 observed neurons , with an experiment length of 5 . 5 hours . As can be seen , the weight matrix can be very accurately estimated for high values of observation probability pobs , and reasonably well even for low value of pobs . For example , even if pobs = 0 . 04 , and only two neurons are observed in each timestep , we get a correlation of C ≈ 0 . 84 between inferred weights and the true weights , and the signs of the non-zero weights are only wrong only for 4 weights ( out of 448 non-zero weights ) . When pobs is decreased , the variance of the estimation increases , more weak weights are inferred as zero weights ( and vice versa ) , and we also see more “shrinkage” of the non-diagonal weights ( a decreased magnitude of the non-zero weights ) due to the L1 penalty imposed on them ( Eq 47 in S1 Text ) . In Fig 4 we demonstrate that our method works well even if the neuron model is not a GLM , as we assume , but a Leaky Integrate and Fire ( LIF ) neuron model ( Fig 4 ) . The model mismatch results in a weight mismatch by a global multiplicative constant , and in a worse estimate of the diagonal weights , due to the hard reset in the LIF model . Besides these issues , inference results are both qualitatively and quantitatively similar to results in the GLM network In Fig 5 , we examine another GLM network with N = 1000 observed neurons , which is closer to the scale of the number of recorded neurons in current calcium imaging experiments ( see activity simulation in Fig S1 S1 Text ) . The experiment duration is again 5 . 5 hours . Results are qualitatively the same as the case of N = 50 , except performance is somewhat decreased ( as we have more parameters to estimate ) . Additional information is available in Fig 6 . On the left ( A , D , G ) , we see that the algorithm converges properly to a single solution . In the middle panels ( B , E , H ) , we see that for pobs = 1 we have very good performance ( in terms of area under the ROC ) , but this performance declines for the excitatory weights as pobs decreases . The inhibitory weights are correctly detected much better than the excitatory weights . This is because most excitatory weights are much weaker , as can be seen on the right column ( C , F , I ) . In that column , we observe that strong weights are more easily detected than weak weights . Specifically , around the median of the excitatory weight distribution ( 0 . 178 ) , we detected 99 . 9% , 34 . 9% and 16 . 1% of all the weights , when pobs = 1 , 0 . 2 and 0 . 1 , respectively . Next , in Fig 7 we quantify how inference performance changes with parameters . We vary the number of neurons , N , observation probability pobs , mean firing rate m and connection sparsity pconn . For the given parameters N , pobs and m , performance monotonically improves when T increases . These scans suggest we can maintain a good quality of connectivity estimation for arbitrarily large or small values of N or pobs , respectively—as long as we sufficiently increase T . Note there is a lower bound on T , below which estimation does not work . Looking at Fig 7 , we find that approximately , this lower bound scales as T ∝ N p obs 2 . ( 18 ) Above this lower bound , estimation quality gradually improves with T . Moreover , in order to maintain good estimation quality ( up to some saturation level ) above this bound , T should be scaled as T ∝ N p obs 2 m 2 . ( 19 ) This scaling can be explained intuitively . Our main sufficient statistic is the partially observed spike covariance Σ ˜ ( k ) ( Eq 17 ) . Each component ( i , j ) of Σ ˜ ( k ) contains a sum of all the observed spike pairs ( T⟨Oi , t Oj , t−k Si , t Sj , t−k⟩T ) divided by the number of observed neurons ( T⟨Oi , t Oj , t−k⟩T ) . The total number of observed neuron pairs is approximately N T p obs 2 ( ignoring observation correlations ) , and the total number of observed spike pairs is approximately N T p obs 2 m 2 ( ignoring spike correlations , and assuming the firing rate is not very high ) , where T is measured in time bins . The total number of components in Σ ˜ ( k ) is N2 . Therefore , in each component of Σ ˜ ( k ) , the average number of observed neuron pairs is T p obs 2 / N , while the average number of observed spike pairs is approximately T p obs 2 m 2 / N ( except on the diagonal of Σ ( 0 ) , where we have Tpobs/N neuron pairs and Tpobs m/N spikes ) . We conclude that the number of both observed neuron pairs and spike pairs must be above a certain threshold so that inference will be able to work properly . Above these thresholds , performance improves further when pconn is decreased ( Fig 7 ) , as this reduces the effective number of parameters we are required to estimate . For analytic results on this issue see [28] . If our goal is to infer all the input connections of a single neuron , then performance can be significantly improved if we always observe the output of that neuron . This is demonstrated in Fig 8 . In this figure , we examine a single neuron with O ( 104 ) observed inputs , O ( 103 ) of which are non-zero ( implementation details in S1 Text , section B . 2 ) . The inputs are partially observed ( with pobs = 1 , 0 . 1 , 0 . 01 ) , but we always observe the output neuron . Therefore , the average number of observed neuron pairs and spike pairs in Σ ( 1 ) is increased to NTpobs and NmTpobs , respectively . This can improve the scaling relations in Eqs ( 18 ) and ( 19 ) to T/pobs and T/ ( pobs m2 ) , respectively , if the off-diagonal terms of Σ ( 0 ) are not too strong ( since the number of observations for these components still scales with ∝ p obs 2 ) . Thus , in Fig 8 , we see that even when pobs = 0 . 01 it is still possible to estimate strong weights with some accuracy , despite the large number of connections . In a real imaging experiment , we would not have direct access to spikes , as we have assumed for simplicity so far . Next , we test the estimation quality when we only have direct access to the fluorescence traces of activity ( Fig 10A ) . The fluorescence traces were generated using a model of GCaMP6f calcium fluorescence indicator . Implementation details are described in section B . 3 in S1 Text . As can be seen in Fig 10A , 10B , our spike inference algorithm works reasonably well , both in high and low noise regimes . We then infer network connectivity both from the inferred spikes and the true spikes . As can be seen in Fig 10C-10H , using the inferred spikes usually somewhat reduces estimation performance . This is due to the temporal inaccuracy in the spike estimation . For example , in the inhibitory neurons , the higher firing rates result in more missing spikes in the inference . This causes shrinkage in the magnitude of the inferred weights , since the cross-correlation is weakened by these missing spikes . Combining this information into the inference algorithm ( as in [9] ) , it may be possible to correct for this; we have not pursued this question further here . However , even at low observation probabilities ( pobs = 0 . 1 ) , strong weights are inferred reasonably well , and the sign of synapse is usually inferred correctly for almost all nonzero weights . Therefore , weight inference is still possible at low firing rates , using current generation fluorescence imaging methods .
Neural connectivity inference has attracted much attention in recent years . One approach to this problem is direct anatomical tracing [31 , 32] . However , this method is computationally challenging [33]; moreover , the magnitudes of the synaptic connections ( which also vary over time [34] ) currently cannot be inferred this way . Another approach , on which we focus here , aims to infer the synaptic connectivity from neural activity . This activity can be either action potentials ( “spikes” ) [16 , 35–37] or calcium fluorescence traces [9 , 18 , 38–40] which are approximately a noisy and filtered version of the spikes . Various inference procedures have been suggested for this purpose . Some works use model-free empirical scores [38 , 40 , 41] . Others assume an explicit generative model for the network activity [16 , 30 , 35–37] , and then infer connectivity by estimating model parameters . So far , only few works have validated the connectivity estimate with some form of “ground truth” . Gerhard et al . [19] inferred small scale anatomical connectivity , comparing different methods . A Generalized Linear Model ( GLM ) approach was successful , while linear models and model-free approaches failed . Volgushev et al . [42] estimated the weights of fictitious synapses ( injected current ) . Again , a GLM-based approach outperformed simple correlation-based approaches . Lastly , Latimer et al . [43] was able to infer the magnitude of intracellular synaptic conductances , using a modified GLM . These results indicate that a GLM-based approach should be the method of choice for estimating synaptic connectivity . The task of inferring synaptic connectivity is severely hindered by technical limitations on the number of neurons that can be simultaneously observed with sufficient quality . Typically , the scanning speed of the imaging device is limited , so we cannot cover the entire network with a high enough frame rate and signal-to-noise ratio to infer spikes from the observed fluorescence traces . Previous studies indicate that at low frame rates ( below 30Hz [9] ) , synaptic connectivity cannot be inferred . In such low frame rate regimes , one may use spike correlations or simple dynamical systems as a coarse measure of effective connectivity ( e . g . , [44] ) , but such measures are not claimed to predict synaptic connectivity , only provide a statistical description of the network dynamics . Therefore , common approaches to infer connectivity of a neural network focus all the observations in one experiment on a small part of the network , in which all neurons are fully observed at a high frame rate . However , unobserved input into this sub-network can generate significant error in the estimation , and this error does not vanish with longer experiments . Various works aimed to deal with this persistent error: [17] inferred connectivity in a simulated two-neuron network in which one neuron was never observed; [45] inferred connectivity in a simulated network with two observed neurons and an unobserved common input; [46] inferred unobserved common input in an experimentally recorded network of 250 neurons using a GLM network with latent variables; [47] inferred connectivity in a simulated network with 100 neurons where 20−50 were never observed , with a varying degree of success . To help deal with the “common input” problem , we propose a “shotgun” approach , in which we reconstruct network connectivity by serially observing small parts of the network—where each part is observed at a high frame rate for a limited duration . Thus , despite the limited scanning speed of the imaging device , by using this method , we can extend the number of the neurons covered by the scanning device and effectively decrease the number ( and therefore the effect ) of unobserved common inputs . Additionally , as only a small part of the network is illuminated together , this method can potentially reduce phototoxicity and photobleaching , and allow long , possibly chronic [48] , imaging experiments . We showed here that the proposed method is capable of incorporating prior information about the sparsity of synaptic connections . More specific information could be included . An abundance of such prior information is available for both connection probabilities and synaptic weight distributions as a function of cell location and identity [52] . Cutting edge labeling and tissue preparation methods such as Brainbow [53] and CLARITY [54] are beginning to provide rich anatomical data about “potential connectivity” ( e . g . , the degree of coarse spatial overlap between a given set of dendrites and axons ) that can be incorporated into these priors . Exploiting such prior information can significantly improve inference quality , as demonstrated in previous network inference papers [9 , 17 , 55] . For example , by adjusting the L1 regularization parameters , we can reflect such additional priors: that the probability of having a connection between two neurons typically decreases with the distance between two neurons , and that it is affected by the neuronal type . Another way to improve connectivity estimates is to use stimulus information . For example , increasing the firing rate can improve quality ( Eq 19 and Fig 7 ) , up to a limit . If the firing rate is too high , it becomes harder to infer spikes from fluorescence . A more sophisticated spatio-temporal stimulus scheme can potentially lead to significant improvements in estimation quality [56] . The type of stimulus used can also affect performance . Sensory stimulation usually affects the measured network indirectly , potentially through many layers of neuronal processing . This may result in undesirable common input ( “noise correlations” ) . Optogenetic stimulation does not have this problem , since it stimulates neurons directly by using light sensitive ion channels . However , this type of optical stimulation can potentially interfere with optical recording . Such cross-talk can be minimized by using persistent ion channels [57] ( which require only a brief optical stimulus to be activated ) , or more sophisticated types of stimulation schemes [58 , 59] . Such optogenetic approaches , coupled with the inference and experimental design methods described here , have the potential to lead to significantly improved connectivity estimates . Even if all the neuronal inputs are eventually observed , if the observation probability pobs is low then the variance due to the unobserved inputs may still be high , since , at any given time , most of the inputs to each neuron will be unobserved ( see also [28] ) . As a result , the duration of the experiment required for accurate inference increases quadratically with the inverse of the observation probability ( Eqs ( 18 ) – ( 19 ) and Fig 7 ) , and weak weights become much harder to infer ( Fig 6 ) . Note this variance may be significantly reduced if we only aim to infer the input connections to only a few neurons ( Fig 8 ) . However , in many cases we wish to infer the entire network . In those cases the variance issue will persist , for any fixed observation strategy that does not take into account any prior information on the network connectivity . However , there might be a significant improvement in performance if we can focus the observations on synaptic connections which are more probable . This way , we can effectively reduce input noise from unobserved neurons , and improve the signal to noise ratio . As a simple example , suppose we know the network is divided into several disconnected components . In this case , we should scan each sub-network separately , i . e . , there is no point in interleaving spike observations from two disconnected sub-networks . How should one focus observations in the more general case , making use of past observations in an online manner ? Again , we leave this “active learning” problem as an important direction for future research . In this work we suggest a “shotgun” experimental design , in which we infer the connectivity of a neural network from highly sub-sampled spike data . This is done in order to overcome experimental limitations stemming from the bounded scanning speed of any imaging device . To do this , we develop a statistical expected loglikelihood-based Bayesian method . This method formally captures the intuitive notion that empiric spike correlations and mean spike rates are approximately the sufficient statistics for connectivity inference . Exploiting these sufficient statistics , our method has two major advantages over previous related approaches: ( 1 ) it is orders of magnitude faster ( 2 ) it can be used even when the spike data is massively sub-sampled . We show that by using a double serial scanning scheme , all spike rates and correlations can be eventually inferred ( and therefore neural connectivity ) . We demonstrate numerically that our method works efficiently in a simulated model with highly sub-sampled data and thousands of neurons . We conclude that the limited scanning speed of an imaging device recording neuronal activity is not a fundamental barrier which prevents consistent estimation of network connectivity . | Optical imaging of the activity in a neuronal network is limited by the scanning speed of the imaging device . Therefore , typically , only a small fixed part of the network is observed during the entire experiment . However , in such an experiment , it can be hard to infer from the observed activity patterns whether ( 1 ) a neuron A directly affects neuron B , or ( 2 ) another , unobserved neuron C affects both A and B . To deal with this issue , we propose a “shotgun” observation scheme , in which , at each time point , we observe a small changing subset of the neurons from the network . Consequently , many fewer neurons remain completely unobserved during the entire experiment , enabling us to eventually distinguish between cases ( 1 ) and ( 2 ) given sufficiently long experiments . Since previous inference algorithms cannot efficiently handle so many missing observations , we develop a scalable algorithm for data acquired using the shotgun observation scheme , in which only a small fraction of the neurons are observed in each time bin . Using this kind of simulated data , we show the algorithm is able to quickly infer connectivity in spiking recurrent networks with thousands of neurons . | [
"Abstract",
"Introduction",
"Methods",
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| 2015 | Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data |
Chagas disease , caused by the vector-borne protozoan Trypanosoma cruzi , is increasingly recognized in the southern U . S . Government-owned working dogs along the Texas-Mexico border could be at heightened risk due to prolonged exposure outdoors in habitats with high densities of vectors . We quantified working dog exposure to T . cruzi , characterized parasite strains , and analyzed associated triatomine vectors along the Texas-Mexico border . In 2015–2016 , we sampled government working dogs in five management areas plus a training center in Texas and collected triatomine vectors from canine environments . Canine serum was tested for anti-T . cruzi antibodies with up to three serological tests including two immunochromatographic assays ( Stat-Pak and Trypanosoma Detect ) and indirect fluorescent antibody ( IFA ) test . The buffy coat fraction of blood and vector hindguts were tested for T . cruzi DNA and parasite discrete typing unit was determined . Overall seroprevalence was 7 . 4 and 18 . 9% ( n = 528 ) in a conservative versus inclusive analysis , respectively , based on classifying weakly reactive samples as negative versus positive . Canines in two western management areas had 2 . 6–2 . 8 ( 95% CI: 1 . 0–6 . 8 p = 0 . 02–0 . 04 ) times greater odds of seropositivity compared to the training center . Parasite DNA was detected in three dogs ( 0 . 6% ) , including TcI and TcI/TcIV mix . Nine of 20 ( 45% ) T . gerstaeckeri and T . rubida were infected with TcI and TcIV; insects analyzed for bloodmeals ( n = 11 ) fed primarily on canine ( 54 . 5% ) . Government working dogs have widespread exposure to T . cruzi across the Texas-Mexico border . Interpretation of sample serostatus was challenged by discordant results across testing platforms and very faint serological bands . In the absence of gold standard methodologies , epidemiological studies will benefit from presenting a range of results based on different tests/interpretation criteria to encompass uncertainty . Working dogs are highly trained in security functions and potential loss of duty from the clinical outcomes of infection could affect the work force and have broad consequences .
Chagas disease , a potentially deadly cardiac disease of humans and dogs , is caused by the flagellated protozoan parasite Trypanosoma cruzi . The parasite is transmitted by infected hematophagous triatomine insects , commonly known as ‘kissing bugs’ . Chagas disease is estimated to infect nearly 6 million people throughout Latin America , and occurs across the southern US in enzootic cycles [1 , 2] , where raccoons and other wildlife serve as reservoirs [2 , 3] . In many areas of Latin America , such as in the Gran Chaco ecosystem , domestic dogs are an important reservoir of T . cruzi and domestic vectors that fed on dogs showed higher infection prevalence than vectors that fed on other domestic hosts [4 , 5] . The importance of canines in the T . cruzi transmission cycle in the US is not yet understood . The occurrence of T . cruzi infected canines in the USA is especially high in the state of Texas [1 , 6 , 7] , where 439 cases were reported across 58 counties between 2013–2015 when there was mandatory reporting of T . cruzi infected dogs [8] . Texas harbors at least seven established species of triatomine vectors capable of transmitting T . cruzi [3] and infected wildlife are widespread [1] . The high frequency of canines infected with T . cruzi likely reflects robust enzootic transmission in the state . Outside of Texas , dogs infected with T . cruzi have been reported in Louisiana [9 , 10] , Oklahoma [11 , 12] , Tennessee [13] and Virginia [14] . Across the studied populations , apparent seroprevalence ranged from 3 . 6–57 . 6% and predispositions of infection status with certain breeds or types of dogs do not appear to be strong , with hunting dogs , working dogs , household pets , shelter and stray dogs all impacted [6 , 7 , 9 , 12 , 14 , 15] . T . cruzi infection can occur by vector-mediated transmission through the introduction of infected bug feces into the bite site or mucous membrane or through the ingestion of infected bugs or their feces [5] . Additionally , congenital transmission may occur [3] . Dogs are more likely to become infected than humans [16 , 17] , which could be from dog’s affinity to consume bugs [12 , 18–21] . T . cruzi-infected dogs may be asymptomatic or may develop debilitating acute or chronic cardiac disease , characterized by myocarditis , hepatomegaly , ascites , cardiac dilatation , or sudden death [22] . There are currently no vaccinations or approved anti-parasitic treatments for T . cruzi infections in dogs in the US , and infected dogs are treated symptomatically . The Department of Homeland Security ( DHS ) of the US government manages over 3 , 000 working dogs in various capacities including the Transportation Security Authority , Coast Guard , Secret Service , Federal Protective Services , Customs and Border Protection , and Federal Operations . These dogs are highly trained in working duties performed in the indoor and outdoor environment including search and rescue functions as well as detection of concealed persons , narcotics , or explosives . DHS working dogs may be at increased risk for contact with vector species from working and sleeping outdoors . Some of the working dogs are kept in group kennels , which have previously been shown to be a risk factor for T . cruzi infection [7] . Their working environment could further be an attractant to the vector , where there is high vehicle traffic emitting CO2- a known attractant [23] , bright lights at night , and concentrations of animals and people in otherwise rural areas . In order to provide a baseline for conducting clinical assessments and developing disease management strategies , we conducted a seroepidemiological investigation to quantify the prevalence of T . cruzi infection in populations of working dogs along the Texas-Mexico border . Additionally , we aimed to determine the infection status and feeding patterns of triatomine vectors in the environments where these dogs work and are kenneled .
All canine samples were collected in adherence with animal use protocols approved by Texas A&M University’s Institutional Animal Care and Use Committee on 08/17/2015 under the number 2015–0289 . Written consent was received for each canine sampled from DHS personnel . Sampled DHS working dog breeds were predominantly Belgian Malinois and German Shepherds . Most dogs were bred in Europe , and less commonly dogs came from vendors within Texas or other parts of the US . Dogs receive over 6 months of training at either a training facility in El Paso , Texas , or Front Royal , Virginia , and specialize in various jobs such as track and trail , detection of humans , narcotics , currency , or agricultural products , and search and rescue . After training , dogs are typically assigned to a specific management area and have limited travel . The dogs in our study perform working duties either immediately adjacent to the geopolitical border ( ports of entry ) or north of the border ( checkpoints ) . Off-duty canines are either kenneled individually at their handler’s residence or in a group kennel . Residential kennels are indoor-outdoor metal kennels raised 2 feet from the ground , giving the dog the option of sleeping inside or outside . Group kennels are indoor-outdoor , concrete kennels , and dogs are confined inside during the night . We used a cross sectional study design to collect blood samples from DHS working dogs during November 2015 and April 2016 . Working dogs were sampled from all 5 management areas , with a goal of sampling at least 60% of the dogs that occurred within each management area . Additionally , we sampled DHS canines that were in training at a training facility in management area #1 ( Fig 1 ) . Sample criteria included dogs over 6 months in age and on active duty or in training . Demographic information was collected on all dogs sampled including age , sex , breed , canine job , sleeping location and station of duty . A minimum of 1 ml of blood was collected by venipuncture and aliquoted into serum and EDTA tubes . Samples were screened for anti-T . cruzi antibodies by Chagas Stat-Pak rapid immunochromatographic test ( ChemBio , NY ) which was designed for use in humans and has been validated in dogs [9] . Stat-Pak assay uses three T . cruzi recombinant antigens that are bound to the assay membrane solid phase . Serum or plasma samples were tested according to manufacturer’s protocol and read for result determination after 15 minutes . Tests were considered negative when no color developed and positive when a clear line developed . Additionally , very faint bands that were not perceptible enough to be consider a clear positive , yet with some low level of color development to differentiate them from negative , were tracked as ‘inconclusive’ and subjected to additional testing . All positive or inconclusive samples as determined by Stat-Pak plus 10% of the negatives were tested by both indirect fluorescent antibody ( IFA ) test and Trypanosoma Detect ( InBios , International , Inc . , Seattle , WA ) . IFA detects anti-T . cruzi IgG antibodies and was performed by the Texas Veterinary Medical Diagnostic Laboratory ( TVMDL , College Station , TX ) on serum or plasma samples . Titer values of 20 or higher were considered positive per TVMDL standard protocol; this titer value cutoff has also been used in human medicine [24] . IFA readers were blinded to previous serologic results . Trypanosoma Detect is a rapid immunochromatographic dipstick assay that employs a multi-epitope recombinant antigen for the detection of anti-T . cruzi antibodies . The Trypanosoma Detect test was designed for use in humans but has been found to have high sensitivity and specificity for use in dogs [25] . Serum or plasma were tested according to manufacturer's protocol and read for result determination after 20 minutes . Test results were scored as positive , inconclusive , or negative using the same criteria as described above for the Chagas Stat-Pak . Serological positive status was assigned to samples that tested positive on at least two independent tests . Amplification of parasite DNA from blood samples by real time PCR was performed on all sampled dogs . DNA was isolated from 250 uL of buffy coat by using E . Z . N . A . Tissue DNA kit ( Omega Bio-Tek , Norcross , GA ) . Negative controls ( phosphate buffered saline or water template ) were included in the DNA extractions and the PCR . To determine if analysis of clot rather than buffy coat may result in a greater ability to detect parasite DNA , we conducted additional work with a subset of samples as follows . From 12 dog samples , we extracted DNA from 1 mL of clot for PCR analysis . These 12 dogs comprised 10 that were seropositive and PCR negative based on buffy coat; 1 that was seropositive and PCR positive based on buffy coat; and 1 that was seronegative and PCR positive based on buffy coat analysis . Samples were first screened for presence of T . cruzi satellite DNA using the Cruzi 1/2 primer set and Cruzi 3 probe in a real-time assay to amplify a 166-bp segment of a repetitive nuclear DNA [26 , 27] . Reactions consisted of five microliters of extracted DNA , primers I and II each at a concentration of 0 . 75 μM , 0 . 25 μM of probe , and iTaq University Probes Supermix ( BioRad Laboratories , Hercules , CA ) , in a 20 μL reaction volume . Previously published thermocycling parameters were followed except with a 3-minute initial denaturation using a Stratagene MxPro3000 ( Agilent Technologies , Santa Clara , CA ) . T . cruzi DNA extracted from isolate Sylvio X10 CL4 ( ATCC 50800 , American Type Culture Collection [ATCC] ) was used for a positive control . Machine-calculated thresholds and reaction curves were visually checked for quality . Samples with Ct values less than 34 were considered suspect positive and subjected to further testing . Suspect positive samples by qPCR were run on a second , independent PCR using T . cruzi 121/122 primers to amplify a 330-bp region of kinetoplast DNA [28 , 29] . Reactions included 1μL template DNA , primers at final concentrations of 0 . 75 μM each , and FailSafe PCR Enzyme Mix with PreMix E ( Epicentre , Madison , WI ) in a final reaction volume of 15 μL . Amplicons were visualized on 1 . 5% agarose gels stained with GreenGlo safe DNA dye ( Denville Scientific Inc . , Metuchen , NJ ) . Samples that yielded a band of the appropriate size were interpreted as positive in this assay . Parasite positive dogs were defined as those that tested positive on both the rt-PCR screening and the secondary PCR assays . We used a multiplex quantitative , real time PCR to determine T . cruzi discrete taxonomic unit ( DTU ) of samples that were positive or suspect positive on the screening assay based on amplification of the nuclear spliced leader intergenic region ( SL-IR ) [30] . Using a QIAGEN Multiplex PCR Kit ( QIAGEN , USA ) reactions were performed using 2μL template DNA in a final volume of 20 μl and run on a BioRad CFX96 ( Hercules , CA , USA ) . The only deviation from the previously described protocol was the extension of cycles from 40 to 45 and substitution of dyes as previously described [7] . Positive controls consisted of DNA from triatomines collected across Texas that were previously characterized as infected with TcI or TcIV based on amplification and sequencing of the TcSC5D gene [31] . Samples with Ct values less than 34 were considered positive , and fluorescence signal determined the strain type . Triatomine bugs were opportunistically collected by dog handlers in summer 2016 from group kennels , outside handler’s residence around canine housing , and at stations where dogs worked . To encourage collections , outreach materials with photos of triatomines and look-alike species were disseminated by email and in printed format to dog handlers prior to the summer peak of adult triatomine activity . Bugs were identified to species using morphologic features [32] and sexed . After bugs were washed in 10% bleach solution and rinsed in distilled water , sterile instruments were used to dissect the bugs , isolate hindgut material and evidence of a recent bloodmeal was noted . DNA was extracted from hindguts and tested for T . cruzi DNA and determination of T . cruzi DTU using the same methods as the above testing of dog samples . In order to determine the source of recent bloodmeals , hindgut DNA was subjected to PCR amplification of vertebrate cytochrome B sequences using previously published primers and cycling conditions [33 , 34] . Reactions included 3 μL template DNA , primers at final concentrations of 0 . 66 μM each , and FailSafe PCR Enzyme Mix with PreMix E ( Epicentre , Madison , WI ) in a final reaction volume of 50 μL . Amplicons were visualized on 1 . 5% agarose gel , prepared for sequencing using ExoSAP-IT ( Affymetrix , Santa Clara , CA , USA ) , and Sanger sequencing was performed ( Eton Bioscience Inc . , San Diego , CA , USA ) . Resulting sequences were compared to existing sequences using Basic Local Alignment Search Tool ( National Center for Biotechnology Information , US National Library of Medicine ) . In recognition of the potential for contamination from the environment , samples that aligned to human were re-run on another PCR assay to provide a secondary line of evidence . Due to the uncertainty of sample serostatus associated with the inconclusive band development , antibody-positive dogs were defined using two methods; a ) in the conservative method , inconclusive band development was interpreted as negative , and b ) in the inclusive method , inconclusive band development was interpreted as positive . In the absence of gold standard serological methodology , these two different criteria of positivity ( method A and B ) were analyzed separately to provide a range of results . To evaluate the relationship between potential risk factors and the serostatus of canines , data were imported into R software [35] for analysis . Assessed variables were dog age ( young = 6 months to <3 years , middle age = ≥ 3 years to <6 years , senior = ≥ 6 years ) , sex , breed , sleeping location ( individual kennel at handler’s residence or group kennel ) and management area ( locations 1–5 or training center ) . Due to the small sample size of dogs in some jobs , canine job was dichotomized based on type of detection . Bivariable analysis using the chi-squared or Fisher’s exact was used to identify putative risk factors . Factors with a p≤ 0 . 25 from the initial screening were used in a logistic regression model , while controlling for management area as a random effect . Generalized linear mixed models were calculated and factors with values of p < 0 . 05 were considered significant . Odds ratios and 95% confidence intervals were calculated . To determine variation in seroprevalence across management areas , a logistic regression model was used in which the training center served as the referent to which all five management areas were compared . Kappa index was used to test the agreement between each pairwise combination of the results of the three serological assays for the samples that were tested on all three assays; this sample set was biased toward Stat-Pak positive samples .
In considering inconclusive bands on immunochromatographic tests as negative , 39 of 528 ( 7 . 4% ) of dogs were seropositive for antibodies to T . cruzi on at least 2 assays . Across management areas and the training center , seroprevalence ranged from 4 . 3% to 10% ( Fig 1 ) . In the bivariable analysis , T . cruzi seroprevalence was significantly different across dog breed ( p = 0 . 03 ) , with seroprevalence of German Shepherds being lowest ( 3 . 7% ) and ‘other’ breeds being highest ( 14 . 3%; Table 1 ) . Dogs that spent off-duty time in residential kennels had a significantly higher seroprevalence ( 29/295 , 9 . 8% , p = 0 . 02 ) than those that were group-kenneled ( 10/233 , 4 . 3% ) . Seroprevalence was significantly different among age groups ( p = 0 . 04 ) , where senior dogs had a seroprevalence of 10 . 4% , middle age dogs a seroprevalence of 7 . 9% and young dogs 3 . 2% . Seroprevalence did not vary significantly by sex or canine job . Multivariable logistic regression analysis showed a significant association ( odds ratio [OR] 0 . 41 , 95% CI 0 . 17–0 . 99 , p = 0 . 047 , Table 2 ) between breed and seropositive dogs , after controlling for management areas as a random effect ( Table 2 ) , in which German Shepherds were associated with a significantly lower seroprevalence ( 3 . 7% ) than Belgian Malinois ( 8 . 6% ) . No significant association was found between age , job , or sleeping location and seroprevalence . In considering inconclusive bands on serologic tests as positive , 100 of 528 ( 18 . 9% ) of dogs were seropositive for antibodies to T . cruzi on at least 2 assays . Seroprevalence ranged from 11 . 6% to 26 . 7% across management areas and the training center ( Fig 1 ) . When running bivariable analysis , dogs that spent off-duty time in residential kennels ( 65/295 , 22% ) were marginally ( p = 0 . 09 , Table 2 ) more likely to be seropositive than dogs sleeping at a group kennel ( 36/233 , 15 . 4% ) . Seroprevalence did not vary significantly by age , breed , sex or canine job . Multivariable logistic regression analysis showed that there was no association between age , job , or sleeping location and seroprevalence . Backwards elimination was performed and when only age was included in the model there was a marginal association in which old dogs had a higher seroprevalence ( 39/182 , 21 . 4% ) than young dogs ( 22/156 , 14 . 1%; p = 0 . 09 ) , after controlling for management areas as a random effect . While seroprevalence did not significantly differ across management areas and the training center when positivity was defined according to Method A , dogs from management area #2 ( OR 2 . 6 , 95% CI 1 . 0–6 . 7 , p = 0 . 04 ) and #3 ( OR 2 . 8 , 95% CI 1 . 2–6 . 8 , p = 0 . 02 ) had significantly higher seroprevalence compared to the training center when seropositivity was determined according to Method B ( Table 3 ) . This indicates that area #2 and #3 were both associated with many samples that produced very faint ( inconclusive ) bands on the immunochromatographic tests . In comparing the results across all three serological testing platforms ( Table 4 ) , all IFA positive samples are positive on Trypanosoma Detect , and all but two samples are Stat-Pak positive-both of these samples having a titer of 20 . When comparing the IFA negative samples 71 . 3% are positive or inconclusive on Stat-Pak and 48 . 4% are positive or inconclusive on Trypanosoma Detect . From the 528 dog samples in the study , 215 samples were tested on all three serology assays . Overall test agreement ranged from slight to moderate agreement based on the Kappa Indices ( Table 5 ) , with agreement between tests being better when interpreting immunochromatographic test results using the conservative method A ( kappa range 0 . 37–0 . 48 ) compared to inclusive method B ( kappa range 0 . 05–027 ) . The best agreement was using method A between Stat- Pak and Trypanosoma Detect , with a Kappa index of 0 . 48 ( moderate agreement ) . Of the 57 randomly-selected Stat-Pak negative samples that were subjected to additional serologic testing , one was positive on both IFA ( titer 20 ) and Trypanosoma Detect; this sample was counted as positive in the seroprevalence estimates . Nine ( 15 . 8% ) samples that were both Stat-Pak and IFA negative were positive on Trypanosoma Detect; these dogs were counted as negative in the seroprevalence estimates , but could be false negatives . When applying this prevalence of potential false negatives to the total number of dogs that were negative by Stat-Pak , an additional 49 dogs are extrapolated to be potential false negatives; including these samples as positive would increase seroprevalence to 15 . 9% ( 84 dogs total ) by conservative method A , and 25 . 4% ( 149 dogs total ) by inclusive method B . Inconclusive bands were reported from 108 ( 20 . 5% ) samples screened on Chagas Stat-Pak . When tested on IFA only 1 ( 0 . 9% ) inconclusive tested positive with a titer of 20 . When inconclusive samples were run on Trypanosoma Detect , 37 ( 29 . 6% ) had inconclusive bands on Trypanosoma Detect , 20 ( 18 . 5% ) were positive , and 51 ( 47 . 2% ) were negative . T . cruzi DNA was detected in the buffy coat fraction of the blood in three of 528 ( 0 . 6% ) dog samples according to our diagnostic method which included amplification in both a screening and confirmatory assay . The first PCR-positive dog was sampled from area # 5 in November and was positive for antibodies by all three serology assays with a relatively high titer ( 640 ) on IFA . Using the multiplex real time PCR to determine T . cruzi DTUs , we found that this dog harbored DTU TcIV . The second PCR-positive dog was from the canine training center , sampled in April , positive on all serology assays with a titer of 320 and harbored a mix TcI/TcIV . The third dog was from area # 2 , sampled in April , was negative by all serological assays , and strain type could not be determined . When this PCR positive yet serologically-negative dog was included in binomial analysis of risk factors and the logistical regression model , no difference was found in significant associations . The subset of 12 samples that were subjected to an additional DNA extraction from 1mL of clot produced PCR results that were identical to the results obtained from the 250 uL buffy coat extractions with the exception of the sample from the seronegative , buffy coat-positive sample . This sample was negative based on clot analysis . In the summer of 2016 , a total of 20 adult triatomine bugs of two species ( 18 Triatoma gerstaeckeri and 2 T . rubida ) were opportunistically collected by canine handlers from three management areas ( Table 6 ) . Kissing bugs were collected from stations where dogs and handlers work ( n = 6 ) , handler’s residence near canine housing ( n = 7 ) , group kennels ( n = 4 ) , from the field ( n = 2 ) and 1 bug was removed from a dog while working . Nine ( 45% ) triatomines were positive for T . cruzi including half of the T . gerstaeckeri specimens but neither of the two T . rubida specimens . Of the 9 positive bugs , parasite strain typing revealed DTU TcI in 6 , TcIV in 1 , and a mixed TcI/TcIV coinfection in 2 . From dissection , 13 of the 20 bugs had evidence of a recent blood meal in their hind gut , and 11 of these yielded results after the blood meal analysis protocols , revealing human , canine , coastal-plain toad ( Bufo nebulifer ) and rat ( Rattus rattus ) DNA ( Table 6 ) .
We found widespread T . cruzi infection in government working dogs along the Texas-Mexico border . DHS working dogs play an important role in detection and security functions in the Unites States and the clinical manifestation of infection may be associated with significant future economic and security consequences . We are aware of only two prior epidemiological investigations of T . cruzi infection in working dogs in the US . In 2007 , a serological survey was conducted on military working dogs ( MWD ) in San Antonio , TX , after veterinarians noted an increase in Chagas disease diagnoses , revealing 8% of the kenneled dogs were positive by IFA [36] . Such findings are of utmost importance in these dogs; in 2009 , MWDs deployed in Iraq were evacuated due to cardiac symptoms and diagnosed with T . cruzi infection leaving troops vulnerable without explosive detection dogs [36] . Recently , populations of working hound dogs in south central Texas that are used for scent detection and track/trail were characterized with an extremely high seroprevalence of 57 . 6% ( n = 85 ) in which positive dogs were reactive on both Stat-Pak and IFA [7] . The study population also included many dogs with parasite DNA in the blood and other organs , and infected triatomines collected from the dog kennels were determined to have fed on dogs , allowing the authors to conclude that multi-dog kennels can be high risk environments of T . cruzi transmission [7] . Exposed dogs were present in all five management areas and the canine training school , with an overall apparent seroprevalence of 7 . 4–18 . 9% . This seroprevalence is similar to that reported from dogs in Chagas-endemic areas in Latin America including populations in Peru ( 12 . 3% ) [37] , Argentina ( 45 . 6% ) [38] , Panama ( 11 . 1% ) [39] , Costa Rica ( 27 . 7% ) [19] , Yucatan State , Mexico ( 9 . 8%-14 . 4% ) [40] and Mexico State , Mexico ( 10%-15 . 8% ) [41] . Previous epidemiological investigations of T . cruzi in canines in the US are limited , and most have focused on stray dogs or those sampled from animal shelters , which may be considered as high risk populations due to outdoor activity . A serosurvey of high risk kenneled dogs in southern Louisiana found that 22 . 1% [9] of dogs tested positive for T . cruzi antibodies using the same three serology assays performed in this study . A study in Oklahoma sampling shelter dogs and pet dogs concluded that 3 . 6% dogs were seropositive when testing by radioimmunoprecipitation assay ( RIPA ) [12] . Earlier studies in southern Texas stray dogs 375 dogs were tested and 7 . 5% were positive by indirect immunofluorescence [15] . Similarly , across Texas shelter dogs had a seroprevalence of 8 . 8% when testing dogs on Chagas Stat-Pak [6] . These studies and ours suggest that despite the regular veterinary care , quality food and shelter , highly-valued working dogs can have similar or greater T . cruzi infection than stray and shelter dogs in the US and free roaming or pet dogs in endemic countries . Both population-level and individual-level T . cruzi studies of naturally-infected hosts suffer from a lack of gold standard tests or diagnostic recommendations . Discordance among tests results is prevalent in human and veterinary Chagas diagnostics . For example , a study looking at seroprevalence in people from Veracruz , Mexico used 5 assays and found that test agreements ranged from 0 . 038–0 . 798 on the Kappa index [42] . Similarly , using the Kappa index we found a high discordance among serology assays used , with agreement ranging from slight to moderate depending on the interpretation method . Assay discordance could be affected by the single freeze-thaw cycle , or the age of the sample . These diagnostic challenges make it difficult to directly compare seroprevalence across populations and diagnostic methods , and presents a challenge in clinical settings for diagnosis . Two of the three serological tests we used are only available for research use for dogs in the US , and a limited number of commercial laboratories offer canine T . cruzi diagnostic test services . As in most diagnostic tests , there is some subjectivity in the interpretation of results , and the development of very faint ‘equivocal’ serological bands on both Chagas Stat-Pak and Trypanosoma Detect posed particular complexities in our analysis . The Stat-Pak and Trypanosoma Detect instructions state that band intensity will vary , but faint bands should be interpreted as positive [43 , 44] and that variation is dependent on the concentration of antibodies present [44] . However , some previous canine studies have counted faint bands as negative [6 , 9] while others have interpreted them as positive for analysis [45] . Our presentation of a seroprevalence calculated both conservatively ( very faint bands interpreted as negative ) and inclusively ( very faint bands interpreted as positive ) is an effort to account for imperfect diagnostics . Until refined T . cruzi diagnostic tools are available , we encourage transparency in presenting results on single vs . multiple tests across all strengths of test response . The discordance between test results and the within assay variation could be caused by parasite heterogeneity [45] . T . cruzi is notably heterogeneous with seven major genotypes or discrete typing units ( DTUs ) described as TcI-TcVI and TcBat which vary be region [46 , 47] . Additionally , a notable intra-DTU variability has been found [47 , 48] . Previous research has found that assay reactivity varies by geographic origin of the patient [49] . O’Connor and others found that strain TcI clusters geographic between North and South America [50] . The Chagas Stat-Pak was validated with human sera from Central America to detect strains circulation in that region [51] and may not be optimized for T . cruzi clones from Texas . When very faint bands were interpreted as positive ( method B ) , seroprevalence was significantly higher in two western management areas ( OR 2 . 6–2 . 8 , 95% CI: 1 . 0–6 . 8 p = 0 . 02–0 . 04 ) compared to the training center ( Table 3 ) , whereas this difference was not evident when very faint bands were interpreted as negative ( method A ) . The disproportionate abundance of very faint bands in this geographic area may be driven by differences in the locally-circulating T . cruzi clones . Diosque et al . performed a genetic survey of T . cruzi isolates within a restricted geographical area ( ~300 km2 ) and found five different clones circulating [52]; such findings are clinically and diagnostically relevant because parasite heterogeneity has been shown to cause varying infectivity and immune response [52–54] . In addition , host biological factors ( exposure history , coinfection , genetic makeup ) could also cause reaction variability within and across serology assays . Sleeping location ( group housed indoors vs . individually housed outdoors ) appeared to be independently associated with T . cruzi status with a higher seroprevalence in dogs sleeping outdoors than indoors by method A ( p = 0 . 02 ) , and marginally significant by method B ( p = 0 . 09 ) in bivariable analysis . Previous studies have indicated dogs housed outdoors where vector contact is more likely to be at a higher risk for exposure [6 , 9 , 39] . Dogs in Tennessee spending 100% of their time outdoors were significantly more likely to be seropositive for T . cruzi than dogs spend ≤50% of their time outdoors [13] . Seroprevalence did increase with age in both method A and B , but was only significant in bivariable analysis in method A , where senior ( >6 years ) and middle age ( ≥ 3 years to <6 years ) , were more likely to be seropositive than young dogs ( <3 years old ) ( Table 1 ) . This is anticipated in infectious disease since exposure increases with age and has been found in previous studies [7 , 13 , 17 , 55] . We found that German Shepherds were associated with a significantly lower seroprevalence ( 3 . 7% ) than Belgian Malinois ( 8 . 6% ) in our study; although the driving factors for this difference are currently unknown , it may relate to host behavior , differences in host immune response , or a physical characteristic . We found three dogs ( 0 . 6% ) harbored parasite PCR in their blood , suggesting that these dogs are parasitemic . While two of the three PCR-positive dogs also harbored detectable anti T-cruzi antibodies , one did not , suggesting this dog may have been in the acute stage of infection [56] . The two dogs with successfully typed infections harbored DTUs TcIV and a TcI/TcIV mix , consistent with previous studies on dogs in the US [57 , 58] . Both strain types infect a variety of hosts and vectors in the southern US [3] . DTU TcI is an ancient strain found throughout South and Central America and the predominant strain infecting humans in the US [3] , where it is also associated with wildlife reservoirs including opossums ( Didelphis virginiana ) [57] . TcIV is also associated with wildlife , especially raccoons ( Procyon lotor ) [3] and to our knowledge has not been implicated in the small number of typed human infections in the US . This study found a lower prevalence of dogs PCR positive then previous studies , which likely reflects the time of sampling ( November and April ) when the vector is less active and dogs in Texas are less likely to come in contact with the kissing bug [58] . In recognition of other datasets that have shown that analysis of clot , rather than buffy coat , may afford a greater the chance of detecting parasite DNA [59] , we subjected 12 clot samples to PCR and compared results to previous results from analysis of buffy coat . We found that buffy coat and clot results were identical across this subset with the exception of a sample from a single seronegative dog which was positive from buffy coat and negative from clot . Based on this small comparison trial , we suggest that the low frequency of encountering PCR-positive dogs in our study was not due to the blood fraction used in the analysis . We found an infection prevalence of 45% in the kissing bugs collected from areas where the working dogs frequent , including kennels , stations and handler’s residence , including DTUs TcI , TcIV , and TcI/IV mix . This infection is slightly lower than previous estimates across the state of Texas of 63% and 51% [59 , 60] . Bloodmeal analysis revealed canine , human , and wildlife DNA within the hindguts of these insects , underscoring the generalist feeding strategies of triatomines that often use the most locally abundant hosts . Strict protocols were used to reduce the risk of contamination of samples by exogenous DNA ( i . e . , human DNA ) , including surface sterilization of vectors and dissection of the hindguts . It is biologically plausible that the insects associated with suspected human blood feeding encountered humans at their residence or station or work . A study in California and Arizona that collected bugs by light traps found that 5 of 13 bugs ( 38% ) bugs were positive for a human blood meal , 4 fed on canine and 1 each for rat , pig , chicken and mouse [61] . In Texas , Gorchakov et al . found 65% ( n = 62 ) of bugs positive for human bloodmeal and 32% for canid bloodmeal [62]; in contrast , Kjos et al . found only 1% of vectors ( n = 96 ) collected from residential settings had fed on a human , and 20% on dogs [63] . Larger sample sizes of engorged vectors from the working dog environments will assist in learning the local vector-host interactions that sculpt disease risk . Using dogs as sentinels has been suggested for targeted vector control programs endemic areas such as Peru [37] and to monitor transmission in Argentina [55] . However , the relative importance of dogs as reservoirs , and whether or not they can be a sentinel species for human disease risk in the US , is unknown . Further , because the triatomines in the US tend not to be colonized within homes , dogs are less likely to be useful sentinels at the household level . Nonetheless , given these infected working dogs signal the presence of infected vectors in the environment , there are public health implications of these findings especially with respect to the human handlers who are exposed to the same environments . Because not all T . cruzi-infected dogs will develop disease [21] , the prognosis and clinical implications of the widespread presence of T . cruzi-infected government working dogs along the US-Mexico border is unknown . Nonetheless , the potential loss of duty days resulting in an inadequate canine workforce must be considered . Additionally , given that the canine training school in west Texas ( Fig 1 ) occurs in an area where triatomines are endemic , vector and canine surveillance must be conducted to determine if young dogs may be exposed to the parasite while in training , which would not only have implications for the health of the dog but also potentially afford dispersal of the parasite to the new areas across the US where these dogs are stationed . Understanding the epidemiology of T . cruzi infection is the first step toward implementing control measures to protect the health of these high-value working dogs . | Chagas disease , a potentially deadly cardiac disease of humans , canines and other mammals is caused by the parasite Trypanosoma cruzi . The parasite is primarily transmitted to dogs by ingestion of infected triatomine ‘kissing bug’ vectors or through contact with the insect’s feces . Previous studies concluded that stray and shelter dogs are at high risk of infection in the southern U . S . We proposed that high-value U . S . government working dogs along the Texas-Mexico border may also be at high risk because of their activities in regions with established , infected vector populations . We sampled 528 working dogs along the Texas-Mexico border , and found that 7 . 4–18 . 9% of dogs were positive for T . cruzi antibodies and a small proportion ( 0 . 6% ) also had parasite circulating in the blood . We collected two species of kissing bugs from the canine environments and used molecular approaches to determine that 45% were positive for T . cruzi and the majority had recently fed on canines . We highlight the need for better diagnostic tools for canine Chagas disease research and diagnosis . The widespread burden of T . cruzi infection in the government working dogs could be associated with far-reaching consequences for both animal and human well-being . | [
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| 2017 | Widespread Trypanosoma cruzi infection in government working dogs along the Texas-Mexico border: Discordant serology, parasite genotyping and associated vectors |
The coronary vasculature is an essential vessel network providing the blood supply to the heart . Disruptions in coronary blood flow contribute to cardiac disease , a major cause of premature death worldwide . The generation of treatments for cardiovascular disease will be aided by a deeper understanding of the developmental processes that underpin coronary vessel formation . From an ENU mutagenesis screen , we have isolated a mouse mutant displaying embryonic hydrocephalus and cardiac defects ( EHC ) . Positional cloning and candidate gene analysis revealed that the EHC phenotype results from a point mutation in a splice donor site of the Myh10 gene , which encodes NMHC IIB . Complementation testing confirmed that the Myh10 mutation causes the EHC phenotype . Characterisation of the EHC cardiac defects revealed abnormalities in myocardial development , consistent with observations from previously generated NMHC IIB null mouse lines . Analysis of the EHC mutant hearts also identified defects in the formation of the coronary vasculature . We attribute the coronary vessel abnormalities to defective epicardial cell function , as the EHC epicardium displays an abnormal cell morphology , reduced capacity to undergo epithelial-mesenchymal transition ( EMT ) , and impaired migration of epicardial-derived cells ( EPDCs ) into the myocardium . Our studies on the EHC mutant demonstrate a requirement for NMHC IIB in epicardial function and coronary vessel formation , highlighting the importance of this protein in cardiac development and ultimately , embryonic survival .
A functional coronary vasculature is essential to supply the heart with oxygenated blood . Cessation of the coronary circulation deprives the working myocardium of oxygen and nutrients , leading to irreversible damage to cardiac muscle and myocardial infarction . Coronary artery disease is the main form of cardiovascular disease , and causes significant morbidity and mortality world-wide [1] . Although mammals and other higher vertebrates have insufficient capacity to restore cardiac function following ischemia , a number of studies that exogenously reactivate elements of embryonic coronary vessel formation have demonstrated neovascularisation and regeneration of the infarcted mouse heart , consequently improving cardiac function [2–5] . Moreover , experiments using thymosin β4 have revealed that the specific restoration of the quiescent adult epicardium , the outer epithelial layer of the heart , to an embryonic state , permits the activation of cardiac precursors that contribute to neovascularisation in vitro [6] and in vivo [2 , 7] . However , our comprehension of both the cellular and molecular mechanisms that control this regeneration remain incomplete . Therefore , a deeper understanding of the processes that underpin coronary vessel formation may facilitate the generation of advanced and novel therapies to repair the injured heart . During mammalian cardiac development cells from the proepicardial organ ( PEO ) migrate onto the surface of the heart and adhere to the nascent myocardium of the post-looped heart tube [8] . This gives rise to an outer epithelial layer , termed the epicardium , which completely envelops the developing heart . Epicardial function is critical for cardiac development , since the epicardium provides a source of paracrine signals for myocardial growth ( reviewed in [9–11] ) . Additionally , as cardiac development progresses , a subset of epicardial cells undergo EMT and migrate through the subepicardial space to contribute to the formation of the coronary vasculature and cardiac fibroblasts ( reviewed in [11–15] ) . While the contribution of the epicardium to the endothelial cell lining of the coronary vessels has been challenged recently [15 , 16] , it is clear that epicardial function is absolutely essential to establish the coronary vasculature and facilitate cardiogenesis . Furthermore , reactivating embryonic processes in the quiescent adult epicardium has been shown to facilitate the repair and regeneration of cardiac tissue in response to injury , highlighting the therapeutic potential of this tissue [5 , 17 , 18] . A comprehensive understanding of the critical molecular mechanisms that underpin epicardial function during mammalian cardiogenesis is needed to facilitate the development of epicardial-based therapies . To this end , we have studied mouse mutants with cardiac defects isolated from a balancer chromosome mutagenesis screen . We found that the l11Jus27 mutant [19] carried two different embryonic lethal mutations , one of which displayed a phenotype of embryonic hydrocephalus and cardiac defects ( EHC ) . EHC mutant embryos fail to form a mature , functional coronary system , resulting in late-gestation lethality . We identified a mutation in a splice-donor site in the Myh10 gene as the cause of the EHC phenotype . Myh10 encodes Non-Muscle Myosin Heavy Chain IIB ( NMHC IIB ) , a component of the protein hexamer Non-Muscle Myosin IIB ( NMIIB ) . There are 3 different NMHC II isoforms ( namely IIA , IIB and IIC ) , each displaying specific cellular expression and functionality ( reviewed extensively in [20 , 21] ) . NMHC IIB is a cytoskeletal protein with diverse functions , including: cytokinesis [22 , 23] , regulation of cell shape [24] , adhesion [25 , 26] and migration [27 , 28] . Prior studies of other mutants with abnormal coronary vessel development have shown that these defects arise primarily from epicardial cell dysfunction [29–32] . Accordingly , EHC mutant epicardial cells form an abnormal epithelial layer on the surface of the heart . In addition , the migration of EHC epicardial-derived cells into the underlying myocardium is impaired . EHC epicardial cells also show decreased expression of EMT markers within the epicardium , suggesting that NMHC IIB not only plays an important role in regulating EPDC migration , but also in promoting epicardial EMT . Ultimately , NMHC IIB may therefore function in multiple processes during coronary vessel formation and cardiogenesis , which could potentially be manipulated to repair and regenerate the heart in the context of cardiovascular disease .
A study of ENU ( N-ethyl-N-nitrosourea ) mutagenised mouse strains with embryonic lethal recessive mutations revealed that homozygous mutant embryos from the l11Jus27 mouse line displayed enlarged , distended hearts with oedema [19] . A meiotic mapping approach was employed to further refine the l11Jus27 candidate region . Animals with recombination events within the balancer chromosome interval were test-crossed to known l11Jus27 carriers , and viable offspring were genotyped for several markers in the balancer chromosome region . Correlations between the inheritance of C57BL/6 genomic DNA ( mutagenised strain ) and the l11Jus27 phenotype ( early embryonic lethality ) were evaluated . The failure of a recombinant animal crossed to a known l11Jus27 carrier to produce homozygous C57BL/6 offspring would suggest that the homozygous C57BL/6 embryos died in utero , consistent with the l11Jus27 phenotype . This finding would indicate that the l11Jus27 mutation is located in the genomic region where the recombinant animal has inherited C57BL/6 DNA . Unexpectedly , two recombinant mice failed to produce homozygous C57BL/6 viable offspring when mated to known l11Jus27 heterozygotes ( Fig 1A ) . Both of these animals carried C57BL/6 DNA in non-overlapping sub-regions of the balancer interval , suggesting that they could not carry the same embryonic lethal mutation . Recombinant 363 carried C57BL/6 genomic DNA in a 4 . 1Mb region extending beyond the Trp53 endpoint of the balancer chromosome , and recombinant 508 carried C57BL/6 genomic DNA in the central region of the balancer interval ( Fig 1A ) . To determine if recombinants 363 or 508 produced mutant embryos with the l11Jus27 phenotype , timed matings were performed , and resulting embryos analysed at embryonic day ( E ) 10 . 5–12 . 5 . At these developmental stages , the l11Jus27 phenotype was apparent , with most mutant embryos dying by E12 . 5 ( Fig 1B ) . However , homozygous C57BL/6 embryos produced from the cross of recombinant 363 to l11Jus27 heterozygotes did not display the l11Jus27 phenotype ( Fig 1C ) . Instead , mutant embryos were viable past mid-gestation , and exhibited severe hydrocephalus in the mid-brain region from E11 . 5 ( Fig 1C , arrows ) . We found that embryos generated from the cross of recombinant 508 did display the l11Jus27 phenotype ( Fig 1B ) , indicating that the l11Jus27 mutation is located in the region between the polymorphic markers D11MIT322 and D11MIT35 on mouse chromosome 11 . Based on the new phenotype displayed in the offspring from recombinant mouse 363 , we concluded that two embryonic lethal mutations were present in the l11Jus27 mice , and that mutant embryos carrying both mutations exhibited the more severe l11Jus27 phenotype . A new line of mice displaying the hydrocephalus phenotype was generated from crossing recombinant 363 to balancer chromosome animals , so that the new mutation could be maintained in trans to the balancer . This new mutant line was named EHC . Candidate gene analysis of the C57BL/6 region inherited by EHC mice revealed Myh10 as a strong candidate gene . Myh10 encodes NMHC IIB , the heavy chain component of the NMIIB protein complex , and targeted deletion of Myh10 results in late-gestation lethality with hydrocephalus and cardiac defects [26 , 33] . Therefore , due to similarities in phenotype , we sequenced Myh10 genomic DNA from EHC mutant mice . We found a ‘G’ to ‘T’ transversion mutation in the splice donor site following exon 18 ( Fig 1D ) . This mutation causes exon 18 to be skipped from the Myh10 EHC mutant transcript , producing a smaller product from an RT-PCR reaction performed with primers in exons 17 and 19 ( Fig 1E ) . An abnormal Myh10 transcript created from the fusion of exon 17 and exon 19 was present in EHC mutant embryos ( Fig 1F ) , while wild type embryos contained a transcript with exons 17 , 18 , and 19 ( Fig 1G ) . EHC mutant embryos showed a reduction in full-length Myh10 transcript levels as analysed by qPCR ( Fig 2A ) or RT-PCR amplifying the region from exons 17–19 ( Fig 2B ) . The NMHC IIB protein is a 230kDa molecule comprising 1976 amino acids [20 , 21 , 34] . The wild type NMHC IIB protein sequence translated from exons 17 and 18 is shown in Fig 2C . The abnormal fusion of exons 17 and 19 in the EHC Myh10 transcript causes a change in the reading frame , resulting in a truncated protein ( 1–703 amino acids ) . Additionally , the final three amino acids are divergent from the wild type sequence with a YEI to SEL change ( Fig 2D , boxed area ) . Using a C-terminal NMHC IIB antibody for western blotting , we confirmed that full-length NMHC IIB protein is not detectable in EHC mutant embryos ( Fig 2E ) . Modelling the effect of the EHC mutation on the NMHC IIB protein demonstrated that the premature stop-codon will cause truncation of the protein in the actin binding head domain , resulting in the synthesis of NMHC IIB devoid of the coiled coil rod domain ( Fig 2F ) . NMIIB is dependent upon interactions between the heavy chain rod domains to associate into bipolar filaments in order to exert its cellular function ( Fig 2G ) [20 , 21] . We predict that if a truncated NMIIB protein was produced in EHC mutant embryos , this aberrant protein lacking the essential rod domain would be unable to partake in molecular interactions and therefore be unable to exert contractile force upon the actin cytoskeleton . To confirm that the EHC causative mutation had been correctly mapped to Myh10 , and subsequently caused loss of NMHC IIB function , we performed a complementation assay with a known Myh10 null allele , denoted as Myh10∆ . The Myh10∆ allele has a deletion of Myh10 exon 2 [33] , and does not synthesise full-length NMHC IIB protein ( S1A Fig ) . As with the EHC mutants , homozygous Myh10∆ mutant embryos demonstrated late gestation embryonic lethality and were not present at the expected Mendelian ratios at birth ( S1B Fig , Chi squared test p = 0 . 0035 ) . Heterozygous EHC and Myh10∆ animals were intercrossed; the resultant progeny were genotyped and analysed for embryonic lethality and developmental defects . Analysis of Mendelian frequencies of EHC/Myh10∆ embryos at birth revealed a deviation from expectations due to late gestation embryonic lethality ( S1C Fig , Chi squared test , p = 0 . 0278 ) . These results indicated a failure of complementation between the two lines , and provided strong support for the hypothesis that the Myh10 mutation causes the EHC phenotype by ablating NMHC IIB function . We concluded that EHC mutant embryos were alive at E12 . 5 since a regular heartbeat was observed at the time of dissection . We therefore examined the phenotype of EHC mutant embryos at late gestation . At E13 . 5 , there is still prominent hydrocephalus in the mesencephalic vesicle ( Fig 3A and 3B ) . At E15 . 5 , EHC mutant embryos often displayed oedema in the spinal cord region ( Fig 3C and 3D , arrowhead ) , whilst excess fluid within the mesencephalic vesicle persists ( Fig 3C and 3D , arrow ) . It was rare to recover EHC mutant embryos past E16 . 5 , but the embryos that did survive had abnormal dome-shaped heads , consistent with developmental hydrocephalus ( Fig 3E and 3F , arrow ) . Based on the reduced recovery of the EHC mutants from dissections after E16 . 5 , we conclude that the EHC phenotype causes late-gestation embryonic lethality , accompanied by defects in cranial development due to severe hydrocephalus in the early embryo . As cardiac defects have been previously described for a targeted deletion of NMHC IIB [23 , 33 , 35] , we examined cardiac development in the EHC mutant mouse . We found several similarities between the EHC cardiac phenotype and the defects described in the NMHC IIB knock out . First , upon dissection at E11 . 5 we observed pericardial effusion and blood in the pericardial sac of EHC mutant embryos ( Fig 4B and 4D ) and on the surface of the EHC mutant heart ( Fig 4D’ ) , phenotypes consistent with cardiac developmental defects . Membranous ventricular septal defects have been reported in NMHC IIB knock out animals [33 , 35 , 36] . However , we found that the endocardial cushions are present and have fused in EHC mutant embryos at E11 . 5 ( Fig 4D , arrow ) , indicative of initial development of the septum . Later in gestation , EHC mutants at E14 . 5 have a thin ventricular septum with tearing in the membranous region , suggestive of a vulnerability to septal defects ( Fig 4F , arrow ) . We also observed that EHC mutant ventricles display reduced trabeculation , a thinner compact myocardium , and disorganisation of cells in the ventricular myocardium ( Fig 4D and 4F , asterisk ) . In addition , we detected defects in myocardial cytokinesis ( S2A–S2D Fig ) , comparable to previously reported data from NMHC IIB knock out mice [23 , 33 , 37] . We observed double-outlet right ventricle ( DORV ) , where the aorta erroneously stems from the right ventricle , in mutant embryos at E16 . 5 ( Fig 4H asterisk ) , similar to findings from NMHC IIB knock out mice [33 , 36] . Interestingly , DORV was not detected when NMHC IIB was specifically deleted in cardiomyocytes [35] . However , the DORV phenotype was completely penetrant in EHC hearts at E16 . 5 . During our morphological inspection of E16 . 5 hearts , we also observed that mutant hearts displayed an abnormally rounded ventricular morphology , which lacked a prominent ventricular apex ( Fig 4H–4J ) . In addition , EHC mutants displayed distended atria , abnormally positioned in relation to the ventricles ( Fig 4F and 4H ) , and the ventricular surface was decorated with multiple blood filled vesicle-like structures ( Fig 4D’ and 4H , arrows ) . These vesicles can be observed as early as E11 . 5 and persist until embryonic lethality . Again , these malformations were fully penetrant in mutant embryos by visual inspection at E16 . 5 . Additionally , the morphology of EHC/Myh10∆ and Myh10∆/Myh10∆ embryonic hearts closely resembles that of the EHC mutants ( Fig 4I and 4J ) . Together , the phenotypic similarity of EHC and Myh10∆ homozygous mutants , combined with the failure of the Myh10∆ allele to complement the EHC allele , confirms that the EHC mutation causes complete loss of Myh10 function , resulting in the observed EHC cardiac abnormalities . It became strikingly apparent during our dissection observations that the EHC mutants lacked blood-filled coronary vessels on their ventricular surface , in contrast to heterozygous EHC control hearts ( Fig 4G compared to 4H ) . The Myh10∆ homozygotes and EHC/Myh10∆ compound heterozygote mutant embryos also displayed a lack of coronary vessels ( Fig 4I and 4J ) . To confirm that EHC and Myh10∆ mutant hearts lacked a mature coronary network , we performed immunohistochemical analysis for markers of cellular components of the coronary architecture , namely vascular endothelial cells ( PECAM-1/CD31 ) , and vascular smooth muscle cells ( SM22α ) . Heterozygous littermates at E16 . 5 displayed clear PECAM-1 immunoreactivity , highlighting mature coronary vasculature in which vascular endothelial cells are organised into a large and extensively branched vascular network ( Fig 5A and 5B arrows ) . In contrast , EHC mutants at E16 . 5 displayed only PECAM-1 immunoreactive surface cell clusters ( Fig 5C and 5D arrows ) . A similar staining profile was observed in EHC/Myh10∆ and Myh10∆/Myh10∆ mutant hearts ( S3 Fig ) . To further examine the extent of coronary defects , we evaluated the localisation of vascular endothelial and smooth muscle cells ( vSMCs ) at E14 . 5 . In heterozygous Myh10∆ controls , we observed intense PECAM-1 staining around the lumen of developing vessel structures , illustrating the presence of endothelial cells around the coronary vessels during maturation ( Fig 5E and 5F arrow ) . However , Myh10∆ homozygous mutant hearts displayed PECAM-1 staining surrounding clusters of blood cells on the cardiac surface ( Fig 5G and 5H arrow ) , consistent with results from whole mount PECAM-1 staining of EHC , EHC/Myh10∆ compound heterozygotes and Myh10∆ homozygous mutant hearts ( S3 Fig ) . Coronary endothelial cells are present in a capillary network on the surface of EHC mutant hearts , but lack organisation into larger vessels ( S5J–S5K Fig ) . Smooth muscle cells were present in the heart of heterozygous control embryos ( Fig 5I–5K ) , including surrounding coronary vessels ( Fig 5J arrowheads ) . In the interventricular septum region , SM22α distribution in Myh10∆ homozygous mutant hearts ( Fig 5L ) was similar to that of control hearts ( Fig 5I ) . In the compact myocardium , Myh10∆ homozygous mutants did not display organised clusters of smooth muscle cells or vascular structures ( Fig 5M and 5N ) . Together , these experiments demonstrate that EHC and Myh10∆ homozygous mutant hearts display similar defects in coronary vessel formation . Prior research has demonstrated that mice with a cardiomyocyte-specific deletion of Myh10 are viable [35] , suggesting that coronary vessel development must not be severely compromised when NMHC IIB function has been lost from cardiomyocytes . To investigate the dependence of coronary vessel development on cardiomyocytes NMHC IIB activity , we implemented the previously described strategy [35] to delete Myh10 exon 2 in cells expressing the cardiomyocyte-specific α-Myosin Heavy Chain-Cre ( αMHC-Cre ) transgene . Confirmation of the genomic deletion of Myh10 exon 2 in cardiac cells , but not tail , brain , or liver cells was demonstrated by genomic PCR for primers surrounding Myh10 exon 2 ( Fig 6A ) . These primers generate a 1 Kb product when exon 2 is present in the genome , and a 600bp product after deletion of Myh10 exon 2 . The 600bp deletion product is visible only in heart tissue ( Fig 6A , arrow ) . We further demonstrated the cardiomyocyte specificity of the deletion of Myh10 by isolating cardiac cells , dissociating them in culture , and subjecting the cells to fibroblast or cardiomyocyte culture protocols [38 , 39] . NMHC IIB protein persists in fibroblast cells , which show characteristic morphology ( Fig 6B–6D ) , but not in cardiomyocytes ( Fig 6E–6G ) , which also have distinctive morphology in culture . Histological sections of hearts at E18 . 5 from control and Myh10 cardiomyocyte-specific knock out embryos were examined by immunofluorescence for NMHC IIB and cardiac troponin T expression ( Fig 6H–6K ) . Embryos inheriting the αMHC-Cre transgene and homozygous Myh10 floxed alleles had reduced NMHC IIB expression within the myocardium , although expression of NMHC IIB can be seen in non-cardiomyocyte cell populations such as the endocardial cells present in the valve leaflets ( Fig 6J , white arrow ) . Analysis of vessel development in control embryos and cardiomyocyte knockouts reveals the presence of blood cells in organised vessels on the cardiac surface , visible directly in dissections at E18 . 5 ( S4A–S4D Fig ) , with PECAM-1 staining at E16 . 5 ( S4E and S4F Fig ) , and following DAB staining of blood cells within heart tissue at E15 . 5 ( Fig 6L–6O ) . These results confirm that cardiomyocyte expression of NMHC IIB is not required for the development of coronary vessels . Due to the evidence that cardiomyocyte NMHC IIB is not required for coronary vessel development , we sought to determine if defects in epicardial cell function may therefore underpin the EHC mutant phenotype . A number of seminal studies have demonstrated that the epicardium plays a crucial role in the formation of the coronary vessels during mammalian development [15 , 40–45] . As the coronary vessels incorporate cells and signals from the epicardium and sinus venosus during their development [13 , 15] , we investigated if the specification of the epicardial precursor , the proepicardial organ , and the sinus venosus occurred correctly in EHC mutant embryos . In situ hybridisation for Tbx18 , a proepicardial marker [46] , and Shox2 , which is expressed in the sinus venosus [47 , 48] , showed that these molecular markers are expressed in a similar pattern in heterozygous and EHC mutant developing embryos ( Fig 7A–7D , arrows ) , suggesting that these coronary vessel precursor tissues were present during early development in mutant embryos . Histology and transmission electron microscopy revealed that at E14 . 5 , heterozygous hearts showed the formation of a characteristic epithelial layer on the surface of the myocardium ( Fig 7E–7G ) with flattened epicardial cells ( Fig 7F , arrow ) . Although epicardial cells were clearly evident on the surface of the EHC mutant heart ( Fig 7H , arrow ) , these cells had an unusual morphology compared to controls ( Fig 7I , arrows ) . In addition , the EHC epicardium did not form a contiguous epithelial layer , and individual epicardial cells did not appear to form appropriate contacts with adjacent cells ( Fig 7I , asterisk ) . The formation and maintenance of epicardial cell-cell gap junctions has previously been shown to be essential for correct epicardial cell function [49] . Interestingly , the ultrastructure of the subepicardial cell nucleus displayed an abnormal , multi-folded morphology , indicating defects in the maintenance of correct cellular architecture ( Fig 7I , arrowhead ) . We calculated the percentage of epicardial cells with visually abnormal morphology in control and EHC mutant EM images , finding a statistically significant increase in the percentage of abnormal cells in EHC mutants ( S2E–S2G Fig; Fisher’s exact test p<0 . 001 ) . The epicardium deposits extracellular matrix into the subepicardial space , which separates it from the underlying myocardium and is thought to play a critical role in the molecular communication between these tissues during embryonic development , homeostasis , and response to injury [13 , 50 , 51] . To evaluate possible defects in epicardial ECM deposition in the EHC mutants , we next analysed the subepicardial ECM by staining E11 . 5 cardiac sections with Alcian blue , a marker of glycosaminoglycans ( GAGs ) ( Fig 7G and 7J ) . Prominent Alcian blue staining clearly delineated the subepicardial ECM in control hearts ( Fig 7G , arrow ) . In contrast , EHC mutants lacked Alcian blue staining at the boundary between the epicardium and myocardium ( Fig 7J , arrow ) , suggestive of abnormalities in the subepicardial ECM . Both mutant and control embryos display similar Alcian blue staining in the endocardial cushion mesenchyme ( S2H–S2K Fig ) , illustrating that the localisation profile of GAGs within the heart is not universally disrupted in EHC mutants . To evaluate potential requirements for NMHC IIB in other cardiac cell types with a role in coronary vessel development , we investigated whether or not NMIIB ablation disrupted the development of the endocardium by analysing the localisation of the endocardial marker endomucin , in both heterozygous control and Myh10∆ homozygous mutants E11 . 5 hearts ( Fig 7K–7P ) . Myh10∆ homozygous mutants were utilised in these experiments to facilitate genotyping , as the loss of Myh10 exon 2 can be determined from a single PCR rather than requiring genomic sequencing as needed to detect the EHC point mutation . No aberrations in endomucin staining were detected in Myh10∆ homozygous mutant embryos ( Fig 7N–7P ) , suggesting that the formation of the endocardium is not dependent upon NMIIB function . Moreover , the developing atrioventricular valves , derived from endocardial tissue , are present in heterozygous control and EHC mutant embryos at E14 . 5 and E16 . 5 ( S5A–S5D Fig ) . As the formation of cardiac valve structures is highly dependent upon a functional endocardium [13 , 52] , the presence of these structures in EHC mutants supports the hypothesis that endocardial function is not significantly impaired following loss of NMHC IIB . A recently published complementary study has demonstrated that coronary vessel development is impaired when NMHC IIB is deleted specifically within the epicardium [53] , supporting our findings that epicardial abnormalities in EHC mutant mice contribute to defective coronary vessel development . We sought to establish why the epicardium in particular demonstrates abnormalities following NMHC IIB ablation that are not exhibited in other tissue types . There are three NMHC II isoforms , IIA , IIB , and IIC , encoded by Myh9 , Myh10 and Myh14 respectively in the mouse [20 , 21] . The NMHC IIB null mouse displays developmental defects primarily in the brain and heart , which the authors attribute to an enrichment of NMHC IIB in these tissues [33 , 37 , 54] . However , the relative expression levels of individual NMHC II isoforms have not been explored specifically within the embryonic epicardium . We analysed NMHC II protein levels in control embryonic hearts using immunofluorescent microscopy . This analysis revealed that NMHC IIB is the predominant NMHC II isoform found in the E14 . 5 heart , and moreover , within the epicardium ( Fig 8A–8C , arrows ) . All NMHC II isoforms were detectable in the developing lung ( Fig 8D–8F , arrows ) , as previously described [37] . In addition , at E11 . 5 we found NMHC IIB together with IIA , were abundant in whole heart protein extracts probed by western blotting ( Fig 8G and 8H ) . Somewhat surprisingly , we found that levels of NMHC IIA appeared to be diminished in NMHC IIB ablated samples ( Fig 8G ) , whilst NMHC IIC was not detectable in either control or mutant preparations ( Fig 8I ) . In light of this , we sought to determine the relative abundance and subcellular localisation of NMHC IIA and IIB in enriched epicardial cell cultures derived from E11 . 5 heart explants by immunocytochemistry . In both control and mutant cultures , NMHC IIA appeared to be primarily localised to the cell periphery in cells at both the leading edge and within the culture monolayer ( Fig 8J–8L , arrows ) . In comparison , not only was fluorescent signal notably increased for NMHC IIB ( Fig 8M and 8N ) , its subcellular localisation appeared to be more diffuse throughout the cell body ( Fig 8N , asterisks ) , and frequently associated with cytoskeletal stress fibres ( Fig 8N , arrows ) . As expected , NMHC IIB was not detectable in NMIIB ablated cultures ( Fig 8O ) . Interestingly , mutant epicardial cells did not display either an increase in NMHC IIA signal intensity , or altered NMHC IIA subcellular distribution when compared to controls ( Fig 8L compared to 8J ) . Higher magnification images of control hearts at E11 . 5 and E14 . 5 demonstrate NMHC IIB protein localisation to the epicardium ( Fig 8P and 8R ) , with higher levels of epicardial expression found at E14 . 5 ( Fig 8R ) as compared to E11 . 5 ( Fig 8P ) . We did not detect any immunofluorescent signal in Myh10∆ homozygous mutant hearts at either E11 . 5 or E14 . 5 using the NMHC IIB C-terminal antibody ( Fig 8Q and 8S ) . Together , these data suggest that NMHC IIB is expressed at both a higher abundance in the epicardium , and in distinct subcellular regions to other NMHC II isoforms . Consequently , NMIIB may be serving a specialised function in the epicardial cells that cannot be compensated by other NMII isoforms when NMIIB is lost , thus exposing epicardial dysfunction . The formation of the epicardium is wholly reliant upon the migration of cells from the PEO to the surface of the developing myocardium [8] . NMHC IIB has been shown to play an important role in cell migration , through the generation of traction forces , and guidance of directional persistence [20 , 21 , 28 , 36] . In light of this , we sought to investigate whether NMHC IIB null epicardial cells demonstrated motility defects in vitro by performing a scratch wound assay on epicardial cells cultured from embryonic heart explants as previously described [55] . Epicardial cells from control ( +/+ or Myh10∆/+ ) and NMHC IIB null ( Myh10∆/Myh10∆ ) E11 . 5 hearts were enriched and cultured for 48 hours to confluence on gelatin-coated 24-well plates . The Myh10∆ line was used for these experiments to expedite genotyping . The epicardial nature of the resultant cell populations was confirmed by immunostaining for the epithelial marker , ZO1 , epicardial marker , Wt1 ( Wilms Tumour 1 ) , and the epithelial ‘cobble-stone’ morphology of filamentous actin staining with rhodamine phalloidin ( Fig 9A and 9B ) . These epicardial monolayers were scratched with a P10 pipette tip and wound closure was imaged over a 20-hour period ( S1 and S2 Movies ) . For each image series , we identified 10 cells at the leading edge of the wound at T0 , and manually tracked their migration ( Fig 9C and 9D ) . Cell migration speed and directional persistence were subsequently analysed using ImageJ . Somewhat surprisingly , we found that NMHC IIB null epicardial cells exhibited normal migratory behaviour when compared to controls , with no significant difference in either the average migration speed ( Fig 9E , Mann-Whitney , p = 0 . 6717 ) , or directional persistence ( Fig 9F , Mann-Whitney , p = 0 . 2494 ) . This finding suggests that epicardial cells do not require NMHC IIB to exhibit normal migratory behaviour in vitro . It is well documented that during development , a sub-population of epicardial cells undergo EMT and give rise to epicardial-derived cells that have acquired the ability to invade the underlying myocardium , where they differentiate into multiple cell lineages , including interstitial and perivascular fibroblasts and vascular smooth muscle cells [15 , 45] . In addition to this cellular contribution , it is suspected that EPDCs regulate myocardial development through expression of paracrine signalling molecules [56–60] . It is clear that the correct execution of epicardial EMT is essential for both coronary and myocardial development . Since we did not detect epicardial motility defects in vitro , we examined epicardial cell migration into the underlying cardiac tissues in vivo by evaluating the localisation of cells expressing the epicardial marker Wt1 in EHC mutant and control embryos ( Fig 10A–10G ) . Measurements of the distance between Wt1 positive cells in the ventricular myocardium of E14 . 5 hearts ( Fig 10C and 10D , yellow crosshairs ) and the cardiac surface indicated that EHC EPDCs had not penetrated as deeply into the myocardium as EPDCs in control hearts ( Fig 10H , Mann Whitney , p<0 . 0001 ) . Consequently , the majority of EHC EPDCs resided in a tightly restricted region of the subepicardial space ( Fig 10F and 10I ) or specifically around the blood-filled ventricular vesicles apparent in mutant hearts ( Fig 10G ) . To confirm that the cells we detected at the cardiac surface were epicardial cells , we used the marker Raldh2 . We found that Raldh2 expressing cells are located at the cardiac surface at E11 . 5 and E14 . 5 in both heterozygous control and Myh10∆ homozygous mutants ( Fig 10K–10P ) . Quantification of the number of Raldh2 positive cells per length of epicardium reveals a significant increase in epicardial cell number in Myh10∆ homozygous mutants at both E11 . 5 and E14 . 5 when compared to control littermates ( Fig 10J ) . We also evaluated vimentin expression at the cardiac surface in EHC mutant embryos and heterozygote controls ( Fig 10Q–10T ) . We did not detect increased vimentin staining in EHC mutant embryos , indicating that there is not an increase in mesenchymal cell populations at the surface of the EHC heart . These in vivo results mirror the defects exhibited in other mouse models with compromised epicardial cell function [32 , 61–63] . In addition , an in vivo cell migration defect may well be predicted from previous studies in which NMHC IIB is ablated , or its activation inhibited [28 , 32] . Together , these data provide a strong evidence base to support our hypothesis that epicardial cell abnormalities contribute to the coronary vessel defects displayed by EHC mutant mice . Following the observation that Myh10∆ homozygous mutant epicardial cells display altered migration in vivo , but not in scratch wound assays in vitro , we hypothesised that the coronary vessel development defects in the EHC and Myh10∆ mutant embryos might be due to an altered in vivo environment affecting epicardial derived cell migration . We have shown that Alcian blue staining for GAGs was reduced in the EHC subepicardial ECM ( Fig 7G and 7J ) . Since GAGs constitute a major molecular component of the extracellular matrix , we hypothesised that the loss of NMIIB disrupts ECM protein distribution in the developing heart , thus hindering epicardial cell motility or migration in vivo . We therefore evaluated the expression of laminin and fibronectin , ECM components documented to be expressed in the human embryonic heart [64] . Fibronectin is of interest particularly in the developing epicardium , since it has been shown to be required for directional persistence during epicardial cell migration [65] . In the mouse heart , fibronectin has been reported to localise to the epicardium from E12 . 5 to E16 . 5 , where it co-localises with collagen I [66] . To evaluate ECM distribution we performed immunofluorescence for laminin , fibronectin , and collagen I in Myh10∆/+ control and Myh10∆ homozygous mutant hearts at E11 . 5 and E14 . 5 . We found that laminin , fibronectin and collagen I were abundantly present in the subepicardial ECM of control hearts , and indeed , throughout the ventricular myocardium ( Fig 11A–11C , arrows ) . The distribution of these ECM components was altered in Myh10∆ homozygous mutant hearts , with little detectable staining in the epicardial layer and generally reduced levels throughout the heart ( Fig 11A–11C , arrows ) . Quantification of the staining intensity in the epicardial region relative to staining intensity in the myocardial region on the same tissue section was performed . All ECM components analysed demonstrated a significantly reduced ratio of expression in Myh10∆ homozygous mutants at E11 . 5 and E14 . 5 ( Fig 11D ) , suggesting that the extracellular environment of the mutant heart has been altered . Furthermore , Myh10∆ homozygous mutant epicardial explants cultured independently from myocardial cells display an altered pattern of fibronectin distribution , with poor organisation and a reduced network of fibrils ( S6 Fig ) , confirming that mutant epicardial cells have an impaired ability to produce fibronectin . Correct deposition of the cardiac ECM is necessary for appropriate cardiac development [67] . In the adult heart , alterations in ECM composition are associated with apoptosis [68] . After detecting ECM defects in NMHC IIB ablated mutants , we therefore assessed apoptosis in the Myh10∆ homozygous mutant heart at E14 . 5 using a TUNEL assay , and compared our findings to control samples ( Fig 12A–12G ) . At this developmental stage , control hearts displayed low levels of apoptosis in both the epicardium and underlying myocardium ( Fig 12A and 12B ) . We found a small but statistically significant increase in the average number of apoptotic cells within the mutant myocardium , compared to control littermates ( Fig 12G; Mann Whitney U test , p<0 . 006 ) . In contrast , whilst the mutant epicardium also displayed elevated apoptosis rates when compared to controls , this finding was not statistically significant ( Fig 12G; Mann Whitney U test p = 0 . 07 ) . Similarly , no statistically significant difference was observed between the number of apoptotic cells in activated caspase-3 stained control ( Fig 12H ) and mutant ( Fig 12I ) hearts during early development at E9 . 5 ( Fig 12J; unpaired 2-tailed Mann-Whitney U-test , p = 0 . 9292 ) . These data suggest that loss of NMHC IIB does not cause significant epicardial apoptosis . We next sought to establish whether the function of EHC epicardial cells was compromised prior to EPDC migration , specifically focusing on the process of epicardial EMT . Analysis of the proliferation marker phosphohistone H3 ( PHH3 ) revealed a distinct increase in proliferation within the epicardium of EHC mutants at E14 . 5 as compared to controls ( Fig 13A–13D , arrows; Fig 13E , unpaired t-test , p<0 . 0001 ) . We found no significant difference in cell proliferation in the underlying cardiac tissue between controls and EHC mutant hearts ( Fig 13E , unpaired t-test , p = 0 . 1684 ) , nor in total cardiac tissue between control and Myh10∆ homozygous mutant hearts at E9 . 5 ( S6C Fig , unpaired t-test , p = 0 . 57 ) . This finding is consistent with a reduced incidence of EMT induction , as cells undergoing EMT attenuate cell division in favour of changes to cell morphology [69] . Correspondingly , we examined the localisation of the EMT marker , Snail , within the epicardium at E14 . 5 . Similarly , we found that the EHC mutants demonstrated a reduction in the number of Snail positive epicardial cells ( Fig 13F–13I , arrowheads ) . Recently it has been reported that NF-κB signaling is required downstream of TGFβ and PDGF inputs to mediate epicardial cellular changes associated with EMT [70] . The NF-κB component p65 is expressed in the mouse epicardium during EMT [70] . We examined p65 expression at E14 . 5 in heterozygote control and Myh10∆ homozygous mutant hearts ( Fig 13J–13K ) and calculated the ratio of epicardial staining intensity compared to myocardial staining intensity . We found a statistically significant reduction in the ratio in Myh10∆ mutant embryos , indicating reduced p65 expression in the mutant epicardium ( Fig 13L ) . Together , this evidence is highly suggestive of epicardial EMT dysregulation specifically in the mutant epicardium . This result suggests an important role for NMHC IIB in the promotion or execution of EMT , via NF-κB signaling , that to our knowledge has not been reported previously .
The present study demonstrates a requirement for NMHC IIB during the formation of the mammalian coronary vasculature . Here , we report the characterisation of a mutant mouse line , generated from a balancer chromosome ENU mutagenesis screen [19] , that displays embryonic hydrocephalus and cardiac defects . The EHC point mutation in Myh10 results in a global loss of NMIIB function , as confirmed by genetic complementation of the EHC allele in trans to the Myh10∆ allele . Notably , the Myh10 gene is located approximately 1 Mb outside the Trp53 endpoint of the balancer chromosome interval [19] . In breeding the EHC mutants we have only had 1 animal out of more than 1180 in total that showed recombination between the Myh10 mutation and the balancer chromosome end point , indicating that the balancer chromosome can be used to maintain balanced heterozygous stocks for embryonic lethal mutations in genes located outside the balancer interval . Our studies of the EHC mouse have made advances in understanding the essential role undertaken by NMHC IIB during cardiogenesis . The EHC mutant coronary vessel abnormalities are markedly similar to phenotypic observations of mice in which the epicardium has been specifically disrupted [29 , 31 , 63] , and are consistent with a primary defect in epicardial cell function . We have shown that EHC mutant epicardial cells have a highly perturbed morphology , impaired epicardial EMT , and disrupted subepicardial ECM composition , in addition to reduced migration of EPDCs into the myocardium . The absence of mature coronary vessels in EHC mutants suggests that in vivo these dysfunctional EPDCs are incapable of contributing to the vascular network . A complementary study has demonstrated that coronary vessel development is impaired when Myh10 is deleted specifically in the epicardium [53] , consistent with our conclusion that epicardial defects underpin disrupted coronary vessel development in EHC mutant mice . NMHC IIB plays a key role in a broad variety of fundamental cellular processes [20 , 21] . Similarly , the defects we have documented encompass a range of physiological events , including , but not limited to: cell migration , adhesion , proliferation and apoptosis . Our finding that EHC mutants display defects in epicardial EMT is of particular interest , as other fundamental developmental processes that depend upon the correct initiation and execution of EMT ( e . g . gastrulation , craniofacial development ) occur in the EHC embryo . Indeed , formation of the atrioventricular valves , a process dependent upon endocardial cushion EMT [13] , is evident in EHC mutant hearts ( S5 Fig ) . We have demonstrated that NMHC IIB is the predominant NMHC II isoform in epicardial cells both in vivo and in vitro , and that NMHC IIB is localised to distinct subcellular regions ( in agreement with the findings of Lo et al . , [28] , and Ma et al . , [53] ) . Therefore , it may be hypothesised that NMHC IIB plays critical roles in epicardial and cardiac development , which cannot be replaced by other NMHC II isoforms when NMHC IIB function is globally ablated . The observation that loss of NMHC IIB leads to decreased expression of NMIIA presents the possibility of altered transcriptional regulation in EHC mutants , which needs to be further explored to understand the roles of NMIIB in epicardial cell function , and embryonic development as a whole . Notably , unlike NMHC IIA or IIC , NMHC IIB displays cell type and cell-cycle specific mechanosenstivity [71] , which may be specifically altered in mutant epicardial cells during development . The myocardium also plays a role in epicardial cell behaviour , through the secretion of paracrine signalling molecules which traverse the subepicardial ECM and communicate with the epicardium to orchestrate correct epicardial function [13 , 15 , 50 , 60] . The requirement for NMHC IIB in myocardial development is well documented , with previous reports observing myocardial disorganisation , cardiomyocyte cytokinesis defects , and a reduction of the myocyte population in NMHC IIB null hearts [23 , 33 , 36 , 37] . However , cardiomyocyte-specific Myh10 ablated mice are viable , and importantly , demonstrate a reduced instance of VSD and the complete absence of DORV [35] . Here we report that NMHC IIB is not required within the cardiomyocyte population for coronary vessel development . This finding suggests that the severe morphological defects present in NMHC IIB null hearts are caused by loss of NMHC IIB from other cardiac cell populations . It has recently been demonstrated that epicardial-specific deletion of Myh10 does impair coronary vessel development [53] , although the reported defects are not as severe as we have documented here for the EHC mutant , EHC/Myh10∆ compound heterozygote , or Myh10∆ homozygous mutant embryos . Further investigation is required to determine if these phenotypic differences are due to differences in assays used or due to requirements for NMHC IIB in multiple cell types during coronary vessel development . NMHC IIB has been directly shown to be important in cell migration as a key component of the actin-myosin cytoskeletal machinery [20 , 21 , 28 , 36] . Our use of the epicardial cell culture model shows that the migration of EHC epicardial cells progresses unimpeded in vitro , both in the context of epicardial cell outgrowth from embryonic heart explants , and in a wound-healing assay . Moreover , cultured primary epicardial cells have been previously shown to express Snail [72] , indicating that the process of epicardial cell migration from explant heart tissue in vitro involves EMT activation . A key difference between the explant model and the EHC mutant is the context of the extracellular environment . We performed our explant assays on gelatin-coated plates , and it has been demonstrated that the provision of exogenous ECM substrate can compensate for migration defects in cells with ECM production deficiencies [73] . It is known that the subepicardial ECM plays an important role in epicardial function , in both the adhesion of the epicardial monolayer to the myocardium and in facilitating molecular communication through the subepicardial space [13 , 50 , 60 , 74] . During EMT , TGFβ signaling ( a key input for epicardial EMT [60] ) is known to be affected by ECM substrate rigidity [75] . Additionally , alterations in cell tension , provoked by changes in the ECM , can disrupt nuclear architecture and chromatin structure , with subsequent effects on transcriptional regulation [76] . Disruption of the subepicardial ECM as seen in EHC and Myh10∆ mutants may therefore alter the ability of EHC EPDCs to migrate into the myocardium . The detection of abnormalities in EMT signaling , indicated by increased epicardial cell proliferation and reduced Snail expression in EHC mutants , is surprising , as NMHC IIB would be expected to be a downstream effector of cell motility in EMT . Although EMT defects have been detected in the epicardial-specific Myh10 knock out [53] , comparison of the results of that study with ours is complicated by the use of different assay methods and the potential for requirements for NMHC IIB in non-epicardial cells to influence our findings . However , our results suggest that the loss of NMHC IIB disrupts processes required for EMT signaling , through the NF-κB pathway acting downstream of TGFβ and PDGF inputs [70] . PDGF is produced in the myocardium and serves as a paracrine signal to promote epicardial EMT [60] . Phenotypically , the epicardial cell morphology defects we note from EM studies are similar to those reported for a PDGFRβ knock out [63] . Interestingly , it has previously been shown that changes in ECM composition , particularly collagen , can alter PDGF responsive gene activation during wound healing [77] . We have detected reduced p65 in Myh10∆ mutants , indicative of impaired NF-κB pathway activation . Since NF-κB pathway activation occurs in response to PDGFBB ligand inputs [70] , we speculate that the observed alterations in the subepicardial ECM of mutant embryos may impede PDGF signalling , which subsequently hinders NF-κB pathway activation , thus contributing to epicardial EMT dysregulation in EHC and Myh10∆ homozygous mutant embryos . In summary we have demonstrated a requirement for NMHC IIB to generate the appropriate cardiac extracellular matrix environment at the subepicardial space . We also demonstrate that signals promoting epicardial cell EMT are deficient in EHC and Myh10∆ mutant embryos , and that migration of EPDCs into the myocardium is impaired . Together , these data indicate that the coronary defects observed in the EHC embryos are underpinned by compromised epicardial function , and suggest that NMHC IIB plays a crucial role in normal epicardial biology . Confirmation of our findings is provided from a recent study demonstrating that mice with an epicardial-specific knock out of Myh10 display defects in coronary vessel development [53] . Myh10 is a pleiotropic gene that performs multiple roles in different developmental processes including tension generation [78] , growth factor receptor internalisation [79] , cell adhesion [20] , and extracellular matrix protein secretion [80] . Moreover , the different NMHC II isoforms have functionally distinct roles [24] . Further investigation of the molecular functions of NMHC IIB in the epicardium and other cardiac cell types may inform therapeutic strategies to reactivate epicardial processes in injured cardiac tissue and enhance coronary vessel repair and regeneration .
Experiments using animals were performed in accordance with legislation in the UK Animals ( Scientific Procedures ) Act of 1986 ( PPL 70/8858 to Graham Morrissey ) . Experiments were approved by the University of Manchester Animal Welfare and Ethical Review Body . The l11Jus27 mouse line was generated from a balancer chromosome mutagenesis screen . The two mutations carried in the l11Jus27 mouse line were maintained in trans to 129S5 . Inv ( 11 ) 8BrdTrp53-Wnt3 [19] . Genomic DNA was prepared from ear punches of adult mice and yolk sacs ( <E11 . 5 ) or tails ( ≥E11 . 5 ) of embryos . Genotypes were determined by PCR analysis with microsatellite marker D11MIT327 , D11MIT35 , D11MIT31 or D11MIT322 to differentiate between C57BL/6 ( mutant ) and 129S5 ( wild type ) strains of mice . Myh10tm7Rsad mice were obtained from the MMRRC , and were crossed to Tg ( Nes-cre ) Wme mice to generate global deletion of Myh10 exon 2 . Tg ( Myh6-cre ) 2182Mds/J mice were bred to Myh10tm7Rsad mice to generate cardiomyocyte-specific deletion of Myh10 exon 2 . Genotyping primers are listed in S1 Table . PCR products were sequenced to confirm specificity of genotyping PCR reactions . For mutation mapping , mice were bred to 129S5 wild type mice . Animals inheriting the l11Jus27 mutation but not the balancer chromosome were selected for further breeding . l11Jus27 mice without the balancer were crossed to 129S5 wild type animals and progeny examined for recombination events . Recombination events were identified using microsatellite and SNP polymorphic markers between C57BL/6 and 129S5 mouse strains ( primer sequences in S1 Table ) . Recombinant mice were intercrossed to l11Jus27 mice and progeny analysed . An absence of homozygous C57BL/6 pups indicated that the mutation was present in the recombinant animal . A timed mating was performed to confirm the mutant phenotype . Recombinant mouse 363 , which only carried the ‘EHC’ point mutation , was bred with 129S5 . Inv ( 11 ) 8BrdTrp53-Wnt3 to generate the EHC mouse line . The EHC point mutation was identified by genomic sequencing of all annotated Myh10 exons ( UCSC genome Browser mm9 assembly ) . PCR amplification was performed on each exon , products precipitated , and cycle sequenced using Big Dye v1 . 1 reaction mix ( ABI ) according to manufacturer’s instructions ( primer sequences in S1 Table ) . To confirm genomic deletion of Myh10 exon 2 , PCR was performed for primers flanking exon 2 ( Fig 6 ) and products were sequenced to confirm specificity to Myh10 . Matings of Myh10∆/+ and EHC/+ male and female adult mice were set up and pregnancies were allowed to proceed to term . Neonate litters were culled and tail biopsies from euthanised mice were used to genotype each animal for the Myh10∆ and EHC mutations . The observed genotypes were compared to expected Mendelian ratios and data sets were analysed using a Chi-squared test with 2 degrees of freedom ( http://graphpad . com/quickcalcs/chisquared1 . cfm ) . Mice were set up for timed matings and the morning of the vaginal plug was defined as day E0 . 5 . Mice were sacrificed according to Home Office Schedule 1 methods . Embryos were then dissected from decidua at the desired time point and subsequently imaged in PBS using a Leica MZ6 microscope and DFC420 camera . The three dimensional structure of residues 1–815 of mouse NMHC IIB were predicted using homology modelling . The sequences of mouse NMHC IIB and chicken smooth muscle myosin were aligned using ClustalW [81] , and structure predicted by Modeller [82] using the known chicken myosin structure ( PDB id 1BR1 ) [83] as a template . The sequence identity between the two proteins was 83% over the aligned region . Twenty models were built , and the one with the best score was used for further analysis . Protein was extracted from E11 . 5 embryos by homogenisation in RIPA buffer . Protein concentrations were determined according to the manufacturer’s protocol ( Biorad Protein Assay ) . Protein lysate ( 30 μg ) was loaded onto a 10% polyacrylamide gel and separated by electrophoresis before being transferred to a PVDF membrane . Membranes were blocked overnight in 5% milk and incubated in anti-nonmuscle myosin II primary antibodies ( NMIIA , Covance , PRB-440P , NMIIB Covance , PRB-445P , NMIIC , Covance , PRB-444P; 1:1000 ) or anti-beta-actin HRP conjugate ( Sigma , A3854; 1:200 , 000 ) for 1 hour at room temperature . Membranes were subsequently incubated in HRP conjugated donkey anti-rabbit secondary antibody ( Santa Cruz , SC-2313; 1:1000 ) for 1 hour at room temperature . Protein detection was performed using the ECL Plus Western Blotting detection system ( GE Healthcare ) according to manufacturer’s instructions . RNA was prepared from E12 . 5 wild type , heterozygous and mutant embryos using Tri reagent ( Sigma ) . RNA ( 5 μg ) was treated with RNase-free DNase 1 ( Promega ) and cDNA generated using random primers ( Promega ) and Bioscript reverse transcriptase ( Bioline ) . Primers for Myh10 qPCR are listed in S1 Table . Embryos were fixed in Bouin’s fixative or 4% paraformaldehyde ( PFA ) , dehydrated and then cleared in xylene or histoclear . Embryos were embedded in paraffin and sectioned at 7 μm . After sectioning paraffin was removed from sections by washing 2 x 10 minutes in xylene or histoclear . Sections were then rehydrated and stained in haemotoxylin and eosin or Alcian blue and/or nuclear fast red before being mounted in depex mounting medium for analysis . Embryos and embryonic hearts were fixed overnight in 4% PFA and stored in 70% ethanol . For immunohistochemistry , whole hearts were incubated with primary antibodies anti-PECAM-1 ( BD Biosciences , 550274; 1:100 ) for 1 hour at room temperature . For immunohistochemistry on tissue sections , samples were incubated in anti-smooth muscle alpha actin ( Sigma , A5228; 1:400 ) for 1 hour at room temperature . Staining was developed using the Vectastain Elite ABC Kit ( Vector , PK-6100 ) and visualised using the DAB substrate kit ( Vector , SK-4100 ) . For immunofluorescence on tissue sections hearts were dehydrated , embedded in paraffin and sectioned at 7 μm . Tissue sections were subjected to antigen retrieval by heating in citrate buffer ( DAKO , S1699 ) as per the manufacturer’s instructions . Tissue was blocked prior to incubation in primary antibodies with either 10% ( v/v ) serum in PBS , or DAKO serum free protein block ( DAKO , X0909 ) . Heart sections were stained with primary antibodies directed against the following proteins: laminin ( custom-made antibody , kind gift from Ray Boot-Handford , University of Manchester; 1:400 ) ; fibronectin ( Santa Cruz , SC-6952; 1:50 dilution ) , collagen 1 ( Gentaur , OARA02579; 1:400 ) , fibronectin ( Santa Cruz , sc-6952 , 1:50 ) , p65 ( Santa Cruz , SC-372; 1:200 ) , cardiac Troponin T ( Abcam , ab106076; 1:400 ) , NMHC IIB ( Eurogentec , PRB-445P-050; 1:400 ) , Raldh2 ( Abcam , ab75674: 1:200 ) , SM22α ( Abcam , ab14106; 1:250 ) , endomucin ( Santa Cruz , SC-65495; 1:50 ) , sarcomeric alpha-actinin ( Sigma , EA-53; 1:500 ) , and beta-catenin ( Sigma , C2206 , 1:250 ) overnight at 4° C . Embryos were incubated in species-specific fluorescent secondary antibodies ( Jackson Immunochemicals or Invitrogen; 1:500 dilution ) and slides mounted in DAPI mounting media ( Vector ) . Whole hearts were fixed for two hours at room temperature in 4% PFA . Hearts were washed 2X5 min in PBS , prior to incubation in the DAB substrate kit with added Nickel solution ( Vector , SK-4100 ) according to manufacturer’s instructions . E14 . 5 embryonic hearts were dissected into ice cold PBS , embedded in OCT ( R . A . Lamb ) and snap frozen in liquid nitrogen . Cryosections ( 14 μm thick ) were fixed in 4% PFA for 15 minutes , permeabilised in PBS + 0 . 1% ( v/v ) Triton X-100 for 15 minutes and subsequently blocked with PBS + 1% ( w/v ) BSA + 10% ( v/v ) normal goat or horse serum ( Vector ) for 1 hour . Sections were then incubated with the following primary antibodies diluted in PBS + 0 . 1% ( v/v ) Triton X-100 for 24 hours at 4°C: rabbit polyclonal anti-mouse Wt-1 ( Calbiochem , CA1026 , 1:300 ) , goat anti-Snail ( Abcam , ab53519 , 1:100 ) , rabbit anti-PHH3 ( Millipore , 06–570 , 1:300 ) , anti-vimentin ( Proteintech , 10366-1-AP , 1:50 ) . Sections were then incubated with species-specific biotinylated secondary antibodies ( Vector ) at 1:500 , for 2 hours , or FITC conjugated goat anti-rabbit secondary antibody ( Sigma , F9887 , 1:160 ) ( detection of PHH3 ) . Sections were incubated with Cy3 conjugated streptavidin ( GE Healthcare , PA43001 ) , diluted 1:3000 or 1:1000 ( detection of Snail ) for 30 minutes . Coverslips were mounted with Vectashield with DAPI ( Vector , H-1200 ) and sealed with nail varnish . Slides were stored in the dark at 4°C and imaged within 48 hours . Cardiac fibroblast and cardiomyocyte cell populations were generated from E15 . 5 embryonic hearts according to previously described protocols [38 , 39] . For the generation of epicardial cell cultures , embryonic hearts were dissected from E10 . 5–12 . 5 embryos and the atria and outflow tract were removed . Ventricular tissue was then carefully dissected into four pieces of comparable size , and each piece was placed onto a coverslip pre-coated with 0 . 1% gelatin ( Sigma , G2500 ) ( 1 hour incubation at 37°C ) in a 24 well tissue culture plate ( Corning ) . Explants were cultured in 500μl DMEM ( Sigma , D5796 ) supplemented with 15% ( v/v ) heat inactivated FBS ( Gibco , 10500064 ) and 1% ( v/v ) Penicillin/Streptomycin ( Sigma , P0781 ) . Explants were incubated at 37°C with 5% CO2 and the media was replaced every 3 days until the cultures were required for experiments . Epicardial cells cultured for 72 hours were washed with tissue culture grade PBS + MgCl2 and CaCl2 ( Sigma , D8662 ) and then fixed in 4% PFA for 10 minutes on an orbital shaker . The coverslips were then washed and cell monolayers were permeabilised by incubating in PBS + 0 . 1% ( v/v ) Triton X-100 for 15 minutes . Cultures were then blocked in either PBS + 10% ( v/v ) goat serum , or PBS + 1% ( w/v ) BSA , for at least 1 hour before addition of relevant primary antibody diluted in PBS + 0 . 1% ( v/v ) Triton X-100 for 1 hour: rabbit anti-ZO-1 ( Invitrogen , 40–2300 , 1:100 ) , rabbit anti-Wt1 ( 1:300 , SantaCruz , SC-192 ) , goat anti-fibronectin ( Santa Cruz , sc-6952 , 1:100 ) or rabbit anti-NMHC IIB ( Biolegend , PRB445P , 1:500 ) . Unbound antibody was removed by washing the cultures with PBS before addition of appropriate biotinylated secondary antibody ( Vector ) diluted 1:500 in PBS + 0 . 1% ( v/v ) Triton X-100 for 1 hour . For visualisation of fibronectin , cultures were incubated with FITC conjugated secondary antibody ( Sigma , F9887 , 1:160 ) or developed using the Vectastain Elite ABC Kit ( Vector , PK-6100 ) and visualised using the DAB substrate kit ( Vector , SK-4100 ) as per manufacturer’s instructions . Cultures were washed and then incubated in Cy5 conjugated streptavidin ( GE Healthcare , PA45001 ) diluted 1:500 in PBS + 0 . 1% ( v/v ) Triton X-100 for 30 minutes . Coverslips were washed and then treated with 100nM rhodamine-phalloidin ( Cytoskeleton , PHDR1 ) for 30 minutes to allow visualisation of the actin cytoskeleton . Following a final wash , coverslips were mounted onto microscope slides using Vectashield with DAPI mounting media ( Vector , H-1200 ) , and stored at 4°C in the dark until imaging within 48 hours . The same protocol was employed for immunofluorescence staining of NMHC IIB in cultured cardiac fibroblasts and myocytes populations on gelatin coated glass coverslips . Paraffin sections were rehydrated and subjected to TUNEL staining ( Promega DeadEnd Fluorometric TUNEL System , G3250 ) as per the manufacturer’s instructions . Images were collected on an Olympus BX51 upright microscope and captured using a Coolsnap ES camera ( Photometrics ) through MetaVue Software ( Molecular Devices ) . Specific band pass filter sets for DAPI , FITC , Cy3 and Cy5 were used to prevent bleed through from one channel to the next . Images were then processed and analysed using ImageJ software ( Wayne Rasband , NIH , USA ) . ( http://rsb . info . nih . gov/ij ) . IMARIS ( Bitplane ) 7 . 3 . 4 software was used to analyse the fluorescence intensity in epicardial and myocardial regions for ECM and p65 quantification . For each individual cell in the epicardial and myocardial layers , mean fluorescence intensities ( MFI ) , Alexa488 , and Alexa 647 were measured in pixels . Twenty randomly selected areas of the epicardium and myocardium were measured per tissue section . Five sections per embryo , and three embryos of each genotype , were measured . Myocardial measurements were taken from the compact myocardium as defined by morphology rather than trabeculae or endocardium . Two-tailed student t-test was used to test the significance of differences between two sets of data . Further details of data analysis techniques , statistical tests and numbers of samples used are provided in figure legends . Epicardial cells were cultured from ventricular explants as described above . After 48 hours in culture , explants were removed and the cell monolayer was briefly washed twice with complete media and returned to the incubator for 24 hours . Cell monolayers were scratched using a P10 pipette tip , rinsed twice in complete media , and photographed at 10 minute intervals for 20 hours using an AS MDW live cell imaging system , maintained at 37°C and 5% CO2 . Ten cells at the leading edge of the denuded area were tracked per field of view , using MTrackJ in ImageJ . Total tracked cells = 240 control and 270 mutant . Cultures were generated from at least four hearts for each genotype . Data sets for directional persistence and migration speed measurements were subjected to Mann-Whitney U test to assess statistical significance . Embryos were fixed overnight in 4% PFA and in situ hybridisation was performed as previously described [84 , 85] . Tbx18 and Shox2 plasmids were obtained from A . Kispert ( Institute of Molecular Biology Hannover , Germany ) . Anti-sense probes were synthesized from linear template DNA using RNA polymerase , digoxygenin nucleotide mix and transcription buffer ( Roche ) . RNA probes were purified by precipitation and added to hybridisation mix at a concentration of 0 . 5 μg/μl . Samples were prepared for electron microscopy as described previously [86] . Sections ( 50–70 nm-thick ) were generated using a Reichert-Jung Ultracut ( Leica Microsystems , UK ) and stained using 2% uranyl acetate and 0 . 3% lead citrate . Sections were examined with an FEI Tecnai 12 Biotwin transmission electron microscope . Images were recorded using a Gatan Orius SC1000 camera ( 11 Mpixels , 4008 x 2672 ) . | In order for the heart to function properly it must have its own blood supply . Blood is delivered to the heart through a system of vessels called the coronary vasculature . During development , some of the cells that form the coronary vessels originate from the epicardium , the outer layer of the heart . These epicardial-derived cells migrate into the cardiac tissue where they contribute to the formation of the coronary vascular network . We have found that a mouse mutant containing a mutation in the gene Myh10 , which encodes the cytoskeletal protein non-muscle myosin IIB , fails to form the coronary vasculature . Our work reveals defects in the epicardium , which contribute to the lack of coronary vessel development in this mutant . Surprisingly , we discovered that whilst the mutant epicardial cells are capable of movement when extracted from the embryo , these cells fail to mobilise into a vascular network in the context of the developing embryonic heart . We propose that this migration failure is due to abnormalities in the extracellular environment in the mutant heart . This work highlights the importance of the Myh10 gene in the critical developmental process of coronary vessel formation . | [
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| 2017 | Non-muscle myosin IIB (Myh10) is required for epicardial function and coronary vessel formation during mammalian development |
When cooperation has a direct cost and an indirect benefit , a selfish behavior is more likely to be selected for than an altruistic one . Kin and group selection do provide evolutionary explanations for the stability of cooperation in nature , but we still lack the full understanding of the genomic mechanisms that can prevent cheater invasion . In our study we used Aevol , an agent-based , in silico genomic platform to evolve populations of digital organisms that compete , reproduce , and cooperate by secreting a public good for tens of thousands of generations . We found that cooperating individuals may share a phenotype , defined as the amount of public good produced , but have very different abilities to resist cheater invasion . To understand the underlying genetic differences between cooperator types , we performed bio-inspired genomics analyses of our digital organisms by recording and comparing the locations of metabolic and secretion genes , as well as the relevant promoters and terminators . Association between metabolic and secretion genes ( promoter sharing , overlap via frame shift or sense-antisense encoding ) was characteristic for populations with robust cooperation and was more likely to evolve when secretion was costly . In mutational analysis experiments , we demonstrated the potential evolutionary consequences of the genetic association by performing a large number of mutations and measuring their phenotypic and fitness effects . The non-cooperating mutants arising from the individuals with genetic association were more likely to have metabolic deleterious mutations that eventually lead to selection eliminating such mutants from the population due to the accompanying fitness decrease . Effectively , cooperation evolved to be protected and robust to mutations through entangled genetic architecture . Our results confirm the importance of second-order selection on evolutionary outcomes , uncover an important genetic mechanism for the evolution and maintenance of cooperation , and suggest promising methods for preventing gene loss in synthetically engineered organisms .
The evolution of cooperation in microbial populations is a fascinating , rich and controversial evolutionary problem [1]–[6] . The theoretical understanding of cooperation has been gradually advancing for decades , and recently those insights have also been applied to practical , medical problems , such as the treatment of infections triggered by cooperating , pathogenic bacteria [7] , [8] . Most evolutionary explanations of cooperation rely on kin selection and group selection theories and are constantly being improved and refined by a host of mathematical tools [9] , [10] . Among them , the game theory and meta-population models have proved to be especially useful in the analysis of long term versus short term , as well as the individual versus population benefit of cooperation [11]–[14] . However , those methods tell us practically nothing about the evolutionary pressure on the structure of genomes that encode the cooperative traits . They typically do not distinguish between genotypes and phenotypes and consider only a finite set of possible behaviors ( often only two: cooperate or not ) with a constant extrinsic probability of switching between them . Although some recent papers do go further than evolving classical binary behavior by considering more complex stochastic strategies that take into account past interactions [15] , they also remain “one locus = one parameter” models , unable to consider genetic architecture of cooperation genes . Several experimental studies have shown the need to go beyond these limitations to understand cooperation in microbial systems . Specifically , Foster et al . demonstrated that the pleiotropic effect of a Dictyostelium discoideum gene involved in a cooperative behavior ( differentiation into prestalk cells ) causes the mutations inducing cheating behavior to be associated with a direct fitness cost to the individual [16] . Similarly , cheating mutations induce a cost in Pseudomonas aeruginosa because of co-regulation of public and “private” goods via the same quorum-sensing mechanism [17] . We postulate that genomic architecture of metabolic and secretion genes – achieved by sense-antisense coding or frameshifts – can provide a mechanism for the evolution and maintenance of cooperation that is similar but more basic than ones relying on genetic pleiotropy or co-regulation . Here we investigate how two specific types of genomic architecture of cooperation genes may affect the evolutionary fate of cooperation itself . The first type relies on the concept of operons , already well described and investigated in the context of co-regulation or co-transfer of genes in the same operon [18] , [19] . We specifically consider metabolic and secretion genes that have the same promoter and terminator sequence , thus sharing an operon . The second architecture type is the overlap , base-pair sharing between metabolic and secretion genes due to being in different reading frames or on different DNA strands . Although more rare in bacterial context , gene overlaps may be caused by the strong constraints on maximum genome size and have similar evolutionary explanations and properties as operons [20] . We describe and quantify the role of both these genetic architecture types and show that physical association of cooperation and metabolic genes , via operon and overlap , introduces an evolutionary constraint , pleiotropy in the broad sense , which prevents non-cooperating , cheater individuals from prospering and protects cooperation . Even though the same DNA coding for multiple proteins can , in a broad sense , be viewed as pleiotropy at the sequence level , as far as we know , its importance has never been described in the context of cooperation . In all our experiments we use the Aevol platform [21] , [22] , an in silico experimental evolution system . While similar to existing individual-based , genetic-algorithm simulations , Aevol embodies a number of features inspired by microbial genetics that make it especially well suited for our study . For example , the phenotype of an Aevol digital organism is a continuous function comprised of a potentially unlimited number of biological processes and their performance level , which in turn allows for a continuous cooperating phenotype instead of the classical binary one . When it evolves , the cooperation among individuals is based on a public good molecule that diffuses and degrades in the environment . Individuals live in a spatially structured world , suitable for the evolution of cooperation [23] , [24] and more similar to natural microbial populations than classical meta-population models . The public good is costly to secrete but may benefit any neighboring organisms . Both indirectly selected secretion genes and metabolic genes contributing to fitness directly are encoded in the double-stranded genomes strings of zeros and ones . A set of rules for transcription , translation and protein synthesis governs the complex genotype to phenotype to fitness mapping . Phenotypically similar or even identical individuals can have different genotypes , thus also having different evolvability , robustness , and evolutionary fate [25] . All these properties of Aevol set the stage for evolutionary experiments where genetic architecture constraints of cooperation can be both observed and described . We first demonstrate the existence of differences in the resistance to cheater invasion among several phenotypically equivalent populations . We then correlate the maintenance of cooperation genes with the abundance of promoter sharing or overlapping between metabolic and secretion genes . We hypothesize that such non-random encoding of the secretion is indirectly selected for in situations when cooperation is favored . Indeed , when evolving populations start from a naive , non-secreting ancestor , the cooperators employed this protective encoding , and more so when the cooperation cost was high . Mutational analysis confirmed that the constrained genetic architecture resulted in cooperation-destroying mutations also having a direct negative fitness effect . Overall , our results highlight the need for considering appropriately detailed and realistic computational systems and generally show the importance of second-order selection pressures and genetic architecture in the study and understanding of the evolution and maintenance of cooperation .
In our modified version of Aevol dedicated to the study of cooperation , the phenotype is divided into two groups of traits: metabolism ( biological processes allowing the individual to live and reproduce ) and secretion ( processes relating to the costly secretion of a diffusible public good molecule ) . Starting from an ancestor with a single , metabolic gene , we independently evolved populations for generations . We effectively put cooperation under direct selection by using a particular fitness calculation in which secretion genes were treated the same as the metabolic ones during evolution . At the end of this phase , we chose the fittest individual from each replicate and , by simply reassigning half of the phenotype from metabolism to secretion , obtained cooperators with high secretion levels . Specifically , their average secretion was % of the maximal secretion in Aevol , and the standard deviation in secretion was % of the mean . These individuals had generally comparable metabolic and secretion part of their phenotype with on average genes in each . Using the cooperators from previous experiments , we started with clonal populations that we then let evolve for an additional generations with a possibility of secreting at a moderately high cost ( , see Materials and Methods for the effect of public good cost and fitness calculation details ) . Each of these populations was replicated times , for a total of experiments . In all cases the amount of secretion greatly decreased , but not by the same amount or at the same rate ( Fig . 1 ) . To quantify these differences , we performed a one-way ANOVA on the average secretion between generation and generation , the visually chosen time interval during which cooperation is stabilizing to a new level after a quick and strong decay . We found a highly significant between groups effect ( , ) , each group consisting of the populations that share a common ancestor , confirming that some cooperators are intrinsically more resistant to cheater invasion than others , even though they initially had very similar phenotypes . Moreover , there was no significant correlation between the ancestral cooperation level and the final one ( , ) , eliminating the possibility of our results being driven by an initial difference in the population cooperation level . When visually inspecting the phenotype of a randomly chosen cooperator and its descendants from the previous experiment , we also noticed that it was the same secretion genes that survived cheater invasion between several independent replicates of evolution . One such example , where the phenomenon was especially striking , is presented in Fig . 2 . While we did not perform any statistical analysis because of the computational difficulty of tracking every protein for several thousands of generations , this observation motivated further experiments: it supports the idea that our populations are different ( in their resistance to cheater invasion ) because their secretion genes are somehow different . To quantify the genetic architecture of ancestral organisms we measured the percentage of secretion genes that ( 1 ) share an operon with at least one metabolic gene , ( 2 ) overlap with at least one metabolic gene , ( 3 ) satisfy at least one of ( 1 ) and ( 2 ) , or ( 4 ) satisfy both ( 1 ) and ( 2 ) ( see Material and Methods for more details ) . We then compared the genetic architecture measures with the resistance to cheater invasion , expressed as the average remaining secretion between generation and generation , as before ( Fig . 3 ) . We found that all four genetic architecture properties strongly correlate with the remaining secretion amount ( and for operon sharing , and for overlapping , and for at least one of them , and for both of them ) , supporting our hypothesis that physical linkage between secretion and metabolic genes confers resistance to cheater invasions . In order to confirm the effect of genetic architecture on cheater resistance , rather than examining the exact locations and interactions between genes and using them to infer population's evolutionary fate , we directly quantified the effect of mutations on secretion and fitness . We constructed mutants of each of the ancestors , and calculated the mutational effect as the percentage of mutations that decrease the amount secreted without decreasing metabolic fitness , weighted by their negative effect on secretion . We found significant correlations between the mutation effect and both the robustness to cheater invasion ( calculated as before , , ) and the genetic architecture ( here defined as the percentage of secretion genes sharing an operon or overlapping with at least one metabolic gene , , ) . Simply put , the individuals with genetic architecture that groups together metabolism and secretion genes exhibit higher resistance to cheaters , because they are subject to fewer mutations that would convert cooperators into cheaters without any direct fitness loss . We thus have two measures that predict well the resistance to cheater invasion: genetic architecture and accessibility of mutations . Since the generation range used to quantify cheater resistance was chosen ad hoc , we also examined the effect of different ranges on the correlations . Interestingly , we found that genetic architecture is better correlated with cheater resistance when it is measured between generations and ( and ) than between generations and ( and ) . Conversely , mutational effects are better correlated with cheater resistance measured in the early ( generations to , and ) than late interval ( generation to , and ) . Overall , both genetic architecture and mutation effects are good predictors of how easily cooperators may be invaded by cheaters , but genetic architecture is better at predicting long-term effects , while mutational effects are more strongly correlated with short-term ones . Mutations may affect long-term maintenance of cooperation in many ways and genetic architecture captures but one of them . As we elaborate in the Discussion section below , these results indicate that while all mutational constraints play a role , it is the overlap and operon ones that have the strongest long-term evolutionary consequences . We tested the importance of gene overlap and operon sharing in maintenance of cooperation by examining the extent of genomic connections between secretion and metabolism before and after the increase in secretion cost and the accompanying decrease in cooperation . After generations of evolution at a higher cost , the secretion genes still present are over times more likely to overlap or share an operon with metabolic genes than the secretion genes a the start of the experiment ( Fig . 4 ) , with the difference being highly significant ( Welch's t test , ) . The proportion of all four categories of association between metabolic and secretion genes ( share an operon , overlap , do at least one of them , do both ) has increased , and all increases were significant ( Welch's t test , for operon sharing , for overlapping , for doing at least one of them , for doing both ) . Note that these categories do not exactly correspond to the partitioning done on Fig . 4 ( see Material and Methods for detailed explanation ) , but capture the same general properties of genetic architecture . In the previous experiments we worked with already evolved cooperators , measured their resistance to cheater invasion and genetic architecture . We now turn to de novo evolution of cooperation , in order to show that gene overlap and operon sharing will evolve , via indirect selection pressures , in conditions moderately favorable for cooperation . Naive ancestors evolved for generations with cooperation cost of . In the final populations , individuals on average had metabolic genes and secretion genes . While the number of secretion genes is low , by pooling data of all individuals from each population we obtained a large number of genes that we could analyze . We compare the shared operons and gene overlap for metabolic and secretion genes with the same measures applied only within metabolic genes , as a control . The genetic architecture links between metabolic and secretion genes are on average times stronger than within metabolic genes alone , the difference being highly significant ( Fig . 5a , comparing the sum of the three bottom categories – dark blue , light blue and green – , Welch's t test , ) . The proportion of all four genetic architecture categories differed between the two gene groupings , and all differences were significant ( Welch's t test , for operon sharing , for overlapping , for at least one of them , for both ) . We repeated the analysis for more populations that evolved under a lower secretion cost ( ) and we observed no difference in genetic association between metabolic and secretion genes compared to associations with metabolic genes alone ( Fig . 5b , Welch's t test , ) . Comparison of the two sets of experiments performed at different secretion costs shows that the preferential association between secretion and metabolic genes evolves only when the cost of cooperation is relatively high .
The study of the evolution and maintenance of cooperation is rich in theories , majority of which rely on higher level properties of individuals , such as relatedness , fitness , or group structure . Our experiments investigate basic , genome-level properties and show that the presence of genetic associations between metabolism and secretion genes aids the maintenance of cooperation across thousands of generations . Operon sharing and gene overlap are selected for when cooperation is costly and directly change populations' evolutionary fate . Second order selection is known to play a major role in the rapid evolution of microbial populations [32] and here we contribute to understanding the specific and much studied case of cooperation via public good secretion . We used an in silico experimental platform , Aevol , which has enabled us to collect and analyze genetic architecture and evolutionary dynamics data in detail previously unattainable with either mathematical or experimental systems . The role of second-order selection and genetic constraints in evolution will undoubtedly continue to motivate experimental and theoretical research but in our case it also has the potential to inform bio-engineering and synthetic biology applications .
In this study we use the Aevol platform , an individual-based model of evolution , especially well suited for the study of selection pressures on genomic architecture [21] , [22] , [25] , [33] . It is free and open-source software and is downloadable from http://www . aevol . fr/download . The specific version of the platform we used in this study , including analysis routines , parameter files , other minor modifications , is available on request . Aevol has already been used in several peer-reviewed publications including some that studied cooperation , so we invite the reader to refer to [34] for more information on how cooperation has been implemented and for characterization of the related parameters , and to [22] , [35] for more general details about the original version of Aevol that did not incorporate cooperation . In Aevol , the individuals are living on a toroidal , two-dimensional square grid , with each location being occupied by exactly one individual . In our experiments the grid contains positions , for a total of individuals . Selection and reproduction are performed locally in a synchronous way: at each generation , for each position in the grid , we compete the nine individuals in the neighborhood to determine which one's descendant in going to occupy this position in the next generation . The phenotype of an individual is represented by a two-dimensional curve describing the level of performance for each point of a continuous set of abstract biological processes . This is a very general way of encoding a phenotype without any restriction on the type of biological processes that can be represented . The genotype is a string of zeros and ones , which is transcribed and translated according to a bacterial genomics-inspired process: promoters and terminators are identified to allow transcription , then the transcribed sequences are searched for ribosomal binding site and start codon , followed by what will be a gene and then by an in-frame stop codon , to allow translation . Our genetic code is an abstract mathematical function transforming the gene , i . e . the binary sequence between the start and stop codons , into three numbers , interpreted as a triangle on the axis of biological processes . These three numbers are ( mean position of the triangle on the phenotypic axis ) , ( half-width of the triangle ) , and ( height of the triangle ) . Base-pairs are read three by three , and our amino-acid space has eight symbols: START and STOP , M0 and M1 which are used to specify , W0 and W1 which are used to specify , H0 and H1 which are used to specify ( Fig . 6 ) . Each of these eight amino-acids is assigned to exactly one of the eight ( ) possible triplets . There is no redundancy in the codon–amino-acid mapping , however there is still a large redundancy in the gene–protein mapping because codons inside genes can be reordered without impacting phenotype . Specifically , what matters is the order of codons specifying the same triangle property ( , , or ) , while the relative order of codons for different properties can be altered freely . Once a coding sequence has been detected using the rules explained above and transformed into an amino-acid sequence , we extract from there three binary words ( for , and ) according to the following process: amino acid X0 adds a to the binary word of and X1 adds a 1 to the binary word of , where X is any of M , W , or H . We obtain an integer value for each of the three binary words by interpreting them using Gray code . Gray code is an alternative binary encoding in which two successive integers are encoded by binary numbers differing in only one digit . The integer values are then normalized by where is the number of codons used , and scaled to a [ , ] interval for , [ , ] for , and [ , ] for . Finally the value is multiplied by the transcription efficiency – a property of the promoter explained below . The mean position specifies the primary trait the protein affects , and as it is a real number , it allows for an infinite number of different traits . The height specifies protein's performance level for the primary trait , while the width determines all the traits a protein affects . Individual's phenotype is computed by summing up all the triangles encoded in its genome . There is no genetic regulation via transcription factors in this version of Aevol , however there are protein-protein interactions ( two proteins contributing to the same biological processes ) and transcription efficiency is regulated by the strength of the promoter ( defined as the distance to a consensus sequence ) . As in natural systems such as bacteria or phages , this genomics allows two genes to cluster on the same mRNA ( operon ) or to physically use the same DNA basis in different reading frames or different senses ( overlap ) . Examples of these different configurations are represented on Fig . 7 . The environment is represented by a two-dimensional curve indicating what is , for every possible biological process , the optimal level of performance in the given environment . The fitness of an individual is a decreasing function of the distance between the individual's phenotype and the optimal phenotype . Individuals are locally selected according to their rank in the neighborhood , with a probability of reproduction exponentially decreasing with the rank . The chosen individual will undergo reproduction with mutations ( insertion , deletion or substitution of a small number of basis and duplication , inversion , translocation or deletion of a larger portion of the genome ) . The rates of different mutation types are parameters of the model and have been set to per basis for small mutations , and for large mutations . Ancestral genome is bases long and contains a single gene , while the typical genome length after several thousands of generations of evolution is around basis . Aevol is a stochastic simulation , the variability coming from the randomness of mutations and the probabilistic selection . One of the parameter is the random seed used to initialize the random number generator . We can replicate an experiment by running it several times with the same exact parameters , but different random seeds . In our experiments , we distinguish two categories of biological processes: the “metabolic” ones ( all traits positioned before on the axis of the biological processes ) , that allow an individual performing them to live and reproduce , and the “secretion” ones ( position after on the axis ) , that determine the level of the production of the public good . We note that under our setup , while genes are generally pleiotropic , simultaneously influencing multiple traits , it is not possible for a gene to affect both metabolic and secretion traits . The public good is costly to secrete , but diffuses in the environment and is beneficial to every individual that comes in contact with it . The cost for the production of one unit of public good varies in our experiments , but is always equal to the cost coefficient ( parameter we set ) multiplied by the amount of the public good produced . The fitness of an individual is given by this equation:Where is metabolic fitness ( calculated as explained before but only considering the left part of the axis ) , is the amount of public good present in the environment at the location the individual inhabits , is the per-unit cost of the public good production , and is the amount of pubic good produced by the individual ( computed similarly to metabolic fitness but considering the right part of the axis ) . is a constant chosen based on previous experiments [25] . The diffusion parameter is per generation , meaning that five percent of the public good present at one position will diffuse in each of the eight neighboring positions during one generation . The degradation rate is set to per generation , meaning that ten percent of the public good at each location will degrade during one generation . This degradation can be thought of as replacing any explicit consumption of the public good , but also as specifying the public good durability . Overall , in all our experiments , % of the public good present at generation at some position will remain at this position at generation . In the Supporting Text S1 , we experimentally show that the secretion mechanism implemented in Aevol , as described above , leads to the usual cooperation dilemma . To evolve a large number of strong cooperators , we assigned biological processes that were usually in the secretion part of the phenotype to the metabolic part of the phenotype , allowing a strong direct selection on them . After generations of evolution under these conditions , the whole phenotype of the individuals closely matches the target phenotype . Thus , when picking the best individual and re-assigning half of the trait axis back to secretion , we get a “near-perfect” cooperator , one that secretes close to the maximal possible amount of the public good . Evolving cooperators in this way makes secretion genes evolve in the same way as the metabolic ones , to a high level , increasing the potential signal in further experiments . We repeated this experiment times , extracted the fittest individual from each population , and obtained a bank of independently evolved cooperators . For each of the cooperators we evolved in the first set of experiments , we analyzed the architecture of all its secretion genes and classified them in four different categories: ( 1 ) genes that share an operon with at least one metabolic gene without overlapping with a metabolic gene , ( 2 ) genes that overlap with at least one metabolic gene without sharing an operon with a metabolic gene ( this is possible because our digital DNA is double stranded and thus allows for two reading senses , in addition to three reading frames for each sense ) , ( 3 ) genes that overlap with at least one metabolic gene and share an operon with at least one metabolic gene ( not necessarily the same one ) , and ( 4 ) genes that share neither operon nor overlap with a metabolic gene . There are multiple ways one could classify the different genes , for example , by distinguishing the number of metabolic genes a secretion gene overlaps or shares an operon with . The four categories we chose have the benefit of intuitive simplicity in addition to including all secretion genes in exactly one category , and we have used them in Fig . 4 and Fig . 5 . The number of genes in each category is always shown as a percentage of all the secretion genes and standardized by the genes' phenotypic area . Here , the phenotypic area refers to the area of the protein ( triangle ) the gene encodes for , and allows us to give more weight to the genes that have a strong impact on secretion as well as enable comparison between replicate experiments that may have different secretion levels . However , when performing the statistical analyses to determine the correlation between the presence of overlap and the resistance to cheater invasion , it does not makes sense to , for example , exclude the secretion genes that also share an operon ( in addition to overlapping ) with a metabolic gene . So we use slightly different , larger , categories for secretion genes: share an operon with at least one metabolic gene ( which is exactly the addition of categories 1 and 3 of our previously explained partitioning ) , overlap with at least one metabolic gene ( addition of categories 2 and 3 ) , do at least one of them ( addition of categories 1 , 2 and 3 ) , do both of them ( same than category 3 ) , do none of them ( same than category 4 ) . The difference is that these categories are no longer exclusive: one gene can be in more than one of the new categories at the same time . The genes are standardized by their phenotypic area , as before . These regrouped categories are used in the statistical analyses throughout the paper , and can easily be visually inferred from the categories of the bar graphs in Fig . 4 and Fig . 5 . In these experiments , each population starts from a randomly constructed organism with a 5 , 000 base pair genome . As random sequences of 0's and 1's are generated , they are screened for the presence of open reading frames with genes . Thousands of sequences are tested and the first one that has exactly one metabolic gene with a positive effect on fitness is selected . This genome is then cloned to fill the population grid and form the starting population . Reason for starting with a single , valid gene rather than an organism with effectively empty genome is that in both cases all the genes except the first one have a very high probability of evolving from duplication followed by divergence of one already existing gene . Indeed , promoters and ribosome binding sites are hard to evolve from scratch . Starting from purely random sequence would only greatly slow down the evolution process ( genomes could evolve for thousands of generation before the first gene appears [22] ) without qualitatively changing the understanding of the evolutionary process in our system . After generations of de novo evolution , we pooled the proteins from all the individuals in each replicate to obtain a measure of average genetic architecture within a population . As before , rather than using just a protein count , we standardized the contribution of each protein by its phenotypic area . | Cooperation is a much studied and debated phenomena in the microbial world marked by a key question: Given the survival of the fittest evolutionary paradigm , why do individuals act in seemingly altruistic ways , paying a cost to help others ? Kin selection and group selection , together with mathematical tools from areas such as economics and game theory , have provided some answers . However , they largely ignored the underlying genetic and genomic mechanisms that drive the evolution of cooperation . In this study , we show that the architecture of the genomes has a major role in shaping the fate of cooperating populations . Specifically , we use an in silico evolution platform and discover that genes for cooperative traits are “hiding” behind metabolic ones by overlapping their sequences or sharing operons . In conditions where cheaters may outcompete the cooperators , this entangled architecture evolves spontaneously and effectively protects cooperation from invasion by cheater mutants . We describe a novel genetic mechanism for the evolution and maintenance of cooperation and , by taking into account the second order selection pressures on the genomes , highlight the need for going beyond simple game theory models in its study . | [
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| 2013 | Genetic Architecture Promotes the Evolution and Maintenance of Cooperation |
Scavenger Receptors ( SRs ) from the host’s innate immune system are known to bind multiple ligands to promote the removal of non-self or altered-self targets . CD5 and CD6 are two highly homologous class I SRs mainly expressed on all T cells and the B1a cell subset , and involved in the fine tuning of activation and differentiation signals delivered by the antigen-specific receptors ( TCR and BCR , respectively ) , to which they physically associate . Additionally , CD5 and CD6 have been shown to interact with and sense the presence of conserved pathogen-associated structures from bacteria , fungi and/or viruses . We report herein the interaction of CD5 and CD6 lymphocyte surface receptors with Echinococcus granulosus sensu lato ( s . l . ) . Binding studies show that both soluble and membrane-bound forms of CD5 and CD6 bind to intact viable protoscoleces from E . granulosus s . l . through recognition of metaperiodate-resistant tegumental components . Proteomic analyses allowed identification of thioredoxin peroxidase for CD5 , and peptidyl-prolyl cis-trans isomerase ( cyclophilin ) and endophilin B1 ( antigen P-29 ) for CD6 , as their potential interactors . Further in vitro assays demonstrate that membrane-bound or soluble CD5 and CD6 forms differentially modulate the pro- and anti-inflammatory cytokine release induced following peritoneal cells exposure to E . granulosus s . l . tegumental components . Importantly , prophylactic infusion of soluble CD5 or CD6 significantly ameliorated the infection outcome in the mouse model of secondary cystic echinococcosis . Taken together , the results expand the pathogen binding properties of CD5 and CD6 and provide novel evidence for their therapeutic potential in human cystic echinococcosis .
The mammalian innate immune system relies on a limited number of germline-encoded and non-clonally distributed receptors for pathogen recognition , which have evolved to identify the so called pathogen associated molecular patterns ( PAMPs ) : conserved microbial structures , essential for their survival and not shared by the host , such as lipopolysaccharide ( LPS ) from Gram-negative bacteria , lipotheichoic acid ( LTA ) from Gram-positive bacteria , lipoarabinomannan from mycobacteria , mannan from fungi , chitin from parasites , and viral RNA [1] . Such kind of receptors are collectively named pattern recognition receptors ( PRRs ) , and can be grouped into structurally diverse classes according to the protein domains involved in pathogen recognition ( e . g . , C-type lectin domains or leucine-rich repeats ) [1 , 2] . This is well exemplified by the Scavenger Receptors ( SRs ) , a large group of cell surface and soluble protein receptors that are structurally diverse and participate in a wide range of biological functions ( endocytosis , phagocytosis , adhesion , and signaling ) following binding to multiple non-self or altered-self ligands [3 , 4] . Some SR ( namely SR-A and SR-I ) are characterized by the presence of one or multiple repeats of an ancient and highly conserved cysteine-rich protein domain named SRCR ( for scavenger receptor cysteine-rich ) and constitute a superfamily ( SRCR-SF ) comprising more than 30 different cell-surface and/or secreted proteins present from lower invertebrates to mammals , as well as in algae and plants [5 , 6] . Despite the high degree of structural conservation among SRCR-SF members , a common single unifying function has not been reported . However , a steadily growing bunch of SRCR-SF members is known to interact with diverse microbial ( bacterial , fungal , parasitic and/or viral ) structures [6 , 7] . This is the case of the functionally and structurally highly homologous lymphocyte SR-I receptors CD5 and CD6 . These two receptors are encoded by contiguous genes thought to derive from duplication of a common ancestral gene and are mainly expressed on all T cells , and a minor subset of B cells ( B1a cells ) [6] . The extracellular regions of both receptors are exclusively composed of 3 consecutive SRCR domains showing extensive sequence identity [8] . Their diverging cytoplasmic tails are devoid of intrinsic catalytic activity but both display several structural motifs compatible with a signaling transduction function [6] . Importantly , CD5 and CD6 are physically associated with the clonotypic antigen-specific receptor complex present on T and B1a cells ( TCR and BCR , respectively ) [9 , 10] and are involved in the fine tuning of the activation and differentiation signals generated by such relevant receptors through still incompletely understood and complex signaling pathways [11] . In addition to their immunomodulatory properties , CD5 and CD6 also exhibit PRRs activities . Available data indicate that soluble and membrane-bound forms of CD6 , but not of CD5 , bind to Gram-negative and Gram-positive bacteria through recognition of LPS and LTA , respectively [12 , 13] . In contrast , soluble and membrane-bound forms of CD5 , but not of CD6 , recognize and bind to saprophytic and pathogenic fungal species through β-glucans [14] . More recently , CD5 has been reported as a key receptor for human hepatitis C virus ( HCV ) entry into T lymphocytes [15] , and preliminary observations indicate that CD6 may interact with human immunodeficiency virus 1 ( HIV-1 ) [16] . It remains to be explored , however , whether the broad PRR activity exhibited by CD5 and CD6 also includes other groups of pathogen besides bacteria , fungi and viruses . Helminths -a diverse group of metazoan parasites able to produce long-lasting infections in immunocompetent hosts- currently affect one third of the world population [17] . Helminthiases are usually chronic infections due to the pathogens’ ability to adapt to the defense mechanisms triggered by infected hosts . Therefore , in most cases host immune responses are ineffective in parasite elimination , and are often associated with polarized and stereotyped Th2-type responses , with rare to no levels of Th1-type components [18] . In most helminthiases , such an early response bias does not associate with protective immunity [18–20] , and therefore identification of innate receptors able to recognize and respond to parasite-derived components during early infection stages is highly relevant . Among helminthiases , cystic echinococcosis ( CE ) -formerly known as hydatidosis- is a zoonotic disease caused by the larval stage of the cestode Echinococcus granulosus sensu lato ( s . l . ) , which shows a cosmopolitan distribution with high prevalence worldwide [21–23] . E . granulosus s . l . is composed of numerous variants initially called genotypes/strains ( G1-G10 ) , which nowadays are recognized as new species: E . granulosus sensu stricto ( s . s . ) ( G1/G2/G3 ) , E . equinus ( G4 ) , E . ortleppi ( G5 ) , E . canadensis ( G6/G7/G8/G10 ) and E . felidis ( ‘lion strain’ ) . Among them , the G1 genotype of E . granulosus s . s . is the most frequently found worldwide in livestock and humans [24] . Primary CE occurs in intermediate hosts ( domestic and wild ungulates; accidentally humans ) via ingestion of eggs containing oncospheres , which later develop into metacestodes -or hydatid cysts- mainly in the liver and lungs of the infected host . Secondary CE occurs after protoscolex ( PSC ) spillage from a fertile hydatid cyst within an infected intermediate host . This kind of CE derives from PSC developmental plasticity , which allows them to develop either into new cysts within intermediate hosts or into adult worms if ingested by a definitive host ( usually dogs ) [25] . Human secondary CE is an important medical problem associated with the surgical removal of primary cysts . In fact , although actual percentages of secondary CE cases post-surgery are debatable , recent studies have reported rates of 10–35% depending mainly on the type of surgery , the geographical location , and the follow-up time [26–28] . The murine model of secondary CE ( inoculation of viable PSC into mice ) has been widely used to study both the basic aspects of E . granulosus s . l . immunobiology [29–35] , and the new chemotherapeutics or therapeutical protocols [36–38] , novel vaccine candidates [39–41] , and diagnostic or follow-up tools [42–44] . In this model , secondary CE can be divided into two stages: an early pre-encystment stage ( until day 20–30 post-inoculation ) with PSC developing into hydatid cysts [45] , and a late or post-encystment stage in which differentiated cysts grow and eventually become fertile cysts [46] . Such a sequential developmental process is associated with a strong local control of inflammation during the initial phase of PSC differentiation into hydatid cysts [32 , 47] . The present report extends PRRs activities of both CD5 and CD6 receptors to helminth parasites , using E . granulosus s . l . as a case study . The data we provide indicate that ectodomains from both receptors recognize specific parasite components present in the tegument of PSC . Additionally , the prophylactic potential of CD5 and CD6 ectodomains infusion is shown using the murine model of secondary CE .
Experimental animal procedures were performed in compliance with the Spanish Animal Experimentation Ethics Committee of Universitat de Barcelona School of Medicine , and the Uruguayan Comisión Honoraria de Experimentación Animal ( Universidad de la República ) according to the Canadian Guidelines on Animals Care and the National Uruguayan Legislation No . 18 . 611 . Protocols were approved by Comité de Ética en el Uso de Animales ( Facultad de Química—Universidad de la República ) and were given the Protocol Approval Number 101900-000361-16 ( www . expe . edu . uy ) . Wild-type Balb/c and C57BL/6N mice ( 8–12 weeks old female ) were obtained from DILAVE-MGAP ( Uruguay ) or Charles River ( France ) , and housed under specific pathogen-free ( for in vitro studies ) or conventional ( for experimental infections ) conditions at the animal facilities of Instituto de Higiene ( Universidad de la República , Uruguay ) and of Universitat de Barcelona School of Medicine ( Spain ) . CD5-deficient ( CD5-/- ) ( provided by C . Raman , University of Alabama , Birmingham , AL ) [48] and CD6-deficient ( CD6-/- ) mice [49] on C57BL/6 background were maintained at the animal facilities of the Universitat de Barcelona School of Medicine ( Spain ) under specific pathogen-free conditions . For tegumental antigens extraction , E . granulosus s . l . PSC were obtained by aseptic puncture of fertile bovine hydatid cysts from Uruguayan abattoirs , washed several times with phosphate buffered saline ( PBS ) pH 7 . 2 containing gentamicin ( 40 μg/mL ) , and their viability assessed [29] . Tegumental proteins were extracted from PSC ( viability ≥80% ) using an extracting solution consisting of PBS plus 1% ( w/v ) MEGA-10 , 5 mM EDTA , and 2 mM PMSF [39] . Briefly , 125 , 000 viable PSC/mL of extracting solution were incubated for 2 h at RT with gentle shaking . Then , PSC were allowed to settle down and the supernatant was removed and extensively dialyzed against PBS through a cellulose membrane ( MW cut-off: 12 , 000 Da ) . Protein content of the obtained antigens ( termed PSEx ) was assessed using BCA Protein Assay Reagent ( Pierce ) . PSEx were stored at -20°C until used . Treated PSC were washed thrice with PBS , and their physical integrity was confirmed by observation under a light microscope . For experimental infections , E . granulosus s . s . PSC were obtained by aseptic puncture of fertile bovine or ovine hydatid cysts provided by Uruguayan abattoirs and Dr . Raúl Manzano-Román ( IRNASA-CSIC , Salamanca , Spain ) , respectively . In both cases -Spanish as well as Uruguayan PSC- only parasite batches with ≥95% viability were used for experimental infections , and E . granulosus s . s . genotype was confirmed to belong to the G1 strain by sequencing a fragment of the gene coding for mitochondrial cytochrome c oxidase subunit 1 ( CO1 ) , as previously described [50] . Production of purified recombinant soluble proteins encompassing the whole ectodomains of human CD5 ( rshCD5; from R25 to D345 ) and CD6 ( rshCD6; from D25 to R397 ) receptors ( in PBS with 10% glycerol , pH 7 . 4 ) was performed based on previously reported methods [51] but using SURE CHO-M Cell line clones from the Selexis SURE-technology Platform ( Geneva , Switzerland ) and subjecting serum-free supernatants to size exclusion chromatography protocols developed at PX’Therapeutics ( Grenoble , France ) . Bovine serum albumin ( BSA ) was from Sigma-Aldrich . Proteins were biotinylated with EZ-Link PEO-maleimide-activated biotin ( Pierce ) following the manufacturer’s instructions . Binding of biotin-labelled recombinant proteins to PSC was assessed according to [51] with slight modifications . Briefly , 5 , 000 PSC ( viability ≥90% ) were incubated in 600 μL of biotinylated rshCD5 , rshCD6 or BSA protein solutions ( 20 μg/mL ) in binding buffer ( veronal buffer saline plus 5 mM CaCl2 ) . After 1 h of incubation at RT with gentle orbital rotation , PSC were pelleted and washed thrice with binding buffer , and 125 μL of each solution was stored for further analyses . A new aliquot of 5 , 000 PSC was added to the remaining solutions and the same procedure was performed . Sequential incubations were performed with four PSC aliquots . Then , PSC pellets and 25 μL of stored supernatants ( from the original solution and the last incubation ) , were mixed with SDS-PAGE reducing sample buffer and heat-denatured during 10 min at 100°C . Biotin-labelled proteins were developed by Western blotting ( see below ) following sample resolution in 12% SDS-PAGE and electro-transfer to PVDF membranes ( Bio-Rad ) . The binding ability of rshCD5 and rshCD6 proteins to PSC tegumental antigens was assessed by using 96-well microtiter plates ( Nunc , Roskilde , Denmark ) coated ON at 4°C with 100 μL/well of PSEx in PBS ( 10 μg/mL ) , and further blocked for 1 h at RT with 200 μL/well of PBS containing 1% ( w/v ) BSA . Increasing concentrations ( 0–40 μg/mL ) of biotinylated rshCD5 , rshCD6 or BSA ( 100 μL/well ) were then added to the wells and incubated ON at 4°C . Bound protein was detected by the addition of 100 μL/well HRP-labelled streptavidin ( 1:5 , 000—Sigma ) for 1 h at 37°C . Between every incubation step , unbound proteins were washed out thrice with PBS containing 0 . 05% ( v/v ) Tween-20 . Enzymatic activity was developed at RT by adding 100 μL/well of 3 , 3’ , 5 , 5’-tetramethylbenzidine ( TMB ) substrate ( Sigma ) . After stopping the reaction with H2SO4 0 . 5 M ( 50μL/well ) , absorbance values were read at 450 nm . To assess whether antigens recognized by rshCD5 and rshCD6 within PSEx were carbohydrates , a similar ELISA was performed including a step of PSEx oxidation with NaIO4 [33] . Briefly , PSEx-coated and BSA-blocked plates were incubated during 1 h with 20 mM NaIO4 in 50 mM acetate buffer pH 4 . 5 ( 200 μL/well ) at RT . After three washings with acetate buffer , treated-wells were incubated for 30 min with 50 mM NaBH4 in PBS ( 250 μL/well ) , and 100 μL/well of biotin-labelled rshCD5 ( 20 μg/mL ) or rshCD6 ( 10 μg/mL ) was added to treated and untreated wells . The remaining ELISA protocol was performed as described above . Binding to NaIO4-resistant antigens was assessed as the percentage of absorbance values in treated wells respect to untreated wells . ELISA competition was performed to explore potential overlapping between rshCD5 and rshCD6 for binding to PSEx . Briefly , PSEx-coated and BSA-blocked plates were incubated ON at 4°C with 100 μL/well of either a mixture made of a fixed amount of biotin-labelled rshCD5 ( 20 μg/mL ) or rshCD6 ( 10 μg/mL ) and increasing concentrations of unlabeled rshCD6 ( 0–40 μg/mL ) or rshCD5 ( 0–20 μg/mL ) , respectively . After washing out unbound proteins , the remaining ELISA protocol was performed as described above . Ligand overlapping was assessed as the percentage of absorbance values in competed wells respect to non-competed wells ( 0 μg/mL of unlabeled protein ) . Analysis of PSEx by 2D SDS-PAGE was performed following standard protocols . Briefly , 300 μg of PSEx antigens were first precipitated by ON incubation at -20°C in 300 μL ice-cold acetone containing 20% of trichloracetic acid ( TCA ) and 0 . 07% dithiothreitol ( DTT ) to remove insoluble proteins and lipids . After centrifugation for 15 min at 10 , 000g and 4°C , the supernatant was discarded and 300 μL of ice-cold acetone containing 20% dimethyl sulfoxide ( DMSO ) and 0 . 07% DTT were added and incubated for 1 h at -20°C . Then , samples were centrifuged for 15 min at 10 , 000g and 4°C , the supernatants discarded , and 300 μL ice-cold acetone containing 0 . 07% DTT were added . This step was repeated twice . Finally , the pellet was lyophilized , rehydrated in immobilized pH gradient ( IPG ) buffer ( GE Healthcare ) and frozen at -80°C for 24 h to improve solubilization . For the first dimension , 7 cm linear pH gradient ( pH 3–10 ) Immobilie DryStrips ( GE Healthcare ) were re-hydrated with the sample and run on an IPGphore isoelectric focusing system ( 9 . 5 h run and a total of 35 . 5 KV/h ) , and stored at -80°C until use . Strips were then soaked for 15 min in equilibration buffer ( 50 mM Tris-Cl pH 8 . 8 , 6 M urea , 30% glycerol , 2% SDS , and traces of bromphenol blue ) containing 10 mg/mL DTT , further soaked for 15 min in equilibration buffer containing 25 mg/mL iodoacetamide , and sealed to 10% acrylamide gels using 0 . 5% agarose in standard Tris-glycine electrophoresis buffer . Second dimension SDS-PAGE was run at 50 V for the first 15 min and then raised to 150 V until ending . Finally , replicates of 2D SDS-PAGE gels were subjected either to mass-spec compatible silver nitrate staining or to electro-transference to PVDF membranes ( Bio-Rad ) for Western blotting . Electro-transferred PVDF membranes either from PSC binding assays or from PSEx 2D SDS-PAGE were blocked with 1% ( w/v , in PBS ) BSA for 2 h at RT . Membranes from PSEx 2D SDS-PAGE were additionally incubated ON at 4°C with solutions of biotin-labeled rshCD5 or rshCD6 ( 15 μg/mL ) . All membranes were then incubated for 1 h at 37°C in a PBS solution of 0 . 1% ( w/v ) BSA and 0 . 05% ( v/v ) Tween-20 containing HRP-streptavidin ( 1:5 , 000—Sigma ) . Finally , membranes were extensively washed with PBS plus 0 . 05% ( v/v ) Tween-20 , and blots were developed by chemo-luminescence ( SuperSignal West Pico Substrate , ThermoScientific ) in a G-Box equipment ( Syngene ) . Clean spots observed in PSEx 2D SDS-PAGE transferred PVDF membranes Western blotted with biotinylated rshCD5 and rshCD6 were manually back-mapped on gels for mass spectrometry identification at the Proteomic Facility of Pasteur Institut ( Montevideo ) . Briefly , spots were excised , faded and tryptic digestions were performed using sequencing-grade modified trypsin ( Promega ) . After gel extraction into 60% acetonitrile containing 0 . 1% TFA , the excess of acetonitrile was removed by speed vacuum . Peptide samples were then combined with an equal volume of matrix , spotted onto a MALDI sample plate , and allowed to dry at RT . Mass spectra were acquired on a 4800 MALDI TOF/TOF Mass Analyzer ( Abi Sciex ) operating in the positive ion reflector mode . Protein identifications were performed using an in-house Mascot v . 2 . 3 search engine by searching a custom database that includes the full proteome of E . granulosus s . l . and E . multilocularis , composed of 20 , 787 sequences ( 10 , 310 , 548 residues ) obtained from the Sanger Helminth Database . Additionally , every mass spectrum was also analyzed using NCBI database to discard possible host related proteins . The search criteria used were cystein carbamidomethylation and methionine oxidation as variable modification , and mass deviation <200 ppm with peptide fragment tolerance of 0 . 45 Da . Scores >56 were considered significant ( P<0 . 05 ) . Assessment of fluorescein isothiocyanate ( FITC ) -labelled PSEx binding to membrane-bound CD5 or CD6 was performed by flow cytometry analyses of parental 2G5 cells ( a Jurkat cell derivative selected for deficient CD5 and CD6 expression [52] ) and stable 2G5-transfectants expressing wild-type human CD5 ( 2G5-CD5 . wt ) or CD6 ( 2G5-CD6 . wt ) [10] . FITC labeling of PSEx was done as previously reported [53] using fluorescein isothyiocyanate ( Sigma ) . Briefly , 1 mg of PSEx was dialyzed against 100 mM NaHCO3 buffer pH 9 , and then 500 μg of FITC ( Sigma ) in DMSO were added . After 8 h of vigorous shaking in the dark , the mixture was extensively dialyzed against PBS and stored at 4°C until use . Binding assays were performed by incubating 2x105 2G5 , 2G5-CD5 . wt and 2G5-CD6 . wt cells with increasing amounts of FITC-labelled PSEx in blocking buffer ( PBS plus 10% human AB serum , 2% FCS and 0 . 02% NaN3 ) for 30 min at 4°C . Then , cells were washed thrice with washing buffer ( PBS plus 2% FCS and 0 . 02% NaN3 ) and analyzed on a FACSCalibur flow cytometer using CellQuest software ( Becton Dickinson ) . Additionally , competition assays were performed in a similar way , but incubating cells with a fixed amount ( 10 μg ) of FITC-labelled PSEx in the presence of increasing amounts ( 5–20 μg ) of unlabeled rshCD5 , rshCD6 or BSA . The influence of the interaction between PSC tegumental antigens and CD5/CD6 on the PSEx-induced cytokine profile was first assessed by cell cultures of spleen and peritoneal cells from naïve CD5-/- [48] , CD6-/- [49] and their corresponding C57BL/6 wild-type littermates in the presence of increasing concentrations of PSEx ( 0–40 μg/mL ) . Secondly , peritoneal cells from naïve C57BL/6 wild-type mice were stimulated with a fixed concentration of PSEx ( 20 μg/mL ) in the presence of increasing amounts of rshCD5 , rshCD6 or BSA ( 0–40 μg/mL ) . Spleen cells were obtained by mechanically disrupting spleens with a syringe plunger through a cell strainer . Harvesting of peritoneal cells was done by repeated washings ( 4 times with 2 mL/washing ) of peritoneal cavities with cold PBS plus 2% FCS . Both procedures were performed under sterile conditions . Cell pellets -either from spleen or peritoneal cells- were treated with red blood cell lysing buffer ( Sigma ) following manufacturer’s instructions , and then suspended in complete culture medium ( RPMI 1640 plus 10% FCS , 50 μM 2-mercaptoethanol , 100 μg/mL streptomycin and 100 U/mL penicillin , all from Sigma ) and counted . Cells were seeded in 96-wells U-bottom plates at 2x105 cells/well in 200μL of complete culture medium and then incubated for 72 h at 37°C in a 5% CO2 atmosphere . Stimulation of cells with 10 μg/mL LPS ( Sigma ) was used as a positive control . Mouse cytokine levels in culture supernatants were determined by commercially available ELISA kits following the manufacturer’s instructions . The IL-17A ELISA kit was from R&D Systems . The IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-12p40 , TNF-α and IFN-γ BD OptEIA-Mouse ELISA Sets were from BD Biosciences Pharmigen . To assess whether rshCD5 or rshCD6 could modulate CE outcome , secondary infections were performed in Balb/c mice [33 , 35] . Mice were administered with rshCD5 , rshCD6 or BSA in 200 μL sterile PBS ( 25 μg , i . p . ) one hour before ( -1h ) and after ( +1h ) i . p . inoculation of 2 , 000 PSC ( viability ≥95% ) in 200 μL of sterile PBS . Mice were euthanized 14 months post-challenge and peritoneal cysts were recovered . Groups were compared in terms of ( i ) frequency of infection ( proportion of mice harboring at least one cyst ) , ( ii ) number of developed cyst within each mouse , and ( iii ) total mass of cyst within each mouse ( cyst wet-weight ) . Depending on the characteristics of the values , statistical analyses were assessed by either Student’s t-test ( parametric values ) , Mann-Whitney U-test ( non-parametric values ) or Fisher’s exact test ( non-parametric contingencies ) . Differences were regarded as significant when P <0 . 05 .
In order to determine whether the human CD5 and CD6 ectodomains are able to directly bind to the surface of viable E . granulosus s . l . PSC , pathogen-binding assays previously used for exploring their putative interaction with fungal and bacterial cell wall components , respectively , were performed [12 , 14] . Thus , biotinylated rshCD5 and rshCD6 proteins were sequentially incubated with viable PSC suspensions , and SDS-PAGE and Western blotting of pellets against streptavidin-HRP further assayed their adsorption to PSC . The results showed that both rshCD5 and rshCD6 ( but not BSA , used as a negative control ) bound to viable PSC ( Fig 1A ) , indicating that they possess helminth-parasite binding activity . Next , it was investigated whether the observed binding of rshCD5 and rshCD6 to viable PSC involved tegumental components . To that end , increasing concentrations of biotin-labeled proteins were assayed on ELISA plates coated with the antigenic fraction termed PSEx , composed of PSC tegumental antigens . Results depicted in Fig 1B show that both , biotinylated rshCD5 and rshCD6 ( but not BSA ) , interact with structures present in the PSEx fraction in a dose-dependent manner . Additionally , when assayed the supernatants resulting from sequential incubations of PSC with biotin-labeled rshCD5 and rshCD6 depicted in Fig 1A , reactivity against PSEx decreased as the number of incubations increased , in accordance with a sequential co-precipitation phenomenon ( Fig 1C ) . Once evidenced the interaction of rshCD5 and rshCD6 with the PSEx fraction , the biochemical characterization of the PSEx components involved was addressed . To that end , an ELISA-based assay was first performed to determine whether rshCD5 and rshCD6 interactors were metaperiodate-sensitive ( i . e . carbohydrates ) or -resistant ( i . e . proteins/lipids ) compounds . Results depicted in Fig 2A indicate that all rshCD6- and most rshCD5-mediated interactions were metaperiodate-resistant , suggesting they are of protein and/or lipid nature . Then , in order to assess if the ligand patterns are similar or different for each molecule , we performed competition experiments in PSEx-coated ELISA plates with a fixed concentration of biotin-labeled rshCD5 incubated with increasing amounts of unlabeled rshCD6 , and vice versa ( i . e . fixed biotin-labeled rshCD6 and increasing amounts of unlabeled rshCD5 ) . The results obtained indicate that rshCD5 and rshCD6 exhibit little overlapping regarding their PSEx interactions ( Fig 2B ) . This was further supported by Western blotting the 2D SDS-PAGE resolved PSEx fraction with biotinylated rshCD5 or rshCD6 and HRP-labeled streptavidin . As illustrated by Fig 2C , rshCD5 and rshCD6 differed regarding their “spot” pattern reactive with the PSEx fraction . Accordingly , MALDI-TOF/TOF analyses identified parasite thioredoxin peroxidase as a potential interactor for rshCD5 , and parasite peptidyl-prolyl cis-trans isomerase ( cyclophilin ) and endophilin B1 ( antigen P-29 ) in the case of rshCD6 ( Table 1 ) . Summing up , these results indicate that both rshCD5 and rshCD6 molecules exhibit binding capacity to different structures -mainly proteins and/or lipids- present in the tegument of E . granulosus s . l . PSC , expanding their known spectrum of pathogen recognition . Next , it was questioned whether membrane-bound forms of human CD5 and/or CD6 lymphocyte receptors also retain their PSEx-binding activity . To that end , binding of FITC-labeled PSEx to parental 2G5 cells ( a Jurkat T cell derivative selected for deficient CD5 and CD6 expression [52] ) and to stable 2G5 transfectants expressing wild-type CD5 ( 2G5-CD5 . wt ) and CD6 ( 2G5-CD6 . wt ) surface receptors [10] was analyzed by flow cytometry . As shown in Fig 3A , mean fluorescence intensity ( MFI ) was significantly higher for 2G5-CD5 . wt and 2G5-CD6 . wt transfectants compared with parental untransfected 2G5 cells . The specificity of these interactions was confirmed by competition binding experiments , in which binding of a fixed amount of FITC-labeled PSEx to 2G5-CD5 . wt and 2G5-CD6 . wt cells was competed in a dose-dependent manner by unlabeled rshCD5 and rshCD6 , respectively ( Fig 3B ) . By contrast , unlabeled BSA ( included as a negative control protein ) did not inhibit binding of FITC-PSEx to 2G5-CD5 . wt nor 2G5-CD6 . wt ( S1 Fig ) . Taken together , this evidence indicates that cell surface-expressed CD5 and CD6 retain PSEx-binding activity as well . The influence of cell surface CD5 or CD6 expression on PSEx-induced cytokine production by spleen and peritoneal cells ( PECs ) from either naïve CD5-/- or CD6-/- mice , as well as their respective wild-type controls was first analyzed . To that end , cells were cultured for 72 h in the presence of increasing amounts of PSEx , and then cytokine production in supernatants was analyzed by capture ELISA . Spleen cells showed no significant PSEx-induced cytokine production over background ( S2 Fig ) , in agreement to previous reports for other PSC-derived antigens [29 , 30 , 54] . By contrast , PSEx simulation of PECs resulted only in significant IL-10 , TNF-α and IL-6 cytokine responses . Therefore , our further analyses focused on those cytokines within supernatants of cultured PECs . Since levels of spontaneous cytokine secretion usually differed between PECs from knockout and wild type mice ( S3 Fig ) , results were further displayed in terms of fold changes for an easier interpretation . As illustrated by Fig 4A , while no differences were observed regarding IL-10 induction , PSEx-stimulated PECs from CD5-/- mice underwent significant higher fold-increases for TNF-α and lower for IL-6 than their wild-type mice counterparts . Regarding CD6 surface expression , the results depicted in Fig 4B showed that PSEx-stimulation of PECs from CD6-/- mice underwent significant higher fold-increases for IL-6 but not TNF-α or IL-10 than their wild-type mice counterparts . On the other hand , LPS-stimulated PECs ( included as a positive stimulation control ) from CD5-/- and CD6-/- mice exhibited higher fold-increases for IL-10 and TNF-α and lower fold-increases only for IL-10 , respectively , than their wild-type counterparts ( Fig 4A and 4B ) , suggesting an overall antigen-independent difference in stimulation threshold . To exclude possible CD5/CD6-independent alterations in knockout mice , TNF-α , IL-6 and IL-10 cytokine levels in supernatants of PSEx-stimulated PECs from wild-type C57BL/6 mice in the presence of increasing amounts of rshCD5 or rshCD6 were also assessed . This strategy might reduce the interaction of PSEx with membrane-bound CD5 and CD6 through direct competition with the soluble forms of the receptor recombinant ectodomains . Regarding PSEx-induced IL-10 secretion , no variations due to rshCD5 addition was observed , while rshCD6 significantly reduced IL-10 levels ( Fig 5A and 5B , top bar charts ) . On the other hand , rshCD5 as well as rshCD6 both modified TNF-α and IL-6 ( Fig 5A and 5B , middle and bottom bar charts ) production in response to PSEx , but in opposite ways . Thus , while rshCD5 addition increased PSEx-induced TNF-α and IL-6 production by wild-type PECs , rshCD6 decreased the secretion levels of both cytokines . BSA addition ( included as a negative control ) did not affect PSEx-stimulated cytokine responses ( Fig 5A and 5B ) . Taken together this set of results indicates that either the absence of cell surface CD5 and CD6 receptors or the presence of both receptors in soluble form modulate the cytokine responses induced by tegumental antigens from E . granulosus s . l . PSC in different ways . Interestingly , the blockade of PSEx components by rshCD5 seems to up-regulate pro-inflammatory cytokine responses ( i . e . increasing TNF-α and IL-6 secretion , without affecting IL-10 production levels ) , while blockade through rshCD6 seems to overall down-regulate the PSEx-induced cytokine response ( i . e . decreasing the production of the three induced cytokines ) . In light of the observed modulation by rshCD5 and rshCD6 of PSEx-induced cytokine responses in PECs from wild-type mice , it was further assessed whether rshCD5 or rshCD6 administration would modify the infection outcome in a mouse model of secondary CE . To that end , rshCD5 and rshCD6 ( 25 μg/mouse ) were i . p . infused 1 h before and after i . p . inoculation of viable PSC ( 2 , 000/mouse ) into Balb/c mice , a highly susceptible mouse strain to secondary CE [33] . The i . p . route for rshCD5/rshCD6 administration was chosen because the peritoneal cavity is the natural anatomical site for infection establishment and hydatid cyst development in this infection model . At 14 months post-infection , mice were euthanized for infection inspection , and the peritoneal hydatid cysts within each mouse were counted and weighted . As illustrated by Fig 6A , rshCD5 infusion exhibited a remarkable prophylactic potential against secondary CE , since it significantly reduced the proportion of infected individuals , as well as the number of hydatid cysts per mouse ( Fig 6B ) , and the total wet weight of hydatid cysts per mouse ( Fig 6C ) . On the other hand , rshCD6 infusion also exhibited some degree of prophylactic potential in secondary CE , since a trend towards reduction in the proportion of infected mice ( Fig 6A ) and the number of hydatid cysts per mouse ( Fig 6B ) was observed . Infusion of equivalent doses of BSA did not affect any parasitological parameter of infection outcome ( Fig 6A–6C ) . Finally , our results showed that rshCD5 -and to a lesser extent rshCD6 as well- exhibit prophylactic potential in the murine model of secondary CE .
Effective mammalian immune responses rely on the early recognition of pathogen-derived components by innate immunity related receptors , otherwise named PRRs . Data on key early steps of helminth parasitic infections is scarce . Functional approaches suggest the involvement of different TLRs ( namely TLR4 , TLR3 , and TLR2 ) [55–59] , and SRs in the recognition of helminth components . Current mammalian SRs include 10 different classes ( SR-A to SR-L; excluding SR-C only present in Drosophila melanogaster ) , being class E ( SR-E ) the most important group in helminth-derived antigens recognition , including Dectin-2 [60] , Mannose Receptor/CD206 [61–63] , CLEC4F/CLECSF13 [64] , and DC-SIGN/CD209a [65] . Our work expands the group of SRs interacting with helminth pathogens to CD5 and CD6 , two lymphoid members of the class I ( SR-I ) . SR-I ectodomains harbor several tandem repeats of the SRCR protein module . In addition to CD5 and CD6 , SR-I members include CD163A/M130 , CD163B/M160 , SCART1 , SCART2 and WC1 [3 , 4] . Macrophages and lymphocyte subsets expressing CD163 or WC1 , respectively , play a role against certain parasite infections ( e . g . Theileria parva [66] , Leishmania braziliensis [67] , Trypanosoma vivax [68] , or Neospora caninum [69] ) , but no direct interaction with parasite helminth structures has been previously reported . A single C-terminal SRCR domain characterizes class A SRs ( SR-A ) , including the SR-AI , MARCO , and SCARA5 receptors [3] . While some evidence supports the involvement of SR-AI and MARCO in parasite infection ( Schistosoma japonicum and Leishmania major , respectively ) [70 , 71] , the ability of SR-AI to directly recognize helminth components has only been shown for Heligmosomoides polygyrus calreticulin [72] . The present study shows that CD5 and CD6 should be added to the list of SRs able to sense helminth components . Soluble CD5 and CD6 physically bind to different components within the tegument of E . granulosus s . l . PSC , modulate their induced cytokine profiles in naïve peritoneal cells , and protect mice from secondary CE . Tegumental components are crucial for helminth physiology ( i . e . nutrients up-take and waste disposal ) and for their ability to modulate immune responses leading to chronic parasite establishments . They are the first parasite structures recognized by soluble and/or membrane-bound host receptors . Therefore , it is highly relevant to identify which receptors hold the ability to recognize them and , if possible , the involved parasite structures . We determine that human CD5 and CD6 ectodomains bind to the surface of viable PSC ( Fig 1A ) . More specifically , CD5 and CD6 interact with components of an antigenic fraction termed PSEx , which is mainly composed of tegumental antigens from PSC ( Fig 1B ) . Such PSEx components are metaperiodate-resistant compounds , indicating their protein and/or lipid nature ( Fig 2A ) . Additionally , bound components differed depending on the receptor analyzed ( Fig 2B ) . 2D SDS-PAGE and MALDI-TOF/TOF analyses of the PSEx fraction provides a differentiated spot pattern ( Fig 2B and 2C ) , and a distinctive set of potential parasite ligands for each receptor: thioredoxin peroxidase for CD5 , and peptidyl-prolyl cis-trans isomerase ( cyclophilin ) as well as endophilin B1 ( P-29 antigen ) for CD6 ( Table 1 ) . These results show that CD5 and CD6 ligands within PSEx do not fully overlap . The CD5 and CD6 ligands besides being present in the PSEx fraction , have been found in other sources of E . granulosus s . l . antigens . Thioredoxin peroxidase has been detected in different PSC antigen sources [73–75] , in hydatid fluid [76] , in adult worms [74] , and in extracellular vesicles obtained from fertile cysts [77] . Endophilin B1 ( P-29 antigen ) has been detected in somatic antigens of PSC and adult worms [74] , in nuclear and cytosolic extracts of PSC [75] , and in hydatid fluid [76] . Finally , peptidyl-prolyl cis-trans isomerase ( cyclophilin ) , has been detected in PSC excretion/secretion products [73] , in nuclear and cytosolic extracts of PSC [75] , and in hydatid fluid [78 , 79] . Implying that CD5/CD6 parasite sensing would span the different stages of E . granulosus s . l . life cycle and not be limited to tegumental structures from PSC . PSEx interaction with CD5 and CD6 is also extendable to their membrane-bound forms , as demonstrated by binding ( and competition binding ) experiments of FITC-labelled PSEx to parental and stably CD5- and CD6-transfected 2G5 cells -a Jurkat cell derivative deficient for surface CD5 and CD6 expression ( Fig 3A and 3B ) . The basal interaction observed between PSEx and parental 2G5 cells suggests other cell surface receptors in parasite interaction ( Fig 3A ) . Jurkat cells ( a human CD4+ T cell lymphoma line ) have been shown to express most TLRs [80] , and 2G5 cell line in particular has been previously checked for surface expression of TLR2 and TLR4 [12 , 14] . Therefore , membrane-expressed CD5 and CD6 receptors specifically interact with PSEx components , even if other surface molecules may concomitantly act as PSEx-binding receptors . Specific recognition of parasite structures by membrane-bound CD5 and CD6 may have relevant functional consequences , since both receptors induce intracellular signalling ( namely MAPK cascade activation ) when ligated by PAMPs [12 , 14] . PECs harbor CD5 and/or CD6 expressing immune cells involved in helminth infection protection . They include all T cells ( comprising Tγδ and iNKT ) and B1a cells as well as a macrophage subset [81] . Interestingly , CD5+ B1a cells are an important source of polyreactive natural IgM antibodies [82] and of IL-10 [83] . Thus , CD5- and CD6-mediated signalling by immune PECs may contribute to relevant biological effects , like modulation of cytokine production and release . Accordingly , PSEx-induced stimulation of PECs from CD5- and CD6-deficient mice resulted in different cytokine responses compared with their wild type controls ( Fig 4 ) . Interestingly , such differences were also observed following LPS-stimulation , suggesting an overall difference in stimulation thresholds ( Fig 4 ) . Indeed , data from knockout mice have shown the involvement of membrane-bound CD5 and CD6 in the fine-tuning of T ( and likely B1a ) cell subset responses [11 , 84] . Thus , the possibility that differences observed in PSEx-induced cytokine production by CD5- and CD6-deficient mice could be due to effects beyond direct recognition of parasite ligands by membrane-bound CD5 or CD6 , was further excluded by similar PSEx stimulation studies in wild type mice in the presence of increasing amounts of soluble CD5 or CD6 . The results showed that rshCD5 up-regulated PSEx-induced pro-inflammatory cytokine responses ( i . e . increased TNF-α and IL-6 secretion , without altering IL-10 production rates ) ( Fig 5A ) , while rshCD6 down regulated the overall ( TNF-α , IL-6 and IL-10 ) cytokine response ( Fig 5B ) . Such findings are highly relevant either in a basic immunological sense , as well as from a potential prophylactic point of view . Cytokine profiles are relevant for host susceptibility/resistance to E . granulosus s . l . infection . Thus , pro-inflammatory responses have been associated with host protection either in experimental infection models [30 , 34 , 85 , 86] as well as in human patients [87–91] , being nitric oxide-mediated mechanisms involved in such protection [89–92] . Our results indicate that CD5 or CD6 binding to tegumental antigens from PSC contribute to cytokine induction associated with parasite establishments , and is not optimal for parasite killing and clearance . From a prophylactic point of view , in vivo infusion of rshCD5 or rshCD6 during the early stages of parasite establishment may be a useful strategy for immunomodulating the host into an anti-parasite state . Accordingly , the assessment of in vivo administration of rshCD5 or rshCD6 in a mouse model of secondary CE resulted in a remarkable prophylactic potential of the former , since it reduced not only the proportion of infected mice , but also the number of developed hydatid cysts per mouse and their parasite loads ( Fig 6 ) . A trend towards reduction in the proportion of infected individuals and the number of developed hydatid cysts was observed in rshCD6 infusion ( Fig 6 ) . The prophylactic potential of rshCD5 may correspond to pro-inflammatory cytokine ( TNF-α and IL-6 ) upregulation ( Fig 5A ) , while CD6 lesser efficiency parallels down-modulating cytokine production , especially IL-10 ( Fig 5B ) , a cytokine usually associated with increased susceptibility to E . granulosus s . l . infection [31 , 34 , 90] . In this sense , our preclinical results might be useful for designing/proposing a novel strategy to reduce secondary infection rates in CE patients . For example , once a hydatid cyst is surgically removed , a concomitant intraperitoneal infusion of rshCD5 -or rshCD6- would help in preventing remaining PSC to develop into new hydatid cysts . Interestingly , besides modulating cytokine production , the potential ligands identified for both CD5 and CD6 receptors are highly relevant for E . granulosus s . l . physiology . Thioredoxin peroxidase is a key enzyme for reactive oxygen species detoxification in E . granulosus s . l . [93 , 94]; while peptidyl-prolyl cis-trans isomerase ( cyclophilin ) has been associated with parasite sensitivity to lethal effects of cyclosporine A [95] , and endophilin B1 ( P-29 antigen ) revealed significant protective activity against secondary CE in mice [96] , as well as against primary infection in sheep [97] , when used as a vaccine antigen . Therefore , the binding of such parasite components by rshCD5 or rshCD6 may also contribute to a better host parasite control . In conclusion , we provide the first evidence for direct recognition of tegumental PSC structures from E . granulosus s . l . by SR-I CD5 and CD6 , in addition to bacterial , fungal , and/or viral components binding . Moreover , we prove the prophylactic potential of soluble CD5 ( and CD6 ) infusion in the mouse model of secondary CE . In this sense , such a prophylactic potential could be ascribed either to: ( i ) a cytokine-modulating activity through the competition with the interaction between tegumental antigens and host membrane-bound forms of CD5/CD6; ( ii ) the blockade of key tegumental components highly relevant for PSC physiology; or ( iii ) a mixture of both situations finally contributing to a better parasite control . Additionally , although CE has a cosmopolitan distribution and represents a major public health problem in regions of South America , Mediterranean , Central and Western Asia , and East Africa [98] , our PSC experimental infections were performed with E . granulosus s . s . G1 genotype , which shows the highest cosmopolitan distribution and is responsible for most human CE cases worldwide [24 , 99] . Further work is required to ascertain CD5 and CD6 prophylactic potential in other helminth-driven pathologies and to explore if additional SRCR-SF members share such interactions . | Scavenger Receptors ( SRs ) are constituents of host’s innate immune system able to sense and remove altered-self and/or pathogen components . Data on their interaction with helminth parasites is scarce . In this work , we describe that CD5 and CD6 -two lymphoid SRs previously reported to interact with conserved structures from bacteria , fungi and viruses- recognize tegumental components in the cestode parasite Echinococcus granulosus sensu lato ( s . l . ) . Moreover , both receptors differentially modulate the cytokine release by host cells exposed to E . granulosus s . l . tegumental components . Importantly , the infusion of soluble forms of CD5 or CD6 improve infection outcomes in a murine model of secondary cystic echinococcosis . In summary , our results expand the pathogen binding properties of CD5 and CD6 and suggest their therapeutic potential against helminth infections . | [
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| 2018 | The ectodomains of the lymphocyte scavenger receptors CD5 and CD6 interact with tegumental antigens from Echinococcus granulosus sensu lato and protect mice against secondary cystic echinococcosis |
Chromosome organization is crucial for genome function . Here , we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization . Using the super-resolution microscopy methods of OligoSTORM and OligoDNA-PAINT , we trace 8 megabases of human chromosome 19 , visualizing structures ranging in size from a few kilobases to over a megabase . Focusing on chromosomal regions that contribute to compartments , we discover distinct structures that , in spite of considerable variability , can predict whether such regions correspond to active ( A-type ) or inactive ( B-type ) compartments . Imaging through the depths of entire nuclei , we capture pairs of homologous regions in diploid cells , obtaining evidence that maternal and paternal homologous regions can be differentially organized . Finally , using restraint-based modeling to integrate imaging and Hi-C data , we implement a method–integrative modeling of genomic regions ( IMGR ) –to increase the genomic resolution of our traces to 10 kb .
In terms of organization , chromosomal DNA is among the most challenging of biological molecules to study . It is massive , differs in sequence and structure from one region to the next , and often comes in pairs of homologs that defy easy distinction . Nevertheless , much progress has been made via genetic , cytogenetic , and epigenetic analyses , including DNA-DNA proximity ligation assays , such as Hi-C and other Chromosome Conformation Capture technologies [1–8] , as well as three-dimensional ( 3D ) modeling ( reviewed by [9 , 10] ) . Recently , researchers have used diffraction-limited microscopy and fluorescent in situ hybridization ( FISH ) probes to image up to 40 locations along single human chromosomes in sequential rounds of hybridization [11] , each round targeting the central 100 kilobase ( kb ) portion of a Hi-C defined self-interacting contact domain , also known as a topologically associating domain , or TAD [3–7] . By connecting the resulting 40 positions , this strategy revealed the overall organization of four chromosomes , including the 3D segregation of compartmental intervals by epigenetic state; compartmental intervals are chromosomal regions whose loci share long-range contact patterns contributing to the relatively active ( A-type ) and inactive ( B-type ) compartments of the genome and appear in Hi-C maps as a plaid pattern extending beyond the Hi-C diagonal [3] . This study has since been used as a guideline for Hi-C contact-based modeling ( bioRxiv [12] ) . Similarly , up to 2 . 5 Mbs of a human chromosome has been traced using contiguous steps of only 30 kb , thus enabling diffraction-limited imaging to provide a super-resolved path and reveal the dynamic nature of contact domains and their relationship to compartments [13] . Super-resolution [13–20] and electron [21] microscopy have also been used to dissect genomic features , including sequence-specific super-resolution imaging of contact domains [13 , 15 , 17 , 20] , bioRxiv [22] , the most recent tracing 1 . 2 Mbs of a human chromosome in contiguous 30 kb steps [13] . Such super-resolution studies have provided first glimpses of the physical nature of contact domains , such as their volumes and shapes . What remains relatively unexplored at super-resolution is the physical nature of compartmental intervals . Here , we present a method that can scan the entire depths of nuclei using super-resolution microscopy and sequential hybridization and , thus , capture genomic regions of the size of compartmental intervals as well as all homologous copies of those regions in a single nucleus . Indeed , using this technology , we have produced an 8 . 16 Mb continuous trace of multiple , contiguous compartmental intervals , including both homologous copies in diploid nuclei , providing measurements of distance , entanglement , surface area , volume , and sphericity as well as gaining insights into inter-homolog differences . We then use a new data-driven protocol that , for the first time , integrates imaging with Hi-C interaction matrices in order to generate 3D models of chromosome organization , achieving 10 kb genomic resolution . We begin by describing our strategy for walking along human chromosome 19 in primary skin fibroblasts of donor PGP1 ( XY , PGP1f; Methods ) , using oligonucleotide-based Oligopaint FISH probes [23 , 24] and imaging in sequential steps using the single-molecule localization microscopy methods of OligoSTORM and OligoDNA-PAINT to achieve super-resolution [14 , 24] . The resulting images resolved the physical nature of individual compartmental intervals , showing considerable variation from cell to cell yet matching , nevertheless , a newly generated Hi-C map of PGP1f cells , including the assignment of A and B compartments . We then integrate our image and Hi-C data to produce a single-cell , 10-kb resolution 3D model for the entire 8 . 16 Mb walk . As the PGP1 genome has been haplotyped and its pedigree is known , ( Fig 1A and 1B; ref . [25]; Methods ) we also describe a strategy whereby the maternal and paternal homologs can be distinguished , discovering that homologous regions within the same nucleus differ in ellipticity more than would be expected at random .
We considered two approaches for walking along the chromosome—one in which the sizes of the steps are in accordance with genomic features and another in which step sizes are uniform—initiating our studies with the first . Thus , we designed Oligopaint probes corresponding to the Hi-C defined features of compartments as well as smaller features , including contact domains , loops , and genes . Because a Hi-C map of PGP1f cells was not available when we initiated our study , we designed our walk based on the Hi-C data of IMR-90 lung fibroblasts ( S1 Fig; ref . [8]; Methods ) . Focusing on an 8 . 16 Mb region extending from chr19:7 , 400 , 000 to chr19:15 , 560 , 000 , we then generated Oligopaint probes to image the region in 9 segments ranging in size from 360 kb to 1 . 8 Mb , including probes that would permit visualization of features as small as 2 . 9 kb ( S1 Table ) . Similar to fluorophore- ( dye- ) conjugated oligonucleotides ( oligos ) pioneered over two decades ago [26] as well as other iterations ( Methods ) , Oligopaint probes consist of computationally designed oligos ( Fig 1C; ref . [23] ) . In addition , they are single-stranded and carry a short region ( e . g . , ~32–42 nucleotides , nts ) of genomic homology as well as nongenomic sequences , called Mainstreet and Backstreet , at their 5' and 3' ends , respectively . Streets permit Oligopaints to be amplified , multiplexed , barcoded , and indirectly visualized via labeled secondary oligos [11 , 13 , 14 , 16 , 23 , 24 , 27–30] . Here , we use Streets to enable OligoSTORM and OligoDNA-PAINT [13 , 14 , 16 , 24] . OligoSTORM melds Oligopaints with STORM ( Stochastic Optical Reconstruction Microscopy; ref . [31] ) or dSTORM ( direct STORM; ref . [32] ) and generates single events of fluorescence ( localizations ) via dye-pairs or single dyes . As for Oligo-DNA-PAINT , it combines Oligopaints with DNA-PAINT ( DNA-points accumulation for imaging in nanoscale topography; ref . [33 , 34] ) and generates localizations via transient hybridizations of short ( ~9–10 nts ) dye-labeled oligos , called imager strands , to complementary sequences , called DNA docking sites , located on the Streets; here the transient binding of imager strands is interpreted as localizations . Consisting of 45 , 407 species of oligos , our Oligopaint library incorporated three key features ( Fig 1C ) . First , all the oligos avoid sequences that differ between the parental genomes of PGP1f and , thus , they bind both homologs of chromosome 19 . Second , both streets of each oligo carry 20-nt long barcodes corresponding to sub-regions of our 8 . 16 Mb walk; these barcodes confer multiple functionalities on the oligo . For example , when enabling OligoSTORM , these barcodes co-localize “bridge” oligos [13 , 14] that recruit the labeled secondary oligos to be visualized by OligoSTORM . When enabling OligoDNA-PAINT , the bridges include a docking site for the imager strand . The barcodes also permit any targeted genomic region to be imaged twice , once via Mainstreet and then , again , via Backstreet . Finally , each street carries a 7-nt sequence ( Fig 1C , asterisk ) that facilitates sequential rounds of imaging; in conjunction with a flanking barcode , these 7 nts generate a region which , when bound by a 27-nt “toehold” oligo [35–37] , dislodge any previously bound bridge . Our workflow ( Fig 1D; Methods ) begins with T7 RNA polymerase-mediated amplification [24 , 27 , 38] of the entirety of an Oligopaint library that is to be used for a walk . The totality of the resulting single-stranded oligos is then hybridized to fixed cells in a single round of denaturation and hybridization [11 , 13] . When implementing OligoSTORM , the sample is then placed on a 3D single-molecule localization microscope ( Bruker Vutara 352 ) and scanned to identify nuclei to be imaged . Sequential rounds of imaging are then conducted with an automated microfluidic system ( Fig 1E ) , each round targeting one step of the walk by hybridizing a specific bridge oligo ( Fig 1E , labeled with ‘b’ ) along with its dye-conjugated oligos , after which images are acquired one nucleus at a time until all nuclei have been imaged; the next round is initiated through the use of toehold oligos ( Fig 1E , labeled with ‘t’ ) , which dislodge bridges bound in the previous round . To traverse the depth of PGP1f nuclei , imaging is conducted in 100 nm increments along the axial ( Z ) dimension for up to 4 μm , well within the volumetric capability of the Vutara . This protocol has given an average localization precision of 11 . 14 +/- 1 . 47 nm and 11 . 02 +/- 1 . 22 nm along the lateral axes and 46 . 96 +/- 6 . 02 nm along the axial dimension , corresponding to average supported optical resolutions of 26 ± 3 nm and 112 ± 11 nm , respectively ( Methods ) . All localizations are then subjected to drift correction with respect to fiducial beads , after which density-based spatial clustering of applications with noise ( DBSCAN; ref . [39] ) extracts clusters of localizations most likely to represent genomic regions ( Fig 1E; Methods ) . Using our workflow , we have walked the entirety of the 8 . 16 Mb region in over 60 chromosomes , separately imaging all 9 chromosomal segments via Mainstreet ( CS1-9; Fig 2A and 2B; S1 Movie; S1 Table ) . These studies also demonstrated the effectiveness of using Backstreet in conjunction with Mainstreet to image smaller features lying within larger regions , all in one imaging run ( S1 Table ) . For example , in a single imaging run that imaged CS1-9 using Mainstreet ( Fig 2A and 2B , rounds 1–9 ) , we used Backstreet to image four subregions comprising CS7 ( Fig 2A , rounds 10–13; S2 Fig ) . We have also imaged features as small as a loop ( 290 kb ) in CS6 , separately visualizing its anchors ( 10 kb ) , central body ( 270 kb ) , as well as flanking regions ( Fig 2C ) , and the Dnmt1 gene ( 59 . 5 kb ) as well as its differentially methylated region ( DMR; 2 . 9 kb ) [40] in CS3 ( Fig 2D ) . As a further demonstration of the capacity of barcodes , Fig 2E illustrates a special use of them to enable simultaneous walks along multiple chromosomes in single nuclei . In particular , taking advantage of the propensity of different chromosomes to lie within their own territories [41] and thus for images of them to be by and large nonoverlapping , we have traced the 8 . 16 Mb region of chromosomes 19 while also walking along chromosomes 3 and 5 in uniform step sizes of 500 and 250 kb , respectively ( Fig 2E; S2 Table; Methods ) . S3 Fig presents a larger field of view that also shows walks on both homologs of chromosome 8 , for which step sizes were 100 kb ( S3A Fig ) , as well as additional images of CS1-9 ( S3B Fig ) , the four subregions of CS7 ( S3C Fig ) , a loop ( S3D Fig ) , and DNMT1 and its DMR ( S3E Fig ) . We have also used our library to image CS7 , 8 , and 9 using OligoDNA-PAINT , achieving an exciting supported resolution of 18 nm ( Fig 2F; S3 Table; Methods; ref . [42] ) . Finally , to assess the reproducibility of our images , we have conducted a number of studies using OligoSTORM ( Methods ) . In one , we imaged CS1 using Mainstreet and then imaged it again 90 hours later using Backstreet , achieving an average spatial overlap of 80 ± 9% ( n = 20; Methods ) with minimal change in the average number of localizations ( n = 4 , 8371 for round 1 and 4 , 912 for round 21; Fig 2G , S4 Fig ) . Comparable results were obtained ( 75%–85% ) when we imaged CS7 via Mainstreet and then reimaged it using barcodes on Backstreet to sequentially visualize four subregions comprising CS7 ( Fig 2H ) . Intriguingly , we noticed early on that a number of consecutive steps of our walk appeared minimally entangled , suggesting that they correspond to distinct physical entities . To explore this , we further analyzed 38 OligoSTORM walks of CS1-9; these 38 represented the 19 nuclei with the highest image quality and for which we observed exactly two foci of signal per chromosomal segment and no evidence of aneuploidy ( Methods ) . These nuclei were analyzed by convolving them with a Gaussian kernel [43] , which enabled us to represent each chromosomal segment of each chromosome as a distribution of localization densities in 3D space ( Methods ) . To assess the structural arrangement of chromosomal segments , we used our density maps and calculated pairwise distances between centers of mass ( Distance score , DS ) and overlap ( Entanglement score , ES ) ( S5 Fig; Methods ) . The results revealed that chromosomal segments are not randomly placed with respect to each other . Although there was considerable variation in distances ( S5A and S5B Fig ) , the observed distributions showed that the five central segments , CS3-7 , were closer than expected in almost all pairwise combinations ( Fig 3A , S5B Fig ) . Overall , these central segments were also spatially separated from the segments lying at the beginning ( CS1 and CS2 ) and end ( CS8 and CS9 ) of the walk . Importantly , even with only 38 walks , these differences from expectation were statistically significant for most pairwise comparisons ( S5B Fig ) . Moreover , segments at the internal edges of the central region were further than expected from segments just beyond the central region; CS3 was further from CS2 , and CS7 was further from CS8 than expected . Interestingly , the beginning ( CS1 and CS2 ) and end ( CS8 and CS9 ) of the walk trended towards being closer than expected . Altogether , these observations suggested the center of the walk to be spatially separated from the ends , with the two ends being closer in space than expected . Our analyses of entanglement also revealed a nonrandom pattern ( Fig 3B , S5C and S5D Fig; Methods [43] ) . Although , again , there was considerable variation ( S5C and S5D Fig ) , the distributions of entanglement scores revealed that the central segments , CS3-7 , were more entangled than expected or trended as such , while the segments lying at the internal boundaries of this region ( CS3 and CS7 ) entangled less than expected with the segments lying upstream and downstream , respectively ( Fig 3B , S5D Fig ) . The contiguous pairs of segments lying at the ends of the walk were also minimally entangled; CS1 and CS2 showed insignificant entanglement , while CS8 and CS9 resulted in the lowest of all the entanglement scores . Thus , it is especially noteworthy that CS1 and CS2 , lying at one end of the walk , nevertheless showed significant entanglement with CS9 and CS8 , respectively , at the other end ( Fig 3B , S5D Fig ) . These observations aligned with our analyses of distance , implying a 3D segregation of the 9 chromosomal segments into two groups , one consisting of the central segments ( CS3-7 ) and the other consisting of the ends of the walk ( CS1 , 2 , 8 , and 9 ) . This was confirmed by unsupervised clustering of the mean distance and entanglement matrices ( S5E and S5F Fig ) . To further explore such segregation , we calculated the surface area , volume , and sphericity for each chromosomal segment , corrected for genomic size ( S6 Fig; Methods ) and then , combining these features with the 8 measures of distance ( DS ) and 8 measures of entanglement ( ES ) , conducted unsupervised clustering via Principal Component Analyses ( PCA ) for all 342 imaged chromosomal segments ( 38 chromosomes x 9 chromosomal segments ) ( Methods; S7 Fig and Fig 3C ) . Despite the variation among them , two major clusters emerged , with the first two principal components accounting for 24 . 2% and 14 . 0% of the variability ( Fig 3C , S7A Fig ) ; the first cluster comprised 84 . 5% of all the images of CS1 and CS3-7 , while the second cluster comprised 62 . 7% of all the images of CS2 , 8 and 9 . Considered together , the segments of cluster 1 had larger area and volume and were less spherical than the aggregate of the segments in cluster 2 ( Fig 3D–3F ) . Interestingly , the segments that were primarily in cluster 1 harbored epigenetic markings of active chromatin , while those primarily in cluster 2 harbored markings of inactive chromatin ( Fig 3G; Methods ) . These findings align with the differing degrees of chromatin compactness that have been observed for different epigenetic states in Drosophila [16] . Our PCA analyses also highlighted variability in the classifications for all chromosomal segments ( S4 Table ) . For example , while CS3-7 fell primarily in cluster 1 and CS2 and CS8 fell primarily in cluster 2 , CS1 and CS9 resulted in much more mixed classifications , with ratios of cluster 1:cluster 2 being 58:42 and 37:63 , respectively ( Fig 3C , bar graph ) . CS1 is particularly noteworthy in this regard; the distributions of PC1 values revealed that all segments were skewed towards one cluster type with the exception of CS1 ( S7B–S7J Fig ) , which was also the most mixed in terms of intra-nuclear and inter-nuclear variation ( S7K Fig ) . In brief , our data suggest that , while chromosomal segments can be broadly classified by their structural properties into categories reminiscent of active and/or inactive chromatin , they are also variable and dynamic in their character , potentially reflecting any number of attributes , including their state of activity and different stages of the cell cycle ( Methods ) . In sum , our analyses revealed organizational traits for the imaged region in single cells . First , CS3-7 , can form an internally entangled entity . Second , this region can be distanced from , as well as less entangled with , its flanking segments , pointing to the potential of distance , per se , to be an organizational principle . Third , the entire region can fold back on itself such that its ends come into proximity and entangle to a modest degree ( see example in Fig 2B ) . Fourth , CS1 , 2 , 8 , and 9 may each correspond to an individual entity , suggesting that the imaged region may be composed of at least 5 structurally distinct sections . Fifth , the two clusters of segments revealed by PCA may correspond to spatially separated compartments ( Figs 2B and 3H ) , active ( A-type ) and relatively inactive ( B-type ) , consistent with observations from Hi-C [3] and diffraction-limited imaging [11] as well as models [44] for how phase separation [45 , 46] may contribute to the physical distinction of compartment types . Finally , while some chromosomal segments were predominantly active or inactive , others could be classified as in either state or mixed , highlighting the capacity of super-resolution imaging to refine our understanding of epigenetic states by contributing measurements of the physical nature as well as variability of structures . Thus , even as our data align with observations that the 3D genome can be compartmentalized and organized by a variety of functions ( e . g . , refs . [3 , 47–51] ) , they also emphasize the potential of chromosomal regions to slip from one milieu into another . How well might our images correlate with population-based data from DNA-DNA proximity ligation based technologies ? To address this , we generated an in situ Hi-C map for PGP1f cells ( Fig 4; S5 Table; Methods ) and assigned loci in the target region to A and B compartments based on GC content [52] . The annotation assigned CS1 , CS3-7 , and CS9 to the A compartment , whereas CS8 and most of CS2 were assigned to the B compartment ( Fig 4 , S8 Fig ) . Notably , the fact that CS3-7 fell in a single compartment meant that all loci in this extended interval exhibited enhanced contact frequencies with one another . Additionally , we observed a statistically significant correlation ( ~ 0 . 6 Pearson Correlation , S9 Fig ) between the enrichment of Hi-C contacts between chromosomal segments and their mean distance score obtained by imaging . These findings recapitulate several features of the imaging data: [i] five physical entities ( CS1 , CS2 , CS3-7 , CS8 , and CS9 ) with distinctive chromatin states , [ii] a single , extended structure spanning CS3-7 , [iii] elevated contact frequency between the endpoints of the target region , and [iv] strong correspondence between the chromosomal segments assigned from Hi-C analysis to the A compartment ( CS1 , CS3-7 , and CS9 ) and those that were most frequently assigned from image analysis to cluster 1 ( CS1 and CS3-7 ) and , likewise , a strong correspondence between segments assigned by Hi-C to the B compartment ( CS2 and CS8 ) and those most frequently assigned by imaging to cluster 2 ( CS2 , CS8 , and CS9 ) , with CS1 and CS9 being the segments that most straddle the two clusters ( S4 Table ) . In brief , our images reveal that compartmental intervals have the capacity to form distinct structures . Moreover , the strong correspondence we observed between the outcomes of two very different technologies , FISH-based super-resolution imaging and Hi-C , lends confidence to our image-based measurements of distance , entanglement , size , volume , sphericity , and structural variability as well as to the overall potential of OligoSTORM to elucidate the organization of large swaths of , if not entire , chromosomes . It demonstrates , in particular , that key features of chromosome structure can be maintained to a reasonable extent through the various steps in which samples are prepared for FISH , including treatment of chromosomal DNA to heat-denaturation and binding of nucleic acid probes . Next , we developed a method , inspired by the fitting of proteins to cryoEM density maps [53] and named integrative modeling of genomic regions ( IMGR ) , that combines different modalities of data . Here , we integrated OligoSTORM images and 10-kb resolution Hi-C data to produce a 3D model of two homologous regions in a single diploid nucleus ( Fig 3I , S10A Fig; S2 Movie; Methods; Supporting information S1 Text ) . Population-based 3D modeling of Hi-C data has been used previously to recapitulate diploid genomes [54] , while single-cell Hi-C datasets have enabled haploid reconstruction [55 , 56] as well as homolog-specific modeling of diploid human cells [57] . In this study , we addressed diploidy through a new , two-step integrative process that fits each conformation in an ensemble of Hi-C derived 3D models ( built as previously described [58 , 59] using 10-kb resolution PGP1f Hi-C data ) into single-cell OligoSTORM density maps at the level of chromosomal segments ( S10A Fig; Methods; Supporting information S1 Text ) . First , the optimal positions and orientations of the 3D models of a chromosomal segment were found by maximizing the goodness-of-fit score with respect to the OligoSTORM density map . This step allowed us to filter out 3D models with conformations that were incompatible with the density map . Second , the resulting optimal rigid-fitted 3D models were optimized with 50 cycles of flexible fitting refinement using a simulated annealing molecular dynamics protocol [53] . Importantly , the scoring function used in this second step included terms ( ECCC ) assessing correlations with density maps as well as additional terms for satisfaction of the spatial restraints imposed by Hi-C data . In this way , we achieved a resolution of 10 kb for a genomic region that had been imaged in step sizes ranging from 0 . 36 to 1 . 8 Mb in size , the outcome being an integrative model that best satisfied both the Hi-C as well as the imaging data . An exciting outcome of using modeling to decipher images at a fine resolution is the potential for gaining insights into the 3D organization of local structures . For example , chromosome 19 is extraordinarily rich in zinc-finger genes , which are clustered in 6 locations [60] , and S10B Fig models the arrangement of the two clusters located in the region we imaged; one lies at one end of CS2 and another extends across CS4 . Similarly , all genes in the imaged region that are classified by OMIM as disease-causing [61] can now be positioned in 3D ( S10C Fig ) . The models can also be used to map ChIP-seq data in 3D space [62] . For instance , it appears that one end of each copy of the region is decorated by a large patch of H3K4me1 , H3K27ac , and H3K4me3 ( S10D–S10F Fig ) . These patches suggest enhancer-promoter clusters and , consistent with this , RNA-seq and DNAse accessibility data indicate that the patches co-localized with foci of expressed genes and greater accessibility ( S10G and S10H Fig ) . In contrast , available ChIP-seq data for facultative ( H3K27me3 ) and constitutive ( H3K9me3 ) heterochromatin suggest multi-foci patches of heterochromatin throughout ( S10I and S10J Fig ) . Finally , we have explored the potential of our approach to address questions regarding the relationship between homologous chromosomal regions . Indeed , studies across a wide variety of species have shown that diploidy does more than provide an heir and a spare , with a burgeoning number of homology effects [63 , 64] demonstrating that a chromosomal region can be far from indifferent to its homolog ( s ) , that there can be molecular awareness , coordination , and even physical pairing ( reviewed by [65 , 66] ) . Structurally , nonrandom allelic differences in accessibility and volume , in some cases showing parent-of-origin effect , have been noted at the level of individual genes ( e . g . , refs . [67 , 68] ) . Here , we asked whether structural differences between homologs can be detected across large chromosomal regions and , if so , whether they reflect parental origin . To address this , we calculated an ellipticity ratio to compare two homologous regions within each of the 19 nuclei examined and then ascertained whether this ratio differed from what might be expected at random . In particular , we fitted each imaged 8 . 16 Mb region to an ellipsoid , calculated an ellipticity score by dividing the length of the longest axis of the ellipsoid by that of the next longest axis , and then , for each nucleus , generated a ratio of the two ellipticity scores representing the two homologous regions , always dividing the larger by the smaller score ( Methods ) . Intriguingly , the median ellipticity ratio was 2 . 5 ( ±0 . 54 ) , which was significantly greater than the score for randomly selected pairs of homologous regions ( 1 . 3 ± 0 . 48; Fig 5A , S11A Fig , S6 Table ) , suggesting that megabase-sized homologous regions of a single nucleus may differ nonrandomly in ellipticity and that the difference is , on average , greater between homologous regions within a nucleus than between nuclei . Whether this difference reflects a regulatory function and , if so , whether that regulation is merely structural or also has functional implications for homolog-specific functions remains to be determined . These observations nevertheless argue that genomic regions can respond to the presence of homology , with one potential outcome being structural distinction from their homologs . Perhaps noteworthy in this context are phenomena that differentially affect homologous chromosomes in a chromosome-wide fashion , such as those underlying X-inactivation or asynchronous replication of random monoallelically expressed genes ( reviewed by [69] ) . In light of these phenomena , our findings may indicate chromosome-wide structural anticorrelations between homologous chromosomes that could even extend genome-wide . To ascertain whether greater ellipticity might correlate with parental origin , we turned to the most recent of our 38 walks as , for these , we had been successful in assigning parental origin . Here , we had implemented homolog-specific Oligopaints ( HOPs; Methods; ref . [14] ) , which target single nucleotide variants ( SNVs; also see ref . [70 , 71] ) that differ between the haplotypes of a genome and , therefore , permit distinction of homologous genomic regions . While proven successful in Drosophila and mice with abundant SNVs ( ~2–5 SNVs per kb; ref . [14] ) , their applicability to humans , which have a lower frequency of SNVs ( ~0 . 77 SNVs per kb for PGP1 ) , was unknown . Thus , we generated two libraries , HOP-M and HOP-P , the former targeting 11 , 259 maternal-specific SNVs extending across the entirety of chromosome 19 , and the latter targeting the analogous 11 , 259 paternal-specific SNVs ( Fig 5B; Methods ) . These proved successful in differentially labeling homologs ( Fig 5C–5G , S11B and S11C Fig; also , S3A and S3B Fig ) and , hence , enabling parental origin to be assigned for 12 walks ( 6 nuclei ) . These studies demonstrated that ellipticity was not absolutely correlated with parental origin ( Fig 5H ) , leaving open , however , the possibility of incomplete skewing . Intriguingly , absence of parental influence would imply that the distinction of homologs is established post-fertilization , perhaps as often as at each cell cycle , lending support to proposals that homologous chromosomal regions are not only molecularly aware of each other , but that that awareness can lead to inter-homolog communication and impact . Interestingly , analyses of even the small number of homologs in our dataset suggested differences in the 3D organization of maternal and paternal homologs ( S11D–S11I Fig ) ; of note , the combined set of maternal and paternal homologs used in our analyses produced matrices ( S11F and S11I Fig ) reminiscent of those of the entire set of 38 chromosomes ( Fig 3A and 3B ) , arguing that the homologs we distinguished by HOPs approximate those of the larger population . While these studies must be greatly expanded to substantiate any homolog-specific trend , they nevertheless indicate that our approach should be useful for exploring the biology of homology . In conclusion , we have described strategies for in situ super-resolution genome visualization through sequential rounds of imaging , followed by integration of the resulting images with Hi-C data to produce 3D models at 10 kb resolution in single nuclei . In conjunction with other efforts ( e . g . , refs . [11 , 13–21] , bioRxiv [22] ) , these strategies should advance our understanding of genome organization and , when augmented with multichromosome walks and parent-of-origin information , may contribute to whole-genome clarity on the relationship between 3D genome configuration and genome function . Indeed , we have already observed patterns of chromosome organization that were otherwise unknown . In line with other studies , including a live-cell analysis correlating chromatin accessibility more with fluctuation than compaction ( bioRxiv [72] ) , we have also observed significant structural variability and speculate , here , as to the source and/or potential of that variability . While such variability may indicate nonhomogeneity of the imaged samples with respect to , for example , the cell cycle , it may also suggest that some features of genome organization function only to enable the genome to be bundled into a small space and that , as long as this can be achieved cell cycle after cell cycle , the specific mechanism of bundling is unimportant , potentially even random . Alternatively , this variability may be intimately related to genomic function . In fact , variability , itself , may be a signature that distinguishes one genomic region , or chromatin state , from another . For instance , while some regulatory factors may recognize their binding regions by the conformation of their targets , it may be that others also , or solely , recognize their targets by the conformational variation of their targets , the number of conformations , and/or the speed with which their targets transition through conformations . If such dynamic variability can be transmitted along chromosomes or be of sufficient magnitude to generate a turbulence that is propagated through the nuclear milieu , it may function to convey information across nuclear distances , perhaps even perturb or promote phase separations . Finally , we note that , as our strategies should be applicable to entire chromosomes , it should be possible to implement them in whole genome studies of entire chromosomes as single , fully integrated units of structure and function , consistent with the unit of inheritance being as much the chromosome as it is the gene .
Akin to a growing category of oligo-based FISH probes ( e . g . , refs . [73–77] ) , Oligopaint FISH probes consist of oligos that have been computationally designed to target specific sequences of the genome [23] ( see the Oligopaints website for preselected whole genome datasets of Oligopaint oligo targets for the human as well as mouse , zebrafish , Arabidopsis , Drosophila , and C . elegans genomes ) . We identified genomic sequences that would serve as good targets for Oligopaint oligos by applying Oligominer to a repeat-masked human hg19 assembly [78] . Specifically , we used the ‘balance’ settings of 35–41 nts of genome homology , 42–47°C for TM , a simulated hybridization temperature of 42°C , and ‘LDA mode’ filtering . Candidate probes were further processed by the ‘kmerFilter’ script that calls the algorithm Jellyfish [79] to eliminate probes containing 18mers that occur in >5 times in hg19 . As the genome of PGP1 has been fully sequenced and phased [25 , 80] ( Kun Zhang and Daniel Jacobsen , unpublished; Bing Ren and Anthony Schmitt , unpublished ) , we were also able to design HOP probes for the homologs of chromosome 19 . First , genomic sequences that would be good targets for HOPs [14] were discovered using the approach described above , except that the input hg19 sequence was not repeat-masked . These probe sequences were then intersected with the locations of phased heterozygous SNVs for PGP1 according to Lo , et al . ( 2013 , ref . [80] ) using the ‘intersectBed’ utility from BEDTools [81] and then processed with in-house software to generate two sets of haplotype-specific HOP probes in which each probe oligo targets ≥1 heterozygous SNV . In order to ascertain which of the two HOP probes corresponds to maternal variants and which corresponds to paternal variants , we took advantage of the full genome sequence of PGP95 ( courtesy of Rigel Chan and Elaine Lim ) , with whom PGP1 shares his maternal parent but not his paternal parent , and presumed that the homolog of chromosome 19 that carries one ( or more ) long blocks of variants in common with PGP95 would be the one that was maternally derived ( Fig 1B ) . First , heterozygous variants that identify the two PGP1 haplotypes were matched to variants from the full genome sequence of PGP95 , and any that were homozygous in the PGP95 genome were discarded . All remaining PGP95 variants were then assigned as matching one of the PGP1 haplotypes ( arbitrarily designated as H0 or H1 ) or neither , permitting us to identify blocks of contiguous matches to H0 or H1 . The H1 haplotype included significantly more of the longest blocks shared between PGP1 and PGP95 , thus identifying the H1 haplotype as maternally derived ( S12 Fig ) . Then , as many long blocks corresponding to H1 were discovered to occupy a segment of PGP1 chromosome 19 at coordinates chr19:48 , 932 , 903–59 , 087 , 560 , we designated the chromosome 19 homolog that carried these blocks the maternal chromosome . Accordingly , we designated the HOP probe corresponding to H1 as HOP-Maternal ( HOP-M ) and that corresponding to H0 as HOP-Paternal ( HOP-P ) . The probes are efficient . Up to 96 . 5% of nuclei producing two fluorescent foci when imaged with HOP-M or HOP-P showed one of the signals to be stronger than the other , with the focus labeled more strongly with one probe being the more weakly labeled focus when imaged with the other probe; dye-swap experiments suggested that the difference between ratios for HOP-M and HOP-P is due to dye chemistries ( S11B and S11C Fig ) . Importantly , the corresponding ratio of signals obtained with probes targeting only the interstitial regions lying between SNVs approached 1 . We designed our Oligopaint libraries based on Hi-C maps that were available at the time our study was initiated . In particular , Geoffrey Fudenberg used IMR-90 Hi-C contact frequencies from Rao , et al . , ( 2014 , ref . [8] ) to identify compartmental intervals and contact domains in the 8 . 16 Mb region extending from chr19:7 , 400 , 000 ( 19p13 . 2 ) to chr19:15 , 560 , 000 ( 19p13 . 12 ) , where the calling of compartmental intervals relied on ICE [82] and a 3-state Gaussian Hidden Markov Model ( HMM ) implemented in scikit-learn [83] , while contact domain calling relied on [84] . Having segmented the 8 . 16 Mb region , we then incorporated Mainstreets and Backstreets into our library such that they would permit individual genomic features to be separately imaged . In particular , streets were generated using an in-house MATLAB ( MathWorks , Natick , MA ) script , ‘MakingStreets’ , available via GitHub ( https://github . com/gnir/OligoLego ) . Street sequences were vetted for predicted performance when serving as primers in PCR reactions using Primer3Plus [85] with default settings , except that the melting temperature was set to be 57–59°C , and the GC content was set to be 40–60% [85] . Street sequences were also checked for pairwise orthogonality using NUPACK [86] to estimate equilibrium hybridization yields between candidate streets and other potential target sites in 390 mM Na+ and 50% formamide at room temperature ( RT ) . Toehold sequences were also validated using NUPACK . Candidate streets passing all upstream checks were then examined for orthogonality to the human genome by using bowtie2 [87] to filter sequences aligning at least once to hg38 with ‘—very-sensitive-local’ alignment mode . Mainstreet and Backstreet sequences were appended to Oligopaint oligos using an in-house MATLAB code available via GitHub ( https://github . com/gnir/OligoLego ) . Here , we designed Mainstreet barcodes so that a ) they could be shared across multiple chromosomes , such that one round of imaging could target anywhere from one up to the maximum number of chromosomes to be imaged , and b ) imaging different chromosomes would be initiated in different rounds , thus permitting each chromosome to be identified by the round in which it first appears . For example , in an imaging scheme that introduces a new chromosome in every round , one could use the following scheme , wherein the walk along Chromosomes 1 , 2 , 3 , 4 , 5 , and N are initiated in Rounds 1 , 2 , 3 , 4 , 5 , and N , respectively . Oligopaint libraries were purchased from CustomArray ( Bothell , WA ) and amplified using in vitro transcription as previously described [24] . Briefly , for each sub-pool to be amplified , we first optimized the template and primer concentrations using real-time PCR . We next performed large-scale limited-cycle PCR , and then used the product as template for in vitro transcription by T7 RNA polymerase ( NEB , E2040S ) . The resulting ssRNA was then reverse transcribed ( EP0752 , ThermoFisher Scientific ) . RNA was digested by adding a mixture of 0 . 5 M NaOH and 0 . 25 M EDTA of equal volume to the reverse transcription reaction at 95° C for 10 minutes . Oligopaint ssDNA oligo probes were purified using the DNA Clean & Concentrator–100 kit ( Zymo Research , Irvine , CA ) paired with an EZ-Vac Vacuum Manifold ( Zymo Research ) . Human primary skin fibroblasts of donor PGP1 ( GM23248 , Coriell Institute; ref . [88] ) were grown at 37°C + 5% CO2 in serum-supplemented ( 10% v/v ) Dulbecco’s Modified Eagle Medium ( DMEM ) ( serum Gibco 10437; media Gibco 10564 ) . The cells were also supplemented with 1% ( v/v ) MEM Non-Essential Amino Acids Solution ( Gibco 11140050 ) . Penicillin and streptomycin ( Gibco 15070 ) were also added to the cell culture media to final concentrations of 50 units/ml and 50 μg/ml , respectively . 40 mm coverslips #1 . 5 ( Bioptechs , Butler , PA ) were cleaned with a bath sonicator and stored in a 75% ( v/v ) ethanol solution at room temperature until used . In order to prepare PGP1f samples for imaging , the pre-cleaned coverslips were first allowed to fully dry in a tissue culture hood and then placed in sterile 150 mm tissue culture dishes ( Falcon 353025 ) . PGP1f cells were then deposited at ~15% confluency onto the 150 mm dishes and allowed to grow in a mammalian tissue culture incubator until ~85% confluent ( ~5 days ) . At this point , the PGP1f cells were rinsed with 1x PBS , fixed using 4% ( w/v ) paraformaldehyde in 1x PBS for 10 minutes , rinsed with 1x PBS , permeabilized with 0 . 5% ( v/v ) Triton X-100 in 1x PBS for 10 minutes , rinsed with 1x PBS , and then stored in a cold room for up to 3 weeks . 3D DNA FISH [89] was performed on fixed and permeabilized coverslips essentially as previously described [14 , 23 , 90] . Briefly , coverglass samples were placed in small glass-bottom tissue culture dishes ( MatTek P50G-1 . 5-30-F ) and incubated with PBST ( 1x PBS + 0 . 1% v/v Tween-20 ) for 2 minutes . Samples were then incubated once with 1N HCL for 5 minutes , twice with 2x SSCT ( 2x SSC + 0 . 1% v/v Tween-20 ) for 1 minute , once with 2x SSCT + 50% ( v/v ) formamide for 2 minutes , and then once for 20 minutes with a pre-heated solution of 2x SSCT + 50% formamide at 60°C . Coverslips were then air-dried and incubated with a hybridization cocktail consisting of 2x SSCT , 50% formamide , 10% ( w/v ) dextran sulfate , 0 . 4 μg/μL of RNase A ( ThermoFisher EN0531 ) , and Oligopaint probe whose concentration was adjusted such that the final amount of probe added was equivalent to ~1 . 4 μM per every 1 Mb targeted ( e . g . ~2 . 8 μM for probe targeting 2 Mb ) . In cases where the parental origin of the homolog was to be determined , HOPs probes were added at a concentration of 2 . 85 μM each . The hybridization reaction was then sealed beneath a 22 x 30 mm , #1 . 5 coverslip ( VWR , Randor , PA ) using rubber cement ( Elmer’s , Westerville , OH ) , which was allowed to dry at 37°C for 7 minutes . Samples were then denatured on top of a heat block at 80°C ( with the heat block standing in water in a water bath set at 80°C ) for 3 minutes . Coverslips were then allowed to hybridize for 2–3 nights at 47°C in a humidified chamber . Following hybridization , coverslips were washed 4 times with 2x SSCT at 60°C for 5 minutes , then twice with 2x SSCT at RT . Coverslips were then rinsed with 1x PBS and allowed to air dry . For the purposes of drift correction , fiducial markers ( gold nano-urchins , GNUs; d = 90 nm , 630 nm abs max ) ( Cytodiagnostics , Ontario , Canada ) were sonicated for 10 minutes and then diluted 1:3 in PBS , pipetted onto the sample coverslips , and sandwiched with a blank , i . e . non-treated , 40 mm coverslip . GNU-coverslip sandwiches were centrifuged at 500 x g for 3 minutes . The sample coverslips were then washed for 2 minutes with 1x PBS . Images were obtained using the widefield mode on the Bruker Vutara 352 ( Bruker Nano Inc . ) microscope ( described in the section in Methods on OligoSTORM imaging ) . SlowFade antifade reagent containing DAPI dye ( ThermoFIsher Scientific ) was used as the imaging buffer . Images were acquired in the Z-dimension , approximating 2–3 um across 4 different channels , with the following dyes: DAPI , Alexa Fluor 647 , Alexa Fluor 488 , and ATTO 561 with 405 , 488 , 561 , and 639 nm lasers ( Coherent ) , the emission filters ( custom-made , Semrock ) 465/30 , 515/40 , 600/50 , respectively , and detected via an sCMOS camera ( Hamamatsu Orca-flash 4 . 0 v3 ) with 100 ms exposure time . These four channels corresponded to nuclear staining , interstitial signals , and the two HOP probes , respectively . Images were analyzed using an in-house MATLAB ( Mathworks ) script , which was written to detect and then derive the intensity ratio of the foci in each channel . Detection of signal from foci was validated by eye . OligoSTORM imaging was performed on a Bruker Vutara 352 commercial 3D biplane single-molecule localization microscope [91–93] , equipped with a 60x water objective ( Olympus ) with a numerical aperture ( NA ) of 1 . 2 . Fig 2C and 2D and S3B–S3E Fig were obtained using a 60X silicone objective ( Olympus ) with an NA of 1 . 3 which , akin to the water objective , does not produce the degree of spherical aberrations common to oil objectives . For illuminating Oligopaint oligos with Alexa Fluor 647 , we used a 639-nm Coherent Genesis laser with ~ 25 kW/cm2 at the back aperture of the objective , and for illuminating with Alexa Fluor 405 , we used a 405-nm Coherent Obis laser for photo-activation with up to ~0 . 05 kW/cm2 at the back aperture ( emission filters were described in the Methods section describing HOPs imaging and processing ) . Fluorescent detection was captured on an sCMOS camera ( Hamamatsu Orca-flash 4 . 0 v2 ) using a 10 ms exposure time . At each imaging session , we imaged 5–10 nuclei at different X , Y stage locations , while taking up to 4 μm z-scan using 100 nm Z-steps at each location , with up to 21 loci per nucleus , collecting 42 , 000–150 , 000 frames for each locus . Image data was collected by a Bruker-provided network attached storage ( NAS ) system . DNA-PAINT imaging was performed on a custom optical set-up based on a Nikon Ti Eclipse microscope featuring a Perfect Focus System and a custom-built TIRF illuminator . DNA-PAINT was performed using Highly Inclined and Laminated Optical sheet illumination ( HILO; ref . [94] ) with 10% of a 1000-mW , 532-nm laser ( MPB Communications ) using a CFI Apo TIRF 100× oil ( N . A . 1 . 49 ) objective at an effective power density of ~2 kW/cm2 . The 532-nm laser excitation light was passed through a quarter-wave plate ( Thorlabs , WPQ05M-532 ) , placed at 45° to the polarization axis and directed to the objective through an excitation filter ( Chroma ZET532/10x ) via a long-pass dichroic mirror ( Chroma ZT532RDC_UF2 ) . Emission light was spectrally filtered ( Chroma ET542LP and Chroma ET550 LP ) , directed into a 4f adaptive optics system containing a deformable mirror ( MicAO 3DSR , Imagine Optic ) and imaged on an sCMOS camera ( Andor Zyla 4 . 2+ ) with 6 . 5-μm pixels using rolling shutter readout at a bandwidth of 200 MHz at 16 bit , resulting in an effective pixel size of 65 nm . In order to estimate the axial positions of single molecule emitters , optical astigmatism was applied using the deformable mirror to cause an asymmetric distortion in the observed point spread functions [95 , 96] . A total of 22 , 500–45 , 000 250-ms frames were acquired for each image by using 0 . 5–1 nM of Cy3B-labeled 10-mer oligos in 1× PBS solution + 500 mM NaCl , 10 nM PCD , 2 . 5 mM PCA , and 2 mM Trolox . Gold nanoparticles ( 40 nm; no . 753637; Sigma-Aldrich ) were used as fiducial markers to facilitate drift correction . The lateral positions of single-molecule localization events were first determined using Picasso [97] by applying the least-square fitting algorithm , and the axial positions were then also determined using Picasso by fitting to a pre-established calibration curve [95 , 96] . Global lateral drift-independent localization precision was estimated by Nearest Neighbor based Analysis ( NeNA ) [98] . Global axial localization precision was estimated by empirical analysis of sub-diffraction sized fiducial markers after fitting to an empirically derived calibration curve with a mean precision of < 20 nm . Two-dimensional DNA-PAINT images were rendered using Picasso , with individual localization events being represented as single point sources blurred by an isotropic two-dimensional Gaussian function whose “sigma” parameter reflects the average X-Y NeNA localization precision of the localizations occurring in the presented field of view and colored according to inferred axial position [98] . Multicolor three-dimensional DNA-PAINT images were rendered using ViSP [99] , with individual localization events being represented as single point sources blurred by an anisotropic three-dimensional Gaussian function whose X-Y “sigma” reflects the average NeNA localization precision of the localizations occurring in the presented field of view and Z “sigma” was informed by the precision of the calibration curve used for axial fitting as determined by Picasso . We exploited a fluidics system as previously described [11 , 27] . Briefly , Bioptech’s FCS2 flow chamber ( v ~ 100 μL ) was integrated with a peristaltic pump ( Gilson minipuls3 ) and 3 valves ( Hamilton HVXM 8–5 ) , with the resulting dead volume in our system being ~700 μL . Integration of the fluidics system and the Bruker Vutara 352 microscope was programmed into SRX , the commercially available microscopy software package that controls the Bruker Vutara 352 . Secondary hybridizations were performed using 1 μM of secondary probe in 2x SSC +30% ( v/v ) formamide , and allowed to hybridize for 30 minutes at RT . Following hybridization , a wash solution ( 40% v/v formamide + 2x SSC ) was introduced for 12 minutes to the flow cell and incubated for 3 minutes without flow . Wash solution was then replaced with imaging buffer consisting of 10% ( w/v ) glucose , 2x SSC , 50 mM Tris , 1% ( v/v ) β-mercaptoethanol , and 2% ( v/v ) of a GLOX stock solution consisting of 75 mg/mL glucose oxidase ( Sigma G2133-250KU ) , 7 . 5 mg/mL catalase ( Sigma C40-500MG ) , 30 mM Tris , and 30 mM NaCl . A thin layer of mineral oil ( Sigma M5904 ) was added at the top to prevent oxygen from penetrating the imaging buffer . Imaging was initiated after the imaging buffer was allowed to incubate for 2 minutes without flow . During localization analysis , the precision of each localization event was determined by calculating the Cramér-Rao Lower Bound , namely the inverse of the Fisher information of the measured point-spread function [100 , 101] . The Cramér-Rao Lower Bound is the lower bound of the variance of the estimation process , which is used to calculate the localization precision of each event . For isolating localizations comprising structures of interest during each round of imaging ( fluidic cycle ) , we first removed localizations of lesser quality according to the following criteria: initial filtering of the localization data consisted of applying a geometric mean goodness-of-fit filter calculated from three separate metrics from the localization data . The individual metrics consisted of calculating the ratio of the photons assigned to the Point Spread Function ( PSF ) in the localization routine to the total number of photons in the camera cutout around a localization event ( first metric ) , total photons in the cutout around a localization event ( second metric ) , and the offset in Z between the calculated localization position of the fluorophore and the physical position of the objective piezo ( third metric ) . The goodness-of-fit metric of each localization event is calculated by sorting each metric through a self-normalization process ( with a value of 0 and 1 being the lowest and highest , respectively , quality value to a given metric ) , multiplying the values of the three metrics together , and taking the cube-root . Localizations with a score closer to 1 are considered of higher quality as compared to localizations with a score closer to 0 . An initial thresholding was set such that only localizations with a rank of 0 . 8 or higher were accepted for statistical analysis . Also , candidate localization events whose peak intensity varied no more than 2 pixels ( pixel size ~100 nm ) in up to 7 frames in a row were considered as one . Finally , we filtered out all localizations with an axial precision worse than 100 nm . We found that the number of localizations increases with genomic size ( S13 Fig ) . Following localization filtering , we performed drift correction using a center-of-mass function between subsets of fiducial markers across the data recording [102] . Finally , clusters of localizations were segmented using DBSCAN [39] . When determining the spatial overlap between two clusters , we used Bruker’s SRX software to generate surfaces of the calculated clusters based upon alpha shapes , using an alpha radius of 150 nm , which reflects the maximum particle distance used for the DBSCAN analysis for CS1 , CS7 , and the merged four subregions that were imaged in rounds 10–13 ( Fig 2A and 2H ) . Average spatial overlap between two clusters was expressed as the average of the fraction of the first cluster overlapping the second and the fraction of the second cluster overlapping the first ( Fig 2G and 2H , S4 Fig ) . When comparing two clusters representing the same genomic region , the difference in number of localizations between the two clusters was assessed using a beeswarm plot ( [103];S4 Fig ) . For comparison , we determined the spatial overlap of a cluster that was randomly divided into two clusters . In particular , we chose two nuclei as an example and divided the localizations obtained in rounds 1 and 21 of imaging into two , generating 8 clusters per diploid nucleus . We noticed that for the nucleus with the higher number of localizations ( ~6 , 500 localizations/cluster ) the average spatial overlap was 90–92% , while for the nucleus with lower number of localizations ( ~3 , 380 localizations/cluster ) the average spatial overlap was 85–89% . This implies that the spatial overlap between rounds is not expected to be more than 90% and is dependent upon the number of localizations . For OligoSTORM analysis , we chose only diploid nuclei for which the homologs were not too close to be distinguished with a single dye ( as we used only Alexa647N for OligoSTORM ) , did not show high levels of anisotropy in Z , and also did not show evidence of aneuploidy . Note , PGP1f cells were confirmed to be karyotypically normal ( XY; n = 20; Cell Line Genetics , Madison , WI ) , and cell sorting experiments show that 72–86% of PGP1f cells are in G1 . DBSCAN-extracted localizations for each chromosomal segment were convolved with a Gaussian kernel to match the mean precision [43] and to reduce the anisotropy of the image acquisition . The density map of each chromosomal segment was represented by intensities at points i ( ρi ) on a cubic grid with fixed voxel size . For each localization , the density value of the nearest voxel was increased by a factor of 1 . The obtained grid was convoluted with a Gaussian function using TEMPy [43] , such that the density ρi was defined as: ρi=∑NZ ( σ2π ) 3e− ( x−xn ) 2+ ( y−yn ) 2+ ( z−zn ) 22σ2 where x , y and z , and xn , yn , zn , are the Cartesian coordinates of grid point i , and localization n respectively . N is the total number of localizations , Z is the number of localizations per grid point , and σ is set to be proportional to the mean precision . The iso-contour threshold for each map was set to be one sigma after shifting of the background peak to zero using TEMPy [43] . The density map of the entire walk was also generated using the same approach . A total of five structural measures were obtained from each OligoSTORM density map obtained for each chromosomal segment . First , the distance score ( DS ) between two chromosomal segments was calculated as the spatial distance between the centers of mass of each chromosomal segment of the OligoSTORM density maps . Next , the DS was corrected for linear genomic distance and expressed relative to the expected distance , as determined via a power-law fit , by subtracting the expected distance from the measured distance . This DS Z-score was calculated for each data point using the power-law function as expected value and is expressed as the value , only , without unit of measure , so as to avoid implications of actual distance . Second , the entanglement score ( ES ) between two chromosomal segments was calculated as the fraction of overlapping voxels within the optimal iso-contour threshold with respect to the smaller of the two density maps as implemented in TEMPy [43 , 104] . As before , the ES was corrected for linear genomic distance using a power-law fit obtained by the ES Z-score . Third , the surface area of each OligoSTORM density map was calculated by summing over the area of the triangles on the surface on the convex hull using the “measure area” option in Chimera [105] . Next , the surface area was corrected for genomic size ( S6A Fig ) and transformed into a surface area Z-score . Fourth , volume was calculated within the optimal iso-contour threshold using the “measure volume” option in Chimera [105] . As before , the volume was corrected for genomic size ( S6B Fig ) and transformed into a volume Z-score . Fifth , a sphericity score ( ψ ) , which measures how closely the shape of an OligoSTORM density map approaches that of a mathematically perfect sphere , was calculated as: Ψ=π13 ( 6VP ) 23AP where VP and AP are the volume and surface area of the OligoSTORM density Map P . To take into account the bias due to genomic size , sphericity was corrected for genomic size ( S6C Fig ) and transformed into a sphericity Z-score . For each homolog , a feature vector was created , which is a binary 1x9 vector encoding the cluster types of each chromosomal segment in the region . Next , a per-nucleus profile of the compartment state was made for comparing the feature vectors of each of the homologs in a single nucleus . The compartment state profile is represented by a 1x9 vector whose values are determined by a comparison score set as: ( i ) 1 , both chromosomal segments belong to cluster 1 , ( ii ) 0 , the chromosomal segments belong to different clusters , or ( iii ) -1 , both chromosomal segments belong to cluster 2 . The resulting matrix of 19 compartment state profiles was hierarchically clustered , resulting in five clusters . The resulting matrix of structural features for the 342 chromosomal segments ( that is , 9 segments in 2 homologs in 19 cells ) was analyzed using the PCA implemented in the Python library Scikit-learn . The clustering resulted best in two major clusters that were named cluster 1 and cluster 2 ( Fig 3C , S7A Fig ) . Data available for RNA-seq , DNase-seq and five chromatin marks ( H3K4me1 , H3K27ac , H3K4me3 , H3K27me3 , H3K9me3 ) were downloaded from the ENCODE project [62] . Mapped reads were obtained from samples ENCFF043JNP and ENCFF280JPQ for H3K4me1 , ENCFF612WGP and ENCFF345SWP for H3K27ac , ENCFF397XVM and ENCFF179TEP for H3K4me3 , ENCFF378EQH for RNA-seq , ENCFF523JNF and ENCFF050OVJ for DNase-seq , ENCFF800CCA and ENCFF876WHO for H3K27me3 , and ENCFF644QGC for H3K9me3 ( https://www . encodeproject . org/ ) . Next , coverage of the chr19:7 ( 19p13 . 2 ) to chr19:15 . 5 ( 19p13 . 12 ) visualized region was calculated using the SamTools bedcov command [106] at 10 kb resolution . All datasets , with the exception of RNA-seq and H3K9me3 ChIP-seq , had two replicates which were highly correlated ( r > 0 . 99 ) and , in such cases , the average coverage per each bin was used . The final number of mapped reads for each dataset was 60 , 550 , 146 for H3K4m1 , 52 , 286 , 921 for H3K27ac , 62 , 084 , 095 for H3K4m3 , 200 , 776 , 708 for RNA-seq , 622 , 389 , 260 for DNAse , and 65 , 357 , 125 for H3K27me3 , and 26 , 947 , 714 for H3K9me3 . The Cartesian coordinates of the location of each homolog in the cell population were fitted to an ellipsoid [107] , and the ellipticity score for any 8 . 16 Mb region was then calculated to be the ratio between the two largest principal axes of the ellipsoid . When comparing homologous chromosomal regions in a single nucleus , we determined the ratio between the more elliptical homolog and the less elliptical one , calling that measure the ellipticity ratio . An ellipticity ratio equal to 1 indicates that the two homologs of a single nucleus had the same ellipticity score . For the evaluation of significance , a random ensemble of elongation ratios was constructed by calculating the ratio between two homologs randomly selected from the analyzed cell population . PGP1f cells were cultured as described in the ‘Cell Culture’ section of Methods and pelleted at low speed centrifugation ( 185 x g ) to maintain cellular and nuclear integrity . We generated 2 in situ Hi-C libraries with three million cells each , utilizing the MboI restriction enzyme ( NEB , R0147M ) , following the protocol described in [8] . Briefly , the Hi-C protocol entails crosslinking cells with 1% formaldehyde ( wt/vol ) , cell permeabilization with nuclei intact , DNA digestion with an appropriate 4-cutter restriction enzyme , and 5’ -overhang filling with the incorporation of a biotinylated nucleotide . The resulting blunt end fragments are ligated , and the DNA is then sheared . Biotinylated ligation junctions are captured with streptavidin beads , and the resulting fragments are analyzed with paired-end sequencing . Ten Hi-C libraries were sequenced with 150 bp paired-end sequencing reads on an Illumina HiSeqX-10 . The resulting sequencing data from the ten libraries ( S9 Table ) was processed separately using Juicer [112] and mapped to the GRCh37/hg19 ( hg19 ) human genome assembly [8 , 112] . To generate statistics and a Hi-C file for a combined map including all ten replicates ( S5 Table; S15 Fig; Supporting information S2 Text ) , the mega . sh script was used ( https://github . com/theaidenlab/juicer/wiki/Usage ) [112] , with the following command: /gpfs0/juicer/scripts/mega . sh . The combined Hi-C map generated a total of 2 , 599 , 264 , 644reads , achieving a resolution of 5 kb , in alignment with Hi-C map resolution parameters described in ref . [8] . This combined map was used with ‘KR normalization’ ( ref . [112 , 113] ) for all subsequent analyses . To label the two manually identified compartments as ‘A’ or ‘B’ , we began by partitioning chromosome 19 into 100kb loci . These loci were assigned to two compartments based on the intrachromosomal contact matrix for chromosome 19 using the eigenvector method ( S8 Fig ) as implemented in Juicer [3 , 112] . Specifically , we used the following command: ‘java -jar /gpfs0/juicer/scripts/juicer_tools . jar eigenvector KR inter . hic 19 -p BP 100000’ . Visual inspection of the Hi-C map for chromosome 19 confirmed that the eigenvector corresponded reliably to the plaid pattern in the data [3] ( S8 Fig and interactive S8 Fig http://bit . ly/2zCG2X6 , for interactive figure documentation see: https://igvteam . github . io/juicebox . js/ ) . Next , we labeled the two compartments as ‘A’ and ‘B’ on the basis of GC content , which is known to be enriched in the ‘A’ compartment and in open chromatin more generally [52] . The GC content track for human hg19 chr19 was downloaded from the UCSC Genome Browser ( http://hgdownload . cse . ucsc . edu/goldenpath/hg19/gc5Base/ ) and encodes the raw data for the gc5Base track on hg19 ( site last updated on 24-Apr-2009 14:48 ) . We binned the data at 100 kb resolution for chromosome 19 , using the following awk command: awk -v res = 100000 ‘BEGIN{fname = “temp”}NF>2{split ( $2 , c , ” = “ ) ; if ( fname ! = c[2] ) {print tot >> fname” . txt”; close ( fname” . txt” ) ; m = res; tot = 0} fname = c[2]; }NF = = 2 && $1>m{while ( $1>m ) {print tot >> fname” . txt”; m = m+res; tot = 0;}}$1< = m{tot+ = $2; }END{print tot >> fname” . txt”; close ( fname” . txt” ) ;}’ my_file . wig . Finally , we calculated the Spearman rank correlation coefficient between the compartment eigenvector and the GC content track . We obtained a Spearman rho value of 0 . 72 , which implies that the ‘A’ compartment corresponds to loci whose eigenvector entry is negative and the ‘B’ compartment corresponds to loci whose eigenvector is positive ( S8 Fig , Interactive Figure http://bit . ly/2oZ3vcU ) . Accordingly , we labeled compartmental intervals 1 , 3 , 5 , 7 , and 9 ( at coordinates 7–8 . 6 Mb , 9 . 3–9 . 5 Mb , 9 . 6–9 . 8 Mb , 9 . 9–14 . 6 Mb , and 15 . 2–15 . 5 Mb ) as compartment ‘A’ and intervals 2 , 4 , 6 , and 8 ( with coordinates 8 . 7–9 . 3 Mb , 9 . 5–9 . 6 Mb , 9 . 8–9 . 9 Mb , and 14 . 6–15 . 2 Mb respectively ) , as compartment ‘B’ . Note , compartmental intervals may differ from chromosomal segments by genomic coordinates and/or compartment classification . | Questions regarding the impact of chromosome structure on genome function are focusing increasingly on the manner in which chromosomes are organized within the nucleus . In fact , studies of processes as diverse as gene activation and repression as well as genome repair and stability are all querying how the 3D organization of chromosomal DNA may be a major player . Here , we apply our strategy for tracing chromosomes at super-resolution , traversing over 8 megabases of human chromosome 19 while visualizing genomic features ranging in size from kilobases to megabases . This technology has enabled exploration of the physical nature of a genomic feature called the compartment; compartments are widely hypothesized to reflect the partitioning of genomes into relatively more and less active regions . Excitingly , we find that compartments are , indeed , physical structures , that they are sometimes distinct and other times entangled . We also find that the maternally-derived and the paternally-derived homologous regions can differ more than would be expected by chance . Finally , by integrating image data with information regarding the frequency with which genomic segments contact each other , we produce a 3D model of how the 8 megabases we have imaged may be organized within a single nucleus , achieving 10 kb genomic resolution . | [
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| 2018 | Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling |
The mosquito Aedes aegypti is the primary global vector for dengue and yellow fever viruses . Sequencing of the Ae . aegypti genome has stimulated research in vector biology and insect genomics . However , the current genome assembly is highly fragmented with only ∼31% of the genome being assigned to chromosomes . A lack of a reliable source of chromosomes for physical mapping has been a major impediment to improving the genome assembly of Ae . aegypti . In this study we demonstrate the utility of mitotic chromosomes from imaginal discs of 4th instar larva for cytogenetic studies of Ae . aegypti . High numbers of mitotic divisions on each slide preparation , large sizes , and reproducible banding patterns of the individual chromosomes simplify cytogenetic procedures . Based on the banding structure of the chromosomes , we have developed idiograms for each of the three Ae . aegypti chromosomes and placed 10 BAC clones and a 18S rDNA probe to precise chromosomal positions . The study identified imaginal discs of 4th instar larva as a superior source of mitotic chromosomes for Ae . aegypti . The proposed approach allows precise mapping of DNA probes to the chromosomal positions and can be utilized for obtaining a high-quality genome assembly of the yellow fever mosquito .
Ae . aegypti is a principal vector for yellow fever , dengue and chikungunya viruses [1] , [2] . These diseases have a significant worldwide impact on human health . Yellow fever affects up to 600 million lives and is responsible for about 30 , 000 deaths annually [3] . Dengue fever is a threat to >2 . 5 billion people in tropical and subtropical regions , where between 50 to 100 million infections occur each year [2] , [4] , [5] . The incidence of dengue fever is increasing globally [6] , for example in developed areas like Singapore where dengue was thought to be well-controlled [7] and is a growing threat to the United States [8] . Despite all control campaigns , Ae . aegypti has expanded its range to most subtropical and tropical regions during the last several decades . This mosquito prefers to feed on humans and breeds in areas that humans inhabit [9] . To facilitate the development of genome-based strategies for mosquito control , genomes for three major disease vectors--the African malaria mosquito Anopheles gambiae , the southern house mosquito Culex quinquefasciatus , and the yellow fever mosquito Ae . aegypti--have been sequenced [10] , [11] . Among genomes of these three species , the genome of Ae . aegypti is the largest [11] . The draft genome sequence consists of 1 , 376 million base pairs , which is ∼5 times larger than the An . gambiae genome [10] and ∼2 times larger than the Cx . quinquefasciatus genome [12] . About half of the genome consists of transposable elements . The genome shows “short period interspersion” meaning that , in general , ∼1–2 kb fragments of unique sequences alternate with ∼0 . 2–4 kb fragments of repetitive DNA [13] . Abundance of repetitive elements in the genome leads to low levels of replication and poor spreading of polytene chromosomes of Ae . aegypti [14] , [15] . The yield of chromosome preparations useful for cytogenetic studies was only 0 . 5% for salivary glands [15] . At the same time , the large size of the genome makes mitotic chromosomes of this mosquito large and easily identifiable . The average size of the biggest metaphase chromosome in Ae . aegypti was estimated as 7 . 7 µm [16] , which is bigger than the average sizes of human metaphase chromosomes and comparable with the size of the human chromosomes at prometaphase [17] . The average size of the biggest human chromosome at prometaphase was estimated as 9 . 24 µm . Most of the classical cytogenetic studies on Ae . aegypti undertaken in the past were performed on mitotic or meiotic chromosomes from larval brain or male testis [18] , [19] , [20] . It has been demonstrated that Ae . aegypti has a karyotype typical to that found in other mosquitoes and includes three pairs of chromosomes . These chromosomes were originally designated as chromosomes I , II , and III in the order of increasing size [18] . However , later chromosomes were renamed in accordance with Ae . aegypti linkage groups as chromosomes 1 , 2 , and 3 [21] . Chromosome 1 was described as the shortest metacentric chromosome; chromosome 2 as the longest , also a metacentric chromosome; and chromosome 3 as a medium-length submetacentric chromosome with the secondary constriction on the longer arm . However , precise measurement of the centromeric index made on spermatagonial metaphase chromosomes has indicated that all Ae . aegypti chromosomes fall into the category of metacentric chromosomes according to the standard classification [22] , [23] . Unlike the anophelines , the sex chromosomes are homomorphic in all culicine mosquitoes , including Ae . aegypti [18] . The sex determination alleles were linked to chromosome 1 and described as Mm in males and mm in females [24] . M . Motara and K . Rai proposed to name sex chromosomes as “m” and “M” chromosomes for female and male determining chromosomes , respectively [20] . However , it was also popular to refer to sex chromosomes in Aedes as “X” and “Y” [19] . The precise measurement of the sex chromosomes in males and females has indicated that the female chromosome 1 is slightly bigger in size [22] . The C-banding technique has also demonstrated differences between male and female sex chromosomes in Ae . aegypti [20] . Typically females have pericentromeric and additional distinct intercalary bands on both chromosomes 1 which are absent on the putative male determining sex chromosome . C-banding has been found to be variable in different strains of Ae . aegypti . For example , an intercalary band can be present on the male chromosome in some strains , and intercalary bands may be differ in size in females [25] , [26] . The silver staining technique [26] and in situ hybridization of 18S and 28S ribosomal genes [27] indicated the location of ribosomal locus on both sex chromosomes of Ae . aegypti . The genetic mapping of the Ae . aegypti genome has been conducted in parallel with cytogenetic studies . An early genetic map included about 70 morphological , insecticide-resistance and isozyme markers [28] . Later , additional genetic maps were developed using restriction fragment-length polymorphism ( RFLP ) markers , random-amplified polymorphic DNA ( RAPD ) loci , single-strand conformation polymorphism ( SSCP ) , and single-nucleotide polymorphism ( SNP ) markers [29] , [30] , [31] . A composite map for RFLP , SSCP , and SNP markers incorporated 146 loci and covered 205 cM [13] . These maps provided the tools to localize a number of quantitative trait loci ( QTLs ) related to the mosquito's ability to transmit the filarioid nematode Brugia malayi [32] , the avian malaria parasite Plasmodium gallinaceum [33] , [34] , and dengue virus [35] , [36] . Advent of the fluorescent in situ hybridization technique allowed mapping of BAC clones , cosmids , and cDNA probes on mitotic chromosomes from the ATC-10 cell line of Ae . aegypti [16] . The chromosome positions of these clones were measured by FLpter: a fractional length from the short arm telomeric end p-terminus . The physical map was integrated with the genetic map by the direct placing cDNA genetic markers that contained the RFLP marker sequence to the chromosomes [37] . Nevertheless , molecular cytogenetic studies on Ae . aegypti mitotic chromosomes remain challenging . The current physical map has relatively low resolution and includes ∼180 markers [11] . Only ∼31% of the Ae . aegypti draft genome assembly has been placed to chromosomes , but without order and orientation . In contrast , a physical map of the malaria vector An . gambiae includes more than 2000 markers and covers about 88% of the genome [10] , [38] . Successful physical mapping for any organism relies on a robust source of high-quality , easily obtainable chromosome preparations . Recently we discovered that imaginal discs ( IDs ) of 4th instar larva can be an excellent source for a high number of large , easily spreadable , banded chromosomes . In this study , we optimized all cytogenetic procedures required for the successful in situ hybridization . Idiograms for each individual chromosome at the metaphase stage have been developed . Based on the banding pattern , 10 BAC clones and a 18S rDNA probe were mapped to their precise chromosomal positions . We propose to use this new cytogenetic tool for the detailed physical mapping of the Ae . aegypti genome .
In this study , we used the Liverpool strain , a parental strain for the Liverpool IB-12 strain , which was used for sequencing the Ae . aegypti genome [11] . Eggs were hatched at 28°C , and after several days , 2nd or 3rd instar larvae were transferred to16°C to obtain a high number of mitotic divisions in IDs . For in situ hybridization and idiogram development , slides were prepared from 4th instar larvae of Ae . aegypti . Before dissection , larvae were placed on ice for several minutes , then transferred to a slide with a drop of cold hypotonic solution ( 0 . 5% sodium citrate ) , and after that dissected under a Olympus SZ microscope ( Olympus America , Inc . , Melville , NY , USA ) . Larvae were decapitated , and cuticle from the ventral side of the larval thorax was slightly cut by dissecting scissors ( Fine Science Tools , Foster City , CA , USA ) . The cuticle was opened to expose the IDs to treatment in hypotonic solution for 10 min . Hypotonic solution was removed using filter paper , and larvae were treated with Carnoy's solution ( ethanol/glacial acetic acid in 3∶1 ratio ) for 1 min . After Carnoy's application , IDs immediately turned white and became easily visible under the microscope . Using dissecting needles ( Fine Science Tolls , Foster City , CA , USA ) , IDs were isolated from larvae , transferred to another slide in a drop of 50% propionic acid , and covered with a 22x22-mm cover slip . After 10 min of propionic acid treatment , IDs were squashed and briefly analyzed using an Olympus CX41 microscope ( Olympus America , Inc . , Melville , NY , USA ) at ×200 magnification . Slides suitable for in situ hybridization , which had >50 chromosome spreads , were then placed in liquid nitrogen , and cover slips were removed . Slides were dehydrated in a series of ethanol ( 70% , 80% , 100% ) and air dried . The percentage of the slides suitable for in situ hybridization was >90% . For the analysis of mitosis dynamics in IDs and brain ganglia , larvae were fixed in Carnoy's solution ( ethanol/glacial acetic acid in 3∶1 ratio ) . After 24 hours , IDs and brain ganglia were dissected from larvae and squashed in 50% propionic acid . Small drops of lactic acid were placed on each corner of the cover slip to prevent slides from drying . Slides were analyzed under the Olympus CX41 microscope at x400 magnification . BAC clone DNA was isolated using the Qiagen Large Construct kit ( Qiagen Science , Germantown , MD , USA ) . BAC-DNA was labeled by nick translation . Each reaction mix contained: 1 µg of DNA; 0 . 05 mM each of unlabeled dATP , dCTP , and dGTP , and 0 . 015 mM of dTTP ( Fermentas , Inc . , Glen Burnie , MD , USA ) ; 1 µl of Cy3 or Cy5 dUTP ( GE Healthcare UK Ltd , Buckinghamshire , UK ) ; 0 . 05 mg/ml of BSA ( Sigma , St . Louis , MO , USA ) ; 5 µl of 10x nick translation buffer; 20 u of DNA polymerase I ( Fermentas , Inc . , Glen Burnie , MD , USA ) ; and 0 . 0012 u of DNAse I ( Fermentas , Inc . , Glen Burnie , MD , USA ) . DNA polymerase/DNAse ratio was selected empirically to obtain the probe with the size range from 300 to 500 bp . To obtain a C0t1 DNA fraction , the genomic DNA was isolated from adult Ae . aegypti mosquitoes using a blood and cell culture maxi kit ( Qiagen Science , Germantown , MD , USA ) . DNA was digested by DNAse I with a concentration 0 . 0002 u/µl ( Fermentas , Inc . , Glen Burnie , MD , USA ) to obtain fragments <100 bp . After that , DNA was denatured at 97°C for 10 min , and DNA fragments were allowed to reassociate in TE buffer for 1 hour at 37°C . Then single-stranded DNA was digested using S1 nuclease ( Invitrogen Corporation , Carlsbad , CA , USA ) with a concentration of 2 . 58 u/µl for 15 min at 37°C . Double-stranded C0t1 DNA fraction was collected by standard ethanol precipitation for further application . Fluorescent in situ hybridization ( FISH ) was performed using a standard protocol [39] . Slides were pretreated with 0 . 1 mg/ml of pepsin ( USB corp . , Cleveland , Ohio ) for 5 min at 37°C; denatured in deionized 70% formamide in 2xSSC at 72°C for 5 min; and dehydrated in an alcohol series ( 70% , 80% , and 100% ) for 5 min each . Hybridization mix contained 50% formamide , 10% dextran sulfate ( Sigma , St . Louis , MO , USA ) , 200 ng of each probe per slide , and 4 µg of C0t1 DNA fraction . To eliminate nonspecific hybridization to the chromosomes , the probe was prehybridized with C0t1 fraction in a tube at 37°C DNA for 30 min . After that , the final 10 µl volume of hybridization mix per slide was overlaid with a 22x22 cover slip and glued by rubber cement . Hybridization on the slide was performed at 37°C in a dark humid chamber over night . Afterward , the slides were washed in a Coplin jar with 0 . 4x SSC , 0 . 3% Nonidet-P40 at 72°C for 2 min , and in 2x SSC , 0 . 1% Nonidet-P40 at RT for 5 min . Slides were thereafter counterstained using 1 µM YOYO-1 iodide solution ( Invitrogen Corporation , Carlsbad , CA , USA ) in 1× PBS for 15 min and enclosed under antifade Prolong Gold reagent ( Invitrogen Corporation , Carlsbad , CA , USA ) by a cover slip . Slides were analyzed using a Zeiss LSM 510 Laser Scanning Microscope ( Carl Zeiss Microimaging , Inc . , Thornwood , NY , USA ) at ×1000 magnification . To develop idiograms , the best images of the chromosomes stained with YOYO-1 were selected . The colored images were inverted in black and white images and contrasted in Adobe Photoshop as described before [40] . The chromosomal images were straightened using ImageJ program [41] and were aligned for comparison . In total , 150 chromosomes at various stages of condensation were analyzed . The sizes of IDs were measured using an SZ dissecting microscope ( Olympus America Inc . , Melville , NY , USA ) . The lengths of the chromosomes were measured using Zen 2009 Light Edition software [42] . The statistic analysis was performed using the JPM8 software program at 95% confidence intervals Heiberger [43] .
To obtain polytene chromosomes for cytogenetic analysis of Ae . aegypti , we have screened several tissues from different developmental stages including 4th instar larvae , pupae , and adults . Polytene chromosomes were found in salivary glands , Malpighian tubules , and ovarian nurse cells . However , polytene chromosomes had poor banding patterns and formed multiple ectopic contacts in all examined tissues . To improve the quality of the polytene chromosomes , we maintained the larval stages at 16°C . Reduced rearing temperature was effectively used to improve the quality of the polytene chromosome in salivary glands of Culex pipiens [44] . In our study , we did not detect any such improvement in the polytenization level or chromosome structure in Ae . aegypti . Finally , we confirmed that polytene chromosomes in Ae . aegypti are not suitable for the physical mapping of the genome . In addition to polytene chromosomes , we analyzed mitotic chromosomes from IDs and brain ganglia . Six IDs , which will develop into legs at the adult stage , are located right under the cuticle on the ventral side of the thorax in larva ( Fig . 1 ) . Although IDs become visible under the dissecting microscope from the 2nd instar larval stage , the best stage for the chromosome preparation is 4th instar larvae when IDs start to develop into legs and accumulate large numbers of mitotic divisions . IDs at different stages of their development are shown in Fig . 1 . The size of IDs in 4th instar larvae ranged from 0 . 1 to 0 . 8 mm . Fig . 1D represents overdeveloped IDs , which are not suitable for slide preparation because of the abundance of already differentiated tissues . In this study , the number of mitotic divisions per slide was compared between: 1 ) IDs of two sizes--0 . 1–0 . 25 mm and 0 . 3-0 . 45 mm ( Fig . 2A ) ; 2 ) IDs from larvae reared at 28°C and 16°C ( Fig . 2B ) ; and 3 ) one ID and two brain ganglia ( Fig . 2C ) . The largest number of mitotic divisions ( ∼175 ) was detected in IDs with an oval shape and length of 0 . 3–0 . 4 mm ( Fig . 1A , B ) . The 16°C temperature stimulated the accumulation of ∼1 . 5 times higher number of mitotic divisions per slide as compared to the normal temperature ( Fig . 2B ) . Finally , our comparison indicated a ∼6 fold difference in number of mitotic divisions between one ID and two brain ganglia ( Fig . 2C ) . This parameter is extremely important for utilizing chromosome preparations for successful in situ hybridization . The major phases of mitosis in IDs of Ae . aegypti are shown in Fig . 1: prophase ( A-C ) prometaphase ( D ) ; metaphase ( E ) and anaphase ( F ) . The interesting feature , which characterizes mitosis in Ae . aegypti , is that homologous chromosomes have strong somatic synapsis during interphase and stay paired up to early metaphase ( Fig . 3A-D ) . As a result of chromosomal pairing , only three separate chromosomes can be detected in all cells at the early mitotic stages . At metaphase , homologous chromosomes finally segregate from each other , and the visible number of chromosomes becomes equal to 6 ( Fig . 3E ) . The synapsis of the homologous chromosomes in Aedes cells has been described before [45] . Prometaphase and metaphase chromosomes ( Fig . 3D , E ) are the most abundant in IDs ( ∼42% ) and easily identifiable by their relative lengths and morphological characteristics . Long prophase chromosomes ( Fig . 3A-C ) , which are present in IDs at the level of ∼35% , are convenient for the mapping and orientation of relatively short scaffolds with sizes ∼1 Mb . Thus , ∼77% of all chromosome spreads on the preparations of squashed IDs can be utilized for the cytogenetic analysis and the physical mapping of Ae . aegypti genome . Another important feature of the mitotic chromosomes in IDs of Ae . aegypti is a clearly visible and reproducible banding pattern that can be used for developing idiograms--the diagrammatical representation of the chromosomes . In this study , idiograms for mid-metaphase chromosomes , the most convenient stage for chromosome recognition , have been developed . To calculate the correct proportion of the idiograms , chromosomes were measured using Zen2009 Light Edition software [42] . The results of these measurements are summarized and compared with previous data in Table 1 . The average lengths of the chromosomes were 7 . 15 µm , 9 . 46 µm , and 8 . 36 µm for chromosomes 1 , 2 , and 3 , respectively . The relative lengths of the chromosomes were 28 . 48% , 37 . 93% , and 33 . 39% . Centromeric indexes ( the relative length of the p-arm ) were 46 . 92% , 48 . 61% , and 47 . 42% , respectively , for chromosomes 1 , 2 , and 3 . Therefore , all three chromosomes should be considered as metacentric regarding current chromosomal nomenclature [23] . The average lengths of the chromosomes from IDs at the metaphase stage were just slightly ∼0 . 8 µm bigger than that from ATC-10 line [16] . The relative lengths of the chromosomes were found to be very similar to the chromosomes from brain [18] , spermatogonia [22] , and ATC-10 line [16] . Interestingly , the centromeric indexes in our study were more similar to that from brain and spermatogonia than to the cell line ( Table 1 ) . Fig . 4 shows the major steps of the idiogram development . The images of the YOYO-1 stained chromosomes ( Fig . 4A ) were converted in black and white images ( Fig . 4B ) and further contrasted in Adobe Photoshop [40] to obtain clear banding patterns . After that , chromosomes were straightened using Image J program plug-in [41] and aligned to each other for the pattern comparison . In total , 150 chromosomes were analyzed . Chromosomal arms were first determined by FISH of the BAC clones with known chromosomal positions ( Fig . 5 ) . These BAC clones contained genetic markers previously genetically mapped to the chromosomes [46] . Based on the human cytogenetic nomenclature , we determined bands with 4 different intensities – intense , medium intensity , low intensity , and negative [47] . The total number of bands per three chromosomes at mid metaphase was equal to 78 . The following regions can be used as cytogenetic landmarks for the chromosomal arm recognition: intense band in the middle of the 1q arm , intense double band in the 2q arm , and 2 low intense bands in the area next to the telomeric band on the 3q arm ( Fig . 4 ) . These regions have consistent distinct morphology and can be easily utilized for the chromosomal arm recognition . To test the reliability of chromosomal banding patterns for physical mapping , 10 BAC clones ( Table 2 ) were placed to their precise chromosomal positions on idiograms ( Fig . 4C ) by FISH . All BAC clones contained genetic markers ( Jimenez et al . , 2004 ) , and their positions on the chromosomes were predicted by previous genetic mapping [29] . In our study , most of the BAC clones followed the order of the previous genetic mapping . Only one BAC clone with genetic marker LF103 was found in slightly different order . The expected position of this BAC clone was between genetic markers LF253 and LF106 on the 3p arm . The actual position of this BAC clone was close to the centromere on 3q arm . Thus , the idiograms for the mitotic chromosomes from the ID cells of Ae . aegypti , which are presented here , can be successfully utilized for the physical mapping of the Ae . aegypti genome .
The genome of Ae . aegypti has several features that make physical mapping and genome assembly difficult . First , Ae . aegypti and other aediines have the largest genomes within the Culicidae family investigated thus far [11] . Second , the Ae . aegypti genome is extremely enriched with DNA repeats: about half of the genome consists of transposable elements . Third , Ae . aegypti lacks well-developed spreadable polytene chromosomes [14] , [15] . Initial physical mapping of the Ae . aegypti genome was performed on metaphase chromosomes from the ATC-10 cell line [16] . Using FLpter , a fractional length from the p-terminus ( short arm telomeric end ) for measuring the location of the signal on each chromosome , provided a very approximate localization on the chromosomes . In addition , using chromosomes from the permanent ( immortalized ) cell lines for the genome mapping can be misleading because these cells usually accumulate chromosomal rearrangements . Two large chromosomal translocations were described in the ATC-10 line [16] . It has been shown that in the cell culture of Ae . albopictus ∼30% of the cells were tetraploid and 30% of the diploid cells had chromosomal aberrations [48] . As a result of these difficulties and limitations , less than one third of the Ae . aegypti draft genome assembly has been placed to chromosomes mostly based on results from genetic recombination mapping efforts , but without order and orientation [11] . Using chromosomes from IDs of 4th instar larvae for the physical mapping of the Ae . aegypti genome as proposed here will help to overcome the above problems . Preparation of the chromosome spreads from IDs is a simple , robust procedure . In this study more than 90% of the slides were suitable for in situ hybridization . The number of the chromosome spreads per slide in IDs was also high . We were able to find ∼150 chromosome spreads per individual ID at the stages appropriate for the mapping . Finally , presence of these chromosomes in the IDs makes any individual mosquito at the larval stage available for cytogenetic analysis and allows avoiding having to use cell culture chromosomes for the physical mapping . The chromosome spreads from ID cells have two features important for physical mapping . First , chromosomes at all stages of mitosis have reproducible banding pattern which can be easily visualized by fluorescent staining with YOYO-1 . Band-based physical mapping can be easily applied to these chromosomes instead of previously used distance–based mapping ( FL-pter , fractional length from the p-terminus ) [16] . This approach will lead to the precise positioning of the BAC clones and genome assemblies on the chromosomes . In addition to band-based mapping , the direct labeling of the DNA probe , which we used in our study , provides more precise location of the signal on the chromosome as compared to antibody-detected probes used before [16] . Second , the significant number of chromosome spreads in IDs ( up to ∼30% ) might be found at early stages of mitosis . Prometaphase and especially prophase chromosomes reflect significantly lower chromatin condensation and can be utilized for the orientation of relatively short scaffolds up to size ∼1 Mb . The average size of the scaffolds in the current Ae . aegypti genome assembly is 1 . 5 Mb [11] . In order to map and orient scaffolds on the chromosomes , the probes for the BAC clones from the opposite sides of the scaffolds must be labeled with two different colors . This approach was successfully used for the mapping of An . gambiae heterochromatic scaffolds [38] . Recently maps for mitotic chromosomes were created and successfully used for the physical mapping of Dr . melanogaster heterochromatin [49] , [50] , [51] , [52] , [53] . Among other organisms , the most detailed cytogenetic analysis was performed for human and mammalian genomes [47] . The highly populated FISH-based physical maps of mammalian genomes included 9528 and 851 markers for human and canine , respectively [54] , [55] . The importance of chromosome-based physical mapping for comparative genomics was recently emphasized by H . Lewin and coauthors in the article titled “Every genome sequence needs a good map” [56] . The authors suggested looking “back in the future” for developing high-resolution physical maps as an important framework for genome annotation and evolutionary analysis . Finding an appropriate source of chromosomes and developing chromosomal idiograms , as conducted in this study , is the first important step toward the assembly and further utilization of the genomic information for the yellow fever mosquito Ae . aegypti . | Dengue fever is an emerging health threat to as much as half of the human population around the world . No vaccines or drug treatments are currently available . Thus , disease prevention is largely based on efforts to control its major mosquito vector Ae . aegypti . Novel vector control strategies , such as population replacement with pathogen-incompetent transgenic mosquitoes , rely on detailed knowledge of the genome organization for the mosquito . However , the current genome assembly of Ae . aegypti is highly fragmented and requires additional physical mapping onto chromosomes . The absence of readable polytene chromosomes makes genome mapping for this mosquito extremely challenging . In this study , we discovered and investigated a new source of chromosomes useful for the cytogenetic analysis in Ae . aegypti – mitotic chromosomes from imaginal discs of 4th instar larvae . Using natural banding patterns of these chromosomes , we developed a new band-based approach for physical mapping of DNA probes to the precise chromosomal positions . Further application of this approach for genome mapping will greatly enhance the utility of the existing draft genome sequence assembly for Ae . aegypti and thereby facilitate application of advanced genome technologies for investigating and developing novel genetic control strategies for dengue transmission . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"biology"
]
| 2011 | Imaginal Discs – A New Source of Chromosomes for Genome Mapping of the Yellow Fever Mosquito Aedes aegypti |
Chikungunya virus ( CHIKV ) is an arthropod-borne virus responsible for recent epidemics in the Asia Pacific regions . A customized gene expression microarray of 18 , 760 transcripts known to target Aedes mosquito genome was used to identify host genes that are differentially regulated during the infectious entry process of CHIKV infection on C6/36 mosquito cells . Several genes such as epsin I ( EPN1 ) , epidermal growth factor receptor pathway substrate 15 ( EPS15 ) and Huntingtin interacting protein I ( HIP1 ) were identified to be differentially expressed during CHIKV infection and known to be involved in clathrin-mediated endocytosis ( CME ) . Transmission electron microscopy analyses further revealed the presence of CHIKV particles within invaginations of the plasma membrane , resembling clathrin-coated pits . Characterization of vesicles involved in the endocytic trafficking processes of CHIKV revealed the translocation of the virus particles to the early endosomes and subsequently to the late endosomes and lysosomes . Treatment with receptor-mediated endocytosis inhibitor , monodansylcadaverine and clathrin-associated drug inhibitors , chlorpromazine and dynasore inhibited CHIKV entry , whereas no inhibition was observed with caveolin-related drug inhibitors . Inhibition of CHIKV entry upon treatment with low-endosomal pH inhibitors indicated that low pH is essential for viral entry processes . CHIKV entry by clathrin-mediated endocytosis was validated via overexpression of a dominant-negative mutant of Eps15 , in which infectious entry was reduced , while siRNA-based knockdown of genes associated with CME , low endosomal pH and RAB trafficking proteins exhibited significant levels of CHIKV inhibition . This study revealed , for the first time , that the infectious entry of CHIKV into mosquito cells is mediated by the clathrin-dependent endocytic pathway .
Chikungunya virus ( CHIKV ) is an arthropod-borne virus of the genus Alphaviruses , belonging to the family Togaviridae . It is an enveloped , single-stranded , positive-sense RNA virus with a genome size of approximately 12 , 000 nucleotides . CHIKV virions measure 60–70 nm in diameter and it contains a spherical capsid with icosahedral symmetry . The viral genome encodes for four non-structural ( nsP1–P4 ) and five structural proteins ( capsid , E1 , E2 , 6K and E3 ) [1] , [2] . Embedded in the lipid bilayer surrounding the viral capsids , the E1 and E2 structural proteins enable the virus to be directed to host cells for attachment and fusion with cellular membranes during infectious entry processes [1] , [2] . Chikungunya is defined as “bent walker” in Makonde , which refers to the hunched posture observed in patients suffering from persisting arthralgia [3] , [4] . Symptoms typically develop from 4–7 days after the bite of an infected mosquito vector . Characterized by high fever , joint pain , headache , vomiting and maculopapular rash , acute CHIKV infection lasts approximately 1–10 days , while chronic CHIKV infection often results in polyarthralgia and myalgia that persist for long periods . Other CHIKV-associated complications reported include lymphopenia , severe skin lesions , lethal hepatitis and encephalitis , with severe neurological symptoms documented during recent outbreaks in Réunion Island [3] , [4] . While human transmission of CHIKV occurs via Aedes ( Ae . ) mosquitoes , particularly Ae . aegypti and Ae . albopictus , other Aedes species such as Ae . furcifer , Ae . taylori , Ae . luteocephalus , Ae . africanus and Ae . Neoafricanus are involved in enzootic cycles [5] , [6] . Alphaviruses can be broadly divided into the New World encephalitic viruses and Old World arthritogenic viruses [7] , [8] . Along with other widely recognized Old World alphaviruses such as Sindbis ( SINV ) , Semliki Forest ( SFV ) , Ross River ( RRV ) viruses , CHIKV is responsible for high morbidity rates , accounting for millions of adverse , albeit non-fatal cases [3] , [9] , [10] . Genomic analysis of previously and recently identified clinical isolates revealed unique molecular features , most prominently a point mutation in the viral envelope E1 glycoprotein ( E1-A226V ) [9] , which was suggested to increase the capability of viral fusion , assembly and tropism that aids in virus transmission [11] , thus accounting for the selective advantage of the viral subtype . The presence of the A226V mutation in the CHIKV E1 gene was also reported during a major outbreak of CHIKV infection in the Indian state of Kerala [12] . Based on an SFV model of infection , replacement of the alanine residue at position 226 of the E1 envelope protein to valine was previously observed to affect membrane fusion and is believed to result in differential cholesterol dependence [10] , [13] . Viruses can enter host cells through various pathways such as phagocytosis , macropinocytosis , and receptor-mediated endocytosis . Viruses have evolved the ability to penetrate and release the viral genome into the cell cytoplasm , after binding to the cellular receptor ( s ) . Penetration for enveloped RNA viruses includes endocytosis and membrane fusion , the latter of which can either take place in a pH independent manner at the cell surface or within intracellular vesicles ( pH-dependent ) . Majority of viruses require endocytic internalization for productive infection , with the virions being led to appropriate replication sites , thus bypassing many cytoplasmic barriers [14] . In particular , RNA viruses posses the ability to hijack multiple portals of cellular entry . Endocytic pathways such as clathrin-mediated , clathrin-independent , macropinocytosis , caveolar-mediated and caveolar-independent , have been shown to be utilized by numerous viruses [15] , [16] . Other less characterized pathways also include lipid raft-mediated endocytosis , in which dynamin participation has been proposed but has not been determined [14] . Microarray studies performed on arboviruses and its mosquito vectors have been limited and aimed at enhancing diagnostics and understanding immune-based antiviral mechanisms [17] , [18] . Such studies were previously performed to analyze gene expression profiles of mosquito midguts in response to Sindbis ( SINV ) infection , and genes associated with vesicle transport and immune cascades were observed to be involved during the infection [19] . Previous studies have been conducted to investigate the different entry pathways of Alphaviruses on various cell lines . SFV and Venezuelan equine encephalitis virus ( VEEV ) have been shown to enter mammalian cells through pH-dependent endocytic pathway [20] . Additionally , SINV was observed to infect both mammalian and mosquito cells at neutral pH [21] , while VEEV was found to enter Ae . albopictus C710 mosquito cells via pH-dependent endocytosis [22] . Analyses of infectious CHIKV entry have been limited to mammalian cells , with several findings noting that CHIKV infection on HEK293T mammalian cells is independent of clathrin heavy chain and dependent of functional Eps15 [3] , [4] . However , little is currently known about the infectious CHIKV entry process and pathway into mosquito cells . Deciphering the much neglected aspects of cellular factors in contributing to the infectious entry of CHIKV into mosquito cells may enhance our understanding on the conservation or diversity of these host factors amongst mammalian and arthropod cells for successful CHIKV replication . This unprecedented study therefore aims to examine the infectious entry processes of CHIKV in mosquito cells . Different strategies targeting cellular endocytosis were used , including customized microarray profiling of mosquito genes involved in endocytic pathways , treatment with specific drug inhibitors , gene knockdown and expression of dominant negative cellular proteins . We demonstrated , for the first time , that CHIKV preferentially uses a clathrin-mediated and Eps15-dependent pathway to enter Ae . albopictus ( C6/36 ) cells . We also showed the importance of endosomal pH acidification in CHIKV entry . Moreover , results from the siRNA-based knockdown of Rab5 and Rab7 genes suggested that CHIKV entry involves the trafficking of virus particles from early to late endosomes . The novelty of deciphering the infectious entry of CHIKV into C6/36 cells potentially allows for better understanding on the pathogenesis of CHIKV infection and the development of potential antiviral strategies .
Ae . albopictus C6/36 cells ( American Type Culture Collection ) were maintained at 28°C in Leibovitz-15 ( L-15 ) growth medium ( Sigma-Aldrich Corp . , St Louis , MO , USA ) supplemented with 10% fetal bovine serum ( FBS ) ( Hyclone , Cramlinton , UK ) . Baby hamster kidney ( BHK-21 ) cells ( American Type Culture Collection , ATCC CCL-10 ) were maintained at 37°C in Rosewell Park Memorial Institute ( RPMI- ) 1640 growth medium ( Sigma-Aldrich Corp ) supplemented with 10% FBS ( Hyclone ) . The cells were passaged in T75 flasks ( Nunc , Denmark ) at a 1∶5 dilution every 3–4 days at 70–80% confluency . For experimental infections , C6/36 cells were seeded in T25 flasks to a confluency of 80% that achieved a cell density of ∼3×106 cells/ml . The C6/36 cells were incubated at 37°C for 1 . 5 hours during virus infection , before being placed at 28°C throughout the remainder of the experiments , in line with the natural temperature for mosquitoes and mosquito cell incubation . Singapore/07/2008 CHIKV strain was obtained from National Public Health Laboratory , Ministry of Health , Singapore and propagated in C6/36 cells . Low passages of the virus were used throughout this study . CHIKV strains SGEHICHD122508 – ( Accession No . : FJ445502 . 2 ) and SGEHIDSD67Y2008 – ( Accession No . : EU441882 . 1 ) were obtained from Environmental Health Institute , National Environmental Agency , Singapore . These virus strains were propagated in C6/36 cells and utilized in low endosomal pH experiments . The virus titers were quantitated using viral infectious plaques assays performed on BHK-21 cells . Growth kinetics were performed on these three different CHIKV strains , with infected and mock-infected samples harvested at various time points of 0 , 6 , 12 , 24 , 36 , 48 , 72 , 96 and 120 hours post infection ( p . i ) on C6/36 cells . A multiplicity of infection ( MOI ) of 10 was used for most of the experiments throughout the study , to allow for more accurate observations and better detection of CHIKV entry processes into host cells . Confluent monolayers of C6/36 cells were infected with CHIKV at an MOI of 10 . At 24 hours p . i , the supernatant was harvested by centrifugation at 4 , 500 rcf for 10 mins . CHIKV particles were then concentrated and partially purified by using a centrifugal filter device ( Millipore , Billerica , MA , USA ) at 1 , 077 rcf for 2 hours . The partially purified viruses were then purified even further by sucrose gradient centrifugation at 74 , 766 rcf for 3 hours at 4°C . Finally , the purified virus pellet was resuspended in Tris buffer ( 50 mM Tris-HCl [pH 7 . 4] ) . The titer of the purified virus preparation was determined by viral infectious plaque assay on BHK-21 cells and was found to be 5×1010 PFU/ml . For negative staining of purified CHIKV preparation , 7 . 5×108 PFU/15 µl of CHIKV was added to freshly glow discharged , carbon-coated grids , and stained with 2% uranyl acetate for 1 min . The grids were then air dried before viewing under the CM120 Biotwin transmission electron microscope ( Philips ) . C6/36 cells growing on coverslips were incubated with CHIKV at an MOI of 10 for 1 hour at 4°C with gentle rocking . The cells were subsequently washed three times in ice-cold 1× phosphate buffer saline ( PBS ) to remove unbound viral particles , prior to further incubation for 1 hour at 37°C in growth medium to enable virus penetration . Extracellular virus particles that failed to enter into cells are then inactivated with acid glycine buffer ( pH 2 . 8 ) ( 0 . 1 M potassium hydrogen phthalate and 0 . 1 M of HCl ) . Infectious virus entry was traced at different time points upon the addition of CHIKV to C6/36 cells for up to 1 hour post-infection and processed for either ultrastructural analysis via transmission electron microscopy or immunofluorescence assay . C6/36 cells ( 1 . 2×106 ) were seeded into 24-well plates , and incubated for 24 hours before the drug treatment assays were performed . Pre-treatment drug assays were performed in favour of co- and post-treatment studies , in order to ensure that potential CHIKV inhibition is most likely to occur at the entry step , as opposed to downstream infective phases , such as viral replication . Hence , to determine the effects of the drugs used to inhibit the CHIKV entry , C6/36 cells were pretreated with drugs at different concentrations for 3 hours at 37°C . The pharmacological inhibitors were then removed and cell monolayers were washed twice with 1× PBS , in order to eliminate the possibility of exposure of the virus to the inhibitors . This is to ensure minimal risk of the inhibitors directly influencing the viability of the virus and its subsequent entry into the cells . After 1 . 5 hours of virus infection at an MOI of 1 , the cells were washed thrice with 1× PBS , replaced with fresh L-15 media and incubated for another 24 hours . At 24 hour p . i . , supernatants from CHIKV-infected cells were harvested for viral infectious plaque assays . Three independent experiments were carried out for each set of drugs used . Inhibition of receptor- and/or clathrin- mediated endocytosis was performed through the use of chlorpromazine ( 42 , 56 , 70 & 84 µM ) ( Sigma Aldrich ) [23] , monodansylcadaverine ( 50 , 100 , 150 & 200 µM ) ( Sigma Aldrich ) [24] and dynasore ( 5 , 10 , 50 & 100 µM ) ( Sigma Aldrich ) [25] . Other inhibitors targeting alternative endocytic pathways included filipin ( 0 . 1 , 0 . 5 , 1 . 0 , 1 . 5 & 2 . 0 µg/ml ) ( Sigma Aldrich ) [26] , nystatin ( 5 , 10 , 20 & 40 µM ) ( Sigma Aldrich ) [26] , methyl-β-cyclodextrin ( 2 . 5 , 5 . 0 , 7 . 5 & 10 µM ) ( Sigma Aldrich ) [26] and EIPA ( 10 , 25 , 50 & 100 µM ) ( Sigma Aldrich ) [27] , [28] . CHIKV infected , 0 . 1% DMSO treated C6/36 cells acted as solvent control . Endosomal acidification was inhibited by drug treatment of C6/36 cells with concanamycin A ( 20 , 60 , 100 , 150 & 300 nM –Singapore/07/2008 CHIKV strain ) and ( 80 , 100 , 150 & 300 nM - CHIKV strains SGEHICHD122508 and SGEHIDSD67Y2008 ) ( Sigma Aldrich ) and bafilomycin A ( 0 . 1 , 1 . 0 , 2 . 0 , 3 . 0 & 4 . 0 µM ) ( Sigma Aldrich ) [29] , [30] . Other inhibitors performed on C6/36 cells include colchicine ( 50 , 100 , 150 & 200 µM ) ( Sigma Aldrich ) [31] , nocodazole ( 1 , 5 , 10 , 15 & 20 µM ) ( Sigma Aldrich ) [32] , cytochalasin B ( 0 . 1 , 1 . 0 , 1 . 5 & 2 . 0 µg/ml ) ( Sigma Aldrich ) [23] , cytochalasin D ( 1 , 3 , 5 , 10 & 20 µg/ml ) ( Sigma Aldrich ) [31] and nifedipine ( 40 , 60 , 80 & 100 µM ) ( Sigma Aldrich ) [33] . Upon infection , C6/36 cells were harvested at 0 min , 15 mins , 30 mins and 120 mins post infection ( pi ) . At 0 min pi , cells were harvested immediately upon virus inoculation . At each time point , C6/36 cells were washed with 2 ml of the pre-warmed ( 28°C ) maintenance medium . After decanting the maintenance medium , 1 ml of Qiagen Cell Protect solution was added to each flask . Detached cells were transferred into a sterile 2 ml tube and were stored immediately at −80°C until total RNA extraction . Cells were homogenized in 350 µl RLT buffer in QIAshredder spin columns ( Qiagen , Hilden , Germany ) prior to total RNA extraction with Qiagen RNeasy Protect cell mini kit ( Qiagen ) according to manufacturer's instructions . Hundred nanograms of total RNA were used for probe synthesis of cy3-labeled cRNA , and hybridizations were carried out on an Aedes mosquito customized gene expression microarray ( 18760 transcripts from Vector Base Aedes aegypti database with 2 best probes per transcript ) in Agilent GE 8×60K array format ( Agilent Technologies , California , USA ) . Hybridization was carried out at 65°C for 17 hours in an Agilent hybridization oven at 10 rpm . After hybridization , microarrays chips were washed in gene expression wash buffer 1 for 1 min at room temperature and 1 min in gene expression wash buffer 2 at 37°C before scanning on the Agilent High Resolution Microarray Scanner ( C-model ) . Raw signal data was extracted from the TIFF image with Agilent Feature Extraction Software ( V10 . 7 . 1 . 1 ) . The raw microarray data was processed and analyzed with Partek Genomics Suite ( Partek , St Louis , Missouri , USA ) to generate values representing fold changes in gene expression . An average of the duplicate values was used to calculate fold change , and each value was then assessed for its statistical significance , using analysis of variance ( ANOVA ) . Host genes demonstrating at least a 1 . 5-fold change in expression upon CHIKV infection were selected for further investigation . Pathway analysis was subsequently detailed with Ingenuity Pathway Analysis ( IPA ) 9 . 0 ( Ingenuity Systems 2011 , Redwood City , California ) and differentially regulated genes involved in the clathrin-mediated endocytic pathway were selected for pathway mapping . To track the infectious entry process of CHIKV into C6/36 cells at various time points p . i , cells infected with CHIKV at an MOI of 10 were fixed with 2 . 5% glutaraldehyde ( Agar Scientific , Stansted , UK ) at 4°C for 20 mins , followed by scraping of the cells and subjecting them to longer fixation at 4°C overnight . The following day , cells were centrifuged and the pellet was washed with PBS and deionized water . The cell pellet was then post-fixed with 1% osmium tetroxide ( Ted Pella , Redding , California , USA ) and 1% potassium ferro-cyanide for 2 hours , followed by dehydration in an ascending graded series of ethanol and acetone , i . e . 25% , 50% , 75% , 95% and 100% for 10 mins at each concentration . On the following day , cells were infiltrated with resins by passing them through three changes of mixture , comprised of a combination of acetone , ethanol and araldite . The following day , cells were infiltrated with four changes of absolute embedding media with 1 hour incubation at room temperature , 40°C , 45°C and 50°C . After the last spin , cell pellet was resuspended in 100–200 µl of araldite . Mixture was embedded using the BEEN capsule ( size 3 ) and was incubated at 60°C for 24 hours to allow polymerization . The samples were trimmed with an ultramicrotome ( Reichert-Jung , New York , USA ) and the sections were stained with 2% uranyl acetate and fixed with lead citrate . The stained sections were viewed under the Philip EM 208 transmission electron microscope and images were captured digitally with a dual view digital camera ( Gatan , Werrendale , USA ) . For immunofluorescence microscopy , C6/36 cell monolayers were first grown on coverslips till 75% confluency . The cells were incubated at 4°C for 30 mins . The cells were allowed to bind to CHIKV at an MOI of 10 for 1 hour at 4°C to allow viral attachment to the cell surface before being shifted to 37°C for 10 mins to promote CHIKV entry into the cell . Cells were fixed in ice-cold methanol at 10 and 15 mins post entry of CHIKV . This is followed by three washes of cold PBS prior to immunofluorescence assay analyses . Rabbit polyclonal antibodies to clathrin ( CLTC , Chemicon ) , early endosomal antigen 1 ( EEA1; Novus Biologicals ) and CHIKV E2 protein ( customized CHIKV13893 B3 rabbit polyclonal , ProSci ) were used for immunofluorescence assays . Texas Red ( TR ) - or FITC-conjugated secondary antibodies were used at a concentration of 1 µg/ml . Lysotracker , a dye for staining live cells were used at a concentration of 75 nM . The specimens were then viewed with Olympus IX81 motorized inverted epifluorescence microscope ( Olympus , Tokyo , Japan ) with an excitation wavelength of 543 nm for TR and 480 nm for FITC at 63× magnification . Cell viability upon drug treatments and siRNA transfection was assessed by the Cell Cytoxicity Assay – alamarBlue ( Invitrogen , CA , USA ) assay according to the manufacturer's recommendations . Briefly , C6/36 cells were seeded in 96-well cell culture plates and subsequently treated with individual siRNAs or drugs for 3 hours , before incubation with alamarBlue reagent solution for 2 hours at 37°C . After which , the plates were subjected to fluorescence detection , at an excitation wavelength of 540 nm–570 nm , and emission wavelength of 580 nm–610 nm ( Tecan iControl Reader , Männedorf , Switzerland ) . Plasmid constructs of dominant-negative Eps15 ( GFP-EΔ95/295 ) was kindly provided by A . Benmerah , Pasteur Institute , and plasmid constructs backbone EGFP-C2 was purchased from Clontech ( CA , USA ) . Transfections were performed by using Lipofectamine LTX reagents according to manufacturer's recommendation ( Invitrogen ) . Briefly , C6/36 cells were grown on coverslips in 24-well tissue culture plates until 75% confluency . Then , 3 . 5 µg plasmid constructs were complexed with 4 µl Plus reagent in 25 µl OPTI-MEM medium ( Gibco , New York , USA ) for 15 mins at room temperature . The mixture was then added to 25 µl OPTI-MEM containing 2 µl Lipofectamine LTX ( Invitrogen , USA ) . After incubation for another 15 mins , the DNA-liposome complexes were added to the cells , prior to further incubation for 3 hours at 37°C . One millilitre of complete growth medium was then added and incubated for another 24 hours before the virus entry assay was carried out . Different siRNAs targeting various Ae . albopictus genes involved in endocytic processes were selected to perform reverse transfection assays in C6/36 cells , including CLTC ( NCBI Accession: XM_001656826 ) , RAB5 ( NCBI Accession: XM_001658641 ) , RAB7 ( NCBI Accession: EF127648 ) and vacuolar ATPase B ( NCBI Accession: AF092934 ) . The siRNA gene sequences used in this study are , CLTC ( CAAUAAAGAUAAUGCCCAU ) , RAB5 ( CGAAUAUUGUGAUUGCGCU ) , RAB7 ( CCUGGAGAAUAGGGCCGUA ) and vacuolar ATPase B ( GUCAUUCAAGGGAUAAUGU ) ( Sigma Aldrich ) . Reverse transfection assays on scrambled siRNA gene sequences were also performed simultaneously to confirm the specificity of the gene targeting siRNAs . The scrambled siRNA gene sequences used in this study are CLTC ( ACAGAAUUAAACUACUUGC ) , RAB5 ( ACAGUUUGAGGUACUGUUC ) , RAB7 ( CUCAGAGGGUAACGUCGAG ) and vacuolar ATPase B ( CUGAAUAUCAGUGGUAUAG ) . Specific gene targeting siRNAs and scrambled siRNAs were dissolved in DEPC-treated reverse osmosis water to a final stock concentration of 100 µM , and incubated at room temperature for 30 mins with gentle agitation . Different siRNAs were diluted to desired working concentrations of 0 . 1 nM , 1 nM , 5 nM , 10 nM with serum-free media ( Dharmacon , US ) and transfection reagent ( Dharmafect-1 ) . The specific individual siRNAs that were directed against each of the respective genes were then transfected into C6/36 cells prior to being subjected to CHIKV infection after 48 hours post transfection . The supernatants were then harvested 24 hours p . i for plaque assays . Validation of gene expression was performed via qRT-PCR . Upon gene silencing , total RNA was extracted from C6/36 cells with RNeasy Extraction Kit ( Qiagen ) . The samples were assayed in a 20 µl reaction mixture containing 10 µl SYBR Green Master Mix ( Fermentas , US ) , 1 µl forward and reverse primer respectively , 1 µl RNA , 1 µl reverse transcriptase and 7 µl nuclease free water . A no-template control was also included . The cycling conditions for one-step SYBR Green-based RT-PCR consisted of a 30-min reverse transcription step at 44°C and 5 mins of Taq polymerase activation at 94°C , followed by 40 cycles of PCR with denaturation occurring at 94°C for 15 s and annealing and extension taking place at 60°C for 30 s . Following amplification , a melting curve analysis was performed to verify the melting temperature of PCR products amplified by the Ae . albopictus gene primer pairs . The primers pairs stated are CLTC ( Forward , 5′-CGTTCGGCCAATGCTGC-3′ , Reverse , 3′- GGGAAGTCGCTCTGCGCT-5′ ) , RAB5 ( Forward , 5′-TCAGCGACAGGCATCGC-3′ , Reverse , 3′-CAGCGGTTTTGGCCGAC-5′ ) , RAB7 ( Forward , 5′-AACGAAGCGTGCCCAGCAGT-3′ , Reverse , 3′-CCGGTTGTTGCGGTCTGCGT-5′ ) , vacuolar ATPase B ( Forward , 5′-GCTCGGTCTTCGAGTCGCT-3′ , Reverse , 3′-CAGTGTCAGGCGCGAGGTC-5′ ) and actin controls ( Forward , 5′-CCACCATGTACCCAGGAATC-3′ , Reverse , 3′-CACCGATCCAGACGGAGTAT-5′ ) . Where applicable , statistical analyses were performed on repeated measurements using the one-tailed Student's t-test . The significance level was set at p<0 . 05 ( * ) , p<0 . 01 ( ** ) or p<0 . 001 ( *** ) . Data shown throughout the study were obtained from three independent experiments .
A customized gene expression microarray chip consisting of 18 , 760 transcripts targeting the Ae . aegypti mosquitoes was used to profile differentiated regulation levels of host genes necessary for the infectious entry of CHIKV . A total of 579 targeted mosquito genes were found to be differentially regulated – defined as fold change of less than −1 . 5 or more than 1 . 5 - upon CHIKV infection . Among these genes – many of which are known to be involved in generalized host immune responses , such as the IFN-associated pathway - are those related to clathrin-mediated endocytosis . Genes associated with other endocytic pathways , such as caveolin-mediated endocytosis and macropinocytosis were not observed to be differentially regulated based on the user-defined criteria . Standard housekeeping genes were also found to exhibit similar expression profiles upon CHIKV infection as mock-infected samples . A brief description of the reported mammalian-based functional roles and the fold changes upon various time points of CHIKV infection for each of the genes is shown in Table 1 and a heat map exhibiting the differential regulation of these genes across all time points of CHIKV infection is shown in Figure 1 . These genes , or related genes , have also been mapped onto the clathrin-mediated endocytotic pathway , as shown in Figure S2 . Genes known to be associated with clathrin-mediated endocytosis include epsin I ( EPN1 ) , epidermal growth factor receptor pathway substrate 15 ( EPS15 ) and Huntingtin interacting protein I ( HIP1 ) . EPN1 and EPS15 were found to be upregulated while HIP1 was downregulated upon CHIKV infection . In addition , genes that targeted kinases ( MAP2K7 , MAP4K4 and MAPK14 ) were downregulated in the first 15 min of CHIKV infection , although MAP2K7 and MAP4K4 were subsequently found to be upregulated after 30 min and 120 min of infection . Genes involved in vesicle and endosomal transport , such as ATP6V1B2 , ATP6V1F , ARFRP1 and RAB34 were also found to be differentially regulated during CHIKV infection . Taken together , analysis of the microarray data suggests the possible involvement of clathrin-mediated endocytosis in the infectious entry of CHIKV . Based on the microarray findings , we proceeded to employ a combination of bio-imaging techniques including transmission electron microscopy ( TEM ) and immunofluorescence assays , to further investigate the infectious entry processes of CHIKV . CHIKV was first prepared by a series of concentration and purification procedures . As revealed by negative staining of the virus preparation , a homogeneous population of CHIKV particles with a uniform size of 60–70 nm in diameter ( Figure 2a ) was obtained . The purified virus particles were subsequently used to map the infectious entry process of the virus into C6/36 cells . In order to visualize synchronized entry of CHIKV into cells , C6/36 cells were first incubated with CHIKV ( MOI = 10 ) at 4°C for 1 hour . Low-temperature treatment allows binding of CHIKV to the cell surface receptors but prevents the internalization of virus particles into the cells . Subsequently , the cells were warmed to 37°C , and the virus-infected cells were processed for embedding and sectioning at appropriate times after warming for transmission electron microscopy . At 5 mins upon warming to 37°C , CHIKV particles ( Figure 2b , arrow ) were observed to attach on the outer surface of the plasma membrane of C6/36 cells and CHIKV particles ( Figure 2b , arrow ) were also noted within invaginations of the plasma membrane . These invaginations resembled those of clathrin-coated pits ( Figure 2b , arrowheads ) . Similarly , attachment and localization of CHIKV particles to clathrin molecules were revealed by double-labeled immunofluorescence staining of the cellular clathrin and CHIKV particles by specific antibodies ( Figure 2c and Figure 2d ) . After 10 mins at 37°C , most of the virus particles were observed within endocytic vesicles . CHIKV virus particles were contained within each of these vesicles ( Figure 3a ) as revealed at the ultrastructural level by transmission electron microscopy . These virus-containing vesicles were predominantly localized to the perinuclear region in close association with the endoplasmic reticulum ( ER ) . To further characterize the origin of the cellular endocytic vesicles that were involved in the endocytic trafficking process of CHIKV , double-labeled immunofluorescence microscopy assays were performed . Antibodies specific for early endosomes ( EEA1 ) and late endosomes and lysosomes ( Lysotracker ) were used . At 10 mins after cells were warmed to 37°C , a double-labeled immunofluorescence assay with anti-CHIKV envelope protein and anti-EEA1 antibodies showed colocalization mainly at the cell periphery region , suggesting that the virus particles were trafficked to the endosomes after endocytosis ( Figure 3b ) . By 15 mins after incubation at 37°C , CHIKV particles were found mainly in vesicles ( Figure 3c ) that were stained with Lysotracker ( Molecular Probes ) , thus indicating the trafficking of the endocytosed CHIKV particles to the late endosomes and lysosomes by this time point . The fluorescent staining was more intense at the perinuclear region . A unique accumulation of a large number of virus-containing late endosomes and lysosomes were observed at the perinuclear region by 15 mins ( Figure 3c ) , and these structures remained predominant until 35 mins p . i . ( data not shown ) . The results presented above suggested the involvement of a clathrin-mediated endocytic pathway in CHIKV entry into C6/36 cells . In order to further characterize the pathway by which CHIKV enters C6/36 cells , studies of various drugs inhibiting endocytosis and related processes were performed in a dose-dependent manner . C6/36 cells were pretreated with drugs that selectively inhibit receptor-mediated endocytosis [monodansylcadverine [24]] , clathrin-dependent endocytosis [chlorpromazine [23] and dynasore [25]] and caveolae-dependent endocytosis [filipin and nystatin [26]] . Involvement of inhibitors associated with other entry pathways such as macropinocytosis [EIPA [27] , [28]] and cholesterol-dependent endocytosis [methyl-β-cyclodextrin [26]] was also evaluated . Furthermore , inhibitors targeting actin polymerization [cytochalasin B [23] and cytochalasin D [31]] , microtubule polymerization [colchicine [31] and nocodazole [32]] were used to investigate the role of cytoskeleton during CHIKV entry . Treatment of inhibitors associated with the acidification of endosomes [concanamycin A and bafilomycin A [4] , [29] , [30] as well as the calcium channel flux [ ( nifedipine [33]] were also performed ( Table S1 ) . Minimal cellular cytotoxicity was observed in drug-treated cells throughout the spectra of concentrations used in these experiments . Viral entry occurs via several endocytic pathways , with the most common being clathrin- and caveolae-mediated endocytosis [34] , [35] . Drug treatment assays were carried out to determine whether CHIKV enters C6/36 cells via receptor-mediated endocytosis , and more specifically clathrin- or caveolae-mediated endocytosis . Upon treatment of monodansylcadverine , a well-known pharmacological drug inhibitor that targets receptor-mediated endocytosis [36] , dose-dependent inhibition of CHIKV infection was observed , with a 2-log reduction at 150 µM ( Figure 4a ) . Clathrin-mediated endocytic pathways can also be specifically inhibited by drugs such as chlorpromazine and dynasore . Chlorpromazine is a cationic , amphiphilic molecule that disrupts the assembly of clathrin lattices at the cell surface and endosomes [23] , [26] , whereas dynasore acts as a potent inhibitor of endocytic pathways by disrupting dynamin , thus preventing clathrin coated vesicles formation , [25] . Data revealed dose-dependent inhibition of CHIKV infection , upon treatment with chlorpromazine ( Figure 4b ) and dynasore ( Figure 4c ) , showing 2-log reductions at 70 µM and 10 µM respectively . This suggests that CHIKV entry into C6/36 cells occurs via clathrin-mediated endocytosis . To eliminate the involvement of other entry pathways during CHIKV infection , drugs known to inhibit caveolae-mediated endocytosis and macropinocytosis were also evaluated . Caveolae-mediated drug inhibitors , filipin and nystatin inhibit virus entry by disrupting the caveolae , thus preventing caveolae formation [26] . Treatment with filipin ( Figure . 4d ) and nystatin ( Figure 4e ) did not exhibit inhibitory effects on CHIKV infection at any of the drug concentrations used . These results suggest minimal involvement of caveolae-mediated endocytosis upon CHIKV infection in C6/36 cells . Early studies on alphaviruses have shown that lipid rafts are crucial players during virus entry , as cholesterol is needed to allow fusion of viruses with the endosomal membrane of host cells [37] . To evaluate the role of membranous cholesterol , treatment with methyl-β-cyclodextrin , a drug inhibitor targeting lipid raft synthesis via the removal of cholesterol by disrupting detergent-insoluble membrane micro-domains ( DIMs ) was evaluated in CHIKV infection [38] , [39] . Results displayed dose-dependent inhibition , showing 2-log reductions at 2 . 5 mM ( Figure 4f ) suggesting that CHIKV entry is dependent on lipid raft synthesis targeting on membranous cholesterol . In a previous study , EIPA , an inhibitor of macropinocytosis , successfully inhibited rhinovirus 2 and Coxsackie B3 virus entry into HeLa cells [40] . However , in this study , at low concentrations of 10 and 25 µM , EIPA only displayed minimal inhibitory effects on the entry pathway of CHIKV infection . Instead , CHIKV infection was observed to be enhanced ( Figure 4g ) . Possible reasons could include the activation of reflex mechanisms in cells , thus causing an increase of endocytic uptake through other pathways . The employment of dominant-negative mutants of Eps15 can be much more specific in targeting the arrestment of clathrin-coated pit formation [41] . GFP-tagged dominant negative mutant of Eps15 , ( GFP-EΔ95/295 ) , GFP-tagged negative control constructs ( GFP-D3Δ2 ) and internal GFP control were transiently transfected into C6/36 cells [42] . Transfection efficiencies for all constructs were observed to be more than 80% by fluorescence microscopy . Transfected cells were then assayed for their capability to internalize Texas Red- ( TR- ) conjugated transferrin , a specific marker for clathrin-dependent endocytosis . Indeed , at 48 hours post-transfection , maximal expression of the transfected gene can be observed and the internalization of TR-transferrin was impaired in cells transfected with GFP-EΔ95/295 . In contrast , the uptake of TR-transferrin was not affected in cells expressing GFP-D3Δ2 or GFP ( data not shown ) . The dominant negative mutant GFP-EΔ95/295 drastically inhibited CHIKV infection by more than 80% but neither of the control constructs exerted any inhibitory effects on CHIKV infection in C6/36 cells ( Figure 5 ) . Most enveloped viruses require low-endosomal pH to enter host cells via endocytosis , which is maintained by vacuolar proton-ATPases , to trigger fusion of the viral envelope with the endosomal membrane and release the nucleocapsid into the cytosol [31] , [42] , [43] . Drug treatment assays were performed to examine the low pH-dependence of CHIKV entry using the vacuolar proton-ATPase inhibitors , namely bafilomycin A1 - which inhibits endosomal and lysosomal acidification [29] , [30] - and concanamycin A - which inhibits acidification of organelles [44] As shown in Figure 6 , bafilomycin A1 and concanamycin A displayed dose-dependent inhibitory levels with at least 2-log reductions at 3 µM ( Figure 6a ) and 60 nM ( Figure 6b ) respectively . These results strongly suggest that CHIKV entry process is dependent on low endosomal pH . In addition , recent studies reported that more sensitive inhibition of E1-226V mutated CHIKV LR-OPY1 strain upon endosomal pH acidification with bafilomycin A1 and chloroquine on Ae . albopictus cells were observed as opposed to CHIKV 37997 African reference strain [45] . Therefore , in our studies , C6/36 cells treated with concanamycin A were tested against local isolates of CHIKV , namely the SGEHIDSD67Y2008 strain , which is similar to the prototypic CHIKV 37997 African reference strain , and the SGEHICHD122508 strain , which closely resembles the E1-226V mutated CHIKV LR-OPY1 strain . Results displayed complete inhibition at 150 nM for the CHIKV SGEHICHD122508 strain ( Figure 6c ) when compared to the CHIKV SGEHIDSD67Y2008 strain ( Figure 6d ) . These findings matched those observed by Gay et al . ( 2012 ) , in which mutations in CHIKV strains result in more sensitive inhibitory levels upon endosomal pH acidification . Involvement of the cellular cytoskeletal network on CHIKV entry was also investigated via treatment with cytoskeleton-disrupting drugs . Actin filaments have been shown to assist the initial uptake of ligands via clathrin-coated pits and the subsequent degradative pathway , whereas microtubules are known to be involved in maintaining endosomal traffic between peripheral early and late endosomes . Cytochalasin B and D are actin-disrupting drugs , which specifically target the actin cytoskeleton by preventing its polymerization into microfilaments and promoting microfilament disassembly [44] . Pretreatment of cells with cytochalasin B and D ( Figures 7a and 7b respectively ) failed to inhibit CHIKV infection . Similarly , treatment with nocodazole ( Figure 7c ) and colchicine ( Figure 7d ) , inhibitors resulting in depolymerization of microtubules , showed no inhibition of CHIKV infection , thus indicating that CHIKV entry does not rely on microtubule polymerization [31] . These results suggest minimal involvement of the cytoskeletons in the entry process of CHIKV infection . Previous studies on herpes simplex viruses have identified the importance of calcium ( Ca2+ ) flux in virus entry for delivering virus capsids to the cytoplasm or nucleus [33] . Therefore , to determine whether Ca2+ flux is important in CHIKV infection , nifedipine , an inhibitor of dihydropyridine L-type voltage sensitive Ca2+ channel flux , was used . However , in this study , nifedipine treatment ( Figure 7e ) failed to inhibit CHIKV infection , thus indicating that Ca2+ flux is not required for CHIKV infection . From these drug treatment assays , it can thus be concluded that CHIKV entry into C6/36 cells occurs via clathrin-mediated endocytosis . Low endosomal pH is found to play a significant role in CHIKV entry , while the cytoskeleton and Ca2+ flux may not be vital for the endocytic process of CHIKV infection . Data from the microarray analyses has revealed the differentiated regulation of genes associated with the clathrin-mediated endocytic pathway , and drug treatment assays have validated the involvement of the pathway in the infectious entry of CHIKV into mosquito cells . To investigate the functional roles of genes related to clathrin-mediated endocytosis , siRNAs targeting clathrin-heavy chain ( CLTC ) , Rab proteins ( RAB5 and RAB7 ) and vacuolar ATPases ( vacuolar ATPase B ) were utilized in further downstream studies . Dose-dependent siRNA-based knockdown of the selected targeted cellular genes was performed in varying siRNA concentrations ( 0 . 1 , 1 , 5 , 10 nM ) on C6/36 cells , prior to being subjected to CHIKV infection . Scrambled siRNAs were included as controls to ensure the specificity of the siRNAs used in this study . Minimal cellular cytotoxicity was observed in siRNA-treated cells throughout the spectra of concentrations used in these experiments ( data not shown ) . RNA expression levels of the knocked-down genes were analyzed , with the non-infected samples being harvested at 48 hours post transfection . Significant reduction was observed in the levels of gene expression of CLTC , RAB5 , RAB7 and vacuolar ATPase B relative to non-transfected cells ( TC ) ( Figure S2a–S2d , solid bars ) . In contrast , data for scrambled siRNA gene expression showed similar levels of gene expression to TC samples ( Figure S2a–S2d , striped bars ) . These results suggested that the siRNA knockdown of the targeted cellular genes is specific . Effects of the scrambled siRNAs showed minimal inhibition of CHIKV infection relative to CHIKV-infected non-transfected cells ( PTC ) ( Figure 8a–8d , striped bars ) . However , cells with specific siRNA knockdown of CLTC gene showed dose-dependent reduction in the infectious viral titre of CHIKV , with a 1-log reduction at 5 nM , relative to the PTC samples ( Figure 8a , solid bars ) . siRNAs targeting the endosomal trafficking pathway ( RAB5 and RAB7 ) , which are involved in viral entry via the trafficking of the early and late endosomes , prevented CHIKV infection in a dose-dependent manner , showing a 3-log reduction in infectious virus titre at 5 nM RAB5 siRNA ( Figure 8b , solid bars ) . A 1-log reduction in CHIKV titre at 1 nM RAB7 siRNA ( Figure 8c , solid bars ) further accounts for the trafficking of internalized CHIKV particles from early endosomes to the late endosomes . In addition , silencing of vacuolar ATPase B , involved in endosomal acidification , also led to a decrease in CHIKV infection in a dose-dependent manner , with a 2-log reduction at 5nM ( Figure 8d , solid bars ) . These results further confirmed our earlier findings that CHIKV entry into Ae . albopictus ( C6/36 ) cells occurs via clathrin-mediated endocytosis and is dependent on low pH endosomal acidification .
Interest on deciphering virus entry into host cells has been steadily gaining momentum over recent years , in the hope to establish potentially powerful anti-viral strategies against these medically important human pathogens . Studies have shown that numerous viruses enter via receptor-mediated and/or clathrin-mediated pathways [34] , [35] . The entry process of many enveloped viruses typically begins with the fusion of viral envelope glycoproteins at the plasma membrane allowing internalization of viral nucleocapsids at neutral pH . Virus entry can also occur via endocytosis prior to fusion with the endocytic membrane , whereby hydrophobic virus fusion proteins undergo conformational changes upon exposure to acidic pH resulting in the release of viral nucleocapsids into the cytoplasm . Receptor-mediated endocytosis forms the predominant mode of entry , often mediated by the formation of clathrin-coated pits , prior to subsequent transport of viruses to early endosomes , where the low pH environment triggers fusion [46] . Meanwhile , clathrin-mediated endocytosis primarily entails the binding of extracellular cargo molecules to specific cell-surface receptors . These receptors , along with other membrane proteins entering via endocytosis , are transported by the intracellular adaptor proteins to endocytic sites . Together with clathrin , the adaptor protein forms an enclosed coat at the plasma membrane . The coated membrane then bends to form invaginations resembling clathrin-coated pits that pinch off to form cargo-filled vesicles [47] . Nevertheless , analyses of these entry modes have been predominantly demonstrated in mammalian cells . Indeed , the involvement of endocytic pathways in the entry of alphaviruses has been extensively studies , with SFV and SINV found to penetrate target cells through clathrin-dependent endocytosis [3] , [15] , [48] , [49] . Few studies have however been documented on endocytic entry pathways of arboviruses into mosquito cells . This study shows , for the first time , CHIKV infectious entry into Ae . mosquitoes cells via clathrin-mediated endocytosis . Although a recent study has shown CHIKV entry in mammalian cells via clathrin-independent endocytosis [3] , [4] , [7] , earlier findings indicated the dependence of CHIKV infectious entry in mammalian cells on clathrin [3] , [4] , [7] . This work thus indicates that the infection mechanism in mosquitoes and mammals may have indeed occurred through a common conserved endocytic pathway . A variety of experimental approaches was used in this novel study including microarray gene profiling , bioimaging studies ( transmission electron microscopy , double-labeled immunofluorescence microscopy ) , pharmacological inhibitors , overexpression of dominant-negative mutant of Eps15 and siRNA-based knockdown of genes involved in the endocytic pathway . A customized gene expression microarray was first conducted to identify host genes necessary for the infectious entry of CHIKV into mosquito cells . Several genes that were differentially regulated during CHIKV infection have been known to be involved in clathrin-mediated endocytosis ( Table 1 ) , including EPN1 , EPS15 and HIP1 . EPN1 is an accessory protein that interacts with EPS15 - a clathrin-coat-associated protein that binds the α-adaptin subunit of the clathrin adaptor AP2 ( AP2A1 ) [50] - and clathrin , as well as with other accessory proteins for the endocytosis of clathrin-coated vesicles . It facilitates the rearrangement of the clathrin lattice , resulting in the formation of clathrin-coated invaginations and fission [51] . HIP1 plays a role in clathrin-mediated endocytosis and trafficking by regulating clathrin assembly via binding to a highly conserved region of clathrin light chain [52] . The microarray analysis also revealed the involvement of kinase-targeting genes ( MAP2K7 , MAP4K4 and MAPK14 ) - associated with the signal transduction processes of viral entry [53] – during early CHIKV infection . In addition , ATP6V1B2 and ATP6V1F , components of V-ATPases , were also found to be differentially expressed during the initial phases of CHIKV infection . This suggests a significant role for V-ATPases , which have been identified in intracellular compartments such as clathrin-coated vesicles and endosomes and are therefore essential in clathrin-mediated endocytosis [54] . The upregulation of ARFRP1 suggests the importance of vesicle and endosomal transport in early CHIKV infection , while the downregulation of RAB34 - which is a member of the Rab family small GTP-ases that regulates vesicle budding , docking and fusion , and has been predominantly associated with membrane ruffles and macropinosomes and promotes macropinosome formation [55] – eliminates the possible engagement of micropinocytosis for CHIKV infectious entry into mosquito cells . Taken together , analysis of the microarray data suggests that CHIKV entry occurs via clathrin-mediated endocytosis . Downstream assays were subsequently performed in order to validate the microarray findings . Transmission electron microscopy analyses showed the presence of CHIKV particles within invaginations of the plasma membrane , resembling those of clathrin-coated pits . Furthermore , characterization of the vesicles involved in the endocytic trafficking processes of CHIKV revealed the translocation of the virus particles to early endosomes and subsequently to late endosomes and lysosomes . To this end , double-labeled immunofluorescence assays were performed with the early endosomal marker , EEA1 and late endosomal and lysosomal marker , Lysotracker . Colocalization of virus particles were observed upon double-labeling with anti-CHIKV envelope protein and anti-EEA1 antibodies , thus indicating the trafficking of CHIKV particles to endosomes upon entry into mosquito cells . These endosomes were also observed to be closer to the cell periphery . Subsequent labeling with Lysotracker showed that endocytosed CHIKV particles were trafficked from early to late endosomes and lysosomes ( Figure 3c ) . Further analyses of CHIKV internalization into C6/36 cells was determined by treating cells with a set of pharmacological inhibitors targeting receptor- , clathrin- , caveolae- mediated endocytosis , cholesterol-dependent endocytosis and macropinocytosis . Significant results from treatment with monodansylcadverine , chlorpromazine and dynasore proved the involvement of receptor- and/or clathrin- mediated endocytosis ( Figure 4a–c ) . The importance of lipid rafts has been widely acknowledged , with studies showing that DIMS , found in the plasma membrane of cell surface , posses the ability to isolate cholesterol into the hydrophobic pocket , thus aiding in entry of viruses [38] , including Simian virus 40 ( SV40 ) [56] . Moreover , studies in RNA viruses , such as HIV-1 , have determined virus entry into host cells via lipid rafts , and treatment with methyl-β cyclodextrin resulted in blockade of trans-epithelial transcytosis of HIV-1 and reduction of envelope fusion [57]–[60] . Similarly , we reported in this study that methyl-β cyclodextrin treatment showed inhibition of CHIKV entry via C6/36 cells , thus suggesting that the infectious entry process of CHIKV is dependent on lipid raft synthesis targeting membranous cholesterol . In contrast , treatment with inhibitors such as flilipin , nystatin and EIPA , had minimal effects on inhibiting CHIKV infection , thus eliminating the possibility of CHIKV entry via other pathways . Earlier studies have shown that the mutant form of Eps15 , EΔ95/295 which contains a 200-amino acid deletion , prevented the association with AP2 , thus inhibiting the entry of VEEV via clathrin-mediated endocytic pathway [30] . We observed similar observations in this study , with the overexpression of EΔ95/295 found to reduce the infectious entry of CHIKV . It can therefore be concluded that CHIKV entry into C6/36 cells occurs via clathrin-mediated endocytosis . Earlier studies have shown that E1 constitutes the fusion protein of the alphaviruses [61]–[63] . In the endosomal vesicles containing endocytosed CHIKV particles , the E1–E2 heterodimer undergoes a conformational change upon exposure to low pH . This causes rearrangement to a homotrimeric complex of E1 formation , leading to increased activity for membrane fusion [64] , [65] . Membrane fusion processes occur rapidly via the insertion of hydrophobic fusion peptides to form pores in cellular and viral membranes [66] , thus releasing the nucleocapsid into the cytoplasm of the cell even before the degradation of the lysosomes [67] . Requiring low pH endosomal exposure , alphaviruses exposed to lysotromphobic weak bases such as bafilomycin A1 , chloroquine and concanamycin A , are unable to undergo membrane fusion due to neutralization of pH in the endosomes [67] , [68] . For instance , infection of SFV on Ae . albopictus cells was inhibited upon treatment with inhibitors targeting low-endosomal acidification [66] . A recent study on E1-A226V mutated CHIKV LR-OPY1 strain showed that it is more sensitive to inhibition via endosomal pH acidification with bafilomycin A1 and chloroquine on Ae . albopictus cells as opposed to the CHIKV 37997 African reference strain [45] . These two strains possess 85% nucleotide sequence identity , differing only in the E1 protein at position 226 [9] . Furthermore , CHIKV infection of C6/36 cells was found to be sensitive to inhibitors of the v-ATPase and chloroquine , a weak base that accumulates in the acidic parts of the cell and inhibits the acidification of endocytic compartments [45] . Similarly , in our own study , we observed lower levels of inhibition for the CHIKV SGEHIDSD67Y2008 strain - which has features common to those of the CHIKV 37997 African reference strain - than those of the CHIKV SGEHICHD122508 strain , which resembles the E1-A226V mutated CHIKV LR-OPY1 strain . The results revealed that while both the CHIKV SGEHICHD122508 and SGEHIDSD67Y2008 strains require endosomal acidification for optimal infection of Ae . albopictus cells , the former is more sensitive to inhibition as compared to the latter . This could be due to the differential sensitivities of the CHIKV strains to lysomotropic agents and weak bases , as similarly reported in previous studies [3] , [4] . Our findings in this study were also further evaluated via siRNA-based dosage dependence analyses of several cellular genes associated with clathrin-mediated endocytosis and endosomal acidification . siRNA targeted against CLTC showed significant inhibition of CHIKV infection , thus further strengthening our earlier findings , in which treatment with clathrin-mediated endocytic associated inhibitors showed similar dose-dependent inhibitory trends of CHIKV infection . Similarly , silencing of vacuolar ATPase B also led to a decrease in CHIKV infection , strongly demonstrating that CHIKV entry requires low endosomal pH . Previous studies have shown that RAB5 and RAB7 proteins are usually associated with the translocation of viruses from the early to late endosomes [69] . In particular , mammalian cells infected with SFV were found to require the integrity of RAB5 proteins for productive infection [4] , while RAB5 and RAB7 proteins were identified to play significant roles in the productive infection of vesicular stomatitis Indiana virus ( VSV ) and SFV in mosquito cells [69] , [70] . Similarly , we observed significant inhibition of CHIKV infection upon siRNA-based knockdown of these genes , thus suggesting that CHIKV entry involves the translocation from early endosomes after clathrin-mediated endocytosis to late endosomes . Based on our unprecedented findings in this novel study , it can thus be concluded that CHIKV infectious entry into Ae . albopictus cells occurs via clathrin-mediated endocytosis and is dependent on low endosomal pH acidification and the presence of membranous cholesterol . Elucidation of the infectious entry of CHIKV into mosquito C6/36 cells will contribute towards better understanding of CHIKV pathogenesis , thus enabling future development of antiviral strategies against the infectious entry process of CHIKV . | Deciphering the much neglected aspects of cellular factors in contributing to the infectious entry of CHIKV into mosquito cells may enhance our understanding of the conservation or diversity of these host factors amongst mammalian and arthropod for successful CHIKV replication . The study revealed that the infectious entry of chikungunya virus ( CHIKV ) into mosquito cells is mediated by the clathrin-dependent endocytic pathway . A customized gene expression microarray known to target the Aedes mosquito genome was used to identify host genes that are differentially regulated upon CHIKV infection . A combination of bio-imaging studies and pharmacological inhibitors confirmed the involvement of clathrin-mediated endocytosis as well as the importance of low endosomal pH during CHIKV infectious entry . Furthermore , the clathrin heavy chain , Eps15 , RAB5 , RAB7 and vacuolar ATPase B are shown to be essential for the infectious entry process of CHIKV . This study aims to underline the importance of cellular factors , particularly those associated with clathrin-dependent endocytosis , in mediating the infectious entry of CHIKV into mosquito cells . | [
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| 2013 | Mosquito Cellular Factors and Functions in Mediating the Infectious entry of Chikungunya Virus |
Farnesylation is an important post-translational modification catalyzed by farnesyltransferase ( FTase ) . Until recently it was believed that a C-terminal CaaX motif is required for farnesylation , but recent experiments have revealed larger substrate diversity . In this study , we propose a general structural modeling scheme to account for peptide binding specificity and recapitulate the experimentally derived selectivity profile of FTase in vitro . In addition to highly accurate recovery of known FTase targets , we also identify a range of novel potential targets in the human genome , including a new substrate class with an acidic C-terminal residue ( CxxD/E ) . In vitro experiments verified farnesylation of 26/29 tested peptides , including both novel human targets , as well as peptides predicted to tightly bind FTase . This study extends the putative range of biological farnesylation substrates . Moreover , it suggests that the ability of a peptide to bind FTase is a main determinant for the farnesylation reaction . Finally , simple adaptation of our approach can contribute to more accurate and complete elucidation of peptide-mediated interactions and modifications in the cell .
Protein prenylation is a post-translational modification in which a prenyl group ( farnesyl or geranylgeranyl ) is attached to the protein via a thioether bond to a cysteine at or near the carboxy terminus of the protein ( reviewed in [1] , [2] ) . Protein farnesyltransferase ( FTase ) and geranylgeranyltransferase type I ( GGTase-I ) are also called CaaX prenyltransferases , due to their ability to catalyze modification of peptides and substrate proteins bearing the carboxy terminal ( C’ ) Cys-aliphatic-aliphatic-variable amino acid ( Ca1a2X ) motif [3] . Upon binding of the substrate and the C-terminal Ca1a2X motif , the catalytic zinc ion of FTase coordinates the thiol side chain of the cysteine and catalyzes the covalent attachment of the lipid anchor to this residue . A detailed view of this mechanism has been obtained by a series of structures solved at different stages of the reaction [4] . After the covalent attachment of the isoprenoid in the cytoplasm , substrate proteins can undergo further processing , resulting in a C’ structure that is able to serve as a specific recognition motif in certain protein-protein interactions [5] and to direct the modified protein towards incorporation into cellular membranes [6] . A wide range of proteins involved in diverse cellular functions require this post-translational modification for their action [2] . While numerous proteins have been experimentally shown to undergo farnesylation in vivo [7] , [8] , [9] , it is likely that many FTase substrates remain to be discovered . There is a wide interest in the mapping of FTase targets in the genome , in part due to the therapeutic potential of FTase inhibitors against cancer [10] , [11] , [12] , as well as parasitic infection [13] , [14] . Identification of new targets might lead to novel therapeutic approaches [15] . Moreover , the elucidation of cellular FTase targets might shed light on the function of various proteins , as well as on the cellular network of interactions . Computational approaches have predicted FTase targets based on sequence features of known targets [7] , [8] . These methods show good performance in terms of sensitivity , i . e . known targets are correctly identified . Thus , prenylation is mainly defined by the last four residues of the protein , although additional weaker sequence constraints have also been identified upstream in the sequence [16] . Other approaches were based on manual inspection and derived from structural features [9] . Substrate specificity has also been examined using peptide libraries . A comprehensive study by Hougland et al . on the farnesylation of a large synthetic peptide library has allowed a detailed characterization of FTase specificity [17] . In addition to compiling a large and clean dataset of peptides that contains both efficient substrates and non-substrates for FTase , this study discovered a third group of sequences that are farnesylated only under single-turnover ( STO ) conditions ( [E]>[S] ) . Analysis of peptide substrates has also demonstrated that reactivity depends on synergy between the side chains at the a2 and X positions [18] . These findings indicate that FTase substrate recognition is more complex than the simple Ca1a2X motif model , and that non-canonical sequences can serve as substrates . A large number of structures have been determined for FTase and FTase-substrate peptide complexes [19] . The peptide binding pocket is well-characterized , although a structure of the ternary FTase•farnesyl diphosphate ( FPP ) •peptide in an active conformation has not been determined [9] . The Ca1a2X cysteine sulfur atom ( prior to the product formation ) coordinates the catalytic Zn2+ ion together with side chains ( D297 , C299 and H362 ) of the FTase β-subunit . The a1 side chain points out of the binding pocket and faces the solvent , while the a2 side chain is buried within the binding pocket and interacts both with the farnesyl chain of FPP and the residues lining the pocket . The C’ X position interacts with residues mostly from the FTase β-subunit and is considered the main determinant for the specificity between FTase and GGTase-I 9 . Finally , two highly conserved hydrogen bonds are formed: 1 ) between the C-terminal carboxylate group and the side chain of FTase Q167α and 2 ) between the a2 backbone carbonyl oxygen and the side chain of FTase R202β ( Figure 1 ) . Despite this detailed structural information , only a handful of different peptide sequences have been solved in complex with FTase . We previously developed a scheme for modeling the structures of peptide-protein complexes ( Rosetta FlexPepDock [20] ) , which is incorporated within the Rosetta modeling suite framework [21] . This protocol is the starting point for the development of a structure-based scheme for the prediction of peptide binding specificity ( FlexPepBind ) . Specifically , to refine FlexPepBind for the prediction of FTase binding peptides , we have incorporated constraints derived from the conserved features in solved FTase structures and adapted the energy function to distinguish between reacting and non-reacting tetrapeptides ( based on an underlying assumption that tetrapeptides that bind will react , while those that do not bind will not react ) . We trained and tested this protocol against the recent dataset published by Hougland et al . [17] . Validation of the protocol against several independent sets showed accurate prediction of peptides that could be farnesylated , both under multiple turnover ( MTO ) and single turnover ( STO ) conditions . Evaluation of all possible Cxxx peptides identified a previously uncharacterized class of farnesylation targets that contain an acidic C-terminal residue . The 13 peptides predicted to bind with best affinity were experimentally shown to indeed undergo farnesylation in vitro . Finally , a genomic scan for novel FTase targets revealed 77 novel putative FTase targets previously undetected by sequence-based approaches . Among these , 13 out of 16 selected novel putative farnesylation targets were indeed farnesylated by FTase in an in vitro experimental validation . FTase-peptide binding is a model system for our approach to peptide-protein binding specificity prediction and design . Our protocol can easily be adapted to additional peptide-protein interactions where both experimental structure and affinity data are available , thereby providing a mechanism to identify targets not detectable by sequence conservation only .
Recently Hougland et al . performed a large-scale study , in which they characterized a TKCxxx peptide library for reactivity with rat protein farnesyltransferase ( rat FTase ) [17] . Out of an initial library of 213 sequences , 77 peptides are farnesylated under multiple turnover ( MTO ) conditions , and 51 sequences are not farnesylated under any conditions . Interestingly , the remaining 85 sequences are farnesylated under single turnover ( STO ) conditions but not under MTO conditions . We set out to use FlexPepBind and the structural data available for FTase to discriminate MTO sequences from non-reactive ( NON ) peptide sequences , using the 77 MTO and 51 NON peptide sequences as our training set ( 128 peptides in total; Dataset S1A ) . Towards this end , we used the high resolution structure of human FTase in complex with a peptide derived from the carboxy terminus of Rap2a and a farnesyl diphosphate ( FPP ) analog ( PDB: 1tn6 [9] ) to create a starting model . The bound peptide was truncated to include only the terminal Ca1a2X motif . Different peptide sequences were then threaded onto the peptide backbone and used as starting structures . Initially , we modeled peptide-FTase complex structures for different peptide sequences by applying the Rosetta FlexPepDock protocol to the threaded starting models . This protocol was developed previously in our lab for the modeling and refinement of peptide-protein complex structures to high resolution [20] . Our simulations included three constraints , namely the conservation of the 2 structurally conserved hydrogen bonds ( C’ carboxylate - FTase Q167α; a2 backbone carbonyl oxygen - FTase R202β ) and the location of the cysteine sulfur atom coordinating the Zn2+ ion ( Figure 1 , see Methods for more details ) . For each simulation , the energy of the best scoring Cxxx peptide was extracted ( see Methods for further details ) . Figure 2A shows the Receiver Operating Characteristic ( ROC ) plot for the ability of the peptide energy to discriminate between MTO sequences and non-substrate sequences . The plot shows very good discrimination with an Area Under the ROC Curve ( AUC ) value of 0 . 915 on our training set . These results demonstrate that a structure-based evaluation of the peptide energy can distinguish very well between farnesylated and non-farnesylated peptide sequences . Since the known constraints restrict the simulation to a closely defined region in the binding site , we reasoned that a simpler and faster protocol might be able to model the peptides with similar accuracy . Our simplified protocol therefore includes only a minimization using the Rosetta energy function [21] , [22] under constraints to retain the 2 structurally conserved hydrogen bonds and the cysteine sulfur atom location coordinating the Zn2+ ion ( see above and Methods for more details ) . This protocol yielded similar results with an AUC value of 0 . 875 on the training set . A peptide energy threshold of -0 . 4 ( i . e . sequences with energy below/above -0 . 4 are predicted to be binders/non-binders and therefore farnesylated/non-farnesylated , respectively ) corresponds to a 69% True Positive Rate ( TPR ) and 8% False Positive Rate ( FPR ) . A more stringent threshold of -1 . 1 energy units corresponds to a 44% TPR and 2% FPR ( Figure 2A ) . With the two protocols exhibiting similar performance , we decided to proceed further using the fast minimization protocol . ( Performance on the training set using additional sampling and scoring schemes is summarized in Table S1 . ) To assess FlexPepBind using the selected thresholds , we evaluated performance on three independent test sets ( Dataset S1B-D online ) . Using FlexPepBind , we modeled all of the 8000 possible Cxxx sequences and scored them according to our protocol . The thresholds for the discrimination of MTO/NON predict that 1349 ( 17%; stringent threshold = −1 . 1 ) and 2309 ( 29%; threshold = −0 . 4 ) of all tetramer peptide sequences could be possible substrates ( see Figure 3 ) . This set of putative farnesylation targets suggest a much more versatile binding motif than previously accepted ( see Figure 4 ) : while position a2 of the Ca1a2X motif is still prominently aliphatic ( ILE/VAL/LEU/PHE ) , positions a1 and X are less restricted than previously reported ( compare Figure 4C to Figures 4A&B ) . In particular , we identify within this set a novel class of farnesylation targets that contain an acidic residue at the C-terminus ( 238/1349 putative targets; ∼20%; see Figure 4D ) . Figure 4C indicates that the minimization-based protocol tends to miss larger residues at the C-terminal X position . Indeed , assessment of the prediction accuracy for this position on the training set shows that only 1/8 CxxF and 0/3 CxxW sequences are correctly predicted with the chosen protocol ( CxxM peptides are predicted with higher accuracy: 10/14 ) . Using the FlexPepDock based protocol , performance increases to: 6/8 CxxF; 2/3 CxxW and 11/14 CxxM , demonstrating that CxxF peptides are indeed rescued by the additional backbone flexibility . Therefore , it might be advisable to use the FlexPepDock based protocol for peptides that contain a bulky C-terminal side chain . We compared our predictions to the PrePS [7] prediction of prenylation targets on the initial training set of peptides . Regarding the discrimination of MTO substrates from non-active peptides , PrePS results are comparable to FlexPepBind ( AUC of 0 . 92 , with a threshold corresponding to 60% TPR for 2% FPR ) . However , the performance for STO peptides is significantly better for our structure-based approach: while FlexPepBind recovers 47% and 32% of the STOs with the loose and stringent thresholds concordantly , PrePS predicts only 14% of these sequences as substrates . Since our retrospective studies indicated that our approach can very accurately retrieve actual farnesylation targets , we were interested in testing it prospectively – could novel targets be indeed identified ? We selected the 13 best scoring peptides ( i . e . predicted tightest binders ) , yet previously uncharacterized for experimental validation . These are mostly ‘non-canonical’ peptides , including 5 peptides with an acidic C-terminal residue . Indeed , PrePS [7] predicts only 2 out of the top-scorers to be FTase substrates . In vitro farnesylation assays indicate that all of these peptides indeed undergo farnesylation catalyzed by FTase: 10 under MTO conditions and 3 under STO conditions ( Table 1A ) . These results demonstrate the robustness of our protocol and its exceptional accuracy . Importantly , they confirm the novel class of farnesylation substrates that contain a negatively charged C-terminal residue ( Figure 4D ) . Structural investigation of this novel class of substrates suggests that the negatively charged C' side-chain is stabilized by FTase residue His 149βwhile accepting a hydrogen bond from Trp102β ( GLU ) and creating an additional hydrogen bond with the side-chain of Ser99β ( GLU & ASP ) ( see Figure S1 ) . Additional polar interactions with water molecules are possible but were not explicitly modeled . Equipped with a score that can predict both known and novel FTase targets , we set out to scan the human genome for proteins that may undergo farnesylation . Our protocol was developed based on experimental assays on rat FTase ( and the structure of human FTase [9] ) . Since rat and human FTases show very high sequence identity ( 92% and 96% for subunits α and β respectively ) , and none of the sequence differences are located at or near the peptide binding site , we are confident that our prediction scheme can be applied to human farnesylation as well . We identified 756 unique proteins in human SwissProt [24] that contain the Cxxx motif at their carboxy terminus . 167 and 309 of these protein sequences obtained scores lower than the −1 . 1 and −0 . 4 threshold values , respectively , indicating that these proteins might be farnesylated by FTase . We focused on the group of 167 proteins detected with the more stringent threshold . Could these proteins indeed be FTase substrates ? Several indications support our predictions: First , amongst the 167 candidates , 42 contain a Cxxx motif of a known FTase substrate . Secondly , the Gene Ontology ( GO ) [25] cellular compartment annotation for most of these 167 proteins is Membrane related ( see Figure S2; see Methods for more details ) . This supports their association with membranes , possibly by farnesylation ( albeit this localization annotation might have been inferred from sequence similarity ) . Furthermore , peptide library studies have demonstrated FTase-catalyzed farnesylation ( under STO or MTO conditions ) of 50 of these Cxxx motifs ( representing 66 human proteins ) [17] . Finally , analysis of the putative target proteins with the PrePS server predicts that most of them ( 90/167 ) are indeed FTase targets , while the other 77 are not predicted to be farnesylated ( see Figure S3 ) . To further characterize the latter , we proceeded with in vitro experimental validation of selected sequences . Among these 77 proteins ( containing 72 unique Cxxx motifs ) , 39 motifs had not yet been tested for in vitro farnesylation . The second set chosen for experimental validation consisted of 16 top-scoring peptides selected from these 39 motifs . Of the 16 tested peptides , 9 and 4 peptides are farnesylated in vitro under MTO and STO conditions , respectively , while only 3 were not farnesylated by FTase ( Table 1B ) . None of the 16 sequences in this second set are predicted to serve as farnesylation targets by PrePS . Interestingly , for 9 of these 16 sequences , PrePS predicts that the upstream context of the motif is suitable for farnesylation . In these cases , the PrePS negative prediction is based on the sequence of the Cxxx motif . This suggests that improved characterization of the contribution of the 4 C-terminal residues to farnesylation can identify more farnesylation targets . Finally , for 8 of these 16 sequences , PrePS would predict farnesylation of the Cxxx motifs in the background of the favorable H-Ras upstream sequence . The balance between the upstream signal and the C-terminal Cxxx motif is therefore an interesting subject for future research . Most of the proteins identified by this study as novel FTase substrates have not been well characterized to date . Consequently , in vivo experiments that evaluate the cellular localization and prenylation status of these proteins , in conjunction with the in vitro farnesylation demonstrated in this study , will advance their functional characterization .
The protocol that we developed essentially estimates the binding affinity of FTase for Cxxx peptides , using a training set of reactive peptides , rather than predicting the farnesylation activity of these sequences . This has several implications and limitations . Remarkably , the ability to discriminate peptides that undergo MTO reaction from non-active peptides according to binding energy suggests that the non-active peptides may bind weakly or not at all to FTase ( see Figure 3 ) . This finding is supported by results from an in vitro inhibition experiment in which none of the tested non-active peptides inhibited FTase-catalyzed farnesylation of a known substrate [17] . In turn , the members of the small class of FlexPepBind false positive peptides may bind to FTase with high affinity but still not be farnesylated . These false positive peptides could therefore serve as FTase inhibitors and represent an interesting set to characterize in future work . Previous studies have shown that the sequence immediately upstream of the conserved cysteine residue may also play a role in substrate selectivity [16] . These sequences modulate peptide affinity and reactivity with FTase , i . e . a high-affinity terminal tetramer sequence does not necessarily ensure farnesylation of the protein . For half of the proteins tested in the study , the PrePS [7] program predicts favorable upstream sequences . This result coupled with the high-affinity -Cxxx motif predicted by FlexPepBind ( see Results and Table 1B ) increases the confidence that the human proteins containing the said Cxxx motif could be farnesylated in vivo . In turn , a favorable upstream sequence might compensate for a weak C-terminal signal . Our future work will therefore further characterize the balance between these two signals in determining farnesylation . One puzzling aspect of FTase substrate recognition is the large number of peptides that exhibit single turnover activity . The single turnover rate constant , kfarn , reflects all of the rate constants up to but not including the release of the farnesylated product [4] , [26] , [27] , [28] . Therefore , the STO peptides bind to FTase and are readily farnesylated , but the product dissociates very slowly so multiple turnover activity is very slow . Consistent with this , FlexPepBind achieves an AUC value of 0 . 776 in the discrimination between STO and non-active peptides on the training set , indicating that STO peptides have higher affinity for FTase than the non-active peptides ( see Figure 3 ) . Our protocol thus identifies STO peptides much better than sequence-based methods ( see Results and Hougland et al . [17] ) . What then discriminates between MTO and STO peptides ? Hougland et al . postulated that the farnesylated STO peptides might bind more tightly to FTase than farnesylated MTO peptides , and as a consequence FPP-catalyzed product dissociation is slow [17] . However , binding energy , as approximated by our approach , seems to be a poor discriminator between MTO and STO peptides ( AUC value of 0 . 625 on the training set – Dataset S1B ) . That is , estimation of the binding affinity of peptides in the context of static conformations of the protein cannot explain the difference in reactivity . Furthermore , application of this approach to models of MTO and STO peptides at different stages of the reaction sequence ( pre-farnesylation , post-farnesylation with the farnesyl group in the exit groove ) was not able to account for this difference as well . Hence , rather than binding affinity , a parameter related to the dynamics of product dissociation might dictate turnover . We therefore conclude that a dynamical approach , such as molecular dynamics , will be required to explain the mechanism that distinguishes STO from MTO peptides . Past in vitro peptide farnesylation experiments with FTase have measured kcat/KMpeptide under MTO conditions and kfarn rate constants under STO conditions [17] . The estimated reactivity of MTO and STO peptides ( see Methods ) measured in this work falls within the range of previously measured activity [17] . Therefore , these peptides have comparable reactivity to other substrates , including peptides that correspond to proteins that are farnesylated in vivo . Measured under MTO conditions , the kinetic parameter kcat/KMpeptide is termed the specificity constant and best reflects the reactivity of an enzyme in the presence of multiple substrates , as observed in vivo [29] . In a cell , the reactivity of a protein substrate with FTase depends on the value of kcat/KMpeptide as well as on the concentration of the substrate within the cytosol . Although a protein substrate with a higher value of kcat/KMpeptide is more likely to be farnesylated in vivo , it is unclear what level of in vitro activity corresponds to a true FTase substrate in vivo . Furthermore , in vivo the optimal levels of farnesylation of a given substrate may vary and a low fraction of modification may still be biologically relevant . Additionally , a substrate must be localized to the proper cellular locale in order for modification to occur and the C-terminus of the protein must be structurally available . Peptide library studies and this work have aided in determining potential FTase substrates and have also identified already known substrates , but more work is needed to characterize the reactivity of these substrates in vivo . As for the STO-only peptides , these substrates are readily farnesylated but the product does not dissociate rapidly . One possibility is that these proteins function as FTase inhibitors and consequently play a regulatory role within the cell [17] . However , both FPP and peptides have been implicated in catalyzing product dissociation of farnesylated STO peptides [17] , [30] , [31] and therefore it is possible that other cellular components could activate product dissociation allowing rapid farnesylation of these proteins in vivo . Therefore , competition or synergy among different FTase substrates could play an important functional role for modification and localization of proteins . Improved identification of STO peptides using the structure-based FlexPepBind approach presented here will expand our understanding of regulatory aspects of this reaction . In addition , the overlap in substrate preference of FTase and GGTase-I [3] indicates that modulation of the type of prenyl modification ( e . g . changes in relative enzyme availability or magnesium concentration ) might be functionally important as well . Our future focus on structure-based characterization of GGTase-I specificity will allow an improved investigation of this regulatory feature , complementary to sequence-based studies [7] , [8] . Scanning the human genome for putative FTase targets using our structure-based approach revealed many putative , not yet detected , farnesylated proteins . These new farnesylation substrates may provide novel disease targets for farnesyltransferase inhibitors . Moreover , the prediction that these proteins are farnesylated might shed light on their function . As an example , the putative proteins Q8NA34 , A6NHS1 , and P0C7P2 ( UniProt identifiers [24] ) all contain C' sequences strongly predicted to serve as farnesylation targets suggesting that the proteins are membrane localized . Additionally , our method also predicts FTase substrates that have recently been identified from in vivo experiments . For example , Kho et al . used a tagging-via-substrate proteomic approach to discover novel farnesylation targets [32] . They found a total of 18 farnesylated proteins: 13 are well known , and of the remaining 5 our approach predicts 4 to be farnesylated , including one hypothetical protein . Furthermore , it was recently found that pathogens can hijack the host farnesylation machinery to their own advantage , for example , anchoring effector proteins to the membrane of Legionella-containing vacuoles [33] , [34] , [35] . Thus , in addition to the identification of putative new farnesylation targets in the human genome , FlexPepBind can be used to scan pathogen genomes for farnesylation as well . 13/16 motifs derived from human proteins tested for in vitro farnesylation indeed undergo the reaction . Will this also happen in vivo ? In the following we compile additional available details on these targets that might help answer this question . One way to assess the in vivo relevance of the observed in vitro ability to undergo farnesylation of the C-terminus of a protein is to look for homologous proteins that also undergo farnesylation . Such information can easily be retrieved from PRENbase [8] . A search in this database revealed that Kinesin-like protein KIF21B variant ( Q2UVF0; CFLT ) maps to a cluster of 9 highly similar eukaryotic sequences ( E-val<e-20 ) that are all predicted to undergo farnesylation by PrePS . Similarly , Ankyrin repeat and BTB/POZ domain-containing protein BTBD11 ( A6QL63-3; CWLS ) maps to a cluster of 25 sequences of related proteins in PRENbase . Zinc finger protein 64 homolog ( Q9NTW7-3; CYVA ) also contains a number of conserved homologs in PRENbase , however in this specific isoform the target cysteine is part of the Zinc-finger structural motif , and therefore it might not readily be farnesylated . Another interesting putative farnesylation target that we have identified is the short isoform of Intersectin-2 protein ( Q9NZM3-3; CCLS ) . This protein is involved in clathrin-mediated endocytosis [36] , [37] , and farnesylation could be a mechanism for regulation and localization to the membrane , similar to the prenylation of Rho GTPases for endocytosis [38] . In particular , the long isoform of intersectin-2 contains additional domains [39] , including a PH domain known to bind phosphoinositides [40] , and a C2 domain known to be involved in Ca-dependent and independent binding of phospholipids [41] . Consequently , in the short isoform that lacks these domains , farnesylation might indeed be used as an alternative way to achieve membrane proximity and attachment . While the localization of some Rho GAP proteins ( e . g . p190 [42] ) is regulated by phosphorylation , the short isoform of Rho GTPase-activating protein ( GAP ) 19 ( Q14CB8-5; CSLI ) exposes a new C' motif that may target it to the membrane ( while keeping the Rho GAP domain intact ) . The same goes for MAPKAP1 isoform 6 ( Q9BPZ7-6; CKLA ) , a subunit of mTORC2 . While the full length protein was shown to contain a functional PH and Ras binding domains [43] , the truncated isoform reveals a C' putative farnesylation motif instead . Thus , for all but three MTO sequences we could gather additional information that supports actual in vivo farnesylation . We further discuss alternative splicing as a regulatory mechanism below . Four motifs were found to undergo in vitro farnesylation under STO conditions . The Homeobox protein ESX1 ( Q8N693; CPFF ) is cleaved into an N' and C' domain; while the N' enters the nucleus , the C' domain is localized to the cytoplasm where it inhibits cyclin degradation[44] . A search for homologues in PRENbase produced a cluster with 2 sequences predicted to undergo farnesylation by PrePS . While the latter could support actual farnesylation of this protein , in this case this modification would serve for purposes other than membrane association , such as the interaction with new partners [5] . Isoform 2 of the integral membrane protein solute carrier family 7 member 13 ( Q8TCU3-2; CHFH ) is missing an intracellular domain , and therefore places its C' in proximity to the membrane . Here farnesylation could play a role in targeting this transmembrane protein to a specific membrane compartment [45] , resulting in different membrane distributions for alternative spliced isoforms . Decaprenyl-diphosphate synthase subunit 1 isoform ( Q5T2R2-2; CTTE ) is a nuclear encoded mitochondrial protein . If indeed farnesylated , this would be a first example where an isoform of a mitochondrial protein is farnesylated in the cytosol . Finally , the proton-coupled amino acid transporter 1 ( Q7Z2H8; CAFI ) is likely not a farnesylation target , since mutation of the target cysteine to alanine did not affect its function [46] . As discussed above , the biological role of farnesylation under STO conditions is not yet clear; furthermore , if these proteins are farnesylated in vivo , the function is likely more complex than localization to the membrane . For the three motifs that were not farnesylated under in vitro conditions , additional information about the cognate proteins indeed suggests that the C-terminal cysteines are likely not farnesylated in vivo . The target cysteines of Growth/differentiation factor 15 ( Q99988; CHCI ) and the extracellular C-type lectin domain family 2 member D isoform ( Q9UHP7-3; CLFE ) are part of a conserved disulfide bridge and therefore most likely not farnesylated in vivo . In this study , we chose peptide motifs for in vitro experimental characterization based on their predicted ability to bind FTase and their novelty ( i . e . not predicted by PrePS , and not yet experimentally tested ) . While our post-hoc literature analysis reinforces some of the predictions , other targets will apparently undergo farnesylation only in vitro . The latter represent an interesting set of proteins that allow the investigation of additional factors that regulate the actual farnesylation in vivo , and that therefore distinguish between the ability of a protein to undergo farnesylation in vitro and in vivo . In any case , future in vivo validation is required for all putative targets to unequivocally define their functional importance in the cell . Approximately half of the proteins strongly predicted by FlexPepBind to undergo farnesylation ( 86/167 ) appear in alternative splicing isoforms ( according to Swissprot [24]; the actual number of isoforms is expected to be higher , as more experimental data accumulate from large scale sequencing efforts ) . Among these 86 proteins , most ( 61 ) contain the Cxxx motif only in some of the isoforms . This may present a second layer of regulation for the localization of such proteins , in which a protein can reside in different cellular compartments as a function of the isoform expressed at a given time or a given tissue and therefore perform different functions . This form of regulation may be a consequence of the irreversible nature of farnesylation . On the other hand , farnesylation can be maintained despite alternative splicing . For example , in Rab28 the two reported isoforms ( hRab28S , hRab28L ) differ only by a 95-bp insertion within the coding region [47] . This insertion generates two alternative sequences in the 30 C-terminal amino acids , which strikingly both contain a high-affinity farnesylation motif ( CSVQ – L isoform , CAVQ – S isoform ) at the C-terminus . This is similar to the case of KRas that also expresses as two splice variants with strong farnesylation motifs ( CIIM - 2A isoform , CVIM - 2B isoform ) and different upstream sequences . In this case one upstream sequence harbors an additional palmitoylation site , and may thus lead to different distribution in the membrane [48] . FlexPepBind is a framework for designing peptides that bind to a given protein , as well as for the prediction of peptide binding specificity . It is based on our previously developed modeling protocol FlexPepDock for peptide-protein structures [20] . Inclusion of constraints derived from known structures with bound peptides allows for the definition of backbone flexibility that is appropriate for the specific system of interest , and optimization of the energy function is based on a given set of binding and non-binding peptides . How much conformational freedom should be given to the peptide in order to sample the correct conformation , without introducing too much noise ? What is the best score for discrimination of active and non-active peptides ? While Grigoryan et al . were able to design peptides that bind to specific members of the bZip family [49] , Goldschmidt et al . identified fibril-forming peptides on a large scale [50] , and Kota et al . defined a binding motif for type I HSP40 peptide substrates [51] using fixed backbone conformations , the incorporation of backbone conformational flexibility has generally improved computer-aided design of functional protein interactions , as well as structure-based prediction of peptide-protein and protein-protein interaction specificity [52] . In particular , a range of backbone conformations created by the backrub method [53] improved computational sequence recovery of experimental phage display results on human growth hormone [54] , and variation along normal modes allowed improved optimization of binding between the anti-apoptotic protein BCL-xl and BH3 helical ligands [55] . Modeling of the structure of HIV protease – peptide targets using a flexible docking protocol allowed the distinction between peptides that are cleaved from those that are not , opening new avenues towards the design of HIV protease inhibitors [56] . In our modeling study of FTase binding peptides , side-chain repacking alone that restricts sampling to a discrete rotameric representation results in a low AUC value of 0 . 606 over the training set . Simple minimization that allows for very subtle backbone , side chain , and rigid-body adjustments relieves clashes that cannot be resolved with a simple rotameric side-chain search , and indeed improves performance significantly ( AUC = 0 . 875 ) . Much more extensive sampling with Rosetta FlexPepDock [20] produces even better AUC values ( up to 0 . 94 ) . Therefore , the more we sample , the better we perform . On the other hand , restricted sampling can also improve performance: the incorporation of conserved structural constraints into the simulations , as well as the inclusion of the FPP farnesyl analog , significantly improves the identification of farnesylation targets . The performance of different sampling and scoring schemes is summarized in Table S1 . Incorporation of additional FTase backbone conformations from additional FTase-substrate complex structures could enhance the predictions . To examine this , we evaluated the FlexPepBind protocol with two additional backbone templates , and assessed for each the performance on the training set . Using PDBs 1tn7 [9] and 2h6f [57] , we achieve comparable and slightly worse AUC values of 0 . 85 and 0 . 75 , respectively . Combining the scores based on 1tn6 and 1tn7 gave a marginally better performance ( AUC = 0 . 88 ) and could indeed represent an avenue for future improvement of the protocol . In addition to sampling , calibration of the energy function can also improve the prediction of binding peptides . In a study on PDZ-peptide interactions , Kaufmann et al . optimized the Rosetta energy function on 28 peptide interactions with PDZ domain 3 of PSD-95 for binding prediction . The resulting interface energy using an increased contribution of the hydrogen bond term produces a ROC plot with an AUC value of 0 . 78 on a general set of 144 peptide-PDZ interactions [58] . In our study we find that scoring with the Rosetta energy provided by the peptide provides the best results for the discrimination of active and non-active peptides . This energy includes the internal peptide energy as well as the interface energy , minus a reference energy term that had been previously introduced to optimize sequence recovery in the design of globular proteins [46] . De-facto , removal of this term favors ( in decreasing order ) C , W , F , H , Y , V , I , A , P and disfavors R , Q , N , E , D , K , S , M , T , G , L . Consequently , without this term , hydrophobic residues will be favored , and performance on the training set improved ( probably due to the significant proportion of hydrophobic residues in this set , see Figure 4B ) . Inferior results are obtained using the Rosetta energy score provided by the interface , as well as the total protein structure . In addition , we would like to note that when using FlexPepDock for sampling , averaging the scores of the best 10 models always gives better results than using merely the top-scoring model ( see Table S1 for the performance of different scoring functions ) . While the FlexPepDock based protocol gives better results , it is computationally expensive , however , and would impede large-scale characterization ( even though it may be the method of choice to make specific decisions once a threshold has been determined from the training set ) . We found that simple minimization worked well for FTase specificity prediction ( and is about 500 times faster than the full FlexPepDock-based protocol ) . This is due to the restricted nature of the binding - three very strong limitations constrain the peptide backbone orientation . Other systems will probably benefit from increased modeling of backbone flexibility . In summary , proper calibration of the energy function together with conformational sampling provides efficient structure-based characterization of peptide-protein interactions . It has been estimated that up to 40% of the cellular protein-protein interaction network is mediated by peptide-protein interactions [59] . FlexPepBind is generic in the sense that very little prior knowledge is needed in order to predict the specificity profile for a certain peptide-protein interaction . Given a structural template and a small set of known examples , prediction can be made to identify additional putative targets . We therefore anticipate that this approach can be expanded to a large scale by adapting it to additional peptide-protein interaction motifs in the cellular peptide-protein interaction network .
Human SwissProt [24] was downloaded from IPI [61] ( newest version available as of 19 . 01 . 10 ) , and was scanned for sequences containing a Cxxx regular motif as the last 4 residues in the protein sequence . Gene Ontology [25] terms were associated with each of the 167 identified candidates for farnesylation ( see Results ) . Enrichment for different cellular compartments , evaluated using DAVID [62] , extracted a subset of 93 proteins that are enriched with 18 GO cellular compartment terms , most of them related to the membrane ( see Figure S2 ) . We used the PrePS web-server [7] to obtain sequence-based predictions on our set of 167 selected proteins . For each protein suggested by our protocol to undergo farnesylation , we calculated its prenylation ability using 30 C-terminal residues as input to the server . Farnesylation screens were performed using radioactivity assays . Different conditions were used to assess the ability of Cxxx sequences to undergo farnesylation under multiple turnover ( MTO ) and single turnover ( STO ) conditions , as detailed below . Peptides that do not undergo farnesylation under either of these conditions were defined as NON ( see Hougland et al . [17] for more details ) . | Linear sequence motifs serve as recognition sites for protein-protein interactions as well as for post-translational modifications . One such motif is the CaaX box located at protein C-termini that serves as prenylation site . This prenylation is critical for many signal transduction related proteins and it is thus an important goal to uncover the range of prenylated proteins . Due to poor generalization ability , sequence based computational methods can only go so far in predicting novel targets . In this study , we introduce a novel structure based modeling approach that allows both recovery of known farnesylation substrates , as well as detection of a new class of farnesylation targets . We demonstrate high accuracy in retrospective discrimination between substrates and non-substrates of farnesyltransferase ( FTase ) . More importantly , in a prospective study , in vitro experiments validate that 26/29 predicted peptides indeed undergo farnesylation . These novel peptides were derived either from actual human proteins , or predicted to bind particularly well to FTase . Other than the discovery of putative novel farnesylation targets in the human genome , as well as possible inhibitors , we provide insights into the main determinants of farnesylation . Our approach could be easily extended to additional peptide-protein interactions and help the elucidation of the cellular peptide-protein interaction network . | [
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| 2011 | Identification of a Novel Class of Farnesylation Targets by Structure-Based Modeling of Binding Specificity |
Manipulation of natural mosquito populations using the endosymbiotic bacteria Wolbachia is being investigated as a novel strategy to reduce the burden of mosquito-borne viruses . To evaluate the efficacy of these interventions , it will be critical to determine Wolbachia infection frequencies in Aedes aegypti mosquito populations . However , current diagnostic tools are not well-suited to fit this need . Morphological methods cannot identify Wolbachia , immunoassays often suffer from low sensitivity and poor throughput , while PCR and spectroscopy require complex instruments and technical expertise , which restrict their use to centralized laboratories . To address this unmet need , we have used loop-mediated isothermal amplification ( LAMP ) and oligonucleotide strand displacement ( OSD ) probes to create a one-pot sample-to-answer nucleic acid diagnostic platform for vector and symbiont surveillance . LAMP-OSD assays can directly amplify target nucleic acids from macerated mosquitoes without requiring nucleic acid purification and yield specific single endpoint yes/no fluorescence signals that are observable to eye or by cellphone camera . We demonstrate cellphone-imaged LAMP-OSD tests for two targets , the Aedes aegypti cytochrome oxidase I ( coi ) gene and the Wolbachia surface protein ( wsp ) gene , and show a limit of detection of 4 and 40 target DNA copies , respectively . In a blinded test of 90 field-caught mosquitoes , the coi LAMP-OSD assay demonstrated 98% specificity and 97% sensitivity in identifying Ae . aegypti mosquitoes even after 3 weeks of storage without desiccant at 37°C . Similarly , the wsp LAMP-OSD assay readily identified the wAlbB Wolbachia strain in field-collected Aedes albopictus mosquitoes without generating any false positive signals . Modest technology requirements , minimal execution steps , simple binary readout , and robust accuracy make the LAMP-OSD-to-cellphone assay platform well suited for field vector surveillance in austere or resource-limited conditions .
Mosquitoes are vectors that can transmit an array of pathogens that often cause devastating human diseases [1] . Traditionally considered a problem for tropical regions , mosquitoes are increasingly becoming a global public health challenge [2 , 3] due to a changing global environment , urbanization , increases in the global movement of populations , and the emergence of insecticide resistance [4] . Estimates suggest nearly half the world’s population is at risk for mosquito-borne diseases [5 , 6] , and as such , there is an urgent need for novel approaches to reduce the burden of disease . One biocontrol countermeasure gaining traction for mosquito control is the release of Wolbachia-infected mosquitoes [7–9] . Wolbachia is a maternally-transmitted endosymbiont that can rapidly become established in the natural mosquito populations and can inhibit a variety of pathogens , including arboviruses , malaria parasites , and filarial nematodes [10–15] . Wolbachia control strategies are currently being deployed into the field to alter the capacity of Aedes aegypti to transmit arboviruses or to suppress mosquito populations [16–18] . Surveillance of transinfected mosquitoes as well as natural vector populations is crucial to evaluate the efficacy of these interventions [19] . However , most current screening methods rely on PCR , which is expensive and relies on laboratory facilities . In addition to screening for Wolbachia infection , it would also be desirable to identify the host mosquito species using these assays since different mosquito species differ in their ability to transmit pathogens [20] . Knowledge of vector species , and prevalence and stability of Wolbachia is essential for effective vector control and pre-emption of disease outbreaks with public health measures [21] . Unfortunately , mosquitoes are most commonly identified using morphological taxonomic keys . This process can be tedious , and requires highly trained personnel and undamaged mosquitoes . Alternative morphological methods such as the identification of morphometric wing characters [22] are low throughput and require microscopes and complex imaging instruments . Moreover , traditional morphological-based approaches cannot detect associated symbionts or pathogens . These limitations restrict widespread accessibility and necessitate sample preservation and transport . On the other end of the spectrum , immunoassay-based tools for identifying pathogen-infected mosquitoes , such as VecTest dipsticks ( Medical Analysis Systems Inc . ) , are portable and inexpensive . However , these tests have poor sensitivity [23–25] and are not necessarily available for distinguishing mosquito species or identifying Wolbachia endosymbionts . Nucleic acid tests can provide the necessary sensitivity and versatility for identifying both Wolbachia and mosquito species . However , since molecular testing is currently heavily reliant on PCR [26–28] , opportunities for field-based determinations are limited , leading to significant delays and gaps in actionable surveillance . To support widespread vector surveillance inexpensive , portable , nucleic acid diagnostic platforms are needed that rapidly produce accurate results without requiring complex procedures , instruments , and laboratory infrastructure . In this regard , isothermal nucleic acid amplification assays such as loop-mediated isothermal amplification ( LAMP ) have begun to be employed because they do not require complex thermocycling instruments [29–31] . However , although LAMP can rival PCR for sensitivity it often produces spurious amplicons , which in turn lead to false positive readouts with non-specific reporters such as Mg2+ precipitation or fluorescent dye intercalation [32–34] . To mitigate the spurious signals that arise with LAMP , we have previously applied principles that were developed for nucleic acid strand exchange circuits [35–38] to the design of short hemiduplex oligonucleotide strand displacement ( OSD ) probes for LAMP [39] . The single stranded ‘toehold’ regions of OSD probes bind to LAMP amplicon loop sequences , and then signal via strand exchange [40] that leads to separation of a fluorophore and quencher [39] . OSDs are the functional equivalents of TaqMan probes and can specifically report single or multiplex LAMP amplicons without interference from non-specific nucleic acids or inhibitors [39 , 41] . OSDs significantly enhance the diagnostic applicability of LAMP , allowing it to match the allelic specificity of real-time PCR . Recently , we engineered these molecular innovations to function fluently in one-pot LAMP-OSD reactions that can directly amplify a few tens to hundreds of copies of DNA and RNA analytes from minimally processed specimens and produce sequence-specific fluorescence signals that are easily observable by the human eye or ( more importantly ) by unmodified cellphone cameras [42] . The fluorescence endpoints that are produced can be used for yes/no determinations of the presence of an analyte , and also estimation of analyte copies on an order of magnitude scale [42 , 43] . Here , we have adapted our smartphone-read one-pot LAMP-OSD system to directly amplify target nucleic acids from crudely macerated mosquitoes and to sequence-specifically report both mosquito and symbiont amplicons as visually readable fluorescence . In particular , we have developed two LAMP-OSD assays–one targeting the Ae . aegypti cytochrome oxidase I gene ( coi ) , and the other the Wolbachia wAlbB surface protein ( wsp ) gene . Using a blinded set of field-caught mosquitoes , we demonstrate the exquisite sensitivity and specificity of our LAMP-OSD platform for identifying mosquito species and detecting Wolbachia infections .
All chemicals were of analytical grade and were purchased from Sigma-Aldrich ( St . Louis , MO , U . S . A . ) unless otherwise indicated . All enzymes and related buffers were purchased from New England Biolabs ( NEB , Ipswich , MA ) unless otherwise indicated . All oligonucleotides and gene blocks ( summarized in S1 Table ) were obtained from Integrated DNA Technologies ( IDT , Coralville , IA , U . S . A . ) . Ae . aegypti coi LAMP-OSD target sequence was amplified by PCR from mosquito genomic DNA . LAMP-OSD target region of the Wolbachia wAlbB wsp gene was purchased as a gBlock fragment . Both amplification targets were cloned into the pCR2 . 1-TOPO vector ( Fisher Scientific , Hampton , NH ) by Gibson assembly according to manufacturer’s ( NEB ) instructions [44] . Cloned plasmids were selected and maintained in an E . coli Top10 strain . Plasmid minipreps were prepared from these strains using the Qiagen miniprep kit ( Qiagen , Valencia , CA , USA ) . All target inserts were verified by sequencing at the Institute of Cellular and Molecular Biology Core DNA Sequencing Facility . Wolbachia wAlbB and wPip strain wsp genes and Ae . aegypti coi gene sequences were obtained from NCBI GenBank . Consensus signature sequences were derived following MUSCLE ( MUltiple Sequence Comparison by Log-Expectation ) alignment analysis of each gene set . Target specificity of these signature sequences was evaluated by comparing them to respective wsp or coi gene sets from phylogenetically-related strains and species such as Wolbachia wMel and Ae . albopictus , respectively . Both MUSCLE alignment as well as NCBI BLAST [45 , 46] analysis were used for this in silico specificity analysis . The Primer Explorer v5 primer design software ( Eiken Chemical Co . , Japan ) was used for generating several potential LAMP primer sets composed of the outer primers F3 and B3 and the inner primers FIP and BIP . Primer design was constrained to include at least a 40 bp gap between the F1 and F2 or between the B1 and B2 priming sites . Primer specificity for targeted sequence and a corresponding lack of significant cross-reactivity to other nucleic acids of human , vector or pathogenic origin were further assessed using NCBI BLAST . These primer sets were functionally tested in LAMP assays using zero to several hundred copies of purified plasmids as templates . Amplification kinetics were measured in real time using the fluorogenic intercalating dye Evagreen and the LightCycler 96 real-time PCR machine ( Roche ) . The fastest primer sets that detected the fewest template copies with negligible spurious reactivity in the absence of templates were selected for further assay development . Fluorogenic OSD probes were then designed to undergo toehold-mediated strand exchange with these Ae . aegypti coi and the Wolbachia wsp LAMP amplicons . Of the two target derived loop regions ( between the F1c and F2 , and the B1c and B2 primer binding sites ) the regions between F1c and F2 were chosen as wsp and coi OSD binding regions ( S1 Fig ) . Fluorogenic OSD probes were designed using the NUPACK software and our previously described engineering principles [39] . Briefly the hemiduplex OSDs were designed to display 11–12 nucleotide long single-stranded toeholds on the longer , fluorophore-labeled strands . All free 3’-OH ends were blocked with inverted dT to prevent extension by DNA polymerase . Single loop primers were designed to bind the second loop region ( between B1c and B2 primer binding sites ) of the wsp and coi LAMP amplicons and accelerate LAMP amplification . LAMP assays were assembled in a total volume of 25 μl of 1X Isothermal buffer ( NEB; 20 mM Tris-HCl , 10 mM ( NH4 ) 2SO4 , 50 mM KCl , 2 mM MgSO4 , 0 . 1% Tween 20 , pH 8 . 8 at 25°C ) . The buffer was supplemented with 0 . 4 mM dNTPs , 0 . 8 M betaine , 2 mM additional MgCl2 , 1 . 6 μM each of FIP and BIP , 0 . 8 μM of loop primer , 0 . 4 μM each of F3 and B3 primers , and 16 units of Bst 2 . 0 DNA polymerase . Plasmid DNA templates were serially diluted in TE buffer ( 10 mM Tris-HCl , pH 7 . 5:0 . 1 mM EDTA , pH 8 . 0 ) immediately prior to use . Zero to several hundred copies of synthetic plasmid or gBlock templates were added to the LAMP reaction mixes followed by 90 min of incubation at 65°C . 1X EvaGreen ( Biotium , Hayward , CA , USA ) was included in LAMP assays that were then analyzed using the LightCycler 96 real-time PCR machine ( Roche , Basel , Switzerland ) . Reactions were subjected to 45 cycles of two-step incubations–step 1:150 sec at 65°C , step 2: 30 sec at 65°C . EvaGreen signal was measured in the FAM channel during step 2 of each cycle . Subsequently , amplicons were subjected to a melt analysis by incubation at 65°C for 1 min followed by incremental rise in temperature to 97°C . Amplicon melting was monitored by measuring fluorescence at the rate of 10 readings per°C change in temperature . The resulting data was analyzed using the LightCycler 96 analysis software to measure Cq values for amplification and amplicon melting temperatures . LAMP reactions monitored in real time using OSD probes were assembled and analyzed as above with the following changes . First , OSD probes were prepared by annealing 1 μM of the fluorophore-labeled OSD strand with 5 μM of the quencher-labeled strand in 1X Isothermal buffer . Annealing was performed by denaturing the oligonucleotide mix at 95°C for 1 min followed by slow cooling at the rate of 0 . 1°C/s to 25°C . Excess annealed probe was stored at -20°C . Annealed OSD probes were added to the LAMP reactions at a final concentration of 100 nM of the fluorophore-bearing strand . LAMP-OSD assays intended for visual readout and smartphone imaging were assembled in 0 . 2 ml optically clear thin-walled tubes with low auto-fluorescence ( Axygen , Union City , CA , USA ) . Following 90 min of amplification at 65°C , LAMP-OSD reactions were incubated at 95°C for 1 min followed by immediate transfer to room temperature and fluorescence imaging . Images were acquired using an unmodified iPhone 6 and an UltraSlim-LED transilluminator ( Syngene , Frederick , MD , USA ) . In some experiments , our previously described in-house 3D-printed imaging device [42] was used for LAMP-OSD fluorescence visualization and smartphone imaging . Briefly , this device uses Super Bright Blue 5 mm light emitting diodes ( LED ) ( Adafruit , New York , NY , USA ) to excite OSD fluorescence . Two cut-to-fit layers of inexpensive >500 nm bandpass orange lighting gel sheets ( Lee Filters , Burbank , CA , USA ) on the observation window filter the OSD fluorescence for observation and imaging . Ae . aegypti , Ae . albopictus , Culex tarsalis , Cx . quinquefasciatus ( Houston ) , and Cx . quinquefasciatus ( Salvador ) mosquitoes were reared under conventional conditions in the insectary at the University of Texas medical Branch , Galveston , TX , USA . Four to seven day old mosquitoes were collected and immediately frozen for shipment , storage and subsequent testing . To obtain blood fed insects , Aedes mosquitoes were starved for a period of 24 hours then offered a sheep blood meal ( Colorado Serum Company , Denver , CO , USA ) using a hemotek membrane system ( Hemotek ) . Unfed mosquitoes were separated and mosquitoes that were engorged were collected 24 hours post feeding and processed in the same manner as unfed mosquitoes . For field collections , female mosquitoes were trapped using Fay prince trap ( John W . Hock ) baited with CO2 in Galveston , Texas . 90 mosquitoes were morphologically identified and sorted into three blinded groups that were stored at -20°C , 4°C and 37°C , respectively for up to 3 weeks prior to LAMP-OSD analysis . For LAMP-OSD analysis individual mosquitoes were prepared either in 1 cc syringes or in 1 . 5 ml microcentrifuge tubes as follows . In-syringe preparation: The plunger was removed from a 1 cc syringe and a 0 . 5 μM pore size 1/8th inch diameter frit ( catalog # 59037 , Sigma-Aldrich , St . Louis , MO , USA ) was placed inside the syringe . A single mosquito was placed on top of the frit and macerated thoroughly using the syringe plunger . 100 μl of water was aspirated into the syringe to fully re-suspend the macerated mosquito prior to evicting this mosquito-containing water from the syringe into a microcentrifuge collection tube . A 2 μl aliquot of this sample was directly tested by LAMP-OSD assays . In-tube preparation: A single mosquito was placed in a 1 . 5 ml microcentrifuge tube and manually macerated using a disposable micropestle ( Fisherbrand RNase-Free Disposable Pellet Pestles , Cat # 12-141-364 , Fisher Scientific , Hampton , NH , USA ) . Each macerated mosquito was resuspended in 100 μl water . A 2 μl 1:10 diluted aliquot of this mosquito sample was directly assessed by LAMP-OSD analysis . For LAMP-OSD analysis of pools of mosquitoes 25 , 50 , or 100 mosquitoes were placed in 1 . 7 ml microcentrifuge tubes and processed by the ‘in-tube’ method . Briefly , the mosquito pools were macerated manually using a disposable micropestle . The macerated pools of 25 , 50 , or 100 mosquitoes were re-suspended in 250 μl , 500 μl , or 1000 μl of water , respectively . 2 μl aliquots of 1:10 and 1:100 dilutions in water of these mosquito pool macerates were then assayed by LAMP-OSD assays . Mosquito pools tested included Pool A ( 99 un-infected Ae . aegypti ) ; Pool B ( 99 un-infected Ae . aegypti and one Wolbachia-infected Ae . albopictus ) ; Pool C ( 49 un-infected Ae . aegypti and one Wolbachia-infected Ae . albopictus ) ; and Pool D ( 24 un-infected Ae . aegypti and one Wolbachia-infected Ae . albopictus ) . The paired results of morphological identification and LAMP-OSD analysis were compared using 2x2 contingency tables . Sensitivity or true positive rate was calculated by using the formula TP/ ( TP+FN ) where TP are true positive samples , and FN are false negative samples . Specificity or true negative rate was calculated using the formula TN/ ( TN+FP ) where TN are true negative samples and FP are false positive samples .
LAMP uses two inner ( FIP and BIP ) and two outer ( F3 and B3 ) primers specific to six consecutive target sequences ( B3 , B2 , B1 , F1c , F2c and F3c ) ( S1 Fig ) [47] . Bst DNA polymerase extends these primers by strand displacement DNA synthesis to form 109 to 1010 copies of concatemerized amplicons with loops between the F1 and F2 , B1 and B2 , F1c and F2c , and B1c and B2c regions . We use an additional fifth primer that binds to one of these loop regions and accelerates amplification [41] . OSD probes , with blocked 3’-ends that prevents spurious signaling from polymerase-mediated extension , hybridize to the second loop region ( S1 Fig ) . To enable molecular identification of Ae . aegypti mosquitoes , we designed a smartphone-imaged LAMP-OSD assay to amplify and detect a signature sequence in the mitochondrial cytochrome c oxidase I ( coi ) gene . Each cell has multiple mitochondria and hence several hundred copies of the coi gene , which should enable detection from a very small amount of sample . Moreover , mitochondrial coi gene sequences are commonly used as barcodes for molecular identification of animal species including distinction of mosquito species; our chosen coi signature sequence was assigned to Ae . aegypti when queried against the Barcode of Life Data Systems ( BOLD; http://www . boldsystems . org/index . php ) coi signature sequence database [48–51] . We developed a second visually read LAMP-OSD assay targeting the Wolbachia surface protein ( wsp ) gene to identify Wolbachia-infected insects . The wsp gene is widely used as a marker for strain typing and screening for infected insect vectors [28 , 52] . We engineered our wsp LAMP outer and inner primers to be complementary to , and hence amplify , two Wolbachia strains , the wAlbB and the closely related wPip ( S2 Fig ) . We deliberately designed our assay to detect both strains in order to ensure that we could assess field-collected mosquitoes irrespective of temporal and spatial variation in relative abundance of wAlbB-infected Ae . albopictus and wPip-infected Cx . quinquefasciatus mosquitoes in our collection area [53 , 54] . Significant nucleic acid sequence variation should prevent amplification of the wsp gene from all other Wolbachia groups ( S2 Fig ) . To enable transduction of both wAlbB and wPip wsp LAMP amplicons to visible fluorescence we designed an OSD probe that is specific to an identical loop sequence present in both amplicons . With a single endpoint visual ‘yes/no’ readout of OSD fluorescence ( either directly observed or imaged using cellphone camera ) , the Ae . aegypti coi LAMP-OSD assay could reliably identify the presence of as few as 4 copies of synthetic target DNA ( Fig 1 ) . Similarly , the cellphone-imaged wsp LAMP-OSD assay produced bright visible fluorescence when presented with only 40 copies of its target wsp sequences while remaining dark in the presence of synthetic wAlbA , wAus , wMors , and wAna wsp templates ( Fig 1 and S2 Fig ) . In the absence of target DNA , neither assay generated spurious signal . Our next goal was to demonstrate the ability of these LAMP-OSD assays to detect naturally occurring target sequences in mosquitoes . At the same time , we wanted to ensure that minimally processed samples would be compatible with our detection platform in order to facilitate rapid in-field vector testing with fewest instruments and user-required steps . Therefore , as an initial approach , we developed the ‘in-syringe’ method for rapid sample preparation wherein individual mosquitoes were crushed inside 1 cc syringes using the syringe plunger as a pestle . A small chromatography column frit placed inside the syringe served as a pedestal that aided maceration and removed larger particulates when the macerated sample was re-suspended in water and recovered . Small portions ( up to 8% of a LAMP-OSD reaction ) of these macerated samples were added directly to LAMP-OSD reactions , which were then incubated for 90 min at 65°C to initiate and sustain amplification . Endpoint visual examination of these assays for the presence or absence of OSD fluorescence revealed that our visually read LAMP-OSD system is compatible with direct analysis of crudely processed mosquitoes ( Fig 2 ) . The coi LAMP-OSD assay generated bright fluorescence readily distinguishable from sample auto-fluorescence when seeded with crudely prepared Ae . aegypti mosquitoes . In contrast , closely related Ae . albopictus failed to instigate false positive signal . Similarly , the wsp LAMP-OSD assay generated bright fluorescence in response to Ae . albopictus and Cx . quinquefasciatus mosquitoes , which are naturally infected with wAlbB and wPip Wolbachia , respectively , but remained negative in the presence of unrelated Wolbachia wMel and uninfected mosquitoes ( Fig 2 and S3 Fig ) . These results indicate that the Wolbachia wsp and Ae . aegypti coi LAMP-OSD assays are able to specifically amplify and signal the presence of their target DNA directly from crudely crushed mosquito samples without requiring any extraction and purification of nucleic acids . Furthermore , the large burden of non-specific nucleic acids as well as other molecular and macroscopic components present in a crude mosquito sample did not compromise signal accuracy . We also confirmed the absence of significant inhibition of amplification and signaling by recapitulating the detection limit of synthetic DNA targets in a background of crude non-specific mosquito sample . The coi and wsp LAMP-OSD assays could detect 4 and 40 target copies , respectively , even in the presence of 8% reaction volume of crude mosquito sample ( S4 Fig ) . Mosquitoes feeding on blood meals have been reported to engorge on 1 nL to as much as 6 μL of blood [55] . It is conceivable that the blood meal might confound visual LAMP-OSD fluorescence analysis by contributing auto-fluorescence . To ascertain compatibility of visually read LAMP-OSD with direct analysis of crudely prepared blood-engorged mosquitoes , we challenged both coi and wsp LAMP-OSD assays with crude in-syringe preparations of blood engorged Ae . aegypti and Ae . albopictus mosquitoes . Mosquitoes that had recently consumed a blood meal could be directly analyzed by visual LAMP-OSD without diminution of signal to noise ratio ( Fig 3 ) . To validate assay performance under more rigorous conditions , we challenged the LAMP-OSD system with a blinded set of 90 field-caught mosquitoes comprised of Ae . aegypti , Ae . albopictus , and Ochlerotatus species . The mosquitoes were divided into three groups of 30 individuals that were stored without desiccant at -20°C , 4°C , or 37°C for 1 , 2 , or 3 weeks prior to testing . To reduce mosquito processing cost , footprint , and time for this large study , we further simplified sample preparation requirements by optimizing the “in-tube” mosquito preparation method wherein each mosquito was crushed with a micropestle directly in a microcentrifuge tube followed by resuspension in water and introduction in a LAMP-OSD reaction . The visually read coi LAMP-OSD assay demonstrated an overall sensitivity ( true positive rate ) of 97% and specificity ( true negative rate ) of 98% when compared to morphological typing of field-caught mosquito species ( Figs 4 , S5 , S6 and S7 ) . On closer inspection of the data , it is evident that even after three weeks of mosquito collection and storage at temperatures as high as 37°C the coi LAMP-OSD assay was correctly able to identify 29 out of 30 Ae . aegypti mosquitoes . The single mosquito that the LAMP-OSD assay failed to identify had been stored at 37°C for a week prior to testing . We ruled out lack of amplifiable nucleic acids or their incompatibility with coi LAMP primers and OSD probe by PCR amplifying the relevant coi LAMP target and verifying its sequence . Of the 60 non-Ae . aegypti mosquitoes analyzed by coi LAMP-OSD , only one mosquito generated a false positive signal . Sequence analysis of its coi gene ruled out mis-firing of the coi LAMP-OSD assay . It is possible that this LAMP assay was inadvertently contaminated with a small amount of a pre-formed Ae . aegypti amplicon . The Wolbachia wAlbB/wPip wsp LAMP-OSD assay did not generate a positive signal from any non-Ae . albopictus mosquito . This is expected since natural populations of Ae . aegypti and most Ochlerotatus species are not infected with Wolbachia [56 , 57] . However , ability of the wsp assay to identify Wolbachia infection was influenced by the storage temperature of mosquitoes . The wsp LAMP assay could readily identify Wolbachia infection in 3 out of 4 Ae . albopictus mosquitoes stored at -20°C for as long as 3 weeks . PCR analysis of the wsp-negative mosquito using previously described primers ( 81F and 691R ) and protocols [28] did not produce amplicons suggesting that this individual was likely uninfected or had Wolbachia levels below the levels detectable by PCR . As the storage temperature was increased the frequency of Wolbachia detection dropped . While 40% of Ae . albopictus mosquitoes stored at 4°C gave a positive wsp LAMP-OSD signal , none of the Ae . albopictus mosquitoes kept at 37°C for even as little as 1 week were wsp positive . All mosquitoes that failed to generate a signal by LAMP also failed to produce wsp PCR amplicons . Since , 95–99% of Ae . albopictus mosquitoes in the wild are typically found to be infected with Wolbachia [58] , these results are suggestive of nucleic acid deterioration in mosquitoes upon storage at high temperature . For time- and cost-efficient mosquito surveillance high-throughput analysis of pooled mosquitoes rather than individual insects is often necessary [59 , 60] . To determine the utility of our assay platform for analyzing mosquito pools we created four sample pools comprising Wolbachia-infected Ae . albopictus and un-infected Ae . aegypti mosquitoes–Pool A: 99 Ae . aegypti , Pool B: 1 Ae . albopictus and 99 Ae . aegypti , Pool C: 1 Ae . albopictus and 49 Ae . aegypti , and Pool D: 1 Ae . albopictus and 24 Ae . aegypti . Entire pools were subjected to ‘in-tube’ crude sample preparation followed directly by LAMP-OSD analysis for Wolbachia wsp . Our data demonstrate that LAMP-OSD could readily detect the presence of a single Wolbachia-infected mosquito in pools of 25 , 50 , and 100 mosquitoes ( Fig 5 ) . No false signals were produced by pools of 99 Wolbachia-free Ae . aegypti mosquitoes . These results suggest that our rapid sample preparation method and smartphone-read LAMP-OSD assays can be used for accurate analysis of both individual and pooled mosquitoes .
Mosquito control strategies that rely on the introduction of Wolbachia are now being deployed around the world [7–9] , and the surveillance of efficacy and spread require agile , field-based methods for both mosquito and symbiont detection . Unfortunately , currently available tools for mosquito diagnostics have several shortcomings . Morphological identification methods are inherently low throughput , require extensive technical expertise , and cannot also readily identify pathogens or biocontrol agents such as Wolbachia . Spectroscopy , such as near infrared spectroscopy [61] and Fourier transform infrared spectroscopy [62] , allow identification of mosquito species , Wolbachia , and pathogens , but require expensive instruments and expertise that are generally incompatible with low-resource settings . Immunoassays can detect pathogen-carrying vectors but are insensitive and cannot also identify vector species . Nucleic acid amplification methods could potentially look at both vector and symbiont sequences , but are heavily reliant on PCR with the ensuing encumbrances of expensive instruments and trained operators , again precluding widespread use . Isothermal nucleic acid amplification assays would facilitate field-based vector monitoring , but most reported approaches rely on nucleic acid purification and non-specific readout , and thus suffer from laborious setup and the risk of false positives [63–65] . Probe-read isothermal methods such as the recombinase polymerase assay ( RPA ) are more reliable but still require expensive and proprietary reaction formulations and probes , which limits their flexibility and versatility in assay engineering . Furthermore , most RPA applications for vector diagnostics [66] also depend on extensive sample processing and nucleic acid purification prior to amplification . These drawbacks led us to develop a simpler , more robust field-deployable assay based on loop-mediated isothermal amplification ( LAMP ) that can identify both mosquito species and specific Wolbachia strains by direct analysis of crudely macerated individual or pooled mosquitoes . While LAMP assays have previously been developed for Wolbachia detection by targeting the 16S rRNA gene for amplification [63] , these assays required extraction and purification of DNA prior to assay , and used non-specific readouts that were highly prone to false positive signals . To overcome these barriers to the use of LAMP , we have previously adopted methods that originated in the field of nucleic acid computation: the use of strand exchange reactions that initiate at complementary single-stranded ‘toeholds’ and progresses via branch migration . The base-pairing predictability and programmability of strand exchange kinetics promotes the construction of exquisitely sequence-specific oligonucleotide strand displacement ( OSD ) probes for LAMP amplicons ( S1 Fig ) , thereby greatly reducing the detection of non-specific amplification background [39 , 41 , 42] . For instance , we recorded positive coi LAMP-OSD signals from field-caught Ae . aegypti mosquitos but did not detect signal from closely related Ae . albopictus and Ochlerotatus species mosquitoes . Similarly , the wsp assay detected wAlbB and wPip , as expected , but not wMel , wMors , wAna , wAus , or wAlbA Wolbachia . Strand exchange circuits have the additional advantage that they can be used to embed algorithms and act as ‘matter computers’ [35–37 , 67] . For example , strand exchange transducers can logically integrate multiple analytes; transform nucleic acids to glucose and human chorionic gonadotrophin ( hCG ) ; adapt readout to beads , paperfluidics , glucose meters , pregnancy test strips , and cellphones , and allow target copy number estimation using a single endpoint yes/no readout of presence or absence of signal above an adjustable threshold [68–71] . In the current instance , we deliberately designed our wsp LAMP-OSD assay to ‘compute’ the presence of both wAlbB and wPip in order to increase our odds of finding infected field-caught mosquitoes . However , the dependence of strand exchange efficiency on toehold binding strength [39] can be exploited to engineer yes/no distinctions between strain-specific single nucleotide polymorphisms [39] , and the same dual wsp assay could be rendered strain-specific by simply substituting an OSD reporter specific to an alternate polymorphic loop sequence ( S2 Fig ) . This might be advantageous for strain discrimination if double infections were released during vector control measures [72 , 73] . By using our one-pot LAMP-OSD assay , macerated mosquito homogenates could be directly analyzed and ‘yes/no’ visual readouts could be quickly ascertained with a cell phone in the field without the requirement for laboratory equipment or technically training . Moreover , since our assays can accurately analyze mosquitoes several days after capture–the coi LAMP-OSD assay could for example identify mosquitoes after 3 weeks at 37°C without desiccant–mosquitoes from remote collection outposts can potentially be analyzed even after delayed retrieval . We are currently automating the assays and workflow on low-cost modular paper and plastic devices that will not only further streamline diagnostic application , especially for high-throughput analysis , but will also provide biohazard and aerosol containment by restricting mosquito maceration and molecular assay in sealed chambers . The flexibility of assay timing is further accommodated by the fact that lyophilized LAMP-OSD reaction mixes can be stored and deployed without cold chain [42] . Our sample preparation and assay workflow not only simplify application of molecular diagnostics for surveillance but should also reduce operational costs by eliminating the need for nucleic acid extraction and complex instruments for assay incubation and readout . Market cost of LAMP-OSD assay reagents is ~$1 . 5/reaction . We are in the process of significantly reducing this cost by substituting commercially sourced purified Bst 2 . 0 DNA polymerase with our recently developed ‘cellular reagents’ as a cheaper alternative [74] . Open system TaqMan qPCR assays have been reported to also cost $1 . 51/reaction [75] . Our re-usable in-house 3D-printed device [42] for fluorescence visualization costs <$5 in parts to build and enables ‘instrument-free’ readout of visual signal at no additional cost . Fluorescence signal may also be captured for posterity using unmodified standalone camera or any camera cellphone including low cost ( <$200 ) models [42] . In contrast , lab-based qPCR testing is estimated to require ~$30 , 000 in startup investment and ~$700 in annual maintenance [75] . These combined features make our assay platform the best tool to date for expanding vector surveillance to resource poor settings [76] , especially in that the ease of use should allow minimally trained citizen scientists to participate in otherwise sophisticated public health monitoring operations in the field . A few caveats must be considered for any nucleic acid based test including qPCR and LAMP-OSD . First , without prior knowledge of integration sites , nucleic acid amplification tests will be unable to differentiate wsp target sequences derived from infectious Wolbachia bacterial genomes versus Wolbachia DNA integrated in nuclear genomes of hosts such as Drosophila ananassae [77] . However , other PCR-based assays also face this challenge . Second , nucleic acid amplification may not be significantly diminished by the presence of one or two mismatches between a primer and its target binding site [41 , 78] . While , ability to generate a positive signal from very closely related target variants that differ by only one or two nucleotides is desirable , it is important for a nucleic acid test to distinguish polymorphic strains . Unlike two-primer systems such as qPCR , use of at least six primer binding sites during LAMP allows LAMP primers to scan a three times larger target sequence space for distinguishable polymorphisms . As a result , LAMP assays are more likely to distinguish unexpected polymorphic strains due to the likelihood of encountering mismatches in multiple primers that would in turn lead to diminution of amplification and false positive signals . The development efforts that we have put into LAMP-OSD should now allow it to be generalized to screening other microbes or mosquito phenotypes in field settings . For example , LAMP-based assays have been developed to identify pathogens transmitted by mosquitoes and insecticide resistance alleles [79–82] , but these rely on purified nucleic acid as templates and non-specific readout whereas our method functions with mosquito homogenate and ensures accuracy using unique sequence-specific strand exchange probes . In addition , this technology could be used to identify gut microbes and insect-specific viruses associated with mosquitoes , which is of growing interest given that it is becoming clear that the microbiome can shape vector competence for human pathogens [83–85] . Overall , we have demonstrated a versatile nucleic acid diagnostic platform for rapid and accurate analyses of both insect vectors and symbionts , and that can now be further configured for additional applications . | Mosquitoes spread many human pathogens and novel approaches are required to reduce the burden of mosquito-borne disease . One promising approach is transferring Wolbachia into Aedes aegypti mosquitoes where it blocks transmission of arboviruses like dengue , Zika and Yellow fever viruses and spreads through mosquito populations . For effective evaluation of this approach , regular surveillance of Wolbachia infections in Ae . aegypti is required . However , current diagnostic tools , such as real time polymerase chain reaction , are not well suited to support these critical surveillance needs in resource poor settings due to their dependence on expensive instruments and technical expertise . To fill this need we developed a simple , robust and inexpensive assay to identify Ae . aegypti mosquitoes and Wolbachia using our unique one-pot assay platform , LAMP-OSD , which uses loop-mediated isothermal amplification to amplify nucleic acid targets at a single temperature . Unlike other LAMP-based tests , our assays assure accuracy by coupling amplification with novel nucleic acid strand displacement ( OSD ) probes that hybridize to specific sequences in LAMP amplification products and thereby generate simple yes/no readout of fluorescence readable by human eye and by off-the-shelf cellphones . To facilitate field use , we developed our assays so they are compatible with crushed mosquito homogenate as the template , meaning no nucleic acid extraction is required . In blinded tests using field collected mosquitoes , LAMP-OSD-cellphone tests performed robustly to identify 29 of 30 Ae . aegypti even after 3 weeks of storage at 37°C while producing only one false positive out of 60 non-specific mosquitoes . Similarly , our assay could identify Wolbachia in field-caught Aedes albopictus without producing any false positives . Our easy to use and easy to interpret assays should facilitate widespread field mosquito surveillance with minimal instrumentation and high accuracy . | [
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| 2018 | Direct nucleic acid analysis of mosquitoes for high fidelity species identification and detection of Wolbachia using a cellphone |
Oxytocin administration has been reported to decrease consumption , withdrawal , and drug-seeking associated with several drugs of abuse and thus represents a promising pharmacological approach to treat drug addiction . We used an established rat model of alcohol dependence to investigate oxytocin’s effects on dependence-induced alcohol drinking , enhanced motivation for alcohol , and altered GABAergic transmission in the central nucleus of the amygdala ( CeA ) . Intraperitoneal oxytocin administration blocked escalated alcohol drinking and the enhanced motivation for alcohol in alcohol-dependent but not nondependent rats . Intranasal oxytocin delivery fully replicated these effects . Intraperitoneal administration had minor but significant effects of reducing locomotion and intake of non-alcoholic palatable solutions , whereas intranasal oxytocin administration did not . In dependent rats , intracerebroventricular administration of oxytocin or the oxytocin receptor agonist PF-06655075 , which does not cross the blood-brain barrier ( i . e . , it would not diffuse to the periphery ) , but not systemic administration of PF-06655075 ( i . e . , it would not reach the brain ) , decreased alcohol drinking . Administration of a peripherally restricted oxytocin receptor antagonist did not reverse the effect of intranasal oxytocin on alcohol drinking . Ex vivo electrophysiological recordings from CeA neurons indicated that oxytocin decreases evoked GABA transmission in nondependent but not in dependent rats , whereas oxytocin decreased the amplitude of spontaneous GABAergic responses in both groups . Oxytocin blocked the facilitatory effects of acute alcohol on GABA release in the CeA of dependent but not nondependent rats . Together , these results provide converging evidence that oxytocin specifically and selectively blocks the enhanced motivation for alcohol drinking that develops in alcohol dependence likely via a central mechanism that may result from altered oxytocin effects on CeA GABA transmission in alcohol dependence . Neuroadaptations in endogenous oxytocin signaling may provide a mechanism to further our understanding of alcohol use disorder .
Alcohol use disorder is a global public health issue; 6% of the world’s population is subject to morbidity and mortality from alcohol [1] . Alcohol dependence ( reflecting dysfunction equivalent to moderate to severe alcohol use disorder ) is a chronic , relapsing disorder that develops as a result of neuroadaptations in brain reward , stress , and executive function systems controlled by neurocircuits that involve the basal ganglia , extended amygdala , and prefrontal cortex , respectively [2] . In alcohol dependence , the motivation to seek and consume alcohol is driven by reward hypofunction and stress sensitization . Both of these processes contribute to a negative emotional state that drives enhanced motivation for alcohol drinking via negative reinforcement [3 , 4] . Oxytocin has been hypothesized to have an important role in social behavior , fear and anxiety , and learning and memory [5] . Recent optogenetic and neuroanatomical mapping studies have advanced our understanding of the complexity of oxytocin’s neurocircuitry and the role of signaling by the endogenous oxytocin system in various behaviors [6] . Oxytocin modulates stress and reward function and has long been suggested as a putative treatment for addiction [7] . Reports that oxytocin administration can reduce drug consumption , withdrawal , and relapse associated with various drugs of abuse in preclinical models has greatly enhanced the interest in this area [8 , 9] . Indeed , oxytocin decreased economic demand for stimulants [10] , reinstatement of stimulant seeking [11 , 12] , opioid tolerance and withdrawal [13] , and development of alcohol tolerance [7] . Particularly relevant for treating alcohol dependence , oxytocin has an anti-stress and anti-anxiety profile [14] . Preliminary clinical studies have demonstrated that intranasal oxytocin administration reduced alcohol-withdrawal–related anxiety and alcohol craving in alcohol-dependent patients in early abstinence [15] , reduced alcohol craving in anxious alcohol-dependent individuals [16] , and reduced neural response to alcohol cues in heavy social drinkers [17] . Oxytocin decreased drinking in nondependent rats [18 , 19] , binge drinking in mice [20 , 21] , and cue-induced reinstatement in postdependent rats [17] . However , oxytocin’s effects on alcohol drinking and motivation for alcohol in currently alcohol-dependent rats remains to be determined . Therefore , in the present study , we used a model of chronic-intermittent exposure to alcohol vapor to induce alcohol dependence and enhanced motivation for alcohol . This model has excellent predictive validity , has allowed dissociation of the neurobiology underlying alcohol dependent versus nondependent behavior , and has been used to determine the likely contribution of a range of neurobiological systems to alcohol use disorder [3 , 39] . Oxytocin has been detected in the brain following both intraperitoneal and intranasal administration in mice and rats [22–24] . Oxytocin receptors are found in the brain and in various peripheral tissues , where binding mediates oxytocin’s classic role in lactation , parturition , and sexual reflexes in males and females [25] . There are several other oxytocin binding sites in the periphery , including the gut , heart , vascular system , and vagus nerve , which all may contribute to behavioral effects of oxytocin , including inhibition of appetite and fear [25 , 26] . The central versus peripheral contributions to oxytocin’s putative effects on alcohol drinking also remain to be determined . Oxytocin receptors are found in many brain regions relevant for alcohol dependence , such as the extended amygdala in rats and humans , including the central nucleus of the amygdala ( CeA ) [27 , 28] . The CeA is well described as a key structure involved in the transition to drug and alcohol dependence [2 , 29] . Silencing of an alcohol-withdrawal–activated CeA neuronal ensemble blocked the enhanced drinking associated with alcohol dependence [30] . The majority of CeA neurons are GABAergic , and increased GABA signaling in the CeA is linked to alcohol dependence . Intra-CeA infusion of a GABAA receptor agonist decreased alcohol drinking in alcohol-dependent rats [31] . We previously demonstrated that spontaneous and evoked GABA transmission is increased in CeA of alcohol-dependent rats [32] . Both pro- and anti-stress neuropeptides interact with CeA GABA signaling and bidirectionally modulate alcohol drinking . Oxytocin has been reported to interact with GABAergic signaling and may block the acute effects of alcohol via a direct action on GABAA receptors [33] . However , the physiological effects of CeA oxytocin in alcohol dependence are currently unknown . Therefore , in the present study , we tested the hypothesis that oxytocin decreases alcohol drinking and motivation for alcohol specifically in alcohol-dependent rats via central and not peripheral actions . Furthermore , we hypothesized that alcohol dependence would alter how oxytocin modulates GABA signaling in the CeA .
For all behavioral and electrophysiological experiments involving anesthesia , isoflurane inhalation was used . For electrophysiological experiments , anesthesia was followed by rapid decapitation to allow brain slice preparation . All behavioral studies were approved by the Institutional Animal Care and Use Committee ( ACUC ) of the National Institute on Drug Abuse Intramural Research Program . All electrophysiological procedures were approved by the IACUC of The Scripps Research Institute . All procedures were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals ( 8th edition ) . Rats used in behavioral ( male Wistar , n = 86; Charles River , Kingston , NY ) and electrophysiology ( male Sprague-Dawley , n = 87; Charles River , Raleigh , NC ) experiments weighed 225 to 275 g upon arrival , were group-housed ( 2 to 3 per cage ) in standard plastic cages in a temperature- and humidity-controlled room , and were maintained under a reverse 12 h/12 h light/dark cycle with food and water available ad libitum except during behavioral testing . For behavioral experiments , alcohol ( ethanol; Warner Graham , Cockeysville , MD ) was dissolved in tap water . Oxytocin ( ChemPep Inc . , Wellington , FL ) was dissolved in 0 . 9% saline . For peripheral administration , the non–blood-brain barrier-penetrant oxytocin receptor agonist PF-06655075 was initially dissolved in 10% ( v/v ) Self Emulsifying Drug Delivery System that consisted of 3:4:3 Miglyol 812:Cremophor RH40:Capmul MCM ( v/v/v ) . The emulsion was completed with 90% ( v/v ) 50 mM aqueous phosphate buffer ( pH 7 . 4 ) . For intracerebroventricular administration , PF-06655075 was initially dissolved in 5% ( v/v ) dimethyl sulfoxide , then combined with 5% ( v/v ) polyethylene glycol 300 , and completed with 90% ( v/v ) saline . PF-06655075 and the vehicle used for its peripheral administration were kindly provided by Pfizer . The non–blood-brain barrier penetrant antagonist L-371 , 257 ( Tocris Bioscience , Ellisville , MO ) was prepared in a vehicle of 5% ( v/v ) dimethyl sulfoxide , 5% ( v/v ) Cremophor , and 90% ( v/v ) sterile water . For electrophysiology experiments , oxytocin and the selective vasopressin receptor 1A antagonist ( d ( CH2 ) 5 , Tyr ( Me ) 2 , Arg8 ) -Vasopressin ( TMA ) [34] were obtained from Tocris ( Ellisville , MO ) . The selective oxytocin receptor antagonist desGly-NH2-d ( CH2 ) 5[D-Tyr2 , Thr4]OVT ( OTA ) was provided by Dr . Maurice Manning ( University of Toledo , OH ) [34] . CGP 55845A , DL-2-amino-5-phosphonovalerate ( DL-AP5 ) , and 6 , 7-dinitroquinoxaline-2 , 3-dione ( DNQX ) were obtained from Tocris ( Ellisville , MO ) . Tetrodotoxin ( TTX ) was purchased from Biotium ( Hayward , CA ) . Alcohol was purchased from Remet ( La Mirada , CA , USA ) . All drugs were dissolved in artificial cerebrospinal fluid ( aCSF ) . Oral alcohol self-administration experiments were conducted in standard operant chambers ( Med Associates , St . Albans , VT ) fitted with 2 retractable levers and a dual-cup liquid receptacle . After initial training , as previously described [29 , 35] , the animals were allowed to lever press for alcohol ( 10% , w/v; 0 . 1 ml ) and water ( 0 . 1 ml ) on separate levers according to a concurrent fixed-ratio 1 ( FR1 ) schedule of reinforcement ( each lever press resulted in fluid delivery ) in 30-min operant sessions . After each training session , the liquid receptacle and surrounding area were inspected to confirm the consumption of earned reinforcers . After response acquisition , rats were split into 2 groups matched by their alcohol consumption . For the remainder of these experiments , rats in the nondependent group were exposed to air without alcohol , whereas rats in the dependent group were exposed to alcohol vapor in daily cycles designed to cause intoxication ( 14 h vapor “on”; 200 mg/dl target blood alcohol levels ) and withdrawal ( 10 h vapor “off” ) to induce alcohol dependence , as previously described [29 , 35] . Dependence is characterized by somatic and motivational signs of withdrawal that include increased anxiety-like behavior , reward deficits , and enhanced motivation to self-administration of alcohol [36–39] . In all operant alcohol self-administration experiments , steady baseline consumption of alcohol was established in dependent and nondependent rats before pharmacological testing began . Operant alcohol self-administration sessions including pharmacological tests were conducted 2 to 3 sessions per week ( never on consecutive days to minimize potential carry-over effects ) during the 10 h “off” period , 6 to 8 h into withdrawal . Intraperitoneal oxytocin ( 0 , 0 . 125 , 0 . 25 , 0 . 5 , and 1 mg/kg; 0 . 5 or 1 ml/kg ) was administered 30 min prior to FR1 alcohol self-administration sessions in dependent ( n = 10 ) and nondependent ( n = 10 ) rats . The pretreatment time was selected based on previous work that determined peak brain oxytocin concentrations following intraperitoneal oxytocin administration [22] . The test order of intraperitoneal doses was counterbalanced using a within-subjects Latin-square design . Self-administration of alcohol and water was recorded during tests . Based on results of the FR1 test , the 0 , 0 . 125 , and 0 . 25 mg/kg intraperitoneal doses were then tested on a progressive ratio ( PR ) schedule of reinforcement , in which the number of lever presses required to obtain the next alcohol reinforcer increased progressively , as follows: 1 , 1 , 2 , 2 , 3 , 3 , 4 , 4 , 5 , 5 , 7 , 7 , 9 , 9 , 11 , 11 , 13 , 13 , etc . The last completed ratio ( breakpoint ) was used as an indication of motivation for alcohol . During the PR test , only the alcohol lever was made available . Sessions lasted 90 min or until 15 min had elapsed without a response . The pretreatment time during the PR test was the same as for the FR1 test . Again , these doses were administered in a Latin-square design , with intervening nontesting days . To reestablish a stable baseline of intake , the same rats described above were moved to different operant chambers with matching operant manipulanda and reinforcer receptacles . They were allowed eight 30-min FR1 sessions to obtain alcohol and water ( S1 Fig ) . Next , intranasal oxytocin was administered 1 h prior to testing responding for alcohol on FR1 and PR schedules of reinforcement ( described above ) . The pretreatment time was selected based on previous work that determined peak brain oxytocin concentrations following intranasal oxytocin administration [22] . Again , tests were conducted in Latin-square designs with intervening nontesting days . For intranasal drug delivery , general anesthesia was rapidly induced by isoflurane ( 5% for 2 to 5 min ) , and then rats were immediately administered intranasal oxytocin ( 0 , 0 . 25 , 0 . 5 , and 1 mg/kg/20 μl; based on the FR1 data , the intranasal doses of 0 . 5 and 1 mg/kg/20 μl were selected for testing on the PR schedule ) using the rat Precision Olfactory Device ( rPOD; Impel NeuroPharma , Seattle , WA ) and allowed to recover before being returned to their home cage . Based on the effects of oxytocin on alcohol drinking and the motivation for alcohol , we selected the doses of intraperitoneal ( 0 . 25 mg/kg ) and intranasal ( 1 mg/kg ) oxytocin for further behavioral testing . These doses by each route were the lowest dose by each route that significantly reduced alcohol drinking and the motivation for alcohol specifically in dependent rats . The pretreatment times were the same as used for testing on alcohol self-administration . Behavioral tests were conducted in separate cohorts of rats to assess potential non–alcohol-specific effects of oxytocin on locomotion and grooming in an open field ( dependent , n = 6; nondependent , n = 6 ) and motor coordination on a rotarod ( dependent , n = 5; nondependent , n = 7 ) . Two blind observers scored any instances of grooming behavior according to previously published work . A single point was given for each instance of vibration of forepaws , face washing , body grooming , body scratching , paw licking , head shaking , body shaking , and genital grooming , according to previously reported studies [40 , 41] . Points were totaled for each animal to yield a grooming score . In addition to alcohol’s pharmacological effect , alcohol contains calories and has a sweet-taste component that contribute to the reinforcer efficacy of alcohol . To assess the potential role of calories and sweet taste in the ability of intraperitoneal and intranasal oxytocin to reduce alcohol consumption , we tested oxytocin on the consumption of a nonalcoholic caloric reinforcer without a sweet taste ( 5% maltodextrin; n = 7 ) and on a nonalcoholic sweet solution without caloric content ( 0 . 1% saccharin; n = 8; additional details provided in S1 Text ) . Three separate cohorts of alcohol-dependent rats were used ( central oxytocin administration: n = 6; peripheral oxytocin receptor agonist: n = 9; peripheral oxytocin receptor antagonist combined with intranasal oxytocin administration: n = 12 ) . A subset of the group used for testing the peripheral oxytocin receptor antagonist combined with intranasal oxytocin administration was given 2 sessions in the absence of any treatment to reestablish a baseline and then used to test the effect of central administration of the oxytocin receptor agonist PF-06655075 ( central oxytocin receptor agonist: n = 7 ) . The central oxytocin and PF-06655075 administration cohorts were surgically implanted with a guide cannula to allow intracerebroventricular administration of these compounds . The oxytocin receptor antagonist L-371 , 257 , which does not cross the blood-brain barrier , was tested in combination with intranasal oxytocin administration to test the ability of peripheral antagonism to reverse the ability of intranasal oxytocin to reduce alcohol drinking in dependent rats . L-371 , 257 is a potent and competitive antagonist of the oxytocin receptor ( pA2 = 8 . 4 ) with high affinity at both the oxytocin receptor ( Ki = 19 nM ) and vasopressin V1a receptor ( Ki = 3 . 7 nM ) [42] . The pretreatment time and dose ( 45 min; 5 mg/kg ) were selected to be in excess of doses that provided blockade of oxytocin-induced uterine contractions for at least several hours following peripheral ( intravenous or intraduodenal ) administration in rats [42] . Doses much lower than this in rats ( 0 . 5 mg/kg intraperitoneal ) [43] and mice ( 300 μg/kg intranasal ) [44 , 45] have been demonstrated to provide behaviorally effective antagonism . Additionally , in previous studies , comparable doses of L-371 , 257 versus oxytocin ( 0 . 5 mg/kg versus 0 . 5 mg/kg intraperitoneal in rats [43]; 300 μg/kg versus 200 μg/kg intranasal in mice [45] ) were administered . Here , we administered 5 times the dose of the antagonist compared to oxytocin ( 5 mg/kg versus 1 mg/kg ) via a preferred route of peripheral absorption ( intraperitoneal injection versus intranasal application [23] ) to conservatively test for the ability of the peripherally acting antagonist to alter the actions of intranasal oxytocin . Rats were tested in a within-subjects Latin-square design with the following combinations of intraperitoneal and intranasal treatments: intraperitoneal vehicle with intranasal vehicle , intraperitoneal vehicle with intranasal oxytocin ( 1 mg/kg/20 μl ) , intraperitoneal L-371 , 257 ( 5 mg/kg/ml ) with intranasal vehicle , and intraperitoneal L-371 , 257 ( 5 mg/kg/ml ) with intranasal oxytocin ( 1 mg/kg/20 μl ) . Operant training and induction of dependence were conducted , as described above . To test the effect of central versus peripheral oxytocin receptor agonism on dependence-induced alcohol drinking , we administered centrally or peripherally a long-acting , large molecule that does not penetrate the blood-brain barrier ( PF-06655075 ) . For central administration , 7 rats were surgically implanted with a guide cannula to allow intracerebroventricular administration of PF-06655075 . These rats were previously used to test the peripheral oxytocin receptor antagonist combined with intranasal oxytocin administration ( data shown in Fig 3B ) . The PF-06655075 dose of 30 μg was selected for intracerebroventricular administration to match the highest dose of oxytocin administered by the same route , a dose that blocked drinking in alcohol-dependent rats . For peripheral administration , the rats were administered 1 mg/kg of PF-06655075 subcutaneously . This dose was selected taking into account the high plasma protein binding of PF-06655075 ( i . e . , resulting in lower unbound concentrations than oxytocin ) as well as its oxytocin receptor binding Ki . Subcutaneous administration of 1 mg/kg of PF-06655075 versus 1 mg/kg of oxytocin would be expected to produce comparable receptor occupancy ( 94 . 2% versus 99 . 7% at Cmax; see S1 Text for calculations based on data of Modi and colleagues [26] ) . Therefore , 1 mg/kg of subcutaneous PF-06655075 was expected to recapitulate the putative peripheral binding of oxytocin . This hypothesis was based on the observation that lower doses of intraperitoneal oxytocin were sufficient to block drinking in alcohol-dependent rats in the present study . Specifically , 0 . 25 mg/kg of intraperitoneal oxytocin reduced alcohol drinking in dependent rats to a similar extent as 30 μg of intracerebroventricular oxytocin . Finally , this systemic dose of 1 mg/kg PF-06655075 has been reported to reduce fear behavior potentially via anti-sympathetic action in the periphery [26] . Dependent and nondependent rats were deeply anesthetized with isoflurane followed by rapid decapitation and immediate removal of the brain into an ice-cold high-sucrose brain slice cutting solution ( sucrose 206 mM; KCl 2 . 5 mM; CaCl2 0 . 5 mM; MgCl2 7 mM; NaH2PO4 1 . 2 mM; NaHCO3 26 mM; glucose 5 mM; HEPES 5 mM [pH 7 . 4] ) . Coronal slices ( 300 to 400 μm ) containing the CeA were continuously superfused ( flow rate of 2 to 4 ml/min ) with 95% O2/5% CO2 equilibrated aCSF of the following composition: NaCl 130 mM , KCl 3 . 5 mM , NaH2PO4 1 . 25 mM , MgSO4·7H2O 1 . 5 mM , CaCl2 2 . 0 mM , NaHCO3 24 mM , and glucose 10 mM . Recordings were performed in neurons from the medial subdivision of the CeA . Each experimental group contained neurons from a minimum of 3 rats . GABAergic activity was pharmacologically isolated with DNQX , DL-AP5 , and CGP . All drugs were applied by bath superfusion . We recorded with sharp micropipettes filled with 3M KCl and evoked GABAergic inhibitory postsynaptic potentials ( eIPSPs ) by stimulating locally within the medial subdivision of CeA through a bipolar electrode . Neurons were held near their resting membrane potential ( −82 . 4 ± 0 . 8 mV ) . We performed an input–output ( I/O ) protocol consisting of a range of 5 current stimulations , starting at the threshold current required to elicit an eIPSP , up to the strength required to elicit the maximum subthreshold amplitude . The middle stimulus intensity was used to monitor drug-induced changes throughout the duration of the experiment . Paired-pulse ratio ( PPR ) was performed at the stimulus intensity giving approximately 50% of the maximal amplitude determined in the I/O protocol . Whole-cell voltage-clamp recordings of GABAergic spontaneous inhibitory postsynaptic currents ( sIPSCs ) and miniature inhibitory postsynaptic currents ( mIPSCs ) were from visualized CeA neurons clamped at −60 mV for the duration of the recordings . Patch pipettes ( 3 to 6 MΩ ) were filled with an internal solution composed of the following ( in mM ) : 145 KCl , 0 . 5 EGTA , 2 MgCl2 , 10 HEPES , 2 Na-ATP , and 0 . 2 Na-GTP . In all experiments , cells with a series resistance greater than 25 MΩ were excluded from analysis , and series resistance was continuously monitored during gap-free recording with a 10-mV pulse . Cells in which series resistance changed more than 25% during the course of the experiment were excluded from analysis . All measures were performed prior to ( baseline ) and during drug application ( details in S1 Text ) . Results are presented as the mean ± standard error of the mean . The level of significance was established as p < 0 . 05 . Statistical analyses were performed in Prism 6 ( Graphpad Software , Inc . , La Jolla , CA ) . Behavioral data were analyzed with one-way ( Dose , Session , or Treatment ) repeated-measures analysis of variance ( R . M . ANOVA ) , two-way ( Group × Dose ) R . M . ANOVA , or by paired-samples t test . The Holms-Sidak test was used for post hoc comparisons . Electrophysiology data were analyzed with two-way ANOVA ( Group × Concentration or Group × Treatment ) followed by Bonferroni post hoc comparisons , one-sample t test , or independent-samples t test , as appropriate .
Passive exposure to alcohol vapor has been demonstrated to cause somatic signs of dependence in alcohol withdrawal as well as a dysregulation of reward and stress systems . The cardinal feature of the model is the increased consumption and motivation for alcohol exhibited by alcohol-dependent rats when allowed to perform an operant response for access to alcohol [3] . Prior to pharmacological testing , alcohol-dependent rats exposed to chronic-intermittent alcohol vapor exhibited significantly enhanced consumption of alcohol in comparison to nondependent rats that were exposed to air in their home cage throughout the study ( S1 Fig ) . Oxytocin abolished the difference in alcohol drinking between dependent and nondependent rats at doses of ≥ 0 . 25 mg/kg ( Fig 1 ) . A 2 × 5 ( Group × Dose ) R . M . ANOVA yielded a significant Group × Dose interaction ( F4 , 72 = 7 . 98 , p < 0 . 0001 ) . Post hoc analyses indicated that dependent rats self-administered significantly more alcohol than the nondependent rats following 0 ( p < 0 . 0001 ) and 0 . 125 mg/kg ( p < 0 . 001 ) oxytocin doses , but this difference disappeared at higher doses . Responding was significantly lowered at the 0 . 25 , 0 . 5 , and 1 mg/kg doses ( all p < 0 . 0001 ) in dependent rats compared with the vehicle condition , whereas only the highest dose of 1 mg/kg significantly lowered lever pressing for alcohol in nondependent rats ( p < 0 . 05 ) . Water intake data during all tests are presented in S2 Fig . Oxytocin ( 0 . 125 or 0 . 25 mg/kg ) selectively eliminated the increased motivation for alcohol in dependent rats ( Fig 1B ) . A 2 × 3 ( Group × Dose ) R . M . ANOVA yielded a significant Group × Dose interaction ( F2 , 36 = 3 . 59 , p < 0 . 05 ) . The difference in PR breakpoint between groups following vehicle treatment was significant ( p < 0 . 05 ) . When treated with either 0 . 125 or 0 . 25 mg/kg doses of oxytocin , dependent rats no longer differed from nondependent rats . Oxytocin ( 0 . 125 and 0 . 25 mg/kg ) significantly reduced PR breakpoint for alcohol in dependent rats ( all p < 0 . 01 ) , whereas responding in the nondependent group was not altered by oxytocin . Intranasal oxytocin dose-dependently decreased alcohol drinking in dependent rats without affecting drinking in nondependent rats ( Fig 1C ) . The 2 × 4 ( Group × Dose ) R . M . ANOVA yielded a significant Group × Dose interaction ( F3 , 54 = 14 . 18 , p < 0 . 0001 ) . Dependent rats drank more alcohol than nondependent rats treated with saline , 0 . 25 , and 0 . 5 mg/kg oxytocin ( all p < 0 . 001 ) . This difference was no longer observed following treatment with 1 mg/kg oxytocin . Alcohol drinking in dependent rats significantly decreased across the doses of oxytocin compared to the saline condition ( all p < 0 . 0001 ) , whereas drinking in the nondependent group was not significantly altered by oxytocin . On the PR test ( Fig 1D ) , two-way R . M . ANOVA yielded a significant Group × Dose interaction ( F2 , 36 = 4 . 27 , p < 0 . 05 ) . Following saline treatment , dependent rats had a significantly higher breakpoint for alcohol than nondependent rats ( t54 = 4 . 50 , p < 0 . 001 ) . This difference was no longer observed following 0 . 5 or 1 mg/kg oxytocin administration . Oxytocin ( 1 mg/kg ) reduced PR breakpoint in the dependent group ( p < 0 . 001 ) without altering the behavior of nondependent rats . Dependent and nondependent rats were not significantly different in their spontaneous locomotion or grooming behavior . Intraperitoneal oxytocin ( 0 . 25 mg/kg ) significantly decreased locomotion in both dependent and nondependent rats ( Fig 2A; Dose: F1 , 10 = 14 . 63 , p < 0 . 01; post hoc tests all p < 0 . 05 ) , whereas intranasal oxytocin ( 1 mg/kg ) had no effect in either group . Intraperitoneal and intranasal oxytocin treatments did not affect grooming behavior ( which remained minimal throughout testing; Fig 2A inset ) . Dependent and nondependent rats did not significantly differ in their rotarod performance , and this performance was not significantly altered by intraperitoneal or intranasal oxytocin treatments ( Fig 2B ) . Intraperitoneal oxytocin reduced saccharin ( t6 = 2 . 80 , p < 0 . 05 ) and maltodextrin ( t6 = 3 . 20 , p < 0 . 05 ) consumption , whereas intranasal oxytocin had no effect ( Fig 2C ) . Baseline sessions conducted between intracerebroventricular oxytocin test sessions did not significantly differ , so they were combined for analysis ( Fig 3A ) . All doses of intracerebroventricular oxytocin reduced alcohol consumption in dependent rats ( Dose: F3 , 15 = 10 . 25 , p < 0 . 001; post hoc tests: all p < 0 . 05 ) . Intranasal oxytocin treatment significantly reduced drinking in dependent rats whether the rats were pretreated with either vehicle ( VEH + VEH versus VEH + OXT ) or with the peripherally restricted oxytocin receptor antagonist L-371 , 257 ( L-371 , 257 + VEH versus L-371 , 257 + OXT; Treatment: F3 , 33 = 10 . 73 , p < 0 . 0001; post hoc tests: all p < 0 . 05 ) . L-371 , 257 did not significantly alter drinking by itself ( VEH + VEH versus L-371 , 257 + VEH ) nor did it reverse the ability of intranasal oxytocin to reduce alcohol drinking in dependent rats ( VEH + OXT versus L-371 , 257 + OXT; Fig 3B ) . Baseline sessions conducted between test sessions with intracerebroventricular administration of PF-06655075 did not significantly differ and thus were combined for analysis ( Fig 3C ) . Intracerebroventricular administration of PF-06655075 , which does not cross the blood-brain barrier and thus was expected to not diffuse to the periphery , significantly reduced responding for alcohol relative to baseline responding and relative to vehicle ( Treatment: F2 , 12 = 42 . 25 , p < 0 . 0001; post hoc tests: all p < 0 . 0001 ) . Vehicle administration did not significantly alter responding relative to baseline ( Fig 3C ) . Systemic administration of PF-06655075 ( expected to not reach the brain ) did not significantly alter responding for alcohol relative to baseline responding or the vehicle condition nor did the vehicle itself significantly alter responding relative to baseline ( Fig 3D ) . We recorded from neurons in the medial subdivision of the CeA , using sharp intracellular whole cell configuration for locally evoked inhibitory GABAergic eIPSP and whole-cell patch-clamp configuration for GABAergic postsynaptic currents . We found no difference in input resistance of neurons from nondependent ( 154 . 4 ± 11 . 4 MΩ ) and dependent ( 159 . 7 ± 7 . 6 MΩ ) rats and no difference in spike frequency ( S3 Fig ) between groups . Baseline GABAergic eIPSP amplitudes stimulated locally within the medial subdivision of CeA were not different between nondependent ( 9 . 7 ± 0 . 7 mV ) and dependent ( 9 . 9 ± 0 . 7 mV ) animals ( S4 Fig ) . No differences were observed in PPR of eIPSPs at 100 ms interstimulus interval in nondependent ( 1 . 06 ± 0 . 08 ) and dependent ( 1 . 01 ± 0 . 09 ) neurons . In CeA neurons of nondependent rats , 100 nM oxytocin ( 10 to 15 min ) did not alter eIPSP amplitudes , whereas 500 nM and 1 , 000 nM significantly decreased amplitudes to 83 . 4% ± 4 . 9% ( t10 = 3 . 40 , p < 0 . 01 ) and 74 . 1% ± 7 . 7% ( t4 = 3 . 37 , p < 0 . 05 ) of baseline , respectively ( Fig 4B ) . Notably , in dependent rats , oxytocin at all 3 concentrations did not affect eIPSPs . A two-way ANOVA ( Group × Concentration ) yielded a significant Group effect ( F1 , 44 = 4 . 55 , p < 0 . 05 ) . Oxytocin did not alter input resistance of nondependent ( baseline: 144 . 3 ± 13 . 8 MΩ , oxytocin: 148 . 3 ± 13 . 7 MΩ ) or dependent ( baseline: 148 . 2 ± 10 . 6 MΩ , oxytocin: 141 . 8 ± 12 . 9 MΩ ) CeA neurons , suggesting that oxytocin effects on eIPSP amplitudes were not due to excitability changes but due to decreased GABAergic transmission . Oxytocin ( 500 nM ) significantly increased the PPR of eIPSPs ( baseline PPR: 0 . 98 ± 0 . 11; oxytocin PPR: 1 . 35 ± 0 . 14; t10 = 2 . 56 , p < 0 . 05 ) in nondependent rats , suggesting a possible decrease in the presynaptic release of GABA ( as changes in PPR are inversely related to changes in release ) [46] . Oxytocin did not alter PPR in dependent rats ( baseline PPR: 1 . 17 ± 0 . 22 , oxytocin PPR: 1 . 03 ± 0 . 07 ) . Acute alcohol ( 44 mM; maximal dose [47] ) application significantly increased eIPSP amplitudes in CeA of both nondependent ( to 140 . 8% ± 8 . 5% of baseline; t7 = 4 . 79 , p < 0 . 01 ) and dependent rats ( to 133 . 9% ± 13 . 4% of baseline; t6 = 2 . 53 , p < 0 . 05; Fig 4C ) , indicating a lack of tolerance for the acute effects of alcohol on stimulated GABAergic signaling , as previously reported [47] . Additionally , alcohol did not alter input resistance of nondependent ( baseline: 169 . 0 ± 19 . 2 MΩ , alcohol: 167 . 6 ± 15 . 8 MΩ ) or dependent ( baseline: 167 . 6 ± 10 . 4 MΩ , alcohol: 161 . 4 ± 11 . 4 MΩ ) neurons . Because oxytocin also binds to vasopressin receptors and there is a CeA subpopulation of neurons sensitive to vasopressin binding ( specifically , via the V1a but not V1b vasopressin receptor ) , we always preapplied the selective vasopressin receptor 1A antagonist TMA ( 500 nM ) to isolate specific oxytocin receptor-mediated actions [34 , 48] . TMA had no effect on eIPSP amplitudes ( nondependent: 8 . 51 ± 1 . 08 mV and dependent: 9 . 68 ± 1 . 31 mV without TMA ) and did not affect the magnitude of the alcohol-induced increase of eIPSPs in both nondependent ( to 148 . 6% ± 10 . 5% of baseline; t5 = 4 . 64 , p < 0 . 01 ) and dependent rats ( 129 . 8% ± 10 . 6% of baseline; t6 = 2 . 80 , p < 0 . 05; S5 Fig ) . To determine potential interactions between oxytocin and acute alcohol , in most of the neurons that received 500 nM oxytocin , we applied alcohol in the presence of oxytocin . In this subset of neurons ( 7 of 9 ) from nondependent slices , coapplication of alcohol only induced a moderate , nonsignificant increase of eIPSP amplitudes ( oxytocin: 83 . 4% ± 4 . 9%; oxytocin + alcohol: 101 . 7% ± 8 . 3% of baseline; Fig 4C ) . In the subset of neurons ( 12 of 15 ) from dependent slices , alcohol did not further increase eIPSPs ( oxytocin: 107 . 6% ± 9 . 3%; oxytocin + alcohol: 123 . 0% ± 16 . 8% of baseline; Fig 4C ) , suggesting a blockade of oxytocin-induced decreased GABA transmission in dependent rats . A two-way ANOVA ( Group × Treatment ) yielded a significant main effect of Treatment ( F2 , 53 = 6 . 33 , p < 0 . 01 ) , and a Bonferroni post hoc test indicated a significant difference between the effects of alcohol and oxytocin ( t53 = 3 . 35 , p < 0 . 01 ) . Similarly , oxytocin at 100 and 1 , 000 nM blunted the alcohol-induced facilitation of eIPSPs in both nondependent ( 84 . 2% ± 11 . 0% and 82 . 1% ± 13 . 2% of baseline , respectively ) and dependent slices ( 124 . 0% ± 18 . 6% and 125 . 5% ± 15 . 3% of baseline , respectively ) . Overall , oxytocin blunted the alcohol-induced increase of GABA transmission ( see Fig 4C ) . To further investigate the pre- versus postsynaptic action of oxytocin on GABA signaling , we performed whole-cell patch-clamp recordings of sIPSCs and mIPSCs in CeA neurons . Generally , changes in IPSC frequency reflect altered transmitter release , and changes in amplitude or kinetics reflect alterations in postsynaptic GABAA receptor sensitivity . However , altered amplitude may also reflect a mix of pre- and postsynaptic effects [49 , 50] . We first investigated sIPSCs , finding no significant differences between nondependent and dependent rats in sIPSC frequency ( 1 . 1 ± 0 . 2 and 0 . 8 ± 0 . 1 Hz , respectively ) , amplitude ( 82 . 0 ± 8 . 4 and 66 . 6 ± 5 . 8 pA , respectively ) , rise time ( 2 . 6 ± 0 . 1 and 2 . 8 ± 0 . 1 ms , respectively ) , or decay time ( 8 . 0 ± 0 . 8 and 9 . 4 ± 1 . 0 ms , respectively ) . We next used the vasopressin receptor 1A antagonist TMA and found no effects of TMA alone in either nondependent or dependent rats ( S6 Fig ) , as observed for evoked GABAergic responses . In CeA neurons of nondependent rats , application of 500 nM oxytocin significantly decreased the amplitude of sIPSCs to 85 . 2% ± 6 . 7% of baseline ( t11 = 2 . 22 , p < 0 . 05 ) and increased rise times to 110 . 5% ± 4 . 7% of baseline ( t11 = 2 . 24 , p < 0 . 05 ) with no changes in frequency or decay times ( Fig 5A ) , indicating mainly postsynaptic actions of oxytocin to decrease GABAA receptor function . In dependent rats ( Fig 5A ) , oxytocin also significantly decreased sIPSC amplitude to 83 . 1% ± 5 . 0% of baseline ( t8 = 3 . 37 , p < 0 . 01 ) , with no changes in sIPSC frequency or kinetics . We next investigated mIPSCs in the presence of the sodium channel blocker TTX to isolate action potential independent currents . Generally , mIPSC analysis reveals specific effects of drugs on vesicular release of GABA , which results from exocytosis of neurotransmitter containing vesicles in a manner independent of action-potential–induced release mechanisms . In addition , sIPSCs and mIPSCs likely result from distinct presynaptic neurotransmitter release mechanisms , e . g . , vesicle fusion machinery , spatial segregation of the vesicles and/or vesicle populations , and synaptic vesicle pool dynamics [51] . Similar to sIPSCs , we did not find any baseline differences between nondependent and dependent rats in mIPSC frequency ( 0 . 8 ± 0 . 1 and 0 . 9 ± 0 . 3 Hz , respectively ) , amplitude ( 53 . 2 ± 4 . 0 and 60 . 7 ± 12 . 3 pA , respectively ) , rise time ( 2 . 5 ± 0 . 1 and 2 . 5 ± 0 . 1 ms , respectively ) , or decay time ( 5 . 5 ± 0 . 6 and 6 . 2 ± 0 . 8 ms , respectively ) . Oxytocin did not alter mIPSC frequency , amplitude , or kinetics for neurons from nondependent or dependent rats ( Fig 5B ) , suggesting no effect of oxytocin on this form of GABA release in the medial subdivision of CeA . We next examined the interaction of oxytocin and acute alcohol on CeA sIPSCs in nondependent and dependent rats . In 8 of the 12 CeA neurons from nondependent rats that received 500 nM oxytocin , and 7 of the 9 neurons from dependent rats , we coapplied alcohol ( 44 mM ) and compared effects between groups on sIPSC measures ( Fig 5D–5G ) . A two-way ANOVA ( Group × Treatment ) yielded a significant effect of Treatment ( F1 , 13 = 4 . 87 , p < 0 . 05 ) and a Group × Treatment interaction ( F1 , 13 = 9 . 84 , p < 0 . 01 ) for sIPSC frequency ( Fig 5D ) . The Bonferroni post hoc comparisons indicated that alcohol with oxytocin significantly increased sIPSC frequency compared with oxytocin alone in nondependent rats ( t13 = 3 . 91 , p < 0 . 01 ) . Therefore , oxytocin blocked the alcohol-induced increase in GABA release only in CeA neurons of alcohol-dependent rats . Additionally , a two-way ANOVA ( Group × Treatment ) yielded a significant Group × Treatment interaction ( F1 , 13 = 20 . 88 , p < 0 . 001 ) for sIPSC decay time ( Fig 5G ) . The Bonferroni post hoc comparisons indicated that alcohol with oxytocin significantly increased decay times compared with oxytocin alone in nondependent rats ( t13 = 4 . 86 , p < 0 . 001 ) . As previously shown [52] , we found that in nondependent and dependent rats , acute alcohol alone significantly increased sIPSC frequency to 145 . 5% ± 13 . 4% of baseline ( t5 = 3 . 40 , p < 0 . 05 ) and 131 . 4% ± 8 . 6% of baseline ( t9 = 3 . 65 , p < 0 . 01 ) , respectively ( Fig 5H ) , with no changes in sIPSC amplitude or kinetics , suggesting increased action-potential–dependent GABA release . We compared the effects of alcohol alone with the effects of alcohol and oxytocin using two-way ANOVAs ( Group × Treatment ) for each sIPSC measure ( Fig 5H–5K ) . We found a significant main effect of Treatment ( F1 , 27 = 9 . 00 , p < 0 . 01 ) and a significant Group × Treatment interaction ( F1 , 27 = 5 . 61 , p < 0 . 05 ) for sIPSC rise times ( Fig 5J ) . The Bonferroni post hoc comparisons indicated a significant difference in the effects of alcohol with oxytocin versus alcohol alone for nondependent neurons . Finally , we found that the selective oxytocin receptor antagonist desGly-NH2-d ( CH2 ) 5[D-Tyr2 , Thr4]OVT alone did not alter sIPSCs ( S7 Fig ) , suggesting no basal activity of these receptors on spontaneous GABAergic transmission in dependent rats . Notably , in neurons from dependent rats , oxytocin in the presence of OTA had no effect on sIPSCs when compared to baseline ( Fig 5L and 5M ) . In a subset of these neurons ( 8 out of 11 ) , we applied alcohol in the presence of both OTA and oxytocin , resulting in a significant increase in sIPSC frequency ( t7 = 3 . 01 , p < 0 . 05; Fig 5M ) . We found a similar effect of OTA to rescue the alcohol effect on eIPSPs from nondependent neurons ( t7 = 2 . 70 , p < 0 . 05; S8 Fig ) . These results confirm that oxytocin receptor antagonism blocked postsynaptic effects of oxytocin and restored the acute alcohol-induced facilitation of GABA release in the CeA of alcohol-dependent rats .
Oxytocin delivered via intraperitoneal , intranasal , and intracerebroventricular routes blocked the enhanced motivation for alcohol drinking that developed in alcohol-dependent rats . Intraperitoneal and intranasal oxytocin at certain doses blocked the increased alcohol consumption and motivation for alcohol in dependent rats , without impacting these behaviors in nondependent rats . Intranasal oxytocin did not disrupt spontaneous locomotion , grooming behavior , motor coordination , or consumption of sweet or caloric palatable solutions . Central administration of oxytocin produced a dose-dependent reduction in alcohol drinking in dependent rats , and this effect was replicated by central administration of an oxytocin receptor agonist ( PF-06655075 ) that does not cross the blood-brain barrier and therefore was expected to not diffuse to the periphery . However , peripheral administration of PF-06655075 ( expected to not reach the brain ) had no effect . Additionally , peripheral administration of an oxytocin receptor antagonist that does not cross the blood-brain barrier did not reverse the ability of intranasal oxytocin to reduce alcohol drinking in dependent rats . Together , the data suggest a central mechanism for oxytocin’s actions on alcohol drinking . Ex vivo electrophysiology recordings indicated that oxytocin inhibits spontaneous action-potential–dependent GABAergic transmission in CeA slices from both dependent and nondependent rats . However , oxytocin’s ability to dose-dependently reduce evoked network GABAergic activity was absent in slices from dependent rats . Furthermore , oxytocin blunted acute alcohol-induced facilitation of evoked GABAergic responses in both nondependent and dependent rats but blocked alcohol-induced facilitation of spontaneous action-potential–dependent GABA release only in CeA slices from alcohol-dependent rats , suggesting differential GABA network effects in nondependent and dependent rats produced by oxytocin . Systemic oxytocin blocked the enhanced motivation for alcohol observed in dependent rats , indexed by escalated alcohol drinking and increased breakpoint in a PR test , at doses that did not change the behavior of nondependent rats . These findings are consistent with previous reports that oxytocin can decrease alcohol drinking in nondependent mice and rats [20 , 21] and that oxytocin is particularly effective in decreasing cue-induced reinstatement of alcohol seeking in postdependent rats compared with nondependent rats [17] . The present findings are unique in several ways from previous work in rats [17] . They demonstrate the following: ( 1 ) Oxytocin can reduce alcohol drinking and the enhanced motivation to “work” for alcohol in currently alcohol-dependent rats . This difference is critical , because it provides a novel indication for oxytocin’s potential therapeutic value . Alcohol drinking during acute ( i . e . , currently alcohol-dependent rats ) and protracted abstinence ( i . e . , alcohol seeking in rats with a history of alcohol dependence ) are proposed to model distinct phases and aspects of alcohol use disorder , with distinct underlying neurocircuitry [2] . The results of the present study indicate that oxytocin may have the potential to reduce heavy drinking in moderate to severe alcohol use disorder . ( 2 ) Oxytocin’s anti-drinking effects in rats can be achieved by intranasal administration , using a device designed to allow noninvasive drug delivery across the blood-brain barrier [53] . ( 3 ) Intranasal administration reduced alcohol consumption and motivation for alcohol in dependent rats without causing nonspecific locomotor , grooming , motor coordination , and consumption of nonalcoholic sweet or caloric palatable solutions , suggesting that oxytocin’s effects on alcohol drinking are specific to the pharmacological effects of alcohol . ( 4 ) The present study provides evidence that peripheral receptors did not make a major contribution to the effect of intranasal oxytocin . More specifically , intracerebroventricular administration of a large molecule ( PF-06655075 ) that does not cross the blood-brain barrier or intracerebroventricular administration of oxytocin significantly reduced alcohol drinking in dependent rats . However , PF-06655075 administered systemically at a dose matching the highest tested dose of systemic oxytocin ( 4 times the dose sufficient to block drinking in alcohol-dependent rats ) did not have an effect . Therefore , oxytocin’s effect of reducing alcohol drinking in the present model of alcohol dependence is likely centrally mediated . Further supporting this hypothesis , application of the nonbrain penetrant antagonist L-371 , 257 did not reverse the ability of intranasal oxytocin to reduce alcohol drinking in alcohol-dependent rats , suggesting that intranasal administration of oxytocin reduces alcohol drinking in dependent rats independent of oxytocin receptor binding in the periphery . Despite the precise mechanism remaining unknown , there is accumulating evidence that peripherally applied oxytocin can cross the blood-brain barrier in adult rodents . As suggested by others [22 , 23] , there is likely a mechanism for direct transport of intranasally applied oxytocin across the blood-brain barrier in adult rodents . Tanaka and colleagues applied oxytocin by intravenous , intraperitoneal , and intranasal routes in rats to determine the peripheral and central levels of oxytocin resulting from each route of administration [23] . The authors used the intraperitoneal administration data to determine the amount of centrally detected oxytocin that can be expected to result from peripherally circulating levels . They used this information to account for centrally detected oxytocin following intranasal administration that may be the result of oxytocin transferring first from the nasal epithelium into the peripheral circulation , then into the brain . It was concluded that , under the conditions of intranasal administration , >95% of the oxytocin detected in the brain is expected to result from direct transport across the blood-brain barrier [22 , 23] . Indeed , oxytocin detected in the brain was higher following intranasal administration compared with intravenous or intraperitoneal administration , despite intranasal administration resulting in far lower oxytocin plasma concentrations . Furthermore , oxytocin concentrations were especially high in the olfactory bulb after intranasal administration , suggesting a direct route of entry [23] . Bustion and colleagues demonstrated that radio-labeled oxytocin administered in mice intranasally was detected throughout the brain , i . e . , dissociating exogenously applied deuterated oxytocin from endogenous oxytocin , thus confirming direct entry [24] . Again , particularly elevated levels were detected in the olfactory bulb , suggesting a point of entry at the nasal epithelium . Although intraperitoneal oxytocin administration decreased locomotion in the open field and the consumption of a nonalcoholic , noncaloric sweet solution and a nonalcoholic , unsweetened caloric solution , intranasal administration did not . Neither intraperitoneal nor intranasal oxytocin administration altered grooming behavior in the open field or performance in the rotarod task , indicating that the intraperitoneal effect on locomotion is likely not a result of altered motor coordination . A possible explanation for nonspecific effects of intraperitoneal oxytocin administration is that that oxytocin may have “side effects” that result from binding at peripheral sites ( e . g . , gut , heart , vascular system , and/or vagus nerve ) that disrupt behavior in general and that were induced following intraperitoneal oxytocin administration in the present study . Congruent with this account is the observation that intraperitoneal oxytocin suppressed water consumption during tests for oxytocin’s effect on alcohol consumption . In contrast , oxytocin treatment did not alter water consumption when administered intranasally , intracerebroventricularly , or intranasally in combination with L-371 , 257 , suggesting that peripheral “side effects” were avoided during these tests ( S2 Fig ) . Intraperitoneal administration of oxytocin , unlike the peripherally restricted agonist PF-06655075 , was able to block alcohol drinking in dependent rats , similarly to intranasal oxytocin administration . Although the transfer of peripherally circulating oxytocin to the central compartment is expected to be very limited , a rapid , dramatic spike in plasma-oxytocin concentrations has been noted following intraperitoneal administration that may allow pharmacologically relevant central concentrations of oxytocin to be achieved , especially following application of supraphysiological oxytocin doses , as used in the present study [18 , 20 , 22 , 23 , 54] . A recent study found deuterated oxytocin administered intravenously was detected centrally in rhesus macaques ( i . e . , dissociating exogenous labeled oxytocin from endogenous oxytocin , confirming direct transfer [55] ) . Although intraperitoneal administration of oxytocin induced some side effects in the present study , the action of intraperitoneal oxytocin that causes reduction of alcohol drinking in dependent rats is likely the same central mechanism as engaged following intranasal administration . Nevertheless , the present data suggest that administration by intranasal compared with intraperitoneal route may result in more favorable pharmacokinetics for achieving central over peripheral oxytocin exposure [22 , 23] . To further test the hypothesis of central oxytocin action , we demonstrated that intracerebroventricular infusion of oxytocin and PF-06655075 ( expected to not diffuse to the periphery ) reduced alcohol drinking in dependent rats . Systemic administration of PF-06655075 ( expected to not to cross the blood-brain barrier ) did not affect alcohol intake in dependent rats . Finally , the peripherally restricted oxytocin receptor antagonist L-371 , 257 did not alter the effects of intranasal oxytocin in reducing alcohol drinking in dependent rats . Together , these data suggest that peripheral receptors make a minimal contribution to intranasal oxytocin’s effects on alcohol drinking . Next , we examined the effects of oxytocin on GABAergic transmission in the CeA , a key brain region of dysregulation in alcohol dependence [3 , 56–59] . To gain mechanistic insight into the contribution of the oxytocin system on CeA GABAergic transmission in the context of acute and chronic alcohol , here , we examined both spontaneous action-potential–dependent and evoked GABAergic transmission wherein the network activity is intact , as well as action-potential–independent transmission . We found that oxytocin decreases GABA signaling in the CeA of both dependent and nondependent rats , but its effects varied between different modes of GABAergic transmission . In alcohol-nondependent rats , oxytocin decreased evoked GABA responses by decreasing evoked GABA release , an effect that is no longer observed in dependent rats , suggesting neuroadaptations of the CeA oxytocin system in dependence . In contrast , oxytocin decreased GABAA receptor function in both nondependent and dependent rats . Similar to previous studies in the medial subdivision of CeA [48] , oxytocin did not affect action-potential–independent GABAergic transmission in either group . Evoked GABA responses are generated by delivering a controlled electrical stimulation locally within the CeA , whereas spontaneous events reflect inhibitory signaling across the broader CeA synaptic network . Miniature currents result from spontaneous presynaptic vesicle fusion independent of sodium entry and more precisely determine pre- and postsynaptic drug effects occurring at the terminals . In addition , spontaneous and evoked forms of GABA transmission may represent distinct forms of GABA transmission , resulting from distinct presynaptic neurotransmitter release mechanisms ( e . g . , vesicle fusion machinery , spatial segregation of the vesicles and/or vesicle populations , synaptic vesicle pool dynamics ) [51] . Of note , oxytocin had no effect on mIPSCs or cellular excitability , therefore its effects on sIPSC amplitude and rise time , as well as in eIPSP amplitude and PPR , suggest synaptic effects rather than cell excitability . These synaptic effects likely occur upstream within the local intact GABAergic network that is shared by spontaneous and evoked forms of synaptic transmission , whereas miniature effects are more localized to the specific terminals in the medial subdivision of the CeA where oxytocin receptors may not be present [48] . These considerations are critical and need to be taken into account when comparing with oxytocin electrophysiological effects reported on by others in the lateral subdivision of the CeA [48 , 60 , 61] . In contrast , these lateral CeA studies reported effects of oxytocin in increasing excitability of lateral CeA GABAergic neurons that project to the medial CeA , resulting in an increase in sIPSC frequency and decreased excitability in the medial CeA [48] . Although the lack of oxytocin effect on mIPSCs in the medial CeA is consistent with our results , we observed that oxytocin decreased GABAergic transmission in the medial CeA without changing cellular excitability . There are two likely reasons for these discrepancies . The first is the use of the native peptide oxytocin in our studies as opposed to the selective peptide oxytocin-receptor agonist ( Thr⁴ , Gly⁷ ) -Oxytocin ( TGOT ) [34] . Although we pharmacologically block the predominant vasopressin receptor in the CeA , native versus selective peptide agonists for the oxytocin receptor may exhibit functional selectivity at the oxytocin receptor or off-target effects . The second possibility is the heterogeneity and interconnectivity of GABAergic neurons in the CeA . The medial CeA contains GABAergic neurons that project out of the CeA as well as synapse locally . In one previous study [48] of the TGOT responsive cells , only about half of the TGOT inhibited neurons were excited by vasopressin . Additionally , Viviani and colleagues reported that projection-specific populations of medial CeA GABAergic neurons were unresponsive to TGOT [60] . Therefore , it is possible that the majority of the neurons from which we recorded were from a subpopulation of GABA neurons that did not receive direct innervation of oxytocin-excited lateral CeA GABAergic neurons . Additionally , from our evoked experiments , the electrical stimulation likely excited neurons in the lateral and medial CeA , and our responses were a composite of these effects . Therefore , future experiments will determine specific cell types to understand the distinct oxytocin effects on the different components of the synaptic network . Understanding the physiological actions of oxytocin in the CeA may benefit by comparison to the pro-stress , pro-drinking effects of corticotropin-releasing factor ( CRF ) and the anti-stress , anti-drinking effects of neuropeptide Y ( NPY ) . The pro-stress neuropeptide CRF is elevated in the CeA , likely mediated by glucocorticoid receptors [62 , 63] in alcohol dependence , and infusion of CRF1 or glucocorticoid receptor antagonists in the CeA suppress drinking specifically in alcohol-dependent rats [38 , 62 , 64–66] . In contrast , the anti-stress neuropeptide NPY is decreased in the CeA in alcohol dependence , and intra-CeA infusion of NPY suppresses alcohol drinking specifically in dependent rats [67 , 68] . We have reported that CRF , similar to alcohol , robustly enhances GABAergic transmission in CeA of rats [38 , 66 , 68] . However , NPY and nociceptin decreased presynaptic GABA release in CeA , normalizing the enhanced GABAergic transmission observed in alcohol dependence [68] . Both NPY and nociceptin also block the presynaptic acute alcohol-induced facilitation of CeA GABA release , whereas oxytocin blocked GABA release in dependent rats only . We previously reported that alcohol may act presynaptically through voltage-gated calcium channels to increase action-potential–dependent GABA release in the CeA and that alcohol dependence disrupts this mechanism , shifting alcohol actions to CRF1 receptors [52] . This may explain why oxytocin has no effect on eIPSPs in dependent animals if alcohol dependence dysregulates calcium channels and oxytocin effects work through action-potential–induced calcium-dependent mechanisms . Additionally , oxytocin , via action at oxytocin receptors , may interfere with the CRF1 receptor-mediated mechanism in dependent rats to blunt the effects of alcohol . Therefore , oxytocin’s actions in the CeA are similar but not identical to anti-stress neuropeptides like NPY [67] . Oxytocin’s actions are particularly complex given the pre- and postsynaptic interactions with alcohol . Future studies will be needed to investigate the functional role of oxytocin in its interactions with other pro-stress and anti-stress systems in the CeA . One important limitation of the present work is that the studies were conducted exclusively in male rats , especially considering sex-specific oxytocin distribution and behavioral mediation [69 , 70] and sex differences in alcohol drinking [35 , 71] . It will be critical to test the effect of oxytocin in female subjects to determine the extent to which the present conclusions can be generalized to females . Second , the behavior and electrophysiology experiments were performed in Wistar and Sprague Dawley rats , respectively . Note that , in general , we find the different rat strains to be better suited to one set of experiments or the other ( Wistar for behavior and Sprague-Dawley for electrophysiology ) . There may be strain differences in the sensitivity of rats to oxytocin treatment ( e . g . , MacFadyen and colleagues reported that 0 . 1 mg/kg oxytocin reduced drinking in nondependent Sprague-Dawley rats [18] ) . However , we included internal controls in behavioral and electrophysiological experiments and have previously shown that baseline CeA synaptic activity and effects of drugs are similar between these strains [72] . As such , we do not find problems conceptually linking the data sets . Lastly , although we tested the oxytocin effects on CeA GABAergic signaling in the presence of a V1a antagonist to rule out the contribution of CeA vasopressin receptors in oxytocin’s effects on CeA GABAergic signaling , the contribution of vasopressin receptors to the observed behavioral effects were not evaluated in the present study . There is evidence suggesting a role of central vasopressin receptors in social and aggressive behaviors , and affective states , including anxiety-like behavior [54] . We would expect that agonism of these receptors by oxytocin would have pro-stress , pro-drinking effects rather than contributing to a reduction in alcohol drinking in dependent rats in this model . Consistent with this hypothesis , Edwards and colleagues reported that V1b antagonism in dependent rats decreased their alcohol drinking [73] . Similar effects have also been reported in Sardinian alcohol-preferring rats [74] and alcohol-dependent humans [75] . In summary , the present study reports that oxytocin via central rather than peripheral action reduced alcohol consumption and motivation for alcohol in an animal model of alcohol dependence . These effects may result from dependence-induced alterations in oxytocin and GABA systems in reward- and stress-related extrahypothalamic brain regions such as the extended amygdala . Intranasal oxytocin administration appears to be advantageous in terms of specificity in reducing alcohol-motivated behavior compared with intraperitoneal administration . Therefore , the present work highlights the oxytocin system as a target for understanding the plasticity of brain stress and anti-stress systems in the etiology of alcohol use disorders . Targeting this system , possibly by intranasal administration , may provide novel pharmaceutical interventions for the treatment of alcohol use disorder . | Alcohol use disorder is characterized by a reduction in reward function and gain in stress function . The neuropeptide oxytocin is involved in the regulation of both reward and stress systems . We tested the hypothesis that oxytocin administration could normalize the dysregulations that occur in alcohol dependence and thereby reduce alcohol drinking in dependent rats . We demonstrated that oxytocin administered systemically , intranasally , or into the brain blocked the enhanced drinking exhibited by alcohol-dependent rats . These effects were demonstrated to be centrally rather than peripherally mediated . Oxytocin blocked this enhanced alcohol drinking at doses that did not alter non–alcohol-related behaviors or alcohol drinking in nondependent rats , suggesting the effect was specific to alcohol drinking in alcohol dependence . Ex vivo electrophysiological recordings in the central nucleus of the amygdala ( CeA; a key brain region in the network of dysregulations induced by alcohol dependence ) indicated that oxytocin blocked the facilitatory effects of alcohol on inhibitory signaling in dependent but not nondependent rats . These results provide compelling evidence that dysregulation in the endogenous oxytocin system is of functional relevance to a mechanistic understanding of alcohol use disorder . | [
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| 2019 | Oxytocin blocks enhanced motivation for alcohol in alcohol dependence and blocks alcohol effects on GABAergic transmission in the central amygdala |
Influenza A viruses are important pathogens that cause acute respiratory diseases and annual epidemics in humans . Macrophages recognize influenza A virus infection with their pattern recognition receptors , and are involved in the activation of proper innate immune response . Here , we have used high-throughput subcellular proteomics combined with bioinformatics to provide a global view of host cellular events that are activated in response to influenza A virus infection in human primary macrophages . We show that viral infection regulates the expression and/or subcellular localization of more than one thousand host proteins at early phases of infection . Our data reveals that there are dramatic changes in mitochondrial and nuclear proteomes in response to infection . We show that a rapid cytoplasmic leakage of lysosomal proteins , including cathepsins , followed by their secretion , contributes to inflammasome activation and apoptosis seen in the infected macrophages . Also , our results demonstrate that P2X7 receptor and src tyrosine kinase activity are essential for inflammasome activation during influenza A virus infection . Finally , we show that influenza A virus infection is associated with robust secretion of different danger-associated molecular patterns ( DAMPs ) suggesting an important role for DAMPs in host response to influenza A virus infection . In conclusion , our high-throughput quantitative proteomics study provides important new insight into host-response against influenza A virus infection in human primary macrophages .
Influenza A viruses are negative-stranded RNA viruses that are capable of infecting a variety of avian and mammalian species . These viruses are responsible for the annual epidemics that cause severe illnesses in millions of people worldwide . Influenza A virus and secondary bacterial infections can cause lethal pneumonia and encephalopathy especially in elder people . Host defence against influenza A virus infection is initiated by the innate immune system . The principal effector cells involved in innate immunity are macrophages , and dendritic cells ( DC ) that kill microbes through phagocytosis , present antigens to T cells , and produce cytokines . These innate immune responses are essential for the development of later adaptive immune responses , which provide specific cell-mediated and humoral protection , and are often necessary for a complete clearance of infection . In viral infections innate immune responses are initiated when viruses or their genetic material is recognized by cellular pattern recognition receptors ( PRRs ) of the innate immune system [1] . This recognition results in an interferon ( IFN ) -α/β-mediated antiviral response , as well as activation of pro-inflammatory response and programmed cell death , apoptosis , of infected cells . PRRs involved in activation of antiviral response include Toll-like receptors ( TLRs ) and Retinoic acid-inducible gene I-like receptors ( RLRs ) and the coordinated activation of TLRs and RLRs results in proper activation of antiviral response during influenza A virus infection [2] , [3] . Influenza A virus infection of human macrophages also results in production of pro-inflammatory cytokines IL-1β and IL-18 [4] , [5] . Both of these cytokines have to be cleaved by cysteine protease caspase-1 to generate the secreted , biologically active forms of these cytokines [6] . Caspase-1 is in turn activated in a cytosolic protein complex called inflammasome [7] . The inflammasomes consist of an adapter molecule called apoptosis-associated speck-like protein containing a caspase recruitment domain ( ASC ) and a PRR that belongs to either the nucleotide-binding and oligomerization domain like receptors ( NLRs ) or the PYHIN receptor gene family . Caspase-1 activating structures include NLRP3 , NOD2/NLRP1 , NOD2/NLRP3 , IPAF/NAIP5 , and AIM2 inflammasomes [8] , [9] . Recent studies in experimental mice models have shown a critical role of NLRP3 inflammasome in the host response against influenza A virus infection [10]–[12] . These studies highlight the importance of IL-1β and IL-18 as mediators of host response to influenza A virus infection . However , the exact mechanism of inflammasome activation during viral infection is not known . Proteomics combined with bioinformatics has emerged as an important tool to extract detailed information of cellular signaling mechanisms [13] . With modern mass spectrometry ( MS ) -based approaches it is possible to identify and quantify thousands of proteins from cellular samples , as illustrated by Luber et al . who studied subset-specific viral recognition in dendritic cells [14] . Most of the large scale quantitative proteomics experiments , however , have focused on studying changes in protein abundances in whole cell lysates or individual organelles [14] , [15] . The use of subcellular proteomics provides a deeper insight into cellular events as protein abundancies can be studied on the level of different subcellular compartments and also protein translocations between different cell parts can be detected [16] , [17] . Moreover , pathway and network analyses can provide mechanistic insights by subsequently linking the proteins found to be differentially regulated to the underlying cellular functions and other key players known to be involved in these events . Here , we have used quantitative subcellular proteomics combined with bioinformatics to provide a global view of host-pathogen-interactions during influenza A virus infection of human primary macrophages . We show that viral infection regulates the expression and/or subcellular localization of more than one thousand host proteins at early phases of infection .
For subcellular proteome and secretome analysis ( Fig . 1A ) human primary macrophages were first infected with influenza A virus for 6 h , 12 h and 18 h ( intracellular fractions ) or 6 h , 9 h and 12 h ( secretome ) . After this the cells were fractionated into mitochondrial , cytoplasmic and nuclear fractions , and macrophage growth media were collected for secretome analysis . The enrichment of mitochondrial , cytoplasmic and nuclear proteins into different fractions was confirmed with Western blots ( Fig . 1B ) . Protein identification and quantification from each subcellular fraction and secretome was done using 4plex iTRAQ ( isobaric tag for relative and absolute quantitation ) labeling combined with liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) analysis ( Fig . 1A ) . A total of eight iTRAQ sample sets were analysed , including two biological replicates of mitochondrial , cytoplasmic and nuclear cell fractions as well as secretomes . Additionally , each iTRAQ sample set was analysed twice with LC-MS/MS to improve the quality of protein identifications and quantifications . Based on the LC-MS/MS data we identified 1999 , 1423 , 1230 and 627 proteins from the mitochondrial , cytoplasmic and nuclear cell fractions and secretome , respectively , with false discovery rates ( FDR ) of the sample sets varying from 0 . 5 to 1 . 6% . More than one thousand proteins were identified from more than one cell fractions and altogether we identified 3477 distinct proteins . From the identified proteins , we reliably quantified 2466 proteins , and of these proteins 1321 were differentially expressed in the intracellular fractions ( fold difference ≥1 . 5 or ≤0 . 67 ) and 544 in the secretome ( fold difference ≥3 ) as a result of infection ( Fig . 1C , Tables S1 and S2 ) . To confirm iTRAQ quantification results , Western blot analyses were performed for a selected set of proteins ( Fig . S1A–D ) . To get an overview of our proteomic data the identified and quantified proteins were analysed further using different bioinformatics tools . The proteins were first classified using the gene ontology ( GO ) annotations of their known subcellular locations ( Fig . 1D ) and biological functions ( Fig . 2 , Fig . S2 ) . Most of the known mitochondrial proteins identified in this study were found from the mitochondrial fraction . In addition to mitochondrial proteins , most of the identified lysosomal and endoplasmic reticulum ( ER ) proteins were located in the mitochondrial fraction implying that these subcellular organelles are also enriched in the mitochondrial fraction . Proteins with a known cytosolic localization were most abundant in the cytoplasmic fraction . Many cytosolic proteins , however , also have a nuclear annotation which can be seen in our data as a large number of ‘nuclear’ proteins in the cytoplasmic fraction . In the nuclear fraction , proteins with a nuclear annotation were clearly the most abundant . A large number of differentially expressed proteins were identified from the nuclear cell fraction at 12 and 18 h post-infection ( Fig . 1C ) . In the nuclear fraction 335 and 376 proteins were overexpressed , and 344 and 368 proteins underexpressed at 12 and 18 h post-infection , respectively . The overexpressed proteins contain several mitochondrial , ER , Golgi and cytosolic proteins . Functionally the overexpressed proteins form a heterogeneous group of proteins including several metabolism , gene expression , calcium ion binding , transport and signaling related proteins ( Fig . 2A ) . Here , proteins related with gene expression are mainly nuclear or ribosomal proteins whereas proteins related with metabolism , transport , signaling and calcium ion binding originate from several different subcellular compartments . Proteins that are underexpressed in the nuclear fraction at 12 and 18 h post-infection are mainly known nuclear proteins . Almost 200 of them are involved in gene expression , especially in nucleotide metabolism and mRNA processing ( Fig . 2B ) . Host mRNA splicing machinery has been suggested to be important for influenza virus gene expression [18] , and interestingly , 40 proteins related with nuclear mRNA splicing were underexpressed in our data . Finally , our proteomics data showed that the amount of several nuclear histones increased clearly in the cytoplasmic fraction at 12 and 18 h post-infection implying that there are major changes in the nuclear architecture after influenza A virus infection . The iTRAQ quantitation results and gene ontology analyses show that the number of differentially expressed apoptosis-related proteins increases in macrophages as the infection proceeds ( Fig . 2 , Fig . 3A , Fig . S2 ) . Our proteomics data showed , for example , that the amount of Bax decreases in the cytoplasmic fraction and increases in the mitochondrial fraction , and the amount of cytochrome c decreases in the mitochondrial fraction and increases in the cytoplasmic fraction upon infection ( Fig . 3A ) . These data together with Western blot analysis of cytochrome c ( Fig . S1 ) are well in accordance with classical signs of mitochondrial apoptosis: translocation of Bax onto the mitochondria and leakage of cytochrome c into the cytoplasm . APOPercentage apoptosis assay was used to confirm the progression of apoptotic events in influenza A virus infected macrophages ( Fig . 3B ) . No apoptotic cells were detected from control samples or influenza A virus infected samples at 6 h post-infection . At 12 h after the infection 19% of the cells were apoptotic , and at 18 h after the infection already 73% of the cells were apoptotic demonstrating the rapid progression of apoptosis at later timepoints . It is known that influenza A virus infection triggers the production of IFN-α/β which is followed by the activation of IFN-inducible genes . Accordingly , a clear increase in the expression of several interferon-inducible proteins was seen in the cytoplasmic fraction of influenza A virus infected macrophages ( Table S1 ) . These proteins included three members of the interferon-inducible protein with tetratricopeptide repeat ( IFIT ) family , a group of proteins functioning as antiviral sensors that inhibit protein synthesis through translational arrest . Finally , the expression of several influenza A virus proteins such as hemagglutinin ( HA ) , neuraminidase ( NA ) and nucleoprotein ( NP ) was clearly increased in the cytoplasmic fraction of the infected cells ( Table S1 ) . Several proteins with mitochondrial , lysosomal and ER annotations were upregulated in the cytoplasmic fraction already at 6 h post-infection indicating rapid changes in these compartments upon infection ( Fig . 1D ) . Based on clustering analysis , these proteins belong to a group where the amount of protein in the cytoplasmic fraction increases rapidly at 6 h post-infection and starts to decrease at later time points after infection ( Fig . 3C ) . Surprisingly , the mitochondrial proteins in this group contain several components of the inner mitochondrial membrane including components of the electron transport chain and oxidative phosphorylation . Lysosomal proteins belonging to this cluster contain for example lysosomal proteases , cathepsins ( Fig . 3C ) . To complete our high-throughput proteomics analysis , we performed secretome characterization of influenza A virus-infected macrophages . Influenza A virus infection of human macrophages clearly induced protein secretion already at 6 h post-infection , and more robust protein secretion was seen at 9 h and 12 h time-points ( Fig . 1C , Table S1 ) . Our data shows that influenza A virus infection of macrophages activates both conventional as well as unconventional protein secretion [19] . Examples of the conventionally secreted proteins include e . g . C-C motif chemokine 3 , C-C motif chemokine 24 , complement C1q subcomponent subunit B , epididymal secretory protein E1 , macrophage colony-stimulating factor 1 , macrophage metalloelastase , matrix metalloproteinase-9 , metalloproteinase inhibitor 2 , and plasminogen activator inhibitor 1 . The unconventionally secreted proteins include several danger-associated molecular pattern molecules ( DAMPs ) like amyloid beta A4 protein , annexins , galectins , heat shock proteins , high-mobility group box proteins ( HMGBs ) , and S100 proteins ( Table 1 ) . Enhanced secretion of amyloid beta A4 protein , galectin-3 , HMGB1 , and S100-A9 protein in response to influenza A virus infection was verified by Western blotting ( Fig . S1D ) . In addition to DAMPs , secretion of several other proteins known to be secreted through unconventional secretory pathway was enhanced . These include cystatins A and B , macrophage migration inhibitory factor , and thioredoxin . Interestingly , influenza A virus infection of macrophages also activated the secretion of Ras-related proteins Rab-1A , Rab-2b , Rab-10 , and Rab11 , as well as several components of vacuolar ATPases ( Table 1 ) . Furthermore , enhanced secretion of several lysosomal proteases , cathepsins , was seen in cell culture supernatants of influenza A virus-infected macrophages . Cytoplasmic leakage of cathepsins has been associated with the activation of NLRP3 inflammasome-associated caspase-1 [20] . Our proteome analysis showed a rapid increase in the amount of cathepsins in the cytoplasm as a result of virus infection . In addition , secretome analysis showed that there is a major increase in the secretion of cathepsins and their regulators in macrophages infected with influenza A virus . To characterize the role of cathepsins in influenza A virus-induced inflammasome activation we performed Western blot analysis which showed that mature 25 kDa cathepsin B and 34 kDa cathepsin D are secreted simultaneously with biologically active p10 form of caspase-1 in response to influenza A virus infection ( Fig . 4A ) . Next , macrophages were infected in the presence and absence of cathepsin B inhibitors , Ca-074-Me and z-FA-fmk , after which IL-18 secretion was analysed by ELISA . Both Ca-074-Me ( Fig . 4B ) and z-FA-fmk ( Fig . S3A ) almost completely abolished IL-18 secretion in response to influenza A virus ( Udorn/72 ) infection . This result was confirmed by Western blot analysis of concentrated cell culture supernatants which showed that the appearance of biologically active form of IL-18 , IL-18 p18 , is abolished in macrophages that have been infected with influenza A virus in the presence of Ca-074-Me ( Fig . 4C ) . In accordance with these results , we did not detect any caspase-1 p10 in cell culture supernatants after Ca-074-Me treatment ( Fig . 4C ) . In addition , Ca-074-Me clearly inhibited IL-18 secretion in response to another influenza A H3N2 strain , Beijing 353/89 ( Fig . S3B ) . Collectively , our data shows that cathepsin B activity is essential for inflammasome activation in response to influenza A virus infection . In addition to inflammasome activation , cytoplasmic leakage of cathepsins is associated with cell death . Activation of caspase-3 is the hallmark of programmed cell death , apoptosis , and we have previously shown that caspase-3 is activated in human macrophages during influenza A virus infection [5] . To study the role of cathepsin B in influenza A virus-induced activation of apoptosis , macrophages were infected in the presence and absence of cathepsin B inhibitor , Ca-074-Me , and caspase-3 activation was analysed by Western blotting . Like caspase-1 , caspase-3 is a latent zymogen , which is processed upon activation into smaller polypeptide chains: p17 and p12 , which in turn form the bioactive enzyme . Influenza A virus ( Udorn/72 ) infection clearly activated the formation of caspase-3 p17/p19 ( Fig . 4D ) . Furthermore , Ca-074-Me inhibited the formation caspase-3 p17/p19 in response to infuenza A viruses Udorn/72 ( Fig . 4D ) and Beijing 353/89 ( Fig . S3C ) confirming the role of cathepsins in the activation of apoptosis during infection . This effect was not dependent on diminished viral replication since Ca-074-Me treatment had only little effect on influenza A virus protein expression ( Fig . 4D ) . Several proteins related with inflammatory response were identified from influenza A virus-infected macrophages . Thus , we created a network based on known protein-protein interactions between all the inflammation-related proteins identified from the intracellular fractions of the infected cells ( Fig . 5A ) . NLPR3 was added to our protein interaction network since it has been shown to be essential for the activation of inflammatory response during influenza A virus infection . Protein interaction network showed that purinenergic P2X7 receptor is directly linked to NLRP3 . P2X7 receptor has been shown to be involved in NLRP3 activation in response to extracellular ATP [21] , [22] , and host tissue damage during influenza A virus infection may result in extracellular leakage of ATP . Therefore we studied the role P2X7 receptor in virus-induced inflammasome activation . Specific inhibitor for P2X7 receptor , AZ11645373 , clearly diminished IL-18 secretion in infected macrophages ( Fig . 5B ) . In addition to IL-18 , NLRP3 inflammasome regulates the secretion of IL-1β . We have previously shown that influenza A virus infection is not able to induce production of proIL-1β in human macrophages [23] . Therefore we stimulated macrophages with LPS for 18 h to activate proIL-1β production , after which the macrophages were infected with influenza A virus for 9 h in the presence and absence of AZ11645373 . This P2X7 receptor inhibitor almost completely abrogated influenza A virus-induced IL-1β secretion ( Fig . 5C ) . In addition to pharmacological inhibition , we used small interfering ( si ) RNA approach to study the role of P2X7 receptor in influenza A virus-induced inflammasome activation . Human macrophages were treated with control siRNA and P2X7 receptor specific siRNAs for 24 h after which the cells were left unstimulated or infected with influenza A virus for 9 h . Silencing of P2X7 receptor clearly reduced influenza A virus-induced IL-18 secretion ( Fig . 5D ) and Western blot analysis confirmed that P2X7 receptor protein expression was decreased in P2X7 receptor siRNA-treated macrophages ( Fig . 5E ) . In conclusion , our results show that P2X7 receptor is essential for influenza A virus-induced inflammasome activation . The inflammation-related proteins identified included also two NADPH oxidase subunits , gp91phox ( CYBB ) and p22phox ( CYBA ) . These proteins have been linked to NLRP3 inflammasome activation through reactive oxygen species ( ROS ) formation in response to various stimuli [24]–[26] . Interestingly , also src tyrosine kinase is known to interact with CYBB and CYBA [27] . To study the role of tyrosine phosphorylation in influenza A virus-induced inflammasome activation we infected human macrophages in the presence and absence of PP2 which is a highly specific inhibitor of src tyrosine kinases . PP2 completely inhibited infuenza A virus Udorn/72- and Beijing 353/89-induced IL-18 secretion ( Fig . 5F and Fig . S3D , respectively ) . Furthermore , PP2 also abrogated IL-1β secretion in response to influenza A virus infection ( Fig . 5G ) demonstrating that influenza A virus-induced inflammasome activation is dependent on src family tyrosine kinase activity .
Influenza A virus genome contains eight pieces of segmented negative-sense RNA which encode 11 proteins . Therefore the virus has to exploit host cell factors to promote its replication and suppress antiviral response . The function of viral proteins during influenza A virus infections is rather well characterized . However , the host response activated by viral infection is less well understood . The development of genome-wide screening techniques such as RNAi has resulted in the identification of hundreds of new host factors that are involved in influenza A virus replication in different cell lines [28]–[33] . The cellular responses to influenza infection as well as other respiratory viruses have also been studied using proteomics in human cell lines with both two-dimensional gel electrophoresis ( 2-DE ) based approach [34]–[36] and newer MS-based strategies [37]–[39] . These studies have shown that similar cellular pathways are activated in response to different viruses . However , these studies have also pointed out that the cellular responses to influenza infection are in part cell-type specific and more studies using human primary cells are needed to elucidate the host response in detail . We have previously characterized host-response to influenza infection in human primary macrophages using traditional 2-DE based proteomics and shown that actin and RIG-I/MAVS signaling components translocate onto mitochondria upon infection [40] . However , 2-DE based proteomics suffers from certain drawbacks , most importantly it shows systematic bias against certain protein groups including membrane proteins as well as very big/small proteins and proteins with extreme pIs . Also , it requires a lot of manual work and is therefore not suitable for high-throughput studies . Here , we have used unbiased high-throughput subcellular proteomics approach to analyse the host response of human primary macrophages during influenza A virus infection in a global manner . We provide evidence that the expression and/or subcellular localization of more than one thousand host proteins is affected at the early phases of infection . Up- and downregulation of proteins in all subcellular fractions reflects the dynamic interplay between these compartments and highlights the importance of subcellular proteome characterization as many of these changes cannot be seen at total proteome level . To defend against virus infection , the host activates antiviral machinery . Interferons and IFN-inducible genes ( ISGs ) are the central components of this response . We detected the upregulation of many IFN-inducible proteins in the cytoplasmic fraction of influenza A virus-infected macrophages ( Table S1 ) . Interestingly , these proteins included three proteins of the IFIT family . It was very recently shown by siRNA approach that interferon-inducible transmembrane proteins ( IFITM ) restrict an early step of influenza A virus replication in A549 lung epithelial cells [29] . With our proteomics approach we did not detect any inducible expression of IFITM proteins in human macrophages in response to influenza A virus infection . It may be that IFITMs and IFITs function in antiviral response in tissue specific manner and similar to IFITMs , IFIT proteins may be a novel family of antiviral restriction factors that mediate cellular innate immunity to influenza A viruses . Influenza A virus infection begins with the binding of viral hemagglutinin to sialyated host plasma membrane glycoprotein . Following endocytosis , viral particles are trafficked through early endosomes to late endosomes/lysosomes . In these compartments the conformation of HA is changed resulting in fusion of host-viral membranes and entry of viral ribonucleoproteins into the cytosol . We detected major changes in subcellular localization of Ras-related small GTPases and vacuolar ATPases , which are involved in the regulation of endosomal recycling pathway and have recently been shown to be essential for influenza A virus replication [28] . Furthermore , it was shown that small molecule inhibitor of vacuolar ATPase can antagonize influenza A virus replication probably by inhibiting the entry of viral ribonucleoproteins to the cytosol . Interestingly , our secretome analysis showed that several Ras-related proteins , Rab-10 , Rab-11A , Rab-1A , and Rap-2b as well as components of vacuolar ATPases , V-type proton ATPases , are rapidly secreted in response to influenza A virus infection . The results suggest that these proteins are involved in protein secretion during influenza A virus infection . We have previously shown that inflammasome-associated caspase-1 is activated in human macrophages in response to influenza A virus infection resulting in the secretion of pro-inflammatory cytokines [4] , [5] . Subsequently , it has been shown that the inflammasome structure that activates caspase-1 during influenza A virus infection is NLRP3 [10]–[12] , [41] , [42] . Our present results show that influenza A virus infection of human macrophages is associated with endolysosomal leakage of cathepsins to the cytosol . This was followed by the activation of inflammasome-associated caspase-1 and secretion of IL-18 , caspase-1 , and mature forms of cathepsins . Furthermore , cathepsin specific inhibitor Ca-074-Me completely inhibited secretion of IL-18 and caspase-1 demonstrating that inflammasome activation during influenza A virus infection is completely dependent on cathepsin activity . These results indicate that lysosomal proteases , cathepsins , are an essential part of pro-inflammatory innate immune response during viral infections . NLRP3 activators are chemically and structurally different suggesting that they are not directly recognized by the NLRP3 inflammasome . It is more likely that they activate inflammasome indirectly by inducing changes in endogenous proteins that are recognized as danger signals . In addition to cathepsin activity , potassium efflux and ROS production are the common features associated with NLRP3 inflammasome activation . It was very recently shown that thioredoxin-interacting protein links oxidative stress to inflammasome activation [43] . Our proteomic data shows that there is re-localization of mitochondrial proteins to the cytoplasm already at 6 h after influenza A virus infection . These proteins contained several components of the inner mitochondrial membrane including proteins involved in electron transport chain and oxidative phosphorylation . The results suggest that there is a substantial change and/or damage in mitochondrial structure at early phases of influenza A virus infection which may contribute to reactive oxygen species production during infection . This is likely to contribute to the inflammasome activation and apoptosis seen in influenza A virus-infected macrophages . Our current results clearly show that src tyrosine kinase activity is essential for inflammasome activation in response to influenza A virus infection: src kinase specific inhibitors PP2 ( Fig . 5 F and G ) and src kinase inhibitor II ( data not shown ) abolished influenza A virus-induced secretion of IL-1β and IL-18 . It has also been recently shown that src tyrosine kinase Lyn plays a critical role in the activation of NLRP3 inflammasome in response to malarial hemozoin [44] . Src tyrosine kinases are responsible for the regulation of ROS formation through NADPH oxidase complex [27] . Activation of src tyrosine kinases results in phosphorylation of its substrates Tks4 and Tks5 . These proteins act as molecular organizers that specifically activate NADPH oxidases resulting in ROS formation [45] . In conclusion , our results demonstrate a novel link between src tyrosine kinase activity , ROS formation , and inflammasome activation during viral infections . Apoptosis is an innate immune response by which infected and other harmful cells are eliminated from the inflamed tissue . In this way intracellular danger signals are not released to extracellular space and inflammation is not further enhanced . We have previously shown that influenza A virus infection of human macrophages is associated with activation of caspase-3 [5] , [23] which is the hallmark of apoptosis . Lysosomal proteases cathepsins are known to act in concert with caspases in apoptotic cell death [46] . Our current data shows that influenza A virus infection of human macrophages is associated with rapid cytoplasmic upregulation of cathepsins indicating lysosomal rupture upon infection . Furthermore , we show that cathepsins act upstream of caspase-3 and that their activity is essential for progression of apoptosis . Most proteins are secreted through conventional protein secretion pathway . These proteins contain signal peptides that direct their transport to the plasma membrane through the ER-Golgi pathway . In our experiments , many classically secreted proteins were detected in cell culture supernatants of macrophages in response to influenza A virus infection . In addition to classical protein secretion , activated immune cells secrete proteins lacking signal peptides through unconventional secretory pathway [47] . DAMPs are nuclear or cytosolic proteins with defined intracellular functions [48] . They are usually hidden in intact cells and released during tissue damage through unconventional protein secretion pathway . Our quantitative high-throughput secretome analysis revealed secretion of several DAMPs in response to influenza A virus infection . These included 50 kDa cleavage product of amyloid precursor protein , HMGBs , galectins , and S100 proteins . Amyloid precursor protein is processed by α- , β- and γ-secretases to generate inflammatory amyloid β peptide [49] . Fibrillogenic amyloid β peptide 42 ( Aβ42 ) is a known activator of NLRP3 inflammasome in migroglial cells [50] and it is involved in the pathogenesis of Alzheimer's disease [51] . We were not able to detect secretion of Aβ42 by ELISA in influenza A virus-infected macrophages ( data not shown ) . However , the secretome data showed that amyloid precursor protein is expressed , processed , and secreted in response to influenza A virus infection . In addition , subcellular proteomics data demonstrates that nicastrin and preselinin , which are components of γ-secretases are expressed in human macrophages . This finding suggests that β-amyloid protein and its processing machinery have a specific function in antiviral response . Taniguchi and coworkers have shown that HMGBs function as universal sentinels for nucleic acid mediated innate immune response [52] . HMGBs operate upstream of cytoplasmic RLRs and endosomal TLRs and they are essential for nucleic acid-induced activation of innate immune response . In our experiments both HMGB1 and HMGB2 were detected in cell culture supernatants of influenza A virus-infected macrophages . These results suggest that HMGBs have an important role in the activation of innate immune response to influenza A virus infection . Our proteomics approach showed also increased secretion of galectin-3 in response to influenza A virus infection . This finding is especially interesting since galectins are known to bind extracellularly carbohydrate structures and the envelope of influenza A virus contains two glycoproteins , hemagglutinin and neuraminidase . Hemagglutinin is required for influenza A virus entry , and it can be speculated that the extracellular galectins restrict viral entry . It is easy to envision that secreted galectins may have also other functions in antiviral response since there is ample evidence about the role of galectins in innate immunity to bacterial and fungal infections [53] . Clearly , future studies are required to characterize the importance of galectins in viral infections . In conclusion , our high-throughput , unbiased quantitative proteomics study provides important new insight into host-response against influenza A virus infection in human primary macrophages ( Fig . 6 ) . First , our data shows that there are dramatic changes in mitochondrial and nuclear proteomes in response to infection . Secondly , our data demonstrates that there is rapid cytoplasmic leakage of lysosomal proteins , including cathepsins , upon infection which contributes to inflammasome activation and apoptosis seen in infected macrophages . Thirdly , our data demonstrates that P2X7 receptor and src tyrosine kinase activity are essential for inflammasome activation during influenza A virus infection . Finally , we show that influenza A virus infection is associated with robust secretion of different DAMPs suggesting an important role for DAMPs in antiviral response .
Human primary macrophages were obtained from leukocyte-rich buffy coats from healthy blood donors ( Finnish Red Cross Blood Transfusion Service , Helsinki , Finland ) . Monocytes were isolated as described previously [4] and differentiated into macrophages by maintenance in Macrophage-SFM medium ( GIBCO ) supplemented with 10 ng/ml GM-CSF ( Biosource International ) and antibiotics . After 7 days of culture , the resulting macrophages were used in experiments . Macrophages were infected with influenza A virus in complete Macrophage-SFM medium . The studied cells were lysed or fractionated into mitochondrial , cytoplasmic and nuclear fractions . Mitochondrial and cytoplasmic fractions were isolated by Qproteome Mitochondria Isolation Kit ( Qiagen ) and the cytoplasmic fractions were further purified with 2-D Clean-Up Kit ( GE Healthcare ) . Nuclear fractions were isolated by Qproteome Nuclear Protein Isolation Kit ( Qiagen ) and the insoluble and soluble nuclear protein fractions were combined before analysis . About 1×107 cells were used for all isolations . For secretome analyses , the cells grown in complete Macrophage-SFM medium were washed tree times with PBS after which the cells were stimulated in RPMI growth media supplemented with 1 mM HEPES , L-Glutamine and antibiotics ( GIBCO ) . The growth media were collected and concentrated in Amicon Ultra centrifugal filter devices ( Millipore Corporation , Billerica , MA ) . Human pathogenic H3N2 influenza A virus strains , Udorn/72 and Beijing/353/89 , were cultured in embryonated hen eggs and stored at −70°C . The hemagglutination titer of both influenza virus strains was 256 , as measured by standard methods . In infection experiments , virus dose of 2 . 56 hemagglutination U/ml was used . The experiments were performed with strain Udorn/72 unless otherwise stated . The protein amount of cell lysates was analysed by SDS-PAGE followed by silver-staining and Western blotting to confirm that viral infection did not decrease the total protein expression level in our experiments ( Fig . S1E ) . Mitochondrial , cytoplasmic or nuclear fractions or secretomes of uninfected control cells and influenza A virus infected cells at given timepoints were labeled with 4plex iTRAQ . The samples were first dissolved into 43 µl of iTRAQ dissolution buffer and 2 µl of each sample was run into an SDS-PAGE gel . For the intracellular fractions , equal protein amounts of each sample were taken for the iTRAQ analyses based on the silver stained gels . For secretome analyses , the whole samples were labeled . Protein alkylation , trypsin digestion and labeling of the resulting peptides were done according to manufacturer's instructions ( AB Sciex ) . After labeling , the samples were pooled , dried and dissolved into 20 mM KH2PO4 ( pH 3 ) . Labeled peptides were fractionated by strong cation exchange chromatography ( SCX ) on an Ettan HPLC system ( Amersham Biosciences ) . Each SCX fraction containing labeled peptides was analysed twice with nano-LC-ESI-MS/MS using Ultimate 3000 nano-LC ( Dionex ) and QSTAR Elite hybrid quadrupole time-of-flight mass spectrometer ( AB Sciex ) with nano-ESI ionization ( approximately 22 SCX fractions for intracellular samples and 13 fractions for secretome ) . MS data were acquired automatically using Analyst QS 2 . 0 software . Information-dependent acquisition method consisted of a 0 . 5 s TOF-MS survey scan of m/z 400–1400 . From every survey scan two most abundant ions with charge states +2 to +4 were selected for product ion scans . Once an ion was selected for MS/MS fragmentation , it was put on an exclusion list for 60 s . Protein identification and relative quantitation was performed using ProteinPilot 2 . 0 . 1 software ( AB Sciex ) . Data files from both technical replicates of an iTRAQ sample set were processed together . The search database was a self-built combination of Uniprot human protein sequences and Uniprot ssRNA negative-strand viruses sequences ( both form the release 55 . 0 , 02/08 ) . The search criteria were: cysteine alkylation with MMTS , trypsin digestion , biological modifications allowed , thorough search and detected protein threshold of 95% confidence ( Unused ProtScore >1 , 3 ) . Additionally , automatic bias correction was used for intracellular fractions to correct for uneven protein loading . ProteinPilot identification and quantitation results were also manually checked: for each identified protein at least two unique peptides with good quality MS/MS data were required , and MS/MS spectra with all reporter ion peak heights below 10 counts were manually removed from quantitation results . False discovery rates were calculated using a concatenated normal and reversed sequence database and a previously reported method [54] . Proteins identified from each subcellular fraction were classified based on their Gene Ontology annotations using GeneTrail [55] . Additionally , k-means clustering analysis was performed for the differentially regulated proteins in each subcellular fraction using Chipster , an open source data analysis tool ( http://chipster . sourceforge . net ) . Clustering was done based on the relative quantitation results from the iTRAQ experiments , and a suitable number of clusters for each subcellular fraction was determined by studying the cluster profiles . Protein-protein interaction networks for a selected group of proteins was created using String [56] . Isolated mitochondrial , cytoplasmic and nuclear fractions from influenza A virus infected and untreated macrophages were dissolved in Laemmli sample buffer . Equal amount of protein from each sample was loaded into an SDS-PAGE gel and transferred onto PVDF-membrane . Membranes were blocked with 5% nonfat milk , stained with different Abs overnight and detected by ECL . The primary antibodies used in this study were cathepsin B ( Calbiochem ) , caspase-3 and HSP90 ( Cell Signaling ) , IFIT3 ( BD Transduction Laboratories ) , actin , annexin A1 , β-Amyloid , APOE , caspase-1 , cathepsin D , cathepsin Z , cytochrome c , galectin-3 , GAPDH , histone H1 , HMGB1 , LAMP-1 , P2X7 receptor , S100-A9 and VDAC1 ( Santa Cruz Biotechnology Inc . ) . IL-18 and influenza A virus ( H3N2 ) antibodies have been previously described [4] , [40] . The IL-1β and IL-18 cytokine concentrations were determined by ELISA according to manufacturer's instructions . Human IL-1β and IL-18 ELISAs were purchased from Diaclone and Bender Medsystems , respectively . The cells grown in complete Macrophage-SFM medium were washed tree times with PBS and the media was changed to RPMI growth media supplemented with 1 mM HEPES , L-Glutamine and antibiotics . The RPMI media has lower content of initial media proteins than Macrophage-SFM medium . The cathepsin B inhibitors , Ca-074 Me ( Calbiochem ) and z-FA-fmk ( Calbiochem ) were added 0 . 5 h before influenza A virus infection and used at final concentration of 40 µM and 50 µM , respectively . The P2X7 receptor inhibitor AZ11645373 and src tyrosine kinase inhibitor PP2 were purchased from Sigma , and they were added to macrophages 0 . 5 h before infection with influenza A virus . AZ11645373 and PP2 were used at final concentration of 1 µM and 5 µM , respectively . After five days of cell culture in 12-well plates , macrophages were transfected with 200 nM non-targeting control siRNA ( AllStars Negative Control siRNA , Qiagen , Hilden , Germany ) and with 100 nM of each of two different P2X7 receptor siRNAs ( Hs_P2RX7_1 , Hs_P2RX7_2; Qiagen ) by using HiPerFect Transfection Reagent ( Qiagen ) according to the manufacturer's instruction . After 4 h of siRNA treatment , fresh macrophage media was added to the cells . On the following day , the cells were left unstimulated or infected with influenza A virus for 9 h , after which the cell culture supernatants were collected and total proteins were isolated for ELISA and Western blot analyses , respectively . The percentage of apoptotic cells was assayed with APOPercentage Apoptosis Assay according to manufacturer's instructions ( Biocolor Life Science Assays ) . Photographs were taken with an Olympus DP70 Digital microscope camera connected to an Olympus IX71 light microscope using DP Controller ( version 2 . 2 . 1 . 227 ) and DP Manager ( version 2 . 2 . 1 . 195 ) softwares . The stained ( apoptotic ) and unstained cells were manually counted , and the percentage of apoptotic cells was calculated . | Influenza A viruses are negative-stranded RNA viruses that are capable of infecting a variety of avian and mammalian species . These viruses are responsible for the annual epidemics that cause severe illnesses in millions of people worldwide . The initial innate immune responses to influenza A viruses have to restrict virus spread before the adaptive immune responses fully develop . Macrophages are the key players of innate immune system and they have a central role in the activation of host response during viral infections . However , the host factors that are involved in the activation of innate immune response during influenza A virus infection are incompletely understood . Here , we have characterized in detail the nuclear , mitochondrial and cytoplasmic proteomes , as well as the secretome from influenza A virus-infected human primary macrophages to get a global view of host factors that are affected by the infection . Our approach allowed us to identify several novel host factors that contribute to innate immune system during influenza A virus infections . These include lysomal proteases cathepsins , P2X7 receptor , src family tyrosine kinases as well as several danger-associated molecular patterns . | [
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"biology/cell",
"signaling",
"immunology/innate",
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| 2011 | Quantitative Subcellular Proteome and Secretome Profiling of Influenza A Virus-Infected Human Primary Macrophages |
Integration of environmental and endogenous cues at plant shoot meristems determines the timing of flowering and reproductive development . The MADS box transcription factor FLOWERING LOCUS C ( FLC ) of Arabidopsis thaliana is an important repressor of floral transition , which blocks flowering until plants are exposed to winter cold . However , the target genes of FLC have not been thoroughly described , and our understanding of the mechanisms by which FLC represses transcription of these targets and how this repression is overcome during floral transition is still fragmentary . Here , we identify and characterize TARGET OF FLC AND SVP1 ( TFS1 ) , a novel target gene of FLC and its interacting protein SHORT VEGETATIVE PHASE ( SVP ) . TFS1 encodes a B3-type transcription factor , and we show that tfs1 mutants are later flowering than wild-type , particularly under short days . FLC and SVP repress TFS1 transcription leading to deposition of trimethylation of Iysine 27 of histone 3 ( H3K27me3 ) by the Polycomb Repressive Complex 2 at the TFS1 locus . During floral transition , after downregulation of FLC by cold , TFS1 transcription is promoted by SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 ( SOC1 ) , a MADS box protein encoded by another target of FLC/SVP . SOC1 opposes PRC function at TFS1 through recruitment of the histone demethylase RELATIVE OF EARLY FLOWERING 6 ( REF6 ) and the SWI/SNF chromatin remodeler ATPase BRAHMA ( BRM ) . This recruitment of BRM is also strictly required for SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 9 ( SPL9 ) binding at TFS1 to coordinate RNAPII recruitment through the Mediator complex . Thus , we show that antagonistic chromatin modifications mediated by different MADS box transcription factor complexes play a crucial role in defining the temporal and spatial patterns of transcription of genes within a network of interactions downstream of FLC/SVP during floral transition .
The transition from vegetative to reproductive development in plants is controlled by a complex transcriptional network that responds both to environmental cues and endogenous hormonal signals [1 , 2] . In Arabidopsis thaliana , the MADS-box transcription factor FLOWERING LOCUS C ( FLC ) plays a major role in this network as an inhibitor of floral transition [3 , 4] . Transcription of FLC is repressed by extended exposure to cold that mimics winter conditions ( vernalization ) so that flowering can proceed when plants are subsequently exposed to warm in spring . The repression of FLC transcription by accumulation of histone modifications in response to cold has been extensively studied [5] and FLC target genes have been described by whole genome chromatin immunoprecipitation ( ChIPseq ) [6–8] . Nevertheless , our understanding of how FLC influences the transcriptional network that controls floral transition and how it represses expression of its target genes is still fragmentary . Here , we utilize data derived from genome-wide binding studies of FLC and its partner MADS box transcription factor SHORT VEGETATIVE PHASE ( SVP ) [7 , 9–12] to identify a common target gene that we named TARGET of FLC and SVP 1 ( TFS1 ) . We show that this gene acts in the network downstream of FLC and other floral regulators , and has an important role on the flanks of the meristem during the early stages of floral development . FLC binds to several hundred target genes , but only a small subset of these are conserved between species [8] . Genes involved in flowering control are enriched among the conserved targets , and FLC represses transcription of several of these , including FLOWERING LOCUS T ( FT ) , SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 15 ( SPL15 ) , SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1 ) and SEPALLATA 3 ( SEP3 ) . MADS box transcription factors are proposed to bind DNA as dimers and tetramers [13 , 14] , and FLC interacts with and binds to a subset of its targets in complexes with the related protein SHORT VEGETATIVE PHASE ( SVP ) [7 , 10] . Mutants at SVP are early flowering and exhibit increased levels of SOC1 and FT mRNAs [9 , 10 , 15 , 16] . How FLC represses transcription of its targets is not completely clear , but appears to involve modification of histones . FLC and its homologue FLOWERING LOCUS M ( FLM ) recruit EMBRYONIC FLOWER 1 ( EMF1 ) , a plant-specific Polycomb Repressive Complex 1 ( PRC1 ) component [17 , 18] , to FLOWERING LOCUS T ( FT ) in leaf veins to repress its transcription [19] . Moreover , cooperativity between FLC or FLM and PRC1 contributes to maintenance of PRC2-induced trimethylation of lysine-27 at histone H3 ( H3K27me3 ) at FT chromatin , most probably through the activity of the histone methyltransferase CURLY LEAF ( CLF ) and the H3K27me3-associated protein LIKE HETEROCHROMATIN PROTEIN1 ( LHP1 ) [19] . Additionally , the JmjC domain-containing trimethyl histone H3 lysine-4 demethylase JUMONJI14/PKDM7B ( JMJ14/PKDM7B ) also associates with PRC1 to further antagonize the active chromatin state at FT [20 , 21] . Other targets of FLC and SVP that are involved in floral induction encode the MADS-box transcription factor SOC1 and the plant-specific transcription factor SPL15 [22–27] . Both SOC1 and SPL15 are expressed in the shoot apical meristem where they cooperate at the promoters of target genes such as FRUITFULL ( FUL ) and MIR172b to activate the basal floral promotion pathway under non-inductive environmental conditions [12 , 22 , 28] . Interestingly , SOC1 coordinates the recruitment of the histone demethylase RELATIVE OF EARLY FLOWERING 6 ( REF6 ) to the promoter of FUL and MIR172b to orchestrate the removal of the H3K27me3 mark and activate transcription [22] . Furthermore , SPL15 activity is repressed post-transcriptionally by miR156 and post-translationally through its physical interaction with the gibberellin ( GA ) -labile DELLA protein REPRESSOR OF GA1-3 ( RGA ) [22] . By contrast , SPL9 , a paralogue of SPL15 that is expressed after floral induction at the periphery of the SAM , requires interaction with DELLA proteins to potentiate its trans-activation activity and contribute to the induction of expression of the floral meristem identity gene APETALA1 ( AP1 ) in the low gibberellin context present in the cells that give rise to the floral primordium [29] . Here , we show that TFS1 , which encodes a B3-type transcription factor that is a member of the REPRODUCTIVE MERISTEM ( REM ) family [30 , 31] , constitutes a target of FLC and SVP and that its transcription is repressed through cooperation with PRC complexes during vegetative growth . After floral induction , TFS1 expression is induced at the periphery of the SAM by SOC1 and the age-regulated transcription factor SPL9 through coordinated recruitment of the histone demethylase REF6 and the chromatin remodeler BRAHMA ( BRM ) [32–34] . This analysis deepens our understanding of the mechanism by which FLC represses the floral network and indicates the importance of antagonistic histone modifications mediated by different MADS box complexes on common target genes .
To further define the regulatory network controlling floral transition at the shoot apex , recently published ChIP-Seq and tissue-specific RNA-Seq datasets were examined to identify genes that are expressed specifically at the shoot apex and are bound by the floral repressor transcription factors FLC and SVP [6–8] . Cross-referencing these datasets identified the gene encoding the B3-type transcription factor TARGET OF FLC AND SVP1 ( TFS1 ) , which was formerly annotated as REPRODUCTIVE MERISTEM 17 ( REM17 ) [30 , 31] . A phylogenetic analysis revealed that TFS1 is part of a gene family including REDUCED VERNALIZATION RESPONSE1 ( VRN1 ) , VERDANDI ( VDD ) and VALKYRIE ( VAL ) , and that REM18 and REM19 are the closest homologs of TFS1 within a sub-branch of the phylogenetic tree ( S1A Fig ) . To verify the binding of FLC and SVP at TFS1 , chromatin-immunoprecipitation ( ChIP ) analysis was performed using chromatin extracted from the aerial parts of 15-day old plants grown under inductive long days ( LDs ) and using antibodies that were raised against FLC [8] and SVP ( S1B Fig , S3 and S4 Tables , Methods ) . In agreement with the ChIP-seq data , specific enrichment of a fragment that encompasses the putative CArG-boxes , designated as CArG-box II , located at the 3’ end of TFS1 was detected after ChIP of FLC or SVP ( Fig 1A and 1B ) . Moreover , the ChIP-qPCR analyses also demonstrated a mutual co-operation between these floral repressors at TFS1 , because binding of FLC and SVP was enhanced in the presence of the other protein ( Fig 1A and 1B ) , as reported previously for several other targets of these transcription factors [7 , 10] . To further characterize the regulation of TFS1 by FLC and SVP , the abundance of TFS1 mRNA in leaves and apices was tested by RT-qPCR in Col-FRI flc-3 , Col-FRI svp-41 and Col-FRI flc-3 svp-41 mutants as well as Col-FRI wild-type . TFS1 mRNA was exclusively detected at the shoot apex and its abundance was increased throughout a developmental time course in the single mutants compared to wild-type and was strongly increased in the double mutant ( Fig 1C , S1C Fig ) . Furthermore , TFS1 expression was increased at the shoot apex , but not in leaves , after Col-FRI plants were exposed to vernalisation and returned to normal growth temperature ( S1D Fig ) , consistent with the repression of TFS1 transcription by FLC . To determine whether the spatial pattern of expression of TFS1 differs in Col-FRI flc-3 and Col-FRI svp-41 mutants compared to Col-FRI , in-situ hybridisation analysis of the shoot apex was performed during floral transition after transfer of plants from short days ( SDs ) to inductive LDs ( Fig1D , S1E Fig ) . The Col-FRI and Col-FRI flc-3 plants grown for 2 wks under SD were still vegetative ( Fig 1D ) , but TFS1 expression level was clearly elevated in Col-FRI flc-3 ( S1C Fig ) , indicating that FLC represses TFS1 expression even in the vegetative stage . After transfer to LDs , TFS1 mRNA appeared at the periphery of the SAM in the Col-FRI wild-type . This spatial pattern was not changed in the Col-FRI svp-41 , Col-FRI flc-3 or Col-FRI flc-3 svp-41 mutants , but the mRNA appeared more rapidly after transfer to LDs in the mutants than in Col-FRI wild-type ( Fig 1D , S1C Fig ) . Therefore , TFS1 transcription is induced during floral transition , while the timing and amplitude of its expression are modulated by SVP and FLC . The temporal expression pattern of TFS1 coincided with the transition to flowering , so the flowering time of tfs1 mutants was determined under inductive and non-inductive conditions . Interestingly , tfs1-1 mutants flowered significantly later than wild-type plants under both conditions , suggesting that TFS1 is involved in promoting floral transition ( Fig 1E and 1F , S2A–S2F Fig ) . The role of TFS1 was confirmed by transgenic complementation of tfs1-1 ( see later ) , and by showing that a second allele ( tfs1-2 ) caused a similar late-flowering phenotype under LDs ( S2E and S2F Fig ) . The tfs1-1 mutation also delayed flowering in the Col-FRI flc-3 svp-41 background , supporting the idea that TFS1 acts downstream of FLC and SVP to promote flowering ( Fig 1E and 1F ) . In addition , the Col-FRI flc-3 svp-41 tfs1-1 plants showed impaired flower development , suggesting redundancy between these genes in flower and inflorescence development ( S2G and S2H Fig ) . Overall , these results demonstrate that TFS1 is a direct target of FLC and SVP and is specifically expressed at the periphery of the SAM during floral transition to promote flowering and floral development . Transcriptional repression by FLC and SVP has been linked to activity of Polycomb Repressive Complex ( PRC ) 2 ( PRC2 ) [10 , 19] . PRC2 catalyzes the methylation of histone 3 ( H3 ) at lysine 27 ( H3K27me3 ) and is associated with the repression of transcription [35] . Therefore , whether TFS1 is subjected to PRC-mediated regulation in a FLC and SVP dependent manner was tested by performing ChIP-qPCR using chromatin extracted from 15-day old plants grown under LDs and antibodies directed against H3K27me3 or H3K4me3 ( S3 Table ) . H3K27me3 was detected in the gene body of TFS1 in Col-FRI plants at much higher levels than in Col-FRI flc-3 , Col-FRI svp-41 and Col-FRI flc-3 svp-41 mutants ( Fig 2A ) . However , in Col-FRI flc-3 svp-41 plants an additional peak in H3K27me3 levels was detected close to the transcriptional termination site of TFS1 ( Fig 2A , S3A Fig ) . Furthermore , a commercially available antibody ( S3 Table ) was used for ChIP-qPCR of LHP1 , which is frequently found associated with H3K27me3 marked chromatin [36 , 37] , and the protein was detected at TFS1 in a similar pattern to H3K27me3 ( Fig 2B ) . Therefore , H3K27me3 and LHP1 are present in the gene body of TFS1 in a FLC and SVP dependent manner correlating with reduced transcription of TFS1 . In contrast to H3K27me3 , K4-trimethylated H3 ( H3K4me3 ) is present at genes that are actively transcribed [38] . Enrichment of H3K4me3 was detected close to the transcriptional start site ( TSS ) of TFS1 in the Col-FRI flc-3 , Col-FRI svp-41 and Col-FRI flc-3 svp-41 mutants , whereas it was not present at the gene in Col-FRI plants ( S3B Fig ) . These data are in agreement with previous reports of the dynamic and antagonistic relationship between H3K27me3 and H3K4me3 during development and the floral transition [35 , 39] . Therefore , under these conditions the dynamic change in these chromatin marks at TFS1 correlates with the repression of FLC transcription , the induction of TFS1 and the transition to flowering . The PRC2 mutation curly leaf ( clf ) , which impairs the activity of an enzyme that catalyses H3K27me3 deposition [40] , partially suppresses the late-flowering phenotype of Col-FRI plants [41] . Strikingly , early flowering Col-FRI clf-2 mutant plants expressed highly elevated levels of TFS1 mRNA in the apex , while still expressing high levels of FLC and SVP mRNA ( S3D–S3F Fig ) [41 , 42] . These observations supported the idea that PRC2 might contribute to transcriptional repression of FLC target genes such as TFS1 . To examine this possibility , ChIP analyses were performed to test for H3K27me3 enrichment across the coding region of TFS1 in Col-FRI clf-2 and Col-FRI flc-3 svp-41 mutant plants . In agreement with the functional role of CLF in the deposition of H3K27me3 , a strong reduction in this mark at TFS1 was detected in Col-FRI clf-2 and this was similar to the reduction observed in Col-FRI flc-3 svp-41 mutant plants when compared to wild-type Col-FRI ( Fig 2C ) . In contrast , ChIP analyses for H3K4me3 at TFS1 showed higher enrichment patterns in Col-FRI clf-2 as well as in Col-FRI flc-3 , Col-FRI svp-41 and Col-FRI flc-3 svp-41 mutants than in wild-type Col-FRI ( S3B and S3C Fig ) . These data indicate that reduction in H3K27me3 at TFS1 in Col-FRI clf-2 mutants correlates with an increase in H3K4me3 , consistent with the antagonistic role of these marks during floral transition . Finally , to determine whether the reduction in H3K27me3 levels at TFS1 in Col-FRI clf-2 plants correlated with reduced FLC binding , ChIP analyses were performed for FLC . FLC binding was strongly compromised in Col-FRI clf-2 , indicating that FLC binding requires and is sustained by PRC2 function ( Fig 2D ) . SVP and FLC interact respectively with LHP1 and the Polycomb Repressive Complex 1 protein EMBRYONIC FLOWER ( EMF1 ) [18 , 19 , 43 , 44] , suggesting a link between PRC function and the activity of these floral repressors . Furthermore , a complex including LHP1 , EMF1 and the H3K4me3 demethylase JMJ14/PKDM7B , has been described to play roles related to PRC1 , including delaying flowering in non-inductive photoperiods [19–21] . These observations , together with the result described above that H3K4me3 levels are lower at TFS1 in the presence of SVP and FLC ( S3B Fig ) , suggested that the H3K4me3 demethylase activity of JMJ14 might be required for FLC and PcG mediated repression of TFS1 . To test this idea , ChIP-qPCR analyses of H3K27me3 were performed on TFS1 in Col-FRI jmj14-2 mutants . In these plants the enrichment levels of the repressive mark H3K27me3 were strongly reduced compared to Col-FRI wild-type ( Fig 2E ) . By contrast , the active chromatin mark H3K4me3 was increased at TFS1 in Col-FRI jmj14-2 compared to Col-FRI and was present at a similar level as in Col-FRI flc-3 svp-41 mutants ( S3C Fig ) . Consistent with the increased levels of H3K4me3 , Col-FRI jmj14-2 also showed higher mRNA levels of TFS1 in apices , but did not affect the expression of SVP or FLC ( S3G–S3I Fig ) . In support of the notion that FLC binding to TFS1 requires PRC2 activity and higher levels of H3K27me3 ( Fig 2D ) , reduced binding of FLC to CArG-box II at TFS1 was also detected in Col-FRI jmj14-2 plants , although FLC mRNA level was unaffected ( Fig 2F , S3H Fig ) . Collectively , these data suggest that for FLC mediated transcriptional repression of TFS1 , the recruitment of PRC2 and deposition of H3K27me3 are required , and that these can also be inhibited by increasing the levels of H3K4me3 through mutation of the JMJ14 demethylase . The observations that FLC and SVP bind 3’ of the TFS1 stop codon ( Figs 1A , 1B , 2D and 2F ) and that they interact with a PRC complex and LHP1 [19 , 43] that are associated with the gene body of TFS1 ( Fig 2B ) , suggested that a chromosomal loop might form between the 3’ distal region and the gene body . To test for the presence of such a loop , a chromosome conformation capture ( 3C ) assay was performed . Indeed , in Col-FRI plants fragments B , C and D ( middle region ) were found to interact with the 3’ end ( G and H ) of TFS1 , suggesting that a ‘locked’ DNA loop was formed ( Fig 3 ) . The presence of this loop was then tested in different mutants to determine whether it required FLC/SVP and PcG function . In Col-FRI flc-3 svp-41 mutants , the interaction between the middle region and the 3’ end was strongly reduced compared to Col-FRI , indicating that FLC and SVP are required for the formation of the ‘locked’ DNA loop ( Fig 3A–3D ) . To define the contribution of PRC2 in the formation of this loop at TFS1 , Col-FRI clf-2 plants were examined . In this genotype , the loop appeared significantly weaker than in Col-FRI plants , indicating that CLF activity is required to support the formation of the ‘locked’ DNA loop at TFS1 ( Fig 3E–3G ) . These data indicate therefore that transcriptional repression of TFS1 by FLC and SVP is associated with the formation of a chromatin loop that requires FLC and SVP binding at the 3’ end of the gene and high levels of H3K27me3 within the gene body . In a genome-wide study of binding sites of the MADS-box protein SOC1 , a site at the 3’ end of TFS1 was detected [28] . To determine whether TFS1 is regulated by SOC1 , TFS1 transcript abundance was tested by RT-qPCR using RNA extracted from leaves and apices in soc1-2 , soc1-2 svp-41 and Col genotypes . TFS1 mRNA levels were much lower in apices of soc1-2 mutants than Col , but were largely restored to Col levels in soc1-2 svp-41 double mutants ( Fig 4A ) . To determine the spatial pattern of TFS1 expression in soc1-2 and soc1-2 svp-41 , in-situ hybridisations were performed on apices during floral transition . The overall spatial expression pattern was similar in Col and soc1-2 svp-41 , however , a significant delay in TFS1 expression at the periphery of the SAM was observed in soc1-2 ( Fig 4B ) . ChIP analyses were then performed with antibodies that were directed against endogenous SOC1 and SVP ( S1B Fig , S3 Table , [22] ) . To verify the results of the previous report on the genome-wide study for SOC1 , ChIP-qPCR was performed and detected SOC1 binding in the region of CArG-box I ( CArGI ) , which is located at the 3’ end of TFS1 ( Figs 1A and 4C ) . The ChIP-qPCR experiment with SVP generated a specific enrichment in the region of CArG-box II ( CArGII ) in Col , and this was enhanced in the soc1-2 mutant , suggesting that SOC1 reduces SVP recruitment to TFS1 ( Fig 4D ) . Similarly , Dexamethasone ( DEX ) -induced translocation of SOC1:GR into the nucleus in 35S::SOC1:GR plants caused higher TFS1 transcription and increased binding of SOC1 to CArGI as well as reduced binding of SVP to CArGII ( S4A–S4C Fig ) . Therefore , binding of SOC1 to the 3’ end of TFS1 occurs during floral transition , whereas SVP binds during vegetative development , and is in agreement with their observed roles in the transcriptional regulation of TFS1 . To test in vivo whether the CArG-boxes identified within the ChIP-qPCR amplicons are responsible for the regulation of TFS1 by SOC1 and SVP , a TFS1::TFS1:9xAla-Venus ( TFS1::TFS1:9AV ) gene fusion was constructed that contained the entire intergenic region flanking TFS1 on the 5’ and 3’ sides ( Fig 4E ) . This gene fusion complemented the tfs1-1 mutant phenotype ( S5A and S5B Fig ) and in the transgenic plants VENUS signal was detected at the periphery of the SAM in a similar pattern as observed by in situ hybridization of TFS1 mRNA ( Figs 1D and 4F , S4E Fig ) . Also , the confocal imaging of the TFS1:9xAla-Venus fusion protein indicated that it predominately localized in the cytosol of slowly dividing meristematic cells ( S6A and S6B Fig ) while treatment with leptomycin B ( LMB ) , which impairs the activity of nuclear exportin [45] , suggested that this was due to active export from the nucleus ( S6C Fig ) . However , in actively dividing cells in young sepals the VENUS signal appeared to be nuclear ( S6D to S6G Fig ) , suggesting the nuclear accumulation and presumably the activity of TFS1 may be closely related to cell division . The CArG-boxes identified in the ChIP amplicons were then mutated in this gene fusion construct . Two mutant plasmids were generated in which CArGII or both CArGI and CArGII were mutated ( Fig 4E ) . Several independent transformants carrying each construct were analysed ( Fig 4F , S4E–S4H Fig ) . Transformants harbouring the mCArGII construct displayed a stronger and broader VENUS signal than those carrying the wild-type construct , whereas the mCArGI+II construct conferred a VENUS signal that was similar to the wild-type construct ( Fig 4F , S4E–S4G Fig ) . The relative strength of these mutant constructs was supported by RT-qPCR analysis performed on RNA extracted from apices of the transgenic plants ( S4H Fig ) . Furthermore , the strong signal of the mCArGII construct was greatly reduced when it was introduced into the soc1-2 mutant by crossing ( Fig 4F ) . The low level of TFS1:9AV expression detected in the soc1 TFS1::TFS1:9AV mCArGII and TFS1::TFS1:9AV mCArGI+II plants was consistent with their delayed flowering time compared to TFS1::TFS1:9AV plants ( S5A–S5H Fig ) . Taken together , these observations were consistent with the transcriptional profile of TFS1 in Col and soc1-2 svp-41 ( Fig 4A ) , and supported the proposal that SOC1 activates and SVP represses transcription of TFS1 at least partly through binding to CArG-box I and CArG-box II , respectively . Consistent with the role of SOC1 in promoting TFS1 transcription , increased levels of the repressive mark H3K27me3 were detected in aerial parts of 15-day old soc1-2 mutants across the TFS1 gene body ( Fig 4G ) . Furthermore , in DEX-induced 35S::SOC1:GR plants , H3K27me3 levels were reduced following binding of SOC1 ( S4D Fig ) . Additionally , the presence of the active chromatin mark H3K4me3 and of RNA polymerase II ( RNAPII ) were monitored along the transcribed region of TFS1 in soc1-2 mutants . ChIP-qPCR analysis revealed that H3K4me3 levels were strongly reduced in soc1-2 mutants , similar to those observed in Col-FRI wild-type ( Figs 2A and 4H ) . Also , the ChIP profile of RNAPII demonstrated enrichment throughout the transcribed region of TFS1 in Col and soc1-2 svp-41 . By contrast , in soc1-2 mutants the loss of RNAPII enrichment was most apparent in regions of the gene body ( Fig 4I ) , which is reminiscent of inactive genes that display a poised RNAPII machinery at promoter regions . Overall , these experiments demonstrate that SOC1-induced activation of TFS1 transcription is invoked by eviction of SVP and reduction in H3K27me3 as well as by releasing RNAPII to transcribe the gene . The histone demethylase REF6 and the chromatin remodeler BRM physically interact to antagonize PcG proteins at target loci , and both proteins were detected at the 3’ end of TFS1 in a genome-wide study [33 , 34 , 46] . Furthermore , REF6 and SOC1 co-purified in the same complex , which was required to facilitate transcriptional activation of target genes through the removal of the repressive histone mark H3K27me3 [22 , 47] . Thus , to understand the effects of REF6 and BRM on TFS1 transcription , TFS1 transcript abundance was monitored by RT-qPCR in ref6-1 and brm-1 mutants grown for 9 to 17 days in LDs . Throughout the time-course , the transcript profile of TFS1 was not changed in leaves of ref6-1 or brm-1 mutants compared to Col , but a dramatic reduction in TFS1 transcript abundance was detected in apices of both mutants ( Fig 5A and 5D ) . Therefore , REF6 and BRM are required for the activation of TFS1 in apices . To validate the previously reported binding of REF6 and BRM to TFS1 [46] , ChIP-qPCR analyses were performed using REF6::REF6:HA ref6-1 and BRM::BRM:HA brm-1 transgenic lines [47 , 48] . Binding of REF6:HA and BRM:HA to sites located at the 3’ end of TFS1 was detected and these sites flank CArGI , to which SOC1 binds ( Fig 5B and 5E ) . To understand whether association of REF6 and BRM with chromatin is dependent on SOC1 ( Fig 4C ) , the soc1-2 mutation was introduced into the REF6::REF6:HA ref6-1 and BRM:BRM:HA brm-1 transgenic lines by genetic crossing . A strong reduction in binding of REF6:HA and BRM:HA was detected by ChIP-qPCR in soc1-2 mutants , indicating that SOC1 supports REF6 and BRM binding to the 3’-end of TFS1 ( Fig 5B and 5E ) . Additionally , the BRM:BRM:HA brm-1 transgene was introduced into the ref6-1 mutant to study binding behaviour of BRM:HA to the 3’ end of TFS1 . Chromatin association of BRM:HA was strongly reduced in ref6-1 compared to Col ( Fig 5G ) , which further corroborated the idea that REF6 is required for BRM recruitment . REF6 is a H3K27me3 demethylase [47] and BRM and REF6 appeared to act as direct activators of TFS1 transcription , so H3K27me3 levels were tested at TFS1 in the respective mutants . Compared to Col , increased H3K27me3 levels were detected by ChIP-qPCR along the TFS1 genomic locus in ref6-1 and brm-1 mutants . The pattern of increase of H3K27me3 was identical in both mutants and consistent with the observed increase in soc1-2 mutants ( Figs 4G , 5C and 5F ) . In summary , these findings suggest that the histone demethylase REF6 and the chromatin remodeler BRM are required to activate transcription of TFS1 in association with SOC1 . To test whether BRM recruitment leads to activation of TFS1 through changes in chromatin accessibility , limited Micrococcal nuclease ( MNase ) digestion followed by tiled oligo qPCR was employed to identify well-positioned nucleosomes near the SOC1 , REF6 and BRM bound site at the 3’ end of TFS1 . The MNase-qPCR analysis identified in ref6-1 and brm-1 mutants a nucleosome at a position that encompasses the binding site for SOC1 and this nucleosome was destabilized in Col ( Fig 5H and 5I ) . Therefore , SOC1 appears to increase chromatin accessibility and transcription of TFS1 through recruitment of REF6 and BRM . The spatial and temporal expression patterns of TFS1 appeared similar to those of SPL9 [22 , 49 , 50] , which encodes a transcription factor that binds to regulatory sequences in the promoter of the floral meristem-identity gene APETALA1 ( AP1 ) to regulate floral fate [29 , 50] . Moreover , SPL15 and SOC1 co-operate to regulate floral commitment under non-inductive conditions [22] . Taken together , the data suggested that SPL9 and SOC1 might cooperate to activate TFS1 . To test for the molecular effect of SPL9 , RNA was extracted from apices of SPL9::GFP:rSPL9 to monitor TFS1 transcript abundance by RT-qPCR . TFS1 transcript levels were strongly increased in SPL9::GFP:rSPL9 plants compared to wild-type ( Fig 6A ) . Next , ChIP-qPCR analysis was employed to test binding of GFP:rSPL9 at the 5’ and 3’ ends of TFS1 ( Fig 6B , S7A and S7B Fig ) . Consistent with direct activation of TFS1 by SPL9 , fragments at the 5’ and 3’ ends of the gene were enriched after immunoprecipitation of GFP:rSPL9 ( Fig 6B , S7A and S7B Fig ) . The presence of markers for transcriptional activity at TFS1 , particularly the Mediator head-module component Med18 , RNAPII and H3K4me3 , was scored by ChIP-qPCR in different genotypes . Higher enrichment levels of these markers were detected at TFS1 in SPL9::GFP:rSPL9 than in wild-type , supporting that SPL9 activates TFS1 ( S7C–S7F Fig ) . In contrast , reduced TFS1 transcript abundance was detected in spl9-1 , spl15-1 and spl9-1 spl15-1 mutants ( S8A–S8E Fig ) and this was accompanied with a reduction in H3K4me3 and an increase in H3K27me3 at the TFS1 locus ( S8F–8H Fig ) . Whether the identified binding sites for SPL9 are responsible for in vivo regulation of TFS1 was then examined . To this end , a reporter gene cassette was constructed , mGTACa1/2 , in which the two GTAC motifs overlapping with the ChIP-qPCR peak of GFP:rSPL9 at the 5’ end of TFS1 were mutated ( Fig 6C ) . Transformants harbouring the mGTACa1/2 mutated form were generated and compared by confocal microscopy with plants harbouring a wild-type construct . VENUS fluorescent signal detected at the periphery of the SAM in wild-type was missing in the mGTACa1/2 plants , supporting the idea that SPL9 binds to these sites to activate transcription ( Fig 6C , S7G Fig ) . Surprisingly , however , VENUS fluorescent signal was retained in the epidermis of mGTACa1/2 plants , indicating that expression in these cells likely takes place independently of SPL9 and other SPL transcription factors ( Fig 6C , S7G Fig ) . Additionally , in-situ hybridisation indicated that TFS1 mRNA appeared more rapidly on the flanks of the meristem after transferring SPL9::GFP:rSPL9 plants from SDs to LDs than after transferring Col wild-type ( S7F Fig ) . These studies are consistent with SPL9 binding to the 5’ end of TFS1 to activate transcription at the periphery of the SAM . In support of the notion of functional cooperativity between SPL9 and SOC1 , co-immunoprecipitation of GFP:rSPL9 and SOC1:MYC ( 9x ) was detected in protein extracts from shoot apical tissue of SPL9::GFP:rSPL9 35S::SOC1:MYC ( 9x ) transgenic lines ( Fig 6D ) . The cooperativity between SPL9 binding at the 5’ end of TFS1 and SOC1 binding at the 3’ end suggested that DNA loop formation might occur between their binding regions . Therefore , chromosome conformation capture ( 3C ) was employed to test for DNA loop formation in Col and 35S::miR156b , in which several redundant SPL transcription factors are reduced in expression [51] leading to reduced transcription of TFS1 ( Fig 6E ) . The 3C analyses suggested that interaction between SPL-binding sites located at the 5’ end and the CArG-box predicted to bind SOC1 that is located at the 3’ end of TFS1 occurred in a SPL9 dependent manner ( Figs 4C , 6F and 6G ) . Together with results described above , the data suggest that SPL9 cooperates with SOC1 to form an ‘active’ DNA-loop that is required for active TFS1 transcription . The chromatin remodeler BRM is recruited to TFS1 in a SOC1 dependent-manner to increase chromatin accessibility and TFS1 transcription ( Fig 5E and 5I ) . In addition , SOC1 is required for increased H3K4me3 at TFS1 ( Fig 4H ) . This chromatin mark is also supported by the COMPASS-like ( Complex Proteins Associated with Set1 ) histone H3 lysine-4 methyltransferase complex component WD40 REPEAT HOMOLOG 5 ( WDR5 ) , which associates with the active elongating RNAPII [52] . Therefore , ChIP analyses were performed using commercial antibody ( S3 Table ) that recognises WDR5a and WDR5b [53] to test WDR5 enrichment at TFS1 in different genotypes . Consistently , the presence of WDR5 at TFS1 was decreased across the gene body in soc1-2 , brm-1 as well as spl9-1 , spl15-1 and spl9-1 spl15-1 mutants ( S9A–S9C Fig ) . Additionally , using commercial antibody ( S3 Table ) ChIP analyses for the histone variant H2A . Z , which marks both transcriptionally active and inactive genes [54 , 55] , detected colocalization of H2A . Z with WDR5 at TFS1 in Col and decreased enrichment in soc1-2 and brm-1 ( S9D–S9F Fig ) . In contrast , no difference in H2A . Z enrichment was detected between spl mutants and Col , further corroborating the idea that SPL functions to orchestrate transcriptional machinery rather than influencing nucleosomal composition ( S9D–S9F Fig ) . The data described so far suggested that SOC1 mediated recruitment of BRM might enable association of SPL9 to chromatin . To test this idea , SPL9::GFP:rSPL9 was introduced into brm-1 mutants by genetic crossing . Unexpectedly , in SPL9::GFP:rSPL9 brm-1 most of the floral structures were converted into carpelloid structures at the primary inflorescence , a more severe phenotype than either parental line ( S10A and S10B Fig ) . To further characterize the molecular effect , TFS1 transcript abundance was examined by RT-qPCR using RNA extracted from leaves and apices . The enhanced apex specific expression of TFS1 in SPL9::GFP:rSPL9 was strongly suppressed by brm-1 , supporting the idea that BRM is required to support SPL9 activity ( Fig 7A ) . Similarly , expression of other floral marker genes such as SOC1 , FUL , LEAFY ( LFY ) , AP1 and SEPALLATA3 ( SEP3 ) was also reduced in this genotype , although SPL9 protein level was not affected ( S10C–S10E Fig ) . Consistent with the idea that BRM alters nucleosomal positioning leading to changes in the exposure of a critical SPL-binding site located at the 3’ end of TFS1 ( Fig 5I ) , reduced binding of GFP:rSPL9 was detected to the 5’ and 3’ end of TFS1 in brm-1 ( Fig 7B ) . This result suggested that BRM facilitates binding of SPL9 to its cognate binding sites . SPL15 recruits RNAPII through Mediator [22] , and consistent with SPL9 having a similar role at TFS1 , a strong reduction in the recruitment of MED18 , RNAPII and markers of active transcription such as WDR5 and H3K4me3 was detected in brm-1 ( Fig 7C–7F ) . Taken together , these data indicate that SOC1-dependent recruitment of BRM is required to allow SPL9 to bind to TFS1 and that Mediator conveys regulatory information from SPL9 to the basal RNAPII transcriptional machinery that is coupled with the COMPASS-like complex to activate TFS1 transcription .
TFS1 is a member of the B3-type transcription factor superfamily that is specific to the Viridiplantae [56] . Within this superfamily , TFS1 falls in the REM family , several of which have established or proposed roles in reproduction of Arabidopsis [30 , 31] . Loss of function alleles of two members of this family , VERNALIZATION 1 ( VRN1 ) and VERDANDI ( VDD ) , provided genetic support for roles in reproductive development [57 , 58] . VRN1 is required for stable transcriptional repression of FLC during induction of flowering by vernalization [57 , 59] , and appears to bind DNA non-specifically [57] , while VDD is involved in ovule development [58] . In addition , several other members of this family are specifically expressed in the inflorescence meristem or developing flowers [31 , 60–62] . REM transcription factors and MADS box proteins , another family of transcription factors with multiple roles in reproductive development , appear to often regulate one another’s expression . For example , VRN1 regulates FLC , VDD transcription is controlled by SEEDSTICK , TFS1 is repressed by FLC/SVP and genome-wide studies of binding sites of MADS box factors AG , AP3 , PI and AGL15 identified several REM genes as direct targets [31] . Both families of transcription factors are amplified in higher plants [30 , 63] , and they may have co-evolved to act in common pathways during the evolution of reproductive development . The mechanism of action of REM proteins is not known , although they are believed to bind DNA via their B3 domains . A GFP-tagged form of VRN1 was found to associate widely with Arabidopsis chromosomes , and this association persisted through mitosis but was lost at meiosis [59] . Interestingly , TFS1 was also previously identified in a targeted proteomics approach as interacting with PCNA , a component of the DNA replication complex [64] . Also , our confocal imaging suggested that the nuclear localization and activity of TFS1 is closely related to cell division . The molecular functions of REM proteins such as TFS1 and how they are related to chromatin structure and cell division are interesting areas for future experimentation . Genome-wide studies demonstrated that binding of FLC and SVP is predominately associated with transcriptional repression of target genes [6–8] . One of these targets is the flowering-time gene FT , whose expression in the vascular tissue of leaves is repressed by FLC and SVP [16 , 65] . The capacity of FLC-like transcription factors to repress FT transcription has been reported to be associated with their ability to recruit PRC components and maintain H3K27me3 levels at the gene [19] . We also found that transcriptional repression of TFS1 by FLC at the shoot meristem is associated with H3K27me3 accumulation , and that this involves formation of a chromatin loop between the 3’-end and intragenic regions of TFS1 that requires PRC complexes . The JMJ14 H3K4 demethylase also associates with EMF1 and LHP1 [19–21] , and we found that in jmj14 mutants H3K27me3 levels as well as binding of both FLC and SVP were significantly reduced at TFS1 , although the expression levels of FLC and SVP were not compromised . Collectively , these data suggest a model whereby PRC complexes involving EMF1 , LHP1 and JMJ14 are recruited by FLC and SVP to TFS1 to sustain H3K27me3 levels and binding of these transcription factors , thereby stably repressing TFS1 transcription ( Fig 8A ) . In this model , how the PRC1-like complexes reinforce binding of FLC and SVP and whether binding of these transcription factors is a prerequisite for PcG recruitment and PcG-mediated gene silencing remain to be resolved . Another possibility is that a co-factor for FLC binding , perhaps another MADS box transcription factor such as AGL16 [66] , is reduced in expression in circumstances in which H3K27me3 levels are reduced . In this case , reduction in H3K27me3 would indirectly lower FLC binding . In contrast to FLC/SVP , the MADS box factor SOC1 activates TFS1 transcription during floral transition . Furthermore , SOC1 binds directly to TFS1 as defined in genome-wide [12 , 28] and targeted ChIP-qPCR experiments performed here . Induction of SOC1:GR was sufficient to activate TFS1 transcription in the presence of SVP , demonstrating that SOC1 activation is epistatic to the repression mediated by SVP and that after SOC1:GR activation SVP binding to TFS1 was strongly reduced . This reduction of SVP at TFS1 could be due to its displacement by SOC1 binding to an adjacent CArG box or to the transcriptional repression of SVP by SOC1 [12 , 15 , 28] . Furthermore , our ChIP data indicate that the repressive state imposed by FLC/SVP is overcome by SOC1 through recruitment of the H3K27me3 demethylase REF6 and the chromatin remodeler BRM to the TFS1 locus . Similarly , REF6 was recently shown to be recruited to targets by other MADS box transcription factors [67] . Our observations suggest that SOC1 displays characteristics associated with pioneer transcription factors , as it resolves condensed chromatin structures and opens chromatin through the combinatorial activity of REF6 and BRM . Similarly , a recent report in Caenorhabditis elegans demonstrated that the pioneer factor PHA-4 binds to promoters required for foregut development to recruit RNAPII and promote chromatin opening [68] . PHA-4 was proposed to facilitate chromatin opening by depositing RNAPII at target gene promoters . Similarly , the Drosophila maternal pioneer factor ZELDA ( Zld ) recruits poised RNAPII to Dorsal ( Dl ) target genes , facilitating chromatin accessibility for Dl which then mediates their zygotic activation [69 , 70] . Accordingly , we found that the SOC1-REF6-BRM complex relaxes and opens chromatin at TFS1 to facilitate binding of SPL9 and to activate poised RNAPII , resulting in a reduction in H3K27me3 levels across the TFS1 genomic locus . Many genes directly repressed by FLC or SVP to maintain vegetative development are likely to be subsequently bound and activated by other MADS box transcription factors during reproductive development . Thus the mechanisms defined here by which SOC1 antagonises the repression of TFS1 transcription imposed by FLC/SVP are likely to be more broadly relevant during the transition to flowering . SOC1 functionally co-operates with SPL15 to form a chromatin loop associated with activation of FUL transcription [22] . Similarly , we showed by co-immunoprecipitation a physical interaction between SPL9 and SOC1 at TFS1 . Similarly , we detected looping at the TFS1 locus between the SPL9-binding region close to the TSS and the SOC1 binding region at the 3’-end of TFS1 that might enable a higher turn-over rate of RNAPII to yield higher transcriptional activity . These observations suggest that the formation of an active chromatin loop could enable SOC1 and SPL9 to recruit respectively REF6 and RNAPII to the TSS , and then the active elongating RNAPII could cause the gene body of TFS1 to change its position relative to the stable SOC1-SPL9 complex enabling REF6 to track along the gene with RNAPII progressively removing H3K27me3 ( Fig 8B ) . This model predicts that the SPL9-SOC1 interaction induces dynamic chromatin folding that facilitates movement of the RNAPII along the gene body , rather than that RNAPII separates from the pre-initiation complex and tracks along the TFS1 gene body . It will be interesting to determine in a genome-wide context whether other targets of SPL9 and SOC1 display similar features . The analysis presented here incorporates TFS1 into a network of interactions among FLC target genes . At the shoot meristem , FLC directly binds to and represses transcription of SOC1 and TFS1 [6 , 7 , 27 , 43] . Furthermore , SOC1 directly activates the transcription of TFS1 . Thus repression of TFS1 by FLC involves both direct repression of expression of its positive activator SOC1 as well as direct repression of TFS1 , a relationship characterized as a coherent feed forward loop type II [71] . The temporal and spatial patterns of TFS1 expression on the flanks of the inflorescence meristem are overlapping with and partially conferred by SPL9 , and may indicate an important role for TFS1 in modulating the expression of genes in cells that will give rise to floral primordia . This suggestion is strengthened by the observation that the Col-FRI flc-3 svp-41 tfs1-1 triple mutant shows a floral morphology defect not shown by any of the single mutants . Previously , the soc1-2 agl24-1 svp-41 combination was also demonstrated to have a synergistic effect on floral development due to redundancy among these transcription factors in the repression of genes involved in floral organ development [43] . Our data suggest that there may also be redundancy among FLC , SVP and TFS1 in the regulation of downstream genes , which could be characterized in a future analysis of TFS1 targets . More generally , our work emphasises that defining the network of genes negatively regulated by FLC/SVP , and understanding how these then interact during the progression to flowering when FLC expression is repressed or lost by mutation , is proving to be a productive approach in defining critical mechanisms controlling floral transition .
All seed stocks are in the Columbia-0 ( Col-0 ) genetic background and were obtained from the Nottingham Arabidopsis Stock Centre ( NASC; S1 Table ) except for 35S::SOC1:GR soc1-1 ( Hyun et al . , 2016 ) , which is in a Landsberg erecta ( Ler-0 ) genetic background . Seeds were sown on soil or on full-strength Murashige and Skoog ( MS ) medium containing 1% sucrose , stratified for 3 days at 4°C , and grown at 22°C under either long-days ( 16hrs light/8hrs dark; 150μmol . m-1 . s-1 ) or short-days ( 8hrs light/16hrs dark; 150μmol . m-1 . s-1 ) . Plant age was measured when seeds were transferred from stratifying to ambient growth conditions . Full-length TFS1 genomic region was cloned by PCR with Phusion Enzyme ( New England Biolabs ) according to the manufacturer’s recommendations and used to generate TFS1::TFS1::9xAla-Venus . To introduce 9xAla-Venus coding sequence , we employed Polymerase Incomplete Primer Extension ( PIPE ) cloning method [72] and plasmids were then introduced into Agrobacterium to transform Col plants by floral dip [73] . The sequences of the primers used for PIPE cloning are listed in S2 Table . Total RNA of indicated genotypes at different days after sowing from leaves and apices was isolated with NucleoSpin RNA plant kit ( Macherey-Nagel ) . DNA was removed by an on-column treatment with rDNase and 2 μg RNA was reverse transcribed with an oligo ( dT ) primer , RNAseOUT Recombinant Ribonuclease Inhibitor ( Thermo Fisher Scientific ) and SuperScript II Reverse Transcriptase ( Thermo Fisher Scientific ) . The cDNA equivalent of 20ng of total RNA was used in a 12 μL qPCR reaction on a Roche Light Cycler 480 instrument ( Roche ) with either iQ SYBR Green Supermix ( BioRad ) or GoTaq qPCR Master Mix ( Promega ) and quantified using the UBC21 ( AT5G25760 ) as a reference gene to which data was normalized [74] . The mean of three biological replicates with standard deviation is shown and list of primers used for expression analyses can be found in S2 Table . ChIP was performed as previously described with minor modifications [22] . In brief , above-ground tissue of 15LD-grown plants was collected at ZT8 and fixed in PBS solution containing 1 . 5% formaldehyde . ChIP-assays in which indirect binding of the protein of interest to chromatin was studied , Di ( N-succinimidyl ) glutarate ( DSG; Synchem ) at a final concentration of 1 μM was used to introduce protein-protein crosslinks prior to formaldehyde-assisted protein-chromatin crosslinking . To determine fold enrichment levels , ChIP-DNA was quantified on a Roche Light Cycler 480 instrument ( Roche ) with iQ SYBR Green Supermix ( BioRad ) and normalized against ACT8 ( AT1G49240 ) . In ChIP assays in which histone modifications were tested , the values of the tested histone marks were normalized against histone H3 . The average of three biological replicates is shown and list of primers used for fold enrichment analyses can be found in S2 Table . 3C assay was performed as described previously with minor modifications . A total of 2g of above-ground tissue of 15 day-old LD-grown plants was used for 3C study . Chromatin DNA was digested for 16 hrs at 37°C with 400U Sau3AI ( New England Biolabs , S3 Table ) while agitating at 900 r . p . m . For intramolecular ligation , digested nuclei were incubated for 5 hrs at 16°C in 500 U T4 DNA Ligase ( Promega , S3 Table ) . In parallel , the cloned TFS1:9xAla-Venus construct was digested and ligated . The 3C DNA ligation products were quantified by RT-qPCR and normalised to the TFS1:9xAla-Venus control using the delta-delta Ct method . The sequences of the primers used in the 3C-assay are listed in S2 Table . Micrococcal nuclease-assay was performed as described previously with minor modifications [75] . For nuclear extraction , above-ground tissue of 15 day-old LD-grown plants was harvested , ground in liquid nitrogen and resuspended in lysis buffer ( LB ) [50 mM HEPES pH7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 10% glycerol , 5 mM ß-mercaptoethanol and protease inhibitor cocktail ( Roche ) ] . After 1 hr of lysis , the lysis mixture was filtered twice through 2 layers of Miracloth ( Calbiochem ) and protocol was followed as previously described . For MNase treatment , nuclei were treated with 5U MNase ( Thermo Fisher Scientific ) for 15 min and digest was stopped by adding 16 μL 250 mM EDTA , and then treated with RNase A and Proteinase K ( Sigma Aldrich ) , each for 1 hr . Total protein extraction and in vivo co-immunoprecipitation were performed as described previously with minor modifications [22 , 76] . For SVP Western-analysis , roughly 50 apices of 15 day-old LD-grown plants were harvested . Protein concentration was determined by Bradford series and a total of 50μg for crude extract and 1mg for immunoprecipitation was used . The amino acid sequences of the epitopes for generating SVP antibody are presented in S4 Table . In-situ hybridisation was performed according to the method described previously [77] . The sequences of the primers used for the in-situ hybridisation experiments are listed in S2 Table . For confocal microscopy , shoot apices at different developmental stages were collected and fixed with 4% paraformaldehyde ( PFA ) prepared in phosphate-buffered saline ( PBS ) at pH7 . 0 . Samples were then vacuum infiltrated for 20 min on ice , transferred to fresh 4% PFA , and stored at 4°C overnight . The fixed samples were washed twice for 1 min in PBS , then cleared with ClearSee [10% ( w/v ) xylitol , 15% ( w/v ) sodium deoxycholate and 25% ( w/v ) urea][78] for 3 to 8 days at room temperature . After clearing , the shoot meristems were imaged by confocal laser scanning microscopy ( Zeiss LSM780 ) , as described previously [79] . All image processing and figure construction was performed with Photoshop ( www . adobe . com ) . Mutant and transgenic lines used in this study , including references for their origin and description in literature , and their respective AGI identifiers are listed in S1 Table . | The initiation of flowering in plants is exquisitely sensitive to environmental signals , ensuring that reproduction occurs at the appropriate time of year . The sensitivity of these responses depends upon strong repression of flowering under inappropriate conditions . FLOWERING LOCUS C ( FLC ) and SHORT VEGETATIVE PHASE ( SVP ) are related transcription factors that act in concert to strongly inhibit flowering in crucifer plants through repressing transcription of their target genes . Many direct FLC/ SVP targets have been identified in genome-wide studies , however few of these genes have been characterized for their roles in regulating flowering time or other aspects of reproductive development . Here , we characterize TARGET OF FLC AND SVP1 ( TFS1 ) as a novel target of FLC and SVP , and demonstrate that TFS1 contributes to proper flowering-time control . Moreover , we provide a detailed mechanistic view of how TFS1 transcription is controlled during reproductive development through the repressive activity of FLC/SVP being overcome by the transcriptional activator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 . Thus we further elucidate the network of genes repressed by FLC/SVP to block flowering and determine mechanisms by which their repressive activity is overcome during the initiation of flowering . | [
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| 2019 | Floral regulators FLC and SOC1 directly regulate expression of the B3-type transcription factor TARGET OF FLC AND SVP 1 at the Arabidopsis shoot apex via antagonistic chromatin modifications |
Cell-type specific gene expression is regulated by the combinatorial action of transcription factors ( TFs ) . In this study , we predict transcription factor ( TF ) combinations that cooperatively bind in a cell-type specific manner . We first divide DNase hypersensitive sites into cell-type specifically open vs . ubiquitously open sites in 64 cell types to describe possible cell-type specific enhancers . Based on the pattern contrast between these two groups of sequences we develop “co-occurring TF predictor on Cell-Type specific Enhancers” ( coTRaCTE ) - a novel statistical method to determine regulatory TF co-occurrences . Contrasting the co-binding of TF pairs between cell-type specific and ubiquitously open chromatin guarantees the high cell-type specificity of the predictions . coTRaCTE predicts more than 2000 co-occurring TF pairs in 64 cell types . The large majority ( 70% ) of these TF pairs is highly cell-type specific and overlaps in TF pair co-occurrence are highly consistent among related cell types . Furthermore , independently validated co-occurring and directly interacting TFs are significantly enriched in our predictions . Focusing on the regulatory network derived from the predicted co-occurring TF pairs in embryonic stem cells ( ESCs ) we find that it consists of three subnetworks with distinct functions: maintenance of pluripotency governed by OCT4 , SOX2 and NANOG , regulation of early development governed by KLF4 , STAT3 , ZIC3 and ZNF148 and general functions governed by MYC , TCF3 and YY1 . In summary , coTRaCTE predicts highly cell-type specific co-occurring TFs which reveal new insights into transcriptional regulatory mechanisms .
In multicellular organisms , all cells carry the same genetic information , yet they differentiate during development into a variety of cell types with different morphology and function . This cell type differentiation is brought about by the execution of distinct gene expression programs . These programs , in turn , depend on regulatory arrangements accomplished by specific transcription factors ( TFs ) , which bind to cis-regulatory sequences , such as enhancers or promoters [1] . Cis-regulatory elements are embedded in chromatin , whose basic repeating unit is the nucleosome . The presence or absence of these nucleosomes determines whether or not cis-regulatory elements are accessible for TF . Thus , accessibility of chromatin is a prerequisite for cis-regulatory elements to exert their regulatory effects . In eukaryotes , regulatory decisions are usually directed by a specific combination of TFs that act cooperatively rather than individually [2] . Therefore , the identification of cell-type specific cooperativity among TFs is a crucial step in understanding cell differentiation . Cell-type specific co-operative binding of TFs has so far been primarily studied using groups of promoter sequences active in the cell type of interest [3–7] . In principle , the significance of such interactions can be tested by comparing the expected and observed number of co-occurrences of two motifs in selected promoters . Usually , the promoters considered are selected from among differentially expressed genes identified using gene expression data . In recent years , three approaches have been developed to investigate cell-type specific cooperativity between TFs in accessible chromatin regions . The first approach uses experimental data on TF binding determined by ChIP-seq or ChIP-chip for several TFs to detect significant co-occurrence among them . Typically the number of ChIP-seq peaks for two TFs co-occurring at a specific location is compared to the number of peaks for each individual TF [8 , 9] . An alternative strategy integrates overrepresentation analysis of secondary motifs in peak regions bound by the primary TF [6 , 8 , 10–12] . Both strategies yield highly precise predictions but are restricted to TFs and cell types for which experimental data is available . The largest available human dataset is provided by the ENCODE project [13] and comprises several thousand ChIP-seq experiments . However , ChIP-seq experiments are available for only 87 distinct TFs in the five most studied cell lines [8] . The number of experiments in other cell lines is much smaller , with only a few distinct TFs represented . The second approach to predict TF cooperativity in a cell-type specific manner combines gene expression measurements with the investigation of the regulatory regions of co-expressed genes for overrepresentation of TF motifs [5 , 14] . For example , one previous study used a combination of DNA accessibility and gene expression data to build regulatory maps of Drosophila embryonic development [15] . The advantage of this approach is that gene expression data provides evidence of the functional effect of a specific combination of TFs . The disadvantage of this approach is that it can only be applied to the analysis of promoter sequences or to the small number of known enhancer-target gene pairs , because the target genes of distant regulatory regions are difficult to identify . Therefore this approach is also limited by the availability of experimental data . The third approach to predict TF co-occurrence uses the experimental evidence of open chromatin derived from DNase-seq experiments to find significantly co-occurring pairs of TFs . Previous studies have focused on predicting direct TF-TF dimerization using fixed spacing and orientation of TF motifs [16–18] . Alternatively , the occupancy of binding motifs in DNaseI footprints can be investigated as this provides precise information on DNA-protein binding due to nonuniform DNaseI cleavage [19] . However , these methods require several thresholds to be specified in advance for the identification of the accessible chromatin regions or for the definition of binding motif hits and their spacing . Crucially , these methods do not focus on the cell-type specificity of the predicted TF cooperativity . To address these limitations , here we propose a new method , coTRaCTE , for detecting pairs of TFs which preferentially co-occur in a cell-type specific manner . Our method incorporates two novel refinements which overcome the limitations of previous approaches . First , we consider accessible chromatin regions as determined by DNase-seq and divide the DNaseI-hypersensitive regions ( DHSs ) into those that are open in many cell-types ( ubiquitously open DHSs , hereafter “ubiq-DHS” ) and those that are accessible in a limited number of cell types only ( cell-type specific DHSs , hereafter “CTS-DHS” ) . It is common practice to use such DHSs as a proxy for enhancer elements [20–22] , in particular since promoters tend to be ubiquitously DNase-accessible [23] . The statistical advantage of contrasting the cell-type specific TF co-occurrences to the ubiquitous ones is in the usage of the ubiquitous sites as background sequences from which we can discriminate the TF co-occurring signal and from which we can assess the significance . Using a large scale DNase-seq data set from the ENCODE project , we identify thousands of CTS-DHSs in 64 distinct cell types , which are likely to represent cell-type specific enhancers . The ubiq-DHSs represent chromatin that is constitutively open in all studied cell types . One advantage of coTRaCTE is that it does not require any thresholds to be defined for the identification of cell-type specific enhancers . The only user-defined parameter is the number of CTS-DHSs that should be taken into account for further analysis . The second advantage of coTRaCTE is that putatively cooperative TF pairs are assigned a statistical significance score based on an appropriate genomic background of open chromatin . We apply coTRaCTE to all possible TF pairs represented among 554 TRANSFAC motifs identified in 64 cell types to produce an atlas of predicted co-occurring TF pairs within cell-type specific enhancers . Besides testing our method globally on 64 cell types , we present a more detailed local analysis of co-occurring TF pairs using embryonic stem cells ( ESCs ) as a study system . We decided to use ESCs as a proof of principle to assess our TF network predictions due to the availability of extensive experimental data for these cells . Over the past decade , the transcriptional regulatory network of embryonic stem cells ( ESCs ) has been intensively studied using various experimental techniques such as mass spectrometry [24] , ChIP-chip and ChIP-seq data with microarray expression data [25] as well as bioinformatic techniques ( [26 , 27] for review ) . Although ESCs have been extensively investigated most studies have focussed on core pluripotent regulators such as OCT4 , NANOG and SOX2 in addition to other known pluripotent regulators such as KLF4 , DAX1 , ESRRB , REX1 and c-MYC . Therefore other TFs that potentially cooperate with these core regulators remain to be investigated in detail . Our method allows us to investigate all TF pairs represented in the motif collection that are putatively cooperative in ESCs . Moreover , we reveal several striking differences in the predicted TF networks found in undifferentiated and differentiated ESCs .
Currently , the standard approach to measure chromatin accessibility genome-wide is to digest chromatin with the endonuclease DNaseI followed by sequencing ( DNase-seq ) . DNaseI is used to preferentially cleave accessible chromatin regions , which are therefore referred to as DNase hypesensitive sites ( DHS ) . The DNase-seq experiment generates a genome-wide map of the accessible chromatin regions [28]; i . e . the greater the number of sequenced reads mapping to a certain region , the greater the sensitivity of that region to DNaseI digestion and therefore the greater the accessibility of its chromatin . To determine hypersensitive regions which are most specific for individual cell types , we used data from 164 ENCODE experiments [13] across 88 healthy and 2 cancer cell lines . Biologically similar cell lines were grouped into one cell type , resulting in a total number of 64 cell types in our study . See S1 File for the exact grouping of all cell lines into cell types . Only healthy cell lines were analysed since we are interested in variations in chromatin accessibility determined by cell type identity rather than those determined by disease state or cell immortality . However , we did include two cancer cell lines ( K562 and HeLa-S3 ) to facilitate comparison of our results with the large number of experimental studies analyzing these two cell lines . To account for the high technical variability among DNase-seq experiments from different research centers only experiments conducted in a single center ( i . e . University of Washington ) were considered for the analysis . To quantify the cell-type specificity of the DHSs , we calculated the t-statistic-based measure as described in [29] . First , the DNase-seq reads from all experiments were counted , and log counts plus one pseudocount were normalized for sequencing depth by multiplying the read counts for each sample by the mean read count over all samples divided by the sample’s mean read count . Here , the available replicates were treated as separate samples . Next , we created a large matrix of log normalized read counts over all 164 samples in 200bp non-overlapping windows ( Fig 1 , bottom right panel ) along the human genome ( hg19 Ensembl assembly from genome . ucsc . edu ) excluding strong repeat sequences . For each window w , X i w denotes the log read count for sample i ∈ {1 , … , n} . The set of all samples belonging to a given cell type ct ∈ {1 , … , m} is denoted as Cct , i . e . Cct ⊆ {1 , … , n} . We assume that each sample i belongs to exactly one cell type , so that Cct are pairwise disjunct . Denoting the cardinality of Cct as nct , the average DNase-seq profile for cell type ct is defined as X ¯ c t = 1 / n c t ∑ i ∈ C c t X i . Thus , the global DNase-seq profile of all cell types is then: X ¯ = 1 / m ∑ c t = 1 m X ¯ c t . The unbiased cell-type variance is then given by: s c t 2 = 1 n c t - 1 ∑ i ∈ C c j ( X i - X ¯ c t ) 2 . Assuming equal variance among all cell types , pooled within-class standard deviation is defined by: s = ∑ j = 1 m ( n j - 1 ) s j 2∑ j = 1 m ( n j - 1 ) = ∑ j = 1 m ∑ i ∈ C j ( X i - X ¯ j ) 2 ∑ j = 1 m ( n j - 1 ) . Then , we weight the differences from the global profile to the cell-type profile by the pooled standard deviation . This provides a t-statistic for cell type ct defined as: t c t = X ¯ c t - X ¯ 1 / m + 1 / n c t · ( s + s 0 ) , with s0 denoting the mean of s over all windows to prevent division by small within-cell-type variance estimates . This calculation is then repeated for each genomic window w ( see Fig 1 ) , so that the t-statistic measures the corresponding cell-type specificity of each DHS . An illustrative example for 14 samples from 7 different cell lines corresponding to 6 different cell types is shown in Fig 1 . The top panel shows the raw DNase-seq tracks of 14 samples . The bottom right panel zooms into a small genomic region with 12 windows showing the matrix of the log-normalised read counts for all 14 samples . Then , all 14 samples originating from 9 cell lines are grouped into 6 cell types and the t-statistic is calculated for each window in each cell type ( matrix shown in bottom left panel ) . Windows 6 and 7 have large read counts in all cell types thus their t-score is small in all cell types and they are referred to as ubiquitous DHSs . Windows 2 and 3 have large read counts in bone marrow thus their t-score is large in bone marrow only and they are referred to as bone marrow-specific DHSs . Genomic regions with the largest positive t-score in each cell type are hereafter referred to as “cell-type specific DNase hypersensitive sites” ( or CTS-DHSs ) . In contrast , ubiquitously open regions with global t-score close to zero are hereafter referred to as “ubiquitous DHSs” ( or ubiq-DHSs ) . In constrast to other methods which identify DHSs that are open specifically in given cell type , the t-based measure accounts for within-cell-type variability of the DNase-I sequencing counts . This is a crucial feature of our method since it produces a ranking of genomic sites that are consistently hypersensitive in a given cell type , relative to an average profile of all studied cell types . Read counting and genomic range manipulation were performed using BEDTools [30] . The t-statistic was calculated within the R statistics environment , using the sparse matrix package Matrix . To predict bound and unbound sites for each particular TF in our study , we use the TRanscription factor Affinity Prediction method TRAP [31] . We first select the top-l cell-type specific CTS-DHSs of each cell type and the top l ubiq-DHSs . Then , for each TF motif of interest , its binding affinity to these sites is estimated using TRAP . TRAP quantifies TF binding using a biophysical model that produces binding affinity values for each TF motif to the particular DHS . This approach is superior to hit-based motif screening algorithms which use a threshold to distinguish between binding sites and non-binding sites . Notably , hit-based methods may fail to consider low-affinity binding sites , which might be essential for cell-type specific gene regulation [32 , 33] . For each individual TF and each cell type , we use the binding affinity prediction for each DHS to rank the selected CTS-DHSs and ubiq-DHSs jointly by their predicted binding affinity for the given TF . DHSs ranked among the top-k are considered to be “bound” and all other DHSs are considered to be “unbound” . Considering the example in Fig 2 ( steps 1 and 2 ) , we first take the FOS motif and select the ESC cell type ( highlighted in red ) . Then , the l = 10 most ESC-specific DHSs ( in red ) and l = 10 most ubiquitous DHSs ( in grey ) are taken and jointly ordered by FOS predicted binding affinity . The same procedure is then repeated for T-cell ( in blue ) , astrocytes ( in green ) and all other cell types . Then , the top k = 7 sites in each list are considered as bound by FOS , the remaining 13 sites are considered as unbound . The same scheme is then repeated for all other TFs ( OCT4 , SP1 , MYC , etc ) . For our analysis a list of 554 known TF motifs obtained from TRANSFAC 2012 database from BIOBASE Corporation ( [34] , www . biobase-international . com ) was used . However , the TRANSFAC database contains redundant entries , since transcription factors are known to recognize more than one consensus sequence [35] . On the other hand , similar DNA sequences can be recognized by different TFs [36] , thus different TFs might have the same motifs in the database . Therefore , we assigned the set of 554 TF motifs ( hereafter “TF motifs” ) to 306 individual TFs or TF groups/families ( hereafter “TFs” ) , using the information provided by the TRANSFAC database and by [11] , see S2 File . The calculation of TRAP affinities was done using the TRAP command line tool , the sorting and data manipulation was conducted within the R statistics environment . To predict pairs of co-occuring TFs in a cell-type specific manner , we quantify ( i ) the degree of overlap between cell-type specific DHSs ( corresponding to enhancers ) bound by both TFs and ( ii ) the degree of overlap between ubiquitous DHSs bound by both TFs . To this end , first , we build two two-way contingency tables: one table for the co-binding of the TF pair in the cell-type under study , and the other table for the co-binding of the TF pair in ubiquitous DHSs . Then , the log ratio of p-values from the two contingency tables is calculated to estimate the likelihood that a TF-pair co-occurrence is cell-type specific rather than ubiquitous . Technically , we define two binary variables X and Y identifying the existence of a binding motif in a particular DHSs for the first TF and for the second TF in a pair , respectively . For the top k DHSs having the highest predicted affinity for the first TF ( “TF1” ) , the binary variable X equals one and we defined these DHSs as bound by the first TF . Correspondingly , for the top k DHSs having the highest predicted affinity for the second TF ( “TF2” ) , the binary variable Y equals one , thus these DHSs are bound by the second TF . Formally for each DHSi , where i = 1 , … , 2l: X ( i ) = { 1 DHS i bound by TF 1 0 otherwise Y ( i ) = { 1 DHS i bound by TF 2 0 otherwise . ( 1 ) The third binary variable Zct indicates cell-type specific DHSs for a particular cell type ct and is defined as follows: Z c t ( i ) = { 1 DHS i is cell-type specific for cell type c t 0 DHS i is ubiquitous . ( 2 ) Then two individual X , Y-tables stratified by Zct according to cell type can be constructed , as shown in Table 1 . Due to the selection of the top l cell-type specific DHSs and the top l ubiquitous DHSs , both of the tables have the same size l and are therefore simply comparable . The independence of both variables X and Y can be assessed using Fisher’s exact test ( FT ) and then compared with respect to variable Z , i . e . in the cell-type specific case and in the ubiquitous case . To quantify the difference between the two tables , we define a score L as the log ratio of the p-value obtained from FT in the cell-type specific table and of the p-value obtained from FT in the ubiquitous table . Formally , the Lct score for cell type ct is defined as a log ratio of the probability that the expected counts mct of CTS-DHSs bound by both TFs are larger than the observed value n11ct and of the probability that the expected counts mu of ubiquitous DHSs bound by both TFs are larger than the observed value n11u: L c t = - log [ P ( m c t ≥ n 11 c t ) P ( m u ≥ n 11 u ) ] . ( 3 ) With this definition , the Lct score contrasts the likelihood of co-occurrence of both TFs on the cell-type specific sites with the likelihood of their co-occurrence on ubiquitous sites . The larger the Lct score , the greater the association between the two TFs on the CTS-DHSs relative to the ubiquitous DHSs . Thus , the ubiquitously open chromatin regions serve as background model to assess the significance of the cell-type specific co-occurrence . The Lct score is computed for all possible TF pairs in each cell type of interest . Thus TF pairs with the largest positive Lct score are more likely to co-occur in the particular cell type than in ubiquitously open chromatin and are predicted as co-occurring TFs in a cell-type specific manner . Moreover , TF pairs with the largest negative Lct score are TF pairs which co-occur generally on the ubiquitous DHSs and not in a cell-type specific way . We refer to these as ubiquitously co-occurring TF pairs . The method described above is summarized in Fig 2 ( Step 5 ) using the TF pair FOS and OCT4 for illustration . The binding affinity of these TFs is predicted for 10 ESC-specific and 10 ubiquitous DHSs . Selecting the top k = 7 DHSs as bound , the two contingency tables are derived and their significance is assessed with Fisher’s exact test . The log score ratio L compares the significance of the joint binding on CTS-DHSs by FOS and OCT4 to the significance of the joint binding on ubiquitous DHSs by this TF pair . After evaluation of various combinations of the parameters k and l ( see S1 Appendix and S2 Fig for more details ) , the following combination , which resulted in the most consistent results , was selected for the prediction of co-occurring motifs: k = 1000 ( i . e . the top 1000 DHSs ordered by binding affinity are considered as “bound” ) and l = 5000 ( i . e . a total of 5000 CTS-DHSs and 5000 ubiq-DHSs are analyzed ) . With 5000 cell-type specific DHSs , the overlap of DHSs between different cell types is still very low and this is why we generally recommend this setting . The condition of low DHS overlap can also be verified on a new data set and the parameter changed accordingly . The number of 1000 “bound” DHSs has always worked well in our experience . The alternative of systematically testing this cut-off in search for the most statistically significant results seems both overly computationally demanding and the mere number of tests may make it hard to find statistically significant results . Testing all possible TF motifs among different TF groups results in a total of 111241 TF pairs in each of 64 cell types , corresponding to total number of 14239 × 106 tests . The obtained p-values from the Fisher’s exact test were corrected for multiple testing using the Benjamini-Hochberg method [37] by considering each cell type separately . The complete contingency table analysis and statistical testing were realized within the R statistics environment using the log-linear models of MASS package [38] . Figures were created within R using packages ggplot2 [39] and circlize [40] and the networks were created using Cytoscape [41] . Using our general study design allows us to investigate not only the co-binding TF pairs but the overrepresented TF motifs in the cell-type specific enhancers . To this end , we constructed a single two-way-contingency table for each TF and for each cell type . The row variable X distinguishes the bound DHSs from the unbound , whereas the column variable Z distinguishes the cell-type specific DHSs from the ubiquitous DHSs . The independence of variables X and Z can be assessed using Fisher’s exact test . TF motifs with the highest significance are considered as overrepresented TF in the particular cell type . The overrepresentation analysis was conducted within the R statistics environment . Gene and protein functions were determined using the Entrez Gene database [42 , 43 , www . ncbi . nlm . nih . gov/gene] , UniProt Knowledgebase [44 , www . uniprot . org] and the GeneMANIA tool [45 , www . genemania . org/] . Expression analysis of TFs in various cell types was derived from Ensembl [46] and from GTEx [47] .
To investigate their genomic location , we selected the 5000 highest scoring cell-type specific sites ( CTS-DHS ) for each cell type and the 5000 highest scoring ubiquitous sites ( ubiq-DHS ) across all cell types according to a t-statistic based measure . We found that the large majority ( 88% ) of the CTS-DHSs are located in intronic and intergenic regions whereas only 8% are situated in promoters ( defined as the region starting 5000bp upstream of an annotated TSS ) and < 4% overlap with annotated exons ( hg19 Ensembl assembly , Release 75 from genome . ucsc . edu ) . The only exception is the primary T-cell for which 19% of CTS-DHSs were located in exons and 22% were located in promoters . In contrast , the genomic distribution of ubiq-DHSs differs markedly from that of the CTS-DHSs , with 43% of ubiq-DHSs overlapping promoter regions ( see S3 Fig ) . Further , we investigated the GC content of the two types of DHSs . The mean GC content of the CTS-DHSs varies between 39% ( cardiac atrial fibroblast and spinal cord astrocytes ) and 63% ( primary T-cell ) . However , the majority of CTS-DHSs has a mean GC content below 50% , whereas the mean GC content of the ubiq-DHSs lies much higher by 58% ( see S9 Fig ) . Our findings suggest that the CTS-DHSs correspond mainly to cell-type specific enhancers , a conclusion supporting previous studies that analyzed a different data set [23 , 29] or analyzed specific cell lines [20 , 22 , 48 , 49] . As a first test of the power of our approach , we considered to what extent our observations for individual TFs recapitulate previous findings . To this end , we investigated individual TFs that are overrepresented within the identified CTS-DHSs representing cell-type specific enhancers . We expected these individual TFs to include only a subset of the transcription factors important for cell-type specificity . Notably , overrepresented TF motifs within CTS-DHSs are motifs having the highest significance ( i . e . , smallest p-value ) of the Fisher’s exact test in the particular cell type . We identified a high confidence set of individual TFs consisting of the 50 most significant TFs binding accessible chromatin in each studied cell type . Within this set , we identified 23 TFs that were observed to be among the most significant TFs occurring in at least 30 out of 64 cell types . These 23 TFs are generally enriched within the CTS-DHSs compared to ubiq-DHSs , regardless of cell type . This observation confirms recently published findings of [50] describing several characteristics common to cell-type specific chromatin accessible regions . Most of the TFs enriched in CTS-DHSs are known regulators of many multiple genes and are primarily involved in general cellular functions such as ( i ) : apoptosis , energy metabolism or cellular growth ( HIF1 , NRF1 , SP1 ) , ( ii ) cell cycle functions ( E2F , MYC , MAX ) or ( iii ) in general development of organs ( CREB1 , TFAP2A , TFAP2C , EGR family , KLF4 , HIC1 , TEAD2 ) . The fact that these TFs are very important transcriptional regulators of general cellular functions is consistent with their enrichment in the majority of CTS-DHSs . Taken together , these findings suggest that individual TFs overrepresented within CTS-DHSs of multiple cell types perform general cellular functions . Significantly co-occuring TF pairs were defined as pairs with the Lct score larger than the 99 . 5%-quantile of the empirical distribution of all Lct scores in the particular cell type ( see Materials and methods ) . In this way , we predicted a total of 5 257 co-occurring pairs of TF-motifs within the identified CTS-DHS . These significant TF-motif pairs were then assigned to their corresponding pairs of TFs , resulting in a total of 2 359 significant TF pairs . To test whether the identified co-occurring TFs are cell-type specific , we investigated the overlap of the predicted sets of co-occurring TFs between all pairs of cell types . We found that the majority of the predicted TF pairs shows a high degree of cell-type specificity: 1641 ( 70% ) of the co-occurring TF pairs are found in 6 or fewer cell types , of which 856 ( 36% ) are found in one cell type only ( see Fig 3A ) confirming the cell-type specificity of our predictions . As expected , highly related cell types originating from the same tissue showed partial overlaps between the sets of their predicted co-occurring TF pairs . For example , microvascular endothelial dermal lymph cells share 65% of their co-occurring TF pairs with microvascular endothelial lung lymph cells , which are closely related cells both morphologically and functionally . Interestingly , primary cell types such as primary T-cell , hematopoietic progenitor cells ( HPCs ) and embryonic stem cells ( ESCs ) possess very distinct sets of co-occurring TF pairs compared to all other differentiated cell types , see Fig 3B . For example , primary T-cells share a maximum of only 12% of their co-occurring TF pairs with HPCs and only 6% with other T-cells . Similarly , differentiated ESCs share a maximum of only 24% of their co-occurring TF pairs with undifferentiated ESCs and only 18% with other ES cell lines . These observations suggest that primary cell types and differentiated cell types differ substantially not only in their sets of co-occurring TF pairs but also in their CTS-DHSs . On the other hand , we identified 158 co-occurring TF pairs common to at least 30 out of 64 cell types . These common TF pairs include mainly homeobox factors ( ALX1 , POU2F1 , ONECUT , HNF1 , homeodomain NKX factors ) and members of the forkhead-box ( FOX ) family ( see S5 Fig ) which have general functions in cellular and organismal development and cell differentiation [42–44 , 51 , 52] . This finding confirms observations describing partial sequence similarity of cell-type specific open chromatic regions [50] . Our results suggest that the CTS-DHSs are enriched for pairs of homeobox and forkhead-box binding motifs and for a large number of highly cell-type specific TF pairs . Further , we compared the co-occurring TF pairs with the individual TFs overrepresented on the CTS-DHSs described above . For all cell types , co-occurring TF pairs are not just combinations of the single TFs , moreover the co-occurring TF pairs include more cell-type specific TFs such as KLF4 and cMYC in ESCs or tumor-related genes STAT5 and TAL1 in leukemia . We conclude that the TF pairs predicted by CoTRaCTE contribute additional information on top of the single TF overrepresentation analysis . Selecting TF pairs with the smallest Lct scores ( smaller than the 0 . 5%-quantile ) over all cell types identifies TF pairs that preferentially co-occur in ubiq-DHSs rather than in CTS-DHSs ( see S1 Appendix and S6 Fig ) . The ubiquitously co-occurring TF pairs are derived for each cell type separately , resulting in 64 distinct sets of ubiquitous TF pairs . Notably , these sets of ubiquitous TF pairs are almost identical regardless of which cell type was employed to generate them ( see S8 Fig ) . This finding supports our claim that cell-type specific TF cooperativity can be detected only when genomic regions of interest are contrasted with an appropriate genomic background having the same chromatin accessibility . Further , the ubiquitous TF pairs include several TFs , such as ATF , CREB , E2F1 , NFY , NRF1 , SP1 , TP and STAT factors which have previously been described as promoter-specific TFs [19 , 53] . These findings agree with our expectation because ubiquitous DHSs largely overlap with promoter regions ( see S3 Fig ) . To biologically verify our computationally predicted co-occurring TF pairs , we compared these with experimentally-validated direct protein-protein interactions ( PPIs ) between TFs . We compared our predicted co-occurring TF pairs with the atlas of TF-TF interactions inferred from mammalian two-hybrid assays [54] and from other forms of experimental evidence listed in PPI databases [55] . After individual TFs in both sets were assigned to each other , 169 TF pairs ( 7 . 1% out of 2376 predicted TF pairs ) were found in both sets . This corresponds to a large enrichment relative to random expectation if there was no agreement between computational and experimental predictions ( with an odds ratio of 2 . 0 , and a corresponding p-value of p = 1 . 87 × 10−14 , Fisher’s exact test ) . Among these validated TF pairs were included for example GATA and MEF2 in hematopoietic progenitor cells; GATA and SRF in primary T-cells; differentiated ESCs and in HPCs; E2F1 and NFKB in skin fibroblast; SMAD4 and FOS/JUN/AP1 in lung fibroblasts , ciliary epithelial and brain microvascular endothelial cells . Over all studied cell types , the highest proportion of experimentally validated PPIs was found among co-occurring TFs from pulmonary artery fibroblasts ( 11 . 0% ) whereas the smallest proportion was found in mammary epithelial cells ( 5 . 3% ) . Among the co-occurring TF pairs within ubiq-DHS regions , 110 out of 1389 ( 7 . 9% ) pairs were also found to be interacting proteins according to the PPI data ( corresponding p-value = 1 . 8 × 10−6 , Fisher’s exact test ) . The proportions of experimental PPIs among predicted co-occurring TF pairs for all cell types are shown in Fig 4 . Notably , these proportions are much larger than the proportion ( 2 , 7% ) which would be expected by a random selection of the same number of TF pairs . Next , we compared our computational predictions with highly precise experimental predictions of TF cooperativity derived from the chromatin immunoprecipitation technique coupled with high-throughput sequencing ( ChIP-seq ) . The largest available experimental mapping of TF binding regions in human cell lines using the ChIP-seq technique was generated by the ENCODE Project [13] . In an accompanying study , [8] analyzed all 457 ChIP-seq data sets for 87 sequence-specific human TFs in 72 cell lines to determine binding cooperativity for different pairs of TFs . The authors identified peak regions bound by a primary TF and then conducted an overrepresentation analysis for secondary motifs associated with additional TFs . In this way , they identified a total of 155 putatively co-binding TF pairs among 69 investigated TFs . Of the 155 TF pairs identified by [8] , 120 were found to be represented in our dataset of putatively co-occurring TF pairs after assignment of the TF motifs across datasets . Of these 120 pairs , 10 were found by CoTRaCTE within CTS-DHS regions ( odds ratio = 2 . 4 , p-value = 0 . 02 , Fisher’s exact test ) . For example , IRF4 and PAX5 cooperativity was previously identified in the ChIP-seq experiment in lymphoblastoid cell line ( GM12878 ) and our method predicted this TF pair as co-occurring in B-lymphocytes , T-cell , fibroblasts and in astrocytes . Notably , the interaction of these TFs has been previously described as relevant to the innate immune response [56] . A further example is provided by the known interaction partners STAT1 and CEBPB which were not only detected in the HeLa ChIP-seq experiment but also predicted with our method as co-occurring in diverse fibroblasts . Furthermore , the ChIP-seq read counts of CEBPB in the peak regions of STAT1 are correlated ( with r > 0 . 3 ) in lymphoblastoid ( GM12878 ) and in leukemia ( K562 ) cell lines [8] . All TF pairs predicted both computationally by CoTRaCTE and experimentally by the ChIP-seq based method of [8] are listed in Table 2 , including the cell type where the TF pair was predicted to co-occur as well as evidence from the literature for known interactions between TF pairs . Among TF pairs identified by CoTRaCTE but not identified by the ChIP-seq method , 19 TF pairs were found as experimental PPIs in BIOGRID database . All the pairs not identified by ChIP-seq including additional information are listed in S4 File . Further , we compared our computational predictions with the high confidence set representing TF-TF interactions from ENCODE [8] which is based not only on the ChIP-seq experiments but includes information from other ChIP-seq datasets with an analysis of preferred binding arrangements of the heterotypic TFs . Similarly to the previous comparison , we found a significant overlap between co-occurring TF pairs predicted by CoTRaCTE and TF pairs in the high confidence set of interacting TFs ( odds ratio = 7 . 2 , p-value = 3 . 9 ⋅ 10−5 ) . The high confidence set of interacting TFs from ENCODE is visualized in Fig 4B , TF pairs predicted also by CoTRaCTE are highlighted in red . Notably , three TF pairs SP1:EGR1 , SP1:E2F1 and AP1:NFE2L2 were predicted by both , CoTRaCTE and ENCODE and are known interacting proteins in the BIOGRID database [55] ) . As a positive control , we next compared our predicted co-occurring TF pairs with a dataset generated by a computational method for predicting cooperative cell-type specific dimerization of TFs on the DNA molecule [17] . This dataset considered the occurrence of more than 450000 TF motif pairs in cell-type specific DHSs in 78 cell types while accounting for the orientation and spacing of the two motifs . Based on ∼1 . 4 billion tests for enrichment of TF motif pairs with specific orientation and spacing , 603 highly significant cell-type specific TF-TF dimers were predicted . There is a relatively low agreement between the co-occurring TF motifs predicted by our method with the set of TF-TF dimers predicted by [17] with only 44 out of 603 TF pairs ( 7 . 3% , p-value = 0 . 37 , Fisher’s exact test ) identified by both methods . Nevertheless , 5 of the top-10 most significant predicted TF-dimers ( E-box dimer , OCT-SOX heterodimer , IRF homotypic dimer , EBF1 dimer , FOXA1:AR dimer ) , were also predicted by our method . Notably , since 3 of the top-10 predicted TF dimers are homodimers ( which can not be predicted using our method which only considers cooperativity between two distinct TFs ) this leaves only 2 out of the top-10 predictions which remain undetected by our method . The dimerization of all top-10 predicted TF dimers has been independently confirmed in other experimental studies , see S2 Table for summary . Besides testing our method globally using 64 different cell types ( see S3 File ) , we conducted a detailed local analysis of the TF networks in embryonic stem cells ( ESCs ) , by contrasting undifferentiated H7-hESC cells and differentiated H7-hESC cells ( see S1 Appendix and S10 Fig for the local analysis of hematopoietic progenitor cells and K562 cells ) . To investigate the occurrence of cell-type specific TFs , all TF pairs appearing in 30 or more cell lines were removed from the dataset and cell-type specific regulatory networks were constructed using all remaining significantly co-occurring TF pairs . The predicted regulatory networks are shown in Fig 5 where nodes correspond to TFs and pairs of TFs predicted to co-occur are connected with edges . The predicted ESC networks consist of 216 and 234 predicted co-occurring TF pairs for differentiated H7-hESC and undifferentiated H7-hESC cells , respectively . These pairs respectively comprise 127 and 147 distinct TFs of which the majority ( 67 and 76% ) has been shown to be expressed in ESCs ( [46]; green nodes in Fig 5 ) . Moreover , approximately 20% of the TFs are known to function in pluripotency or early development ( rectangle nodes with yellow border in Fig 5 ) . We conclude that the known expression and function of these TFs may provide independent evidence for their potential activity and co-regulation in ESCs . The main regulators in the predicted network in undifferentiated ESCs are OCT4 , NANOG , SOX2 , POU2F1 , LHX3 , ZBTB16 and PAX4 ( see Fig 5A ) . Accordingly , the most important human pluripotent factors OCT4 , NANOG and SOX2 [26] have the highest number of predicted TF partners . Further , other pluripotent factors such as MYC and KLF4 and the majority of known early developmental regulators such as STAT3 , FOXD3 , ESRR , TCF3 , zinc finger proteins and YY1 [26 , 57] are also present in our predicted network . Previous studies of ESCs have shown that the main pluripotent factors OCT4 , NANOG and SOX2 bind in complexes to the regulatory regions of their target genes [57] . This experimental finding is in agreement with our predictions as illustrated by the TF pairs OCT4:NANOG , OCT4:SOX2 , NANOG:SOX/SRY which we predicted as co-occurring in the ESC-specific DHS regions . Further , our predicted ESC networks contain other experimentally verified PPIs including NANOG:TCF/LIF1 , CEBP:OCT/POU2F , STAT3:NFKB and OCT/POU2F:TBP ( red edges in Fig 5 ) . Our predicted undifferentiated ESC-specific network consists of three separated subnetworks connected only via PAX4 and STAT6 . The delineation of these subnetworks suggests that they perform different functions during ESC regulation . One subnetwork includes the main pluripotent factors OCT4 , NANOG , SOX together with CDX , FOXD3 , ZBTB16 , LHX3 , as well as other FOX genes and the NK-Homeoboxes ( blue subnetwork in Fig 5A ) . These TFs are involved in regulation of development decisions and chromatin remodeling and in the maintenance of the pluripotency [25 , 26] . For example , the association of OCT4 and ZBTB16 was shown in the transcriptional network derived by [58] . The second subnetwork ( highlighted in pink in Fig 5A ) includes regulators such as KLF4 , STAT3 , ZIC3 and ZNF148 which are all known targets of the pluripotent factors OCT4 , NANOG and SOX2 and regulators of early embryonic development [19 , 25] . Our findings suggest that this subnetwork might be activated by the pluripotent factors and might carry out a distinct function from that of the first subnetwork . Correspondingly , a distinct regulatory mechanism of KLF4 compared to the subnetwork consisting of OCT4 , SOX2 and NANOG has been previously suggested [25] . Finally , we identified a third subnetwork including known ESC-regulators such as MYC/MAX , TCF3 , TBX5 and YY1 ( orange subnetwork in Fig 5A ) . This subnetwork functions mainly in the regulation of stem cell differentiation and embryonic organ development [45] . Our identification of this subnetwork is supported by three types of independent evidence . First , [12] found that the the core pluripotent factors OCT4 , SOX2 and NANOG co-regulate their target genes in the absence of MYC consistent with our prediction that MYC is localised in a separate subnetwork . Second , the findings of [57] that KLF4 and ESRR co-occurr more frequently with OCT4 than with MYC confirms the separation of this subnetwork . Third , YY1 has also been shown to be an active component of the MYC transcription network in ESCs [59] which is in agreement with our results . For comparison with undifferentiated cells , we investigated the predicted regulatory network of co-occurring TFs in the differentiated H7-hESC cell line . This predicted network shows clear differences from that of undifferentiated ESCs ( see Fig 5B ) and can be divided into four subnetworks . The first subnetwork ( highlighted in blue in Fig 5B ) consists of the pluripotent factors SOX2 , NANOG , FOXC1 and is connected to the second subnetwork ( highlighted in red in Fig 5A ) dominated by the GATA proteins which are known for their important roles in transducing nuclear events that regulate cellular differentiation and embryonic morphogenesis [60] . Following an analysis conducted with GeneMANIA [45] , we observe a clustering of GATA proteins with several TFs involved in the development of organs and tissues such as endocrine system ( ONECUT , NKX2-1 , NKX2-2 , HNF1B ) , muscles ( SRF , POU6F1 ) and hematopoiesis ( TAL1 , EVI1 , ZBTB16 ) . The third subnetwork ( highlighted in orange in Fig 5B ) is also connected with the pluripotent factors and is dominated by the transcription factor ESR ( which possesses similar motifs to those of ESRRA and ESRRB ) . ESR is the most important target of NANOG and serves to maintain cell pluripotency [61 , 62] . In our predicted regulatory network , ESR clusters with the HNF4 and NR2F transcription factors which are important regulators of mesoderm differentiation . The fourth subnetwork ( highlighted in blue in Fig 5B ) is MYC-centered and includes the transcription factors E2F1 , KLF4 as well as zinc finger proteins and the general regulators TFAP2 and SP1 . This finding agrees with the observation of MYC-centric complexes consisting of E2F1 , the zinc finger protein ZFX and CTCF in a previous ChIP-seq study [57] . In conclusion , the predicted transcriptional network in differentiated ESCs includes more cases of pluripotent TFs co-occurring with TFs involved in early cell differentiation than the predicted transcriptional network in undifferentiated ESCs . Another insight into the transcriptional regulatory mechanism is provided by coTRaCTE . Specifically , it can be used to investigate the predicted co-factors of a specific TF of interest in various cell types . As a proof-of-principle , we demonstrate this type of TF-centric analysis using GATA1 , see Fig 6 . GATA1 is a protein which plays an important role in erythroid development [42 , 43] but is also expressed in many other cell types [47] suggesting that it regulates diverse functions in different cell types . Using our approach , we identified several TFs that co-occur with GATA1 in a cell-type specific manner . For example , GATA1 partners with HNF1 in hematopoietic progenitor cells; with PPARA:RXRA in leukemia; with SRF , CDX and FOXP3 in primary T-cells; with SRY , NKX2-1 , FOXF1 , OCT4 and ZBTB16 in differentiated ESCs and with TAL1:TCF3 motif co-occurring in various fibroblasts . Strikingly , EVI1 was identified as a co-factor of GATA in 19 cell types , indicating that it serves as a more general partner of GATA1 . EVI1 is thought to be involved in hematopoiesis , development and cell differentiation as part of the MECOM complex [42 , 43] and is expressed in a large number of tissues [47] . This evidence suggests that EVI1 ( MECOMB ) is a general transcriptional co-factor of GATA1 .
Here , we present coTRaCTE , a statistical method for detecting putatively cooperative TF pairs co-occurring in a cell-type specific manner within accessible chromatin regions . Our approach incorporates two novel refinements which guarantee highly specific predictions of TF cooperativity and which address the limitations of previous methods . First , coTRaCTE distinguishes cell-type specific DNase hypersensitive sites ( CTS-DHSs ) from ubiquitous DNase hypersensitive sites ( ubiq-DHSs ) using 90 DNase-seq experiments and employing a t-statistic-based measure . This statistical method , which was previously applied to another data set [29] , provides a ranking of genomic sites that are consistently DNase hypersensitive in a given cell type , relative to an average profile of all studied cell types . By analysing 64 different cell types in this way , we predict not only chromatin regions that are open in a cell-type specific manner ( i . e . cell-type specific enhancers ) but also chromatin regions that are open ubiquitously among all cells . Second , coTRaCTE determines the cooperativity of TF pairs by contrasting their co-occurrence in the cell-type specific enhancers and ubiquitously open regions . Using the TRANSFAC database of TF binding motifs and the predicted TF binding affinity for both types of region , we determine bound and unbound DHSs for each individual TF . Then , for each pair of TFs , we quantify the overlap between the cell-type specific regions bound by the TF pair using a Fisher’s exact test . We then quantify the overlap between the ubiquitously open regions bound by the TF pair using a second Fisher’s exact test . Cell-type specific regions and ubiquitous regions bound by both TFs are then compared using the log-ratio of p-values from the two Fisher’s exact tests . Notably , by using the ubiquitous regions as background coTRaCTE can detect cooperative TF pairs that are cell-type specific . Using this approach we predicted 2359 TF pairs as co-occurring in the cell-type specific enhancers of 64 cell types . The large majority ( 70% ) of these pairs are either highly specific for a single cell type or show a large degree of overlap in their predicted TF pairs among related cell types originating from the same tissue . Conversely , we identified 158 TF pairs common to at least 30 out of 64 cell types . According to our observations , these TF pairs are more likely to co-occur within cell-type specific enhancers than within ubiquitously open chromatin regions regardless of the cell type considered . This finding agrees with a recent study of accessible chromatin regions [50] which described partial sequence similarity among cell-type specific DHSs . Interestingly , the set of TF pairs found to preferentially co-occurre on ubiquitously open chromatin in contrast to enhancers specific to a given cell-type is almost invariant regardless of the cell-type considered . This finding confirms our expectation that cell-type specific transcriptional regulation takes place mainly within cell-type specific enhancers , whereas general regulation takes place mainly within ubiquitously open chromatin regions . Importantly , the enrichment of individual TFs within cell-type specific enhancers does not show such a clear cell-type specific pattern than that seen for TF pairs . Thus , the cooperative TF pairs contribute additional information on top of the single TF overrepresentation analysis . This result confirms previous findings that regulatory decisions are usually governed by a specific combination of TFs that act cooperatively rather than individually [2 , 63] . To assess the validity of our predictions , we derived cell-type specific regulatory networks from the predicted TF pairs in each particular cell type and investigated TFs present in these networks . In general , more than 75% of TFs in the networks are expressed in the particular cell type as measured by an independent study using RNA-seq experiments [46] . Moreover , roughly one quarter of TFs in the cell-type specific networks are known regulators in the particular cell type . Notably , all TFs in the predicted networks were selected only by their high Lct score without any knowledge of their possible function or expression in the corresponding cell type . This fact underlines the plausibility of our predictions . To validate our results systematically , we compared the predicted co-occurring TF pairs with ( i ) large-scale experimental databases of PPIs [54 , 55] , ( ii ) predictions derived from an analysis of ChIP-seq experiments [8] and ( iii ) a statistical prediction of TF-dimerization [17] . The experimentally-determined PPIs and relationships between TFs derived from ChIP-seq are significantly enriched among our predicted set of cell-type specific co-occurring TFs . When comparing coTRaCTE predictions with the experimentally derived database of direct PPIs , it is important to consider the sensitivity ( true positive rate ) and the false discovery rate ( FDR ) of the experimental method , estimated by [54] to be 25% and 53% , respectively . For this reason , even if the coTRaCTE predictions showed optimal sensitivity , we could only expect a maximum of 25% of the computationally predicted TF pairs to be represented in the experimentally derived atlas . Similarly , even if the coTRaCTE predictions showed optimal specificity , we could only expect a maximum of 47% of the experimentally determined PPIs to be represented among the computationally predicted TF pairs . Furthermore , the differing results from these two methods are consistent with the fundamental methodological differences between coTRaCTE ( which considers cooperative TF binding on the DNA molecule ) and the experimental approaches to detect PPIs ( which measure the general ability of two proteins to form a complex ) . Interestingly , the agreement between the predicted TF-TF dimers [17] and our predicted set of co-occurring TFs is relatively low ( 7% ) . Nevertheless , out of the top seven most significant predicted TF-TF heterodimers having further supporting evidence from the literature , five were also represented in our set of predicted co-occurring TF pairs . This leaves only 2 of the top 7 predicted TF heterodimers undetected by our method . The relatively small concordance between the coTRaCTE set of predictions and the set of TF-TF dimer predictions might be explained by the differing rationales of both prediction methods . Our method is designed to predict pairs of TFs which co-occur preferentially in cell-type specific enhancers compared to ubiquitously open chromatin regions . In contrast , the predicted TF-TF dimer set consists of directly interacting TFs which bind as a dimer to regulatory DNA regions with a fixed spacing and orientation [17] . Apart from validating the coTRaCTE predictions globally across a range of cell types , we performed a detailed local analysis of the predicted TF networks in embryonic stem cells . As expected , we recovered the core pluripotent factors OCT4 , NANOG and SOX2 as the dominant regulators in undifferentiated ESCs as well as recovering the known co-binding of these three TFs . However , we also observed the early developmental regulators such as KLF4 , ESRR and MYC in the predicted networks , as well as several known direct protein interactions ( e . g . NANOG:TCF/LIF1 , CEBP:OCT4 , STAT3:NFKB ) . The predicted network in undifferentiated ESCs is characterized by three subnetworks performing distinct regulatory functions . One subnetwork including OCT4 , SOX2 and NANOG is responsible for the maintenance of the pluripotency . The second subnetwork consists of several regulators of early embryonic development such as KLF4 , STAT3 , ZIC3 and ZNF148 , which are known to be direct targets of the core pluripotent factors . The third subnetwork contains MYC/MAX proteins and is completely isolated from the other subnetworks , consistent with several previous studies [12 , 57 , 59] . In contrast , the predicted regulatory network in differentiated ESCs includes transcriptional regulators with a higher degree of cell-specificity such as LEF1/TCF , GATA4 , EGR and TFAP2 . In the transcriptional network for differentiated ESCs , we identified four smaller subnetworks which perform the following functions: ( 1 ) pluripotency determined by a subnetwork consisting of SOX and NANOG; ( 2 ) early development of organs and tissues; ( 3 ) mesoderm differentiation and ( 4 ) a subnetwork carrying out more general functions . These results suggest that several cell-type specific TFs are highly active after only a few days of ESC differentiation and that this can drive cell differentiation along a developmental trajectory to the determined cell type . In addition to predicting regulatory networks for a particular cell type of interest , coTRaCTE provides information about the co-regulators of a selected TF in various cell types . For example , for GATA1 , we found several cell-type specific co-regulators such as HNF1 in hematopoietic progenitor cells , PPARA:RXRA in leukemia and a general co-regulator EVI1 found to cooperate with GATA1 in more than 15 cell types . Overall , the validation of our predicted co-occurring TF pairs and further analysis of cell-type specific networks confirms that these predictions include a significant proportion of TFs independently identified as either co-occurring or directly interacting . Moreover , the large majority of regulators observed in the transcriptional network specific to a given cell type are actually expressed in the corresponding cell type . In addition , roughly one quarter of these regulators are known to function in the corresponding cell type . Thus , our findings are not only verified by previously reported observations but also reveal novel potential TF co-occupancies that can be validated by further experimentation . Despite these advantages of coTRaCTE , the method also has some limitations . First , it is insensitive to homodimers because we can recognize an interaction only when two different binding sites are bound . Another question that might arise concerns the detection of competitively binding TFs . coTRaCTE clearly is not designed to detect this since our approach does not involve a physical interaction between the competing factors . It would , nevertheless , appear feasible to design an alternative analysis of the cell-type specific DHSs aiming at the delineation of competing factors , e . g . , by including cell-type specific expression data . In summary , our predicted co-occurring TFs provide further insight into cell-type specific combinatorial regulation by transcription factors . We recommend coTRaCTE as a powerful tool for the generation of statistically-rigorous predictions of cooperativity between TF pairs thus accelerating the elucidation of gene regulatory networks not just in human but in any species for which chromatin accessibility data is available . | Differentiation of multicellular organisms into a variety of cell types with different morphology and function is the result of cell-type specific gene expression . The most important regulators of gene expression are transcription factors ( TFs ) binding to cis-regulatory sequences , such as enhancers or promoters . In particular , the combinatorial cooperativity of TFs is essential for defining the cell-type specificity . However , the experimental detection of cooperative TFs on a large scale is very difficult . Here , we develop a new strategy for predicting co-occurring TFs in a cell-type specific manner . We use a simple statistical test of TF co-binding between cell-type specific enhancers and globally active regulatory regions which assures the specificity and significance of predicted TF co-occurrences . Using chromatin accessibility data in 90 cell lines enables us to predict more than 2000 co-occurring TF pairs in 64 cell types . We confirm our predictions by multiple means , including comparison with large-scale experimental data . Based on our method , we obtain new insights into the cell-type specific TF cooperativity , and the complexity of transcriptional regulatory mechanisms . | [
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| 2018 | coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers |
Cytokinesis terminates mitosis , resulting in separation of the two sister cells . Septins , a conserved family of GTP-binding cytoskeletal proteins , are an absolute requirement for cytokinesis in budding yeast . We demonstrate that septin-dependence of mammalian cytokinesis differs greatly between cell types: genetic loss of the pivotal septin subunit SEPT7 in vivo reveals that septins are indispensable for cytokinesis in fibroblasts , but expendable in cells of the hematopoietic system . SEPT7-deficient mouse embryos fail to gastrulate , and septin-deficient fibroblasts exhibit pleiotropic defects in the major cytokinetic machinery , including hyperacetylation/stabilization of microtubules and stalled midbody abscission , leading to constitutive multinucleation . We identified the microtubule depolymerizing protein stathmin as a key molecule aiding in septin-independent cytokinesis , demonstrated that stathmin supplementation is sufficient to override cytokinesis failure in SEPT7-null fibroblasts , and that knockdown of stathmin makes proliferation of a hematopoietic cell line sensitive to the septin inhibitor forchlorfenuron . Identification of septin-independent cytokinesis in the hematopoietic system could serve as a key to identify solid tumor-specific molecular targets for inhibition of cell proliferation .
Cytokinesis as final step of cell division is essential for cell proliferation , but there is a considerable degree of diversity in its underlying mechanisms among eukaryotes . Even within one organism , such as the amoeba Dictyostelium discoideum , cytokinesis may proceed by different mechanisms for cells growing in suspension or in an attachment-dependent manner . This has been impressively demonstrated for the myosin II-deletion mutant of D . discoideum , which could not further complete cytokinesis in suspension but successfully proliferates when attached to surfaces [1] . Hence , it could be speculated that other cells also confine different molecular requirements for attachment-dependent and -independent cytokinesis , although there is little molecular proof for this idea in mammalian cells . Recent support for this idea comes from the observation that in lymphocytes the hematopoietic linage-specific Rho-GAP ARHGAP19 is essential for cytoskeleton remodeling resulting in cell division [2] while in most other cells M-phase GAP ( MP-GAP ) is the major factor restraining RhoA during cell division [3] . Septins , a conserved family of polymerizing GTP-binding proteins regarded as the forth component of the cytoskeleton [4] , organize a ring that serves as a submembranous scaffold and diffusion barrier for various molecules , which is an absolute requirement for cytokinesis in budding yeast [5] , [6] . In metazoans , septins associate with the mitotic spindle , contractile ring , intercellular bridge and midbody at varying degrees [7] , [8] . For example , anillin-dependent recruitment of septins to the intercellular bridge is required for constriction site formation and ingression in HeLa cells [9] , maturation of the midbody ring in Drosophila melanogaster requires septin-dependent removal of anillin via its C-terminal PH-domain [10] , and septins are required for the release of midbody and midbody ring into daughter cells during the subsequent cell division in Caenorhabditis elegans [11] . Perturbation or depletion of one of the major septin subunits , such as the pivotal subunit SEPT7 [12] , [13] , affects multiple steps in mitosis [4] , [14] . In vitro studies with mammalian cell lines have revealed pleiotropic defects in mitotic spindle organization and chromosome alignment [15] , cleavage furrow ingression [16] , and midbody abscission [17] , [18] . Intriguingly , however , depletion of each septin subunit in adherent cells by RNAi abolishes cytokinesis only at low penetrance ( <25% ) [15] , [18] , [19] . Further , mitosis is completely unaffected in T lymphocytes depleted for the pivotal subunit SEPT7 [20] . To explore the molecular mechanism underlying the relative and cell-type specific requirement of septins in physiological systems we manipulated the Sept7 gene in mice and analyzed cytokinesis of cells with deleted Sept7 .
We floxed Sept7 gene ( exon 4 , encoding the GTP-binding P-loop ) in the mouse genome using the Cre-loxP system ( Figure 1A ) . The Sept7flox allele was converted to Sept7− ( null ) allele by using oocyte-specific expression of Cre-recombinase ( ZP3-Cre ) . Sept7−/− ( KO ) embryos were found in utero up to embryonic day 6 . 5 ( E6 . 5 ) -E7 . 0 , but not after E10 . 5 , indicating early embryonic lethality ( Figure 1B ) . As the genetic loss of SEPT9 or SEPT11 causes embryonic death by E10 [21] and E13 [22] respectively , SEPT7 appears no less vital than these major subunits . These data obviously indicate that septins are dispensable for the majority of cells to execute mitosis in early mouse embryo . To probe the impact of the genetic loss of SEPT7 on mitosis in vitro , we prepared primary fibroblasts ( MEFs ) and SV40-large T-immortalized tail fibroblasts ( TFs ) from Sept7flox/flox mice . Cre-transduction via adeno- or retroviral vectors caused significant reduction of SEPT7 and collateral depletion of SEPT2 , SEPT6 , and SEPT9 ( Figure 2A , 2C ) [20] , [23] , following the deletion of the exon 4 ( Figure 2B ) . Of note , a septin-binding contractile ring protein anillin was also reduced ( Figure 2C ) down to 26%–88% depending on the cell line and multiplicity of infection ( cf . Figure S1 ) . Consequently , Sept7−/− MEFs arrested at G2/M in the cell cycle , as was indicated by the absence of a proliferation marker Ki67 , remarkable phosphorylation of histone H3 and decreased overall proliferation ( Figure 2D–2F ) without increased apoptosis ( Figure S2 ) . The incomplete efficiency in infection and/or recombination ( Figure 2G ) caused a Sept7flox/flox/Sept7−/− mosaic culture and a heterogeneity in SEPT7 level after 12 days , which demonstrated that cells without SEPT7 expression were almost exclusively multinucleated and significantly larger than the neighboring mononucleated cells with residual SEPT7 ( Figure 2H and Figure S3A , S3D ) . In detail , of 223 SEPT7-positive cells analyzed by imaging , 222 cells ( 99 , 55% ) were mono-nucleated . Of 56 SEPT7-negative cells , 54 cells ( 96 , 4% ) were bi- ( 38 cells , 67 , 8% ) or multinucleated ( 16 cells , 28 , 6% ) . Time-lapse observation of the same population identified two subsets; one completed cytokinesis normally within 70–130 min ( about 70% of cells ) , while another could not complete cytokinesis within 130 min , displaying stalled cytokinesis yielding binucleated cells after unsuccessful severing of the intercellular bridge ( about 30% of cells ) ( Figure 3A , Figure S4 and video S1 ) . Immunofluorescence analysis of the intercellular bridges and midbodies did not show obvious disorganization of α-tubulin and F-actin in the absence of SEPT7 ( Figure 3B and Figure S3A , S3B , S3D ) . Improper segregation of chromosomes can lead to the formation of chromatin-bridges associated with a delay in abscission and multinucleation [24] . Analysis of the arrested midbody structures in the Sept7−/− revealed absence of persistent chromatin bridges as shown by LAP2 staining ( Figure S5 ) . However , Sept7−/− cells were often accompanied by unresolved α-tubulin aggregates ( arrowheads in Figure 3C ) and about two-fold hyperacetylation of α-tubulin ( Figure 3D and Figure S3C , S3E ) . These data indicate hyperstabilization of microtubules in Sept7−/− cells , as has been observed in interphase HeLa cells [25] and postmitotic primary neurons [26] . Anillin , a contractile ring organizer which interacts with actomyosin and septins , was reduced in interphase nuclei of Sept7−/− cells ( Figure 3E , cf . Figure 2C and Figure S1 ) . However , SEPT7 was dispensable for the targeting of anillin to the cleavage furrow ( Figure 3F ) . Thus , genetic loss of SEPT7 in fibroblasts appeared to affect mitotic spindle and midbody rather than the contractile ring . Next , we examined the aforementioned presumed dispensability of SEPT7 in non-adherent cell lineages . We introduced a bidirectional γ-retroviral mCherry-Cre construct [27] ( Figure S6A , 6b ) into Sept7flox/flox bone marrow cells , which successfully induced recombination ( Figure 4A ) . An interleukin ( IL ) -3/IL-6/SCF-dependent myeloid colony formation assay ( Figure S6C ) revealed that each subpopulation of the Sept7−/− leukocytes exhibited subnormal but sufficient proliferative activity in vitro ( Figure 4B ) . Given that most of these Sept7−/− cells ( Figure 4C ) had undergone more than 10 replication cycles , SEPT7 protein carried over from the original Sept7flox/flox cell had been eliminated . These data indicate that the resistance to the loss of SEPT7 in mitosis is a common trait of the myeloid lineage . To corroborate the dispensability of SEPT7 in myeloid cell mitosis in vivo , we generated lymphocyte-specific Sept7−/− mice , by intercrossing Sept7flox/flox and CD2-iCre lines [28] . We detected efficient recombination in the bone marrow ( Figure S7A ) , spleen , thymus , and lymph nodes ( Figure 4D ) with recognizable volume loss in the spleen and thymus ( Figure 4E ) . Flow cytometric analysis demonstrated complete loss of SEPT7 in cells collected from thymus , while those from spleen contained a minor population that fully expressed SEPT7 ( Figure 4F ) . Viability of lymphocytes from spleen , peripheral lymph nodes ( Figure 4G ) , thymus ( Figure 4H ) , bone marrow ( Figure S7B ) and a number of peripheral blood cells ( Figure S8 ) showed no differences with or without Sept7 . Although SEPT7/6/2/9 had been depleted from Sept7−/− thymocytes ( Figure S9 ) , flow cytometric DNA content analysis did not detect any multinucleated population ( Figure 4I ) . Intriguingly , as opposed to fibroblasts , HeLa cells [25] and neurons [26] , thymocytes did not exhibit microtubule hyperacetylation after septin depletion ( Figure S10 ) . Sept7−/− splenocytes proliferated normally in vitro in response to concanavalin A and IL-2 ( Figure 4J , 4K ) , without forming multinucleated cells ( Figure 4L ) . Taken together , we conclude that Sept7 is dispensable in the proliferation and maturation of B- and T-lymphocytes in vivo , and in the proliferation of splenocytes and myeloid progenitors in vitro . In our search for the factor enabling diverse hematopoietic cell lineages to go through the cell cycle without SEPT7 , we compared the proteome between the fibroblasts and myeloid cells . From a number of candidate proteins we focused our studies on stathmin ( STMN1 ) because of its specific abundance in the blood cell lineages ( Figure 5A ) and biochemical activity . The stathmin family is known to facilitate microtubule depolymerization by sequestering α/β-tubulin heterodimers [29] , [30] . We hypothesized that the scarcity of stathmin in fibroblasts contributes to the stability of the microtubule network , while the abundance of stathmin in hematopoietic cells facilitates the disassembly of spindle microtubules and the disposal of midbodies . To test the latter possibility , we generated Sept7flox/flox MEFs that express stathmin via a doxycycline-regulatable promoter ( Figure 5B ) . Indeed , stathmin overexpression ( to the level of thymocytes ) was sufficient to rescue the mitotic failure of Sept7−/− MEFs ( Figure 5C , 5D ) without changing other complex cellular properties as represented by cell mobility and adhesion measured in a scratch assay ( Figure S11 ) . We then asked whether stathmin overexpression also rescues multinucleation of the Sept7−/− MEFs . For this reason we co-transduced MEFs with pRBid–Cre and the doxycylin-inducible stathmin construct and DAPI-stained and counted mCherry-positive mono- and multinucleated cells after 5 days of cultivation in the presence or absence of doxycycline ( Figure 5E and Figure S12 ) . While the majority of control cells are multinucleated , overexpression of stathmin clearly shifted the MEFs to the mononucleated phenotype . Finally , we ask whether hematopoietic cells proliferating septin-independently require stathmin and whether stathmin-knockdown renders these cells sensitive to septin inactivation . To answer these questions we used the Jurkat human lymphocyte cell line , because manipulation of primary mouse hematopoietic cells in culture was not feasible . To inactivate septins in Jurkat cells , we applied the septin inhibitor forchlorfenuron ( FCF ) [31] , which dampens septin dynamics and induces the assembly of abnormally large septin structures [32] . Stathmin knockdown by siRNA was performed and cells were further cultivated for 48 hours in the presence or absence of different concentrations of FCF ( Figure 5F , 5G ) . siSTMN1 treatment efficiently reduced stathmin levels while the control siRNA did not ( Figure 5F ) . Remarkably , while 50 µM FCF did not inhibit proliferation of Jurkat cells transfected with the control siRNA , siSTMN1-treated Jurkat cells displayed a clear proliferation defect at this concentration of FCF . At higher concentrations of FCF ( 100 µM ) slight cytotoxic effects also reduced proliferation of the control , but the stronger reduction in the siSTMN1-treated cells remained . Taken together , we demonstrated that stathmin can rescue the proliferation block in SEPT7-deficient MEFs and that stathmin is necessary for proliferation of hematopoietic cells in the absence of functional septins . Thus , stathmin is a critical permissive factor whose abundance enables cells to proliferate without septins .
This study has revealed two distinct types of mammalian cytokinesis which vary by the requirement for SEPT7/septins . Consistent with previous studies [5] , [16] , [18] our findings indicate that cell division requires septins in two spatiotemporally distinct processes , first for the organization of the contractile ring and later for midbody abscission . The former became known early on due to the high prominence and its evolutionarily conservation from budding yeast to humans , while the latter had remained unknown due to its cell-type-dependence . Fibroblasts , typical adherent cells , divide in contact with other cells and/or connective tissue in vivo and extracellular matrices and artificial substrate in vitro . In contrast , amoeboid hematopoietic cells grow planktonically in vivo and divide individually in suspension . Our study confirm the role of septins in the recruitment of the microtubule cleaving machinery ( multi-protein membrane associated abscission machinery probably including spastin for local microtubule destabilization ) [7] , [8] to the midbody for final microtubules scission . This system seems to be inactive in the absence of SEPT7 in fibroblasts , leading to midbody stabilization . In the hematopoietic system the abundance of stathmin leads to a passive rescue due to general microtubule destabilization and thus cytokinesis proceeds in a septin independent manner . The supplementation of stathmin is sufficient for fibroblasts to override the loss of SEPT7 and to complete cytokinesis . The abundant expression of stathmin in early embryo [33] , [34] may account for the dispensability of septins up to midgestation . These data indicate that the synergy between septins and stathmin , among other microtubule-regulating proteins , is critical for completion of cytokinesis and midbody abscission . The entire process should depend not only on the quantitative balance of tubulin/stathmin/septin but also on the phosphorylation level of stathmin [29] , [30] . Of note , β1-integrin-blocking antibodies can inhibit cytokinesis of adherent cells , but not their cytokinesis in suspension [35] . Given these and our findings , it is conceivable that non-adherent cells develop less cytoskeletal network than adherent cells , which should reduce the burden for midbody abscission . Conversely , myosin II-deficient Dictyostelium cells can complete cytokinesis on a substrate but not in suspension [36] , indicating that microtubule is not a critical determinant in this case . A recent study with Drosophila revealed that the SEPT7 ortholog peanut ( Pnut ) and other septins are required for planar cell cytokinesis but dispensable for orthogonal cell division in the single-layered neuroepithelium of the dorsal thorax [37] . This finding supports our notion that SEPT7/septins play a context-dependent role in mammalian cytokinesis . Accordingly , SEPT7 is a promising target for the development of solid tumor-selective anti-proliferative therapy without damaging hematopoietic cells . Reciprocally , stathmin could be selectively targeted in hematopoietic malignancies and p53-compromized cancer [38] , [39] .
Two independently developed Sept7 floxed mice strains were used in this study , both targeting exon4 of mouse Sept7 gene using similar targeting strategies . Sept7flox/flox mice ( Sept7tm1Mgl ) were generated as indicated in Figure 1A . Briefly , the targeting vector containing lox sites and FRT sites flanked neomycin cassette was linearized and electroporated in 129Ola ES-cells . Two positive clones ( 42A3 and 44A1 ) obtained by PCR screen were injected into blastocysts for the generation of chimeric mice . Agouti germ line pups were derived from the mating of chimeric male mice , obtained following the blastocyst injection of Sept7 targeted ES-cell clone 44A1 , with C57Bl/6 Flip females . The resulting sept7loxNeo mice were crossed with C57BL/6- ( C3 ) -Tg ( Pgk1-FLPo ) 10Sykr/J Flippase- expressing mice [40] to delete the neomycin cassette retaining the lox-P-flanked ( floxed ) exon 4 leading to Sept7lox mice . Subsequent Cre-recombinase expression will then catalyze exon 4-excision resulting in an additional frame-shift mutation downstream to this exon . For generation of Oocyte specific knockout animals , Sept7 homozygous floxed mice were crossed with B6-Zp3CretmTgCre [41] . Sept7wt/flox:Zp3Cre mice were bred to generate Sept7wt/del mice . Lymphocyte-specific Sept7 knockouts were generated by mating floxed animals with B6-hCD2-iCre mice [28] . In animal experiments age and sex matched , Cre-expressing Sept7 wt and floxed mice were compared . Tail biopsies , cells and colonies were overnight digested at 53 °C in lysis buffer ( 50 mM Tris-Cl ( pH 8 . 0 ) , 100 mM EDTA , 100 mM NaCl and 1% SDS ) containing proteinase-K ( 0 . 5 mg/mL ) . For tissue samples proteins were salted out with extra NaCl . DNA was precipitated with isopropanol , washed with 70% ethanol and dissolved in water . Genotyping PCR were performed with Hotstar Taq ( Qiagen ) with extra Mg2+ under standard conditions with annealing temperature at 53 °C . The primers used were- Sept7-p1 ( 5′- GGT ATA GGG GAC TTT GGG G-3′ ) , Sept7-p2 ( 5′- CTT TGC ACA TAT GAC TAA GC -3′ ) , Sept7-p3 ( 5′- GCT TCT TTT ATG TAA TCC AGG -3′ ) , Cre-sense ( 5′- GAA CCT GAT GGA CAT GTT CAG G -3′ ) , Cre-antisense ( 5′- AGT GCG TTC GAA CGC TAG AGC CTG T -3′ ) , iCre-fwd ( 5′-AGA TGC CAG GAC ATC AGG AAC CTG- 3′ ) , iCre-rev ( 5′-ATC AGC CAC ACC AGA CAC AGA GAT C- 3′ ) , IL2-fwd ( 5′-CTA GGC CAC AGA ATT GAA AGA TCT- 3′ ) , Il2-rev ( 5′-GTA GGT GGA AAT TCT AGC ATC ATC C- 3′ ) , Myo-fwd ( 5′- TTA CGT CCA TCG TGG ACA GC -3′ ) , Myo-rev ( 5′- TGG GCT GGG TGT TAG CCT TA -3′ ) . Myogenin and IL2 gene fragments were amplified as controls for Cre and iCre genotyping respectively . PCR reactions were separated on 2% agarose gels and images acquired using INTAS Gel documentation system . Sept7wt/del mice were mated and plug checked for embryo analysis . Pregnant mice were sacrificed between embryonic day 6–7 . 0 or 10 . 5 days . The embryos were dissected out in cold PBS and cleaned up from extra-embryonic tissues . Whole embryos were overnight digested for DNA isolation and genotyping . Deviations from Mendelian ratios were calculated by Chi-squared test . Sept7 floxed mouse embryonic fibroblasts were generated from E15 day embryos and maintained under standard conditions . Sept7 floxed adult tail fibroblasts ( TFs ) were isolated from 6–8 weeks old mice tail tips . Minced tail tips were sequentially digested with collagenase and trypsin at 37°C and plated on collagen coated dishes in DMEM supplemented with 20% serum , non-essential amino acids and antibiotics . The cells were splitted 1∶4 and maintained in the same growth medium without coated dishes . To immortalize primary TFs , cells were co-transfected with pSV40Tag encoding simian virus 40 large T antigen and pREP8 plasmid ( Invitrogen ) in a 10∶1 mixture; colonies were selected with 2 mM histidinol ( Sigma ) . Jurkat cells were maintained in RPMI-1640 medium supplemented with 15% serum , 1 mM pyruvate and antibiotics . Post electroporation cells were additionally supported by 2 ng/ml IL2 . Antibody against SEPT7 was from IBL international ( #JP18991 ) , Rabbit anti-anillin antibodies were reported earlier ( Watanabe et al . , 2010 ) . Antibodies used for western blot analysis were SEPT2 ( #11397-1-AP , Acris ) , SEPT9 ( #10769-1-AP , Acris ) , SEPT6 ( sc-20180 , Santa Cruz Biotech ) , SEPT8 ( sc-48937 , Santa Cruz Biotech ) , EF2 ( sc-13004-R , Santa Cruz Biotech ) , GAPDH ( #MAB374 , Millipore ) , GFP ( sc-9996 , Santa Cruz Biotech ) and Stathmin ( #3352 , Cell Signaling Technology ) . Antibodies used for Immunofluorescence staining were rabbit anti SEPT7 , SEPT2 , SEPT6 [23] , Ki67 , phospho-Histone-H3 , cleaved caspase-3 ( Cell Signaling Technology ) , tubulin-α ( T6199 , Sigma and sc-31779 , Santa Cruz Biotech ) , LAP2 ( #611000 , BD Transduction lab ) and acetyl tubulin ( T6793 , Sigma ) . All alexa-dye labeled secondary antibodies , tetramethyl rhodamine-conjugated WGA ( #W849 ) and Alexa fluor-647-conjugated phalloidin ( #A22287 ) were from Invitrogen . DAPI for DNA staining was from Carl Roth ( #6335 . 1 ) . Polybrene ( H9268 ) , doxycycline ( D9891 ) , RNAse A ( R4875 ) and propidium iodide ( P4170 ) were from Sigma . Forchlorfenuron ( FCF ) was obtained from Santa Cruz Biotech . IL2 was from ImmunoTools . IL3 , IL6 and SCF were from Peprotech . Primary MEFs were transduced with commercially available adenoviral Cre particles ( AxCANCre2 , TaKaRa , Japan ) . Gammaretroviral particles ( SF91-nlsCre and pRBid–Cre ) were packaged as described previously [27] . Doxycycline inducible retroviral expression vector used for generating Stathmin-IRES-EGFP cell line was packaged as described previously [42] . Immortalized tail fibroblasts were seeded in 24 well plates ( 2 . 5×104 cells/well ) day before transduction . Plates with viral particles in the presence of polybrene ( 8 µg/mL ) were spun at 1200× g for 1 h at 32°C . After overnight virus treatment , cells were washed , medium changed and processed as indicated . For Sept7 deletion in primary lineage negative bone marrow progenitors , cells were trasduced by spinoculation with pRBid-Cre as described for tail fibroblasts . The transduction was repeated to achieve better transduction efficiency . Cells were lysed directly in SDS gel loading dye and western blotting was performed as previously described using gradient SDS-PAGE gels [43] . Cells were grown on glass coverslips and fixed with 4% paraformaldehyde ( PFA ) in PBS . Fixation was performed for 2–5 min at room temperature ( RT ) followed by 20 min at 4°C . Cells were permeabilized with 0 . 25% Triton X-100–PBS for 30 min at RT . Blocking was done using 4% bovine serum albumin ( BSA ) for 1 h at 4°C . Primary antibodies were used at a 1∶50 to 1∶200 dilution in 1% BSA–PBS for 1–2 h . Secondary antibodies or Alexa Fluor 647-conjugated phalloidin/tetramethy rhodamine conjugated WGA was used at a 1∶500 dilution in 1% BSA–PBS . Imaging was performed using a Leica TCS SP2 confocal microscope with standard settings . Fluorescent intensities were quantified using Image J program ( NIH- http://rsb . info . nih . gov/ij/ ) . For immunophenotyping analysis of hematopoietic cells , spleen , thymus and bone marrow cells were isolated , RBC lysed ( Pharmlyse , BD Biosciences ) and analyzed for surface staining with αCD3-FITC ( Clone 17A2 ) [44] , αB220-eFluor450 ( Clone RA3-6B2 , eBioscience ) , αCD4-PerCP ( Clone RM4-5 , Biolegend ) and αCD8β-Cy5 ( Clone RmCD8-2 ) [44] . Samples were analyzed using an LSRII ( BD Biosciences ) . For SEPT7 , acetyl tubulin and propidium iodide staining , thymocytes/splenocytes were fixed with 3× by volume PFA ( 4% ) at RT for 30 min . Washed and resuspended in PBS and absolute methanol was added to 90% concentration final with constant mixing . The methanol permeabilization was continued for 30 min on ice . After 2× PBS wash cells were resuspended in 4% BSA-PBS and blocked at 4°C for 30 min . Cells were stained with primary antibodies ( 1∶100 in 1%BSA-PBS ) at RT for 30 min . After 1× PBS wash , samples were resuspended in secondary antibody dilution ( anti rabbit Alexa fluor-488/anti mouse Alexa fluor-546 - 1∶500 diluted in 1% BSA-PBS ) and incubated for additional 30 min before PBS wash and FACS analysis . For analysis of DNA content fixed cells were treated with nuclear stain solution ( 1× PBS , 100 µg/mL propidium iodide , 100 µg/mL RNAse-A ) at RT for 15 min and analyzed by flow cytometry in Accuri- C6 flow cytometer . Cells were grown on 8 well chamber slides and time-lapse DIC images were acquired ( 1 per 10 min×16 h ) using OLYMPUS FV1000 microscope fitted with 37°C/humid chamber . Bone marrow lineage negative cells were isolated by MACS separation ( Miltenyl Biotech ) and cells cultured for 2 days in the presence of IL3/SCF/IL6 medium . Cell were transduced on day 3 and 4 and left in suspension culture for another 4days . mCherry positive cells were sorted and seeded in 3 cm plates with methyl cellulose medium ( IL3/IL6/SCF ) ( 1000cells/ml/plate ) as described previously [45] . Colonies were photographed , counted and genotyped after 2 weeks of growth . Blood samples were collected in lithium-heparin tubes ( BD Microtainer- LH tubes ) and subjected to differential blood count and analysis with Vet ABC hematology analyzer ( Scil animal care company GmbH , Viernheim , Germany ) . Spleens were asceptically isolated in RPMI medium ( 10% foetal calf serum , non-essential amino acids , antibiotics and 50 µM 2-mercapto ethanol ) and splenocyte suspension obtained by passing through a 10 µm cell strainer . After RBC lysis cells/spleen were plated in a 6 cm plate and incubated at 37°C for 1 h to remove adherent cells . The suspension cells were collected and counted . 5–6×105 cells/100 µl medium/well were seeded in 96 well plates in the presence or absence of 5 µg/ml concanavalin A and 10 ng/mL murine IL2 . Cell proliferation was assayed using WST1 reagent ( Roche Applied Sciences ) as per manufacturer protocol . For measuring fibroblast proliferation , 500cells/100 µl/well were seeded in 96 well plates . Viable cells were quantified daily using WST1 reagent as per manufacturer protocol . Jurkat cells were microporated with control siRNA ( Allstars negative control siRNA- Qiagen ) or siRNA against human Stathmin ( Hs_STMN1_1: 5′-GCUGAGGUCUUGAAGCAGCTT-3′-Qiagen ) using a Microporator MP-100 system . 200 picomoles of siRNA were used per one million cells microporated at 1400 V/20 msec/single pulse following the standard manufacturer's protocol . 24 h post transfection cells were counted and re-seeded at 3×105/ml density in the presence or absence of FCF in v-bottom 96well plates ( triplicate wells ) . After 48 h of treatment cells were collected and viable cell numbers quantified by flow cytometry in the presence of 2 µg/ml propidium iodide and 2 mM EDTA . Quantitative microplate scratch assays were performed with mitomycin-C treated fibroblasts as described previously [46] . Stathmin-IRES-EGFP transduced and sorted Sept7flox/flox cells were stained and seeded in 96 well plates in the presence or absence of 2 µg/ml doxycycline and scratches were made 24 h later after mitomycin pre-treatment . Infrared fluorescent images were acquired using a Li-COR odyssey scanner at 0 h , 6 h and 18 h . Migration indices were calculated and plotted . All mice experiments were conducted according to German and international guidelines and were approved by the ethics committee of Hannover Medical School ( MHH ) . | Cytokinesis is the finalizing step of the complex scenario of mitosis , leading to separation of two sister cells . The cellular mechanism of cytokinesis in eukaryotes differs at least between yeasts , plants and animals . So far , it is also not clear whether all mammalian cells follow the same mechanistic rules of cytokinesis . Here , we demonstrate that , depending on the mammalian cell type , two different pathways could result in completion of cytokinesis , a septin-dependent pathway and a distinct mechanism , which does not require septins prevalent in the hematopoietic system . Using multiple conditional knockouts , we demonstrate this cell type specificity in vitro and in vivo , and present evidence for the involvement of cell-type specific alteration of the microtubule cytoskeleton . Our data , together with the previously available septin knockdown data in cancer cell lines , suggest septins as plausible antitumor targets with high therapeutic index due to lack of off-target effects on hematopoiesis . | [
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| 2014 | Genetic Deletion of SEPT7 Reveals a Cell Type-Specific Role of Septins in Microtubule Destabilization for the Completion of Cytokinesis |
Although the concept that dendritic cells ( DCs ) recognize pathogens through the engagement of Toll-like receptors is widely accepted , we recently suggested that immature DCs might sense kinin-releasing strains of Trypanosoma cruzi through the triggering of G-protein-coupled bradykinin B2 receptors ( B2R ) . Here we report that C57BL/6 . B2R−/− mice infected intraperitoneally with T . cruzi display higher parasitemia and mortality rates as compared to B2R+/+ mice . qRT-PCR revealed a 5-fold increase in T . cruzi DNA ( 14 d post-infection [p . i . ] ) in B2R−/− heart , while spleen parasitism was negligible in both mice strains . Analysis of recall responses ( 14 d p . i . ) showed high and comparable frequencies of IFN-γ-producing CD4+ and CD8+ T cells in the spleen of B2R−/− and wild-type mice . However , production of IFN-γ by effector T cells isolated from B2R−/− heart was significantly reduced as compared with wild-type mice . As the infection continued , wild-type mice presented IFN-γ-producing ( CD4+CD44+ and CD8+CD44+ ) T cells both in the spleen and heart while B2R−/− mice showed negligible frequencies of such activated T cells . Furthermore , the collapse of type-1 immune responses in B2R−/− mice was linked to upregulated secretion of IL-17 and TNF-α by antigen-responsive CD4+ T cells . In vitro analysis of tissue culture trypomastigote interaction with splenic CD11c+ DCs indicated that DC maturation ( IL-12 , CD40 , and CD86 ) is controlled by the kinin/B2R pathway . Further , systemic injection of trypomastigotes induced IL-12 production by CD11c+ DCs isolated from B2R+/+ spleen , but not by DCs from B2R−/− mice . Notably , adoptive transfer of B2R+/+ CD11c+ DCs ( intravenously ) into B2R−/− mice rendered them resistant to acute challenge , rescued development of type-1 immunity , and repressed TH17 responses . Collectively , our results demonstrate that activation of B2R , a DC sensor of endogenous maturation signals , is critically required for development of acquired resistance to T . cruzi infection .
Chagas disease , the chronic cardiomyopathy caused by infection with the intracellular parasitic protozoan Trypanosoma cruzi , remains a major health problem in Central and South America [1] . Although acute Chagas disease may have a fatal outcome , the blood parasitemia , tissue parasite burden ( liver , spleen , and heart ) , and the inflammatory sequel tend to subside with the onset of adaptive immunity . After several years of asymptomatic infection , approximately 30% of infected patients develop a chronic and progressive form of cardiomyopathy [2] . While not excluding a secondary pathogenic role for autoimmunity , studies in humans and animal models support the concept that parasite persistence in myocardial tissues is the primary cause of chronic immunopathology [3–6] . Cohort studies with chagasic patients have linked chronic heart pathology to TH1-type responses [7] , but this proposition was recently called into question by a report indicating that the frequency of IFN-γ-producing effector/memory T cells is inversely correlated with the severity of chronic Chagas disease [8] . Animal model studies established that acquired resistance depends on development of serum antibodies as well as on IFN-γ-producing CD4+ and CD8+ T cells [9–12] . Recent studies indicated that CCR5 has a suceptible phenotype , attributed to impaired recruitment of effector T cells to parasitized heart tissues [13 , 14] . Although the dominant epitope specificities recognized by cytotoxic CD8 T cells are encoded by highly polymorphic genes [15] , it is still unclear how T . cruzi escapes from immune surveillance [16–18] . In the present work , we set out to investigate the mechanims linking innate to adaptive immunity in the mouse model of T . cruzi infection . Early studies about innate resistance mechanisms indicated that macrophages upregulate nitric oxide ( NO ) -dependent trypanocydal responses [19] due to ligand-induced signaling of Toll-like 2 receptors ( TLR2 ) [20 , 21] or TLR4 [22] . More recently , Bafica et al . reported that macrophages sense T . cruzi DNA via triggering of intracellular TLR9 [23] . Interestingly , they showed that acute infection is more severe in TLR2−/− TLR9−/− mice than in TLR9−/− mice or either TLR2−/−- [23] or TLR4-deficient mice [22] , albeit not as much as in the overtly susceptible MyD88−/− mice [24] . While not formally excluding an additive innate role for TLR4 , these collective studies suggested that cooperative activation of TLR2 and TR9 may account for the bulk of protective IFN-γ responses generated by MyD88-dependent signaling pathways [23 , 24] . Of note , analysis of macrophage activation by MyD88-independent pathways revealed that TLR/TRIF coupling promotes NO-dependent microbicidal responses through upregulation of type I interferons [25 , 26] . In spite of evidence that mice deficient in IL-12 [27] are highly susceptible to T . cruzi infection , it is still uncertain if induction of TH1-responses is strictly dependent on dendritic cell ( DC ) maturation by TLRs/MyD88-dependent pathways . Pertinently , it was reported that spleen cells from MyD88−/− mice display small yet significant production of IL-12 and IFN-γ [24 , 28] . These observations imply that IL-12-dependent Th1 responses may be also controlled by MyD88-independent mechanisms , such as the NKT/CD1d pathway [29] , or by endogenously released bradykinin ( BK ) , an endogenous danger signal driving DC maturation [30–32] . “Kinins” , a small group of mediators related to the nonapeptide BK , activate immature DCs [30] as well as several other cell types through the binding to distinct subtypes of G-protein-coupled receptors: B2R ( constitutive ) and B1R ( inducible ) [33–36] . The B2R agonists , BK or lysyl-BK ( LBK ) , are proteolytically excised from an internal segment of their parental ( glyco ) proteins , high or low molecular weight kininogens , by plasma or tissue kallikreins , respectively [33] . In the settings of infections , however , kinins can be generated through the direct action of microbial cysteine proteases , such as gingipain of Porphyromonas gingivalis [37] and cruzipain ( CZP ) , the major cysteine protease of T . cruzi [38–41] . Using a subcutaneous model of T . cruzi infection , we recently demonstrated that trypomastigotes release kinins in peripheral tissues through the activity of CZP [31] . Once liberated from plasma borne–kininogens , the short-lived kinin peptides activate CD11c+DCs via B2R , inducing IL-12 production and stimulating the migration of these antigen-presenting cells ( APCs ) from the periphery to the draining lymph nodes , where they initiate TH1-like responses against T . cruzi [31 , 32] . Here we report that B2R-deficient mice infected intraperitoneally by T . cruzi display a typical susceptible phenotype . Adoptive cell transfer experiments demonstrate that CD11c+ DCs activated by the endogenous kinin/B2R-signaling pathway are critically required for the induction and/or maintenance of activated effector CD4+ and CD8+ T cells , while limiting the development of potentially detrimental IL-17-producing CD4+ T cell ( TH17 ) responses in mice acutely infected with T . cruzi .
In order to test the hypothesis that kinins may contribute to immune control of T . cruzi infection [30 , 31] , we injected intraperitoneally B2R+/+ C57BL/6 and B2R−/− mice with tissue culture trypomastigotes ( TCT ) of either Dm28c strain ( 1 × 106 ) or Brazil strain ( 1 × 104 ) . The data shown in Figure 1 indicate that wild-type mice infected with Dm28c TCT developed a low blood parasitemia and all the animals survived ( Figure 1A , higher panel ) . In contrast , B2R−/− mice infected with Dm28c showed a precocious blood parasitemia ( day 13 post-infection [p . i . ] ) , which further increased ( approximately 3-fold ) as the infection continued ( 23 d p . i . ) . Mortality rates indicated that B2R−/− mice infected by Dm28c TCT started to die earlier ( day 16 ) than wild-type mice and were all dead by day 27 ( Figure 1A , lower panel ) . We then studied the outcome of infection with the Brazil strain . The results ( Figure S1 ) show that wild-type mice displayed a relatively low blood parasitemia and the mortality rate did not exceed 20% . In contrast , the B2R−/− mice infected by Brazil strain developed increased blood parasitemia , and 80% of these animals were dead by day 28 ( Figure S1 ) . We then further characterized the outcome of intraperitoneal infection with the Dm28c strain , using a lower inoculum ( 6 × 105 ) . Analysis by real-time PCR ( qPCR ) showed that heart tissues of infected B2R−/− mice ( 14 d p . i . ) contained approximately 5-fold higher content of parasite DNA as compared to wild-type heart ( Figure 1B ) . Surprisingly , we found that the parasite tissue burden in the spleen was very low both in B2R+/+ ( 0 . 30 ± 0 . 09 fg/100 ng host DNA ) and B2R−/− ( 0 . 46 ± 0 . 21 fg/100 ng host DNA ) mice ( Figure 1B ) . Thus , unlike the scenario observed in extra-lymphoid tissues , parasite outgrowth in the spleen is controlled by mechanisms that do not critically depend on activation of the kinin/B2R pathway , at least so at relatively early stages ( 14 d ) of infection . Since the tissue parasitism in the spleen of wild-type and B2R−/− mice ( 14 d p . i . ) was marginal , we checked whether type-1 effector cells were generated in lymphoid tissues of both mice strains . Recall assays indicated that splenocytes from wild-type or B2R−/− vigorously secreted IFN-γ upon stimulation with soluble T . cruzi antigen ( Ag ) ( Figure 2A ) . Controls showed that , in the absence of T . cruzi soluble Ag , there was no significant production of IFN-γ by the splenocytes ( Figure 2A ) . We then scrutinized the ex vivo recall responses of CD4+ or CD8+ T cells derived from either wild-type or B2R−/− spleen ( isolated from infected or naïve mice , as controls ) using wild-type CD11c+ DCs ( purified from normal spleen ) as APCs , to exclude the possibility that eventual defects in Ag processing/presentation by B2R−/− DCs could interfere with our “read-outs” . In keeping with the potent type-1 response elicited by unfractionated wild-type and B2R−/− splenocytes ( 14 d p . i . ) , fluorescent activated cell sorting ( FACS ) analysis showed presence of high and comparable frequencies ( Figure 2B , lower panel ) of IFN-γ-producing CD4+ and CD8+ T cells in the spleens of wild-type and B2R−/− mice ( Figure 2B ) . Controls performed with Ag-stimulated CD4+ or CD8+ T cells isolated from naïve mice did not generate significant frequencies of IFN-γ-producing cells . Consistent with the similar FACS profiles , ELISA assays showed that IFN-γ was vigorously secreted by Ag-responsive splenic CD4+ or CD8+ T cells , irrespective of the mouse strain origin ( Figure 2C ) . We then checked if the presence of type-1 CD4+ and CD8+ effector T cells was maintained in the spleen as the infection continued . Recall assays performed 2 wk later ( 28 d p . i . ) indicated that IFN-γ production by wild-type splenocytes remained vigorous , while the type-1 response of Ag-stimulated B2R−/− splenocytes declined sharply ( Figure 3A ) . We then repeated this analysis using CD4+ or CD8+ T cells purified from the spleens of infected wild-type mice or B2R−/− mice , using wild-type DCs as APCs . Consistent with the data obtained with splenocytes , we found that Ag-stimulated T lymphocytes ( CD4+ or CD8+ ) isolated from B2R−/− spleen ( 28 d p . i . ) secreted significantly lower levels of IFN-γ as compared to wild-type splenic T cells ( unpublished data ) . We then performed FACS analysis to further characterize the phenotypic changes that occurred in the spleen , as the acute infection advanced ( 28 d p . i . ) . Our results ( Figure 3B ) showed that Ag-stimulated T cells isolated from wild-type spleen showed high frequencies of IFN-γ-producing CD4+ and CD8+ T lymphocytes . Moreover , a significant fraction of activated CD4+ and CD8+ T cells isolated from spleen of wild-type infected mice displayed the CD44 surface marker . As expected , addition of Ag to CD4+ or CD8+ T cell cultures from naïve mice did not lead to IFN-γ production ( Figure 3B , lower panel ) . In contrast , B2R−/− spleen presented low frequencies of IFN-γ-producing CD4+ or CD8+ effectors ( CD44− ) ( Figure 3B ) . Although we have no direct evidence that the Ag-responsive T cells detected ex vivo include functionally active effectors , it is worthwhile mentioning that adoptive transfer of CD4+/CD8+ T cells ( isolated from wild-type mice at 60 d p . i . ) into B2R−/− mice rendered these recipient mice resistant to lethal infection ( 0% mortality , n = 5; three independent experiments ) , as compared to non-manipulated B2R−/− mice ( 100% mortality ) or B2R−/− mice that received CD4+/CD8+ T cells from normal wild-type mice ( 100% mortality ) . As mentioned earlier in this section , we found a 5-fold increase of T . cruzi DNA in the heart of B2R-deficient mice at day 14 p . i . , as compared to wild-type heart ( Figure 1C ) . In view of these findings , we set out to determine if cardiac tissues of wild-type and B2R−/− mice contained type-1 effector T cells . Recall assays ( again using wild-type splenic CD11c+ DCs as APCs ) showed that IFN-γ production by intracardiac B2R−/− CD4+ T cells was significantly diminished ( over 50% ) as compared to responses elicited by experienced CD4+ T lymphocytes isolated from wild-type heart at 14 d p . i . ( p < 0 . 01 ) ( Figure 4 ) . Similarly , the initial recall response of intracardiac CD8+ T cells isolated from B2R−/− mice was approximately 60% lower than that of wild-type CD8+ T cells ( Figure 4 ) . We then checked if the type-1 cytokine response of intracardiac T cells from B2R−/− mice was further compromised as the infection continued . The FACS profiles of wild-type-infected mice ( 28 d p . i . ) revealed high frequencies of IFN-γ-producing intracardiac CD4+ and CD8+ T cells ( Figure 5 ) . In addition , we found that the CD44 marker characteristic of activated T cells was present in a significant proportion of wild-type intracardiac CD4+ T cells , and ( to lower extent ) also in the CD8+ T cell subset ( Figure 5 , upper and lower panels ) . In contrast , B2R−/− mice exhibited very low frequencies of CD4+ and CD8+ T cells in the intracardiac CD3+ T cell pool at day 28 p . i . ( Figure 5 ) . Following the same trend , IFN-γ-producing CD4+ or CD8+ effector T cells , and activated phenotypes ( CD44+CD4+ and CD44+CD8+ T cells ) were virtually absent from B2R−/− heart . Collectively , these results suggest that activation of the endogenous kinin/B2R signaling pathway in T . cruzi–infected mice may have an impact on the control mechanisms affecting the temporal and spatial activity of type-1 effectors . Considering that the type-1 responses of B2R−/− mice were depressed both in the heart ( as early as 14 d p . i . ) and spleen ( 28 d p . i . ) , we then asked if these effects were coupled to TH2 upregulation . Our results indicated that Ag-stimulated T CD4+ T cells ( isolated from B2R−/− heart or spleen ) did not upregulate IL-4 production ( unpublished data ) . Since IFN-γ inhibits TH17 lineage development in vitro [42 , 43] , we wondered if the reduced TH1 responses observed in B2R−/− mice were accompanied by rises of IL-17- and TNF-α-producing T cells . Recall responses made at 28 d p . i . ( Figure 6A ) revealed that splenic CD4+ T lymphocytes from wild-type mice did not secrete significant levels of IL-17 , while splenic B2R−/− CD4+ T cells upregulated IL-17 . The same trend was found when we measured TNF-α levels secreted by experienced B2R−/− CD4+ T cells ( Figure 6B ) . Similar data were obtained when we compared Ag-stimulated responses of intracardiac CD4+ T cells isolated from B2R−/− versus wild-type mice , as discussed later in this section . Collectively , these data suggest that the TH17/TH1 ratio was drastically increased as the acute infection advanced in the highly susceptible B2R−/− mice . Since type-1 responses were impaired in infected B2R−/− mice , we sought to determine if IL-12 responses were preserved , or not , in these mutant mice . To this end , we inoculated Dm28c TCT ( 1 × 106 ) intravenously in wild-type and B2R−/− mice , isolated splenic CD11c+ DCs 18 h p . i . , and measured IL-12 production by FACS . The results ( Figure 7A ) showed a marked increase in the frequency of IL-12-producing CD11c+ DCs ( 8% ) in B2R+/+ in relation to non-infected controls ( no IL-12 staining ) . In contrast , splenic CD11c+ DCs isolated from infected B2R−/− mice showed a low frequency ( 2% ) of IL-12-positive cells ( Figure 7A ) . These results were corroborated by ELISA determinations of IL-12 responses produced by DCs isolated from intravenously infected mice ( Figure 7B ) . Of note , we found that macrophages ( CD11b+ F4/80+ ) from infected wild-type and B2R−/− mice show enhanced production of IL-12 as compared to naïve mice , suggesting that alternative mechanisms ( i . e . , B2R-independent ) may govern IL-12 production by splenic macrophages ( unpublished data ) . Extending these in vivo studies to BALB/c mice , these animals were pre-treated , or not , with the B2R antagonist HOE-140 before intravenous injection of TCT . The FACS profiles showed a sharp increase of IL-12-positive CD11c+ DCs in BALB/c mice injected with either TCT ( Figure S2 ) or BK ( positive control ) ( Figure S2 ) . In contrast , BALB/c mice pre-treated with HOE-140 showed a reduced frequency of IL-12-positive CD11c+ DCs ( Figure S2 ) . Collectively , the data indicate that B2R drives IL-12 production by splenic DCs , at least at very early stages of the infection . We then carried out in vitro studies to verify if the parasites could induce the maturation of CD11c+ ( splenic ) DCs through the activation of the kinin/B2R signaling pathway . IL-12 production and surface expression of co-stimulatory proteins were used as read-out for DC maturation . FACS analyses showed that CD11c+ DCs ( BALB/c ) did not produce significant IL-12 levels in the absence of parasites ( Figure 7C ) . In contrast , IL-12 production was drastically increased upon addition of exogenous BK ( positive control ) or TCT , whereas HOE-140 cancelled both stimuli ( Figure 7C ) . Notably , TCT induced IL-12-producing DCs irrespective of the presence/absence of lisinopril , a rather selective inhibitor angiotensin-converting enzyme ( ACEi ) ( Figure 7 ) . Specificity controls confirmed that HOE-140 did not interfere at all with the magnitude of IL-12 responses induced by lipopolysaccharide ( LPS ) ( Figure 7C ) . In agreement with the FACS data , ELISA determinations of IL-12 levels in cultures supplemented with HOE-140 confirmed that TCT activate immature DCs through B2R ( Figure 7D ) . Controls in the absence of pathogen indicated that lisinopril or HOE-140 as such did not induce IL-12 production by DCs ( Figure 7C ) . Additionally , DCs cultivated with either TCT or BK ( positive control ) displayed increased surface expression of CD40 and CD86 ( Figure 7E ) . Of note , HOE-140 cancelled the phenotypic changes induced by TCT ( Figure 7E , upper and lower panels ) , while responses induced by BK were significantly reduced by this B2R antagonist ( Figure 7E , lower panel ) . Since TCT generate kinins via CZP while invading endothelial cells , we next asked if parasite cysteine proteases were required for DC activation . This question was addressed by pre-incubating TCT with methylpiperazine-Phe-homoPhe-vinylsulfone-benzene ( VSPh ) , an irreversible inhibitor of CZP . After washing the VSPh-TCT , they were added to DC cultures . Whether using FACS and ELISA , we found that VSPh-TCT failed to drive significant IL-12 production by DCs ( Figure 7C and 7D ) , adding weight to the concept that the parasite relies on CZP to generate the innate kinin stimuli . In order to verify whether the B2R−/− CD11c+ DCs were fully capable of responding to TLR agonists , we compared the in vitro response profile induced by cytosine-phosphate-guanine ( CpG ) and LPS . As shown in Figure 7F , IL-12 responses were of the same magnitude as compared to wild-type C57BL/6 DCs . Moreover , HOE-140 did not interfere with wild-type DC responsiveness to CpG and LPS ( Figure 7F ) . Notably , the magnitude of B2R−/− DC response to TCT was nearly 10% of IL-12 responses observed in wild-type CD11c+ DCs ( Figure 7F ) . As expected , TCT or BK elicited vigorous IL-12 production in CD11c+ DCs from wild-type mice . In both cases , the IL-12 response was partially blocked by HOE-140 ( Figure 7F ) . In contrast , BK did not induce IL-12 in B2R−/− DCs ( Figure 7F ) . As mentioned earlier , we found that production of IFN-γ by Ag-experienced CD4+ and CD8+ T cells from B2R−/− spleen and heart declined sharply as the infection continued ( 28 d p . i . ) . In view of those findings , we asked whether the deficient type-1 responses of B2R−/− mice were restored upon adoptive transfer of wild-type DCs . To address this question , we adoptively transferred ( intravenously ) immature B2R+/+ CD11c+ DCs ( 106 cells ) into B2R−/− mice before injection of the parasites . As controls , recipient B2R−/− mice received an equivalent number of CD11c+ DCs isolated from donor B2R−/− spleen . As expected , our controls showed that B2R−/− mice succumbed ( 100% mortality , n = 5; three independent experiments ) at day 30 . In contrast , 100% of the B2R−/− recipient mice reconstituted with B2R+/+ DCs survived the acute challenge . Of note , the mice of the specificity control group ( B2R−/− DCs → B2R−/− mice ) succumbed ( 100% ) to the infection , thus ruling out the possibility that adaptive immune function was restored due to non-specific activation of these APCs during the DC isolation procedure . We then ran another set of experiments to verify if the DC transfer maneuver had restored ( type-1 ) acquired immunity of B2R−/− recipient mice . Recall assays performed at day 28 p . i . confirmed that splenic or intracardiac ( CD4+ or CD8+ ) T cells from control B2R−/− mice secreted lower levels of IFN-γ as compared to experienced CD4+ or CD8+ T cells isolated from B2R+/+ spleen or heart ( Figure 8A ) . Notably , B2R−/− mice that received adoptive transfer of B2R+/+ DCs recovered the ability to generate IFN-γ-producing CD4+ and CD8+ T cells ( Figure 8A ) . Conversely , the DC transfer to B2R−/− mice repressed the secretion of IL-17 ( Figure 8B ) and TNF-α ( Figure 8C ) by Ag-experienced ( splenic or intracardiac ) CD4+ T cells of the reconstituted B2R−/− mice , therefore simulating the phenotype of wild-type-infected mice .
In the present work , we have demonstrated that the immune dysfunction of B2R−/− mice infected intraperitoneally with T . cruzi is a consequence of defective sensing of endogenously released kinins by immature CD11c+ DCs . Our analysis of the adaptive immune responses of infected B2R−/− appointed a role for the kinin signaling pathway in the development of type-1 effector T cells . The critical importance of DCs as sensors of kinins was confirmed by adoptive cell transfers ( wild type DC→ B2R−/− mice ) , which reversed the susceptible phenotype of B2R−/− mice while restoring the development of type-1 effector T cells , both in the spleen and cardiac tissues of recipient B2R−/− mice . The notion that the kinin-releasing trypomastigotes induce DC maturation through B2R is supported by the following experimental evidence . First , our in vitro studies showed that TCT vigorously induced IL-12 responses in splenic DCs originating from wild-type ( C57BL/6 ) mice , while failing to activate B2R−/− DCs . Second , we demonstrated that HOE-140 , a specific antagonist of B2R , efficiently blocked DC maturation ( IL-12 induction , upregulation of CD80 , CD86 , and CD40 ) . Furthermore , the irreversible inhibitor of CZP ( K11777 ) mitigated the IL-12 stimulatory activity ( B2R-driven ) of TCT , thus implicating the major cysteine protease of T . cruzi in the kinin generation mechanism . Extending these observations to the in vivo settings , we then analyzed IL-12 production by splenic CD11c+ DCs isolated 18 h after systemic inoculation ( intravenously ) of Dm28c TCT . Experiments performed with BALB/c mice showed that mice pre-treated with HOE-140 presented reduced frequencies of splenic CD11c+ IL-12+ DCs . Adding weight to these results , we demonstrated that TCT induced high frequencies of CD11c+ IL-12+ DCs in wild-type ( C57BL/6 ) spleen , while failing to evoke significant IL-12 responses in DCs isolated from B2R−/− spleen . Notably , preliminary studies indicated that macrophages ( CD11b+F4/80+ ) isolated from the spleen of these wild-type and B2R−/− mice develop comparable IL-12 responses . Given that type-1 immune responses in the spleen of B2R−/− mice are well preserved at day 14 p . i . , it is possible that macrophages activated by alternative routes provide the IL-12 signals that drive adaptive immunity in this secondary lymphoid tissue . Although we cannot claim that conventional DCs are the primary or even unique in vivo targets of T . cruzi in the spleen , the above mentioned results support the concept that kinin-releasing pathogens may drive DC maturation in vivo through the activation of G-protein-coupled B2 receptors [32] . Since lymphoid tissues are irrigated by non-fenestrated capillaries , we may predict that trypomastigotes invading the splenic stroma are faced with an abundant supply of blood-borne proteins , such as kininogens . Given biochemical evidence that interactions of high molecular weight kininogens with heparan sulfate proteoglycans potentiate the kinin-releasing activity of CZP [40] , it is plausible that the extracellular trypomastigotes might promptly liberate these paracrine signaling peptides while moving through extracellular matrices , hence driving DC maturation via B2R [31 , 32] . At first sight , our finding that TCT induce DC maturation via the endogenous kinin/B2R pathway appears to conflict with the well-established concept that innate sentinel cells sense pathogens via pattern recognition receptors ( PRRs ) , such as the members of the TLR family [28 , 44] . Indeed , early studies of macrophage ( IFN-γ-primed ) interaction with T . cruzi ( Y strain ) suggested that TLR2 and TLR4 ligands [20–22] are major drivers of innate responses in T . cruzi infection . In a limited attempt to investigate the functional relationship of B2R and TLRs , we examined the outcome of TCT interaction in vitro with CD11c+ DCs ( splenic origin ) derived from either TLR2−/− or TLR4d/d mice . Our results indicated that TCT induced vigorous IL-12 responses both in TLR2−/− DCs and TLR4d/d DCs ( unpublished data ) . Moreover , we found that addition of HOE-140 to the TCT/DC culture system blocked IL-12 responses by TLR2−/− or TLR4d/d DCs ( unpublished data ) . Admittedly , complementary studies with DCs from double TLR2/TLR4 knockout mice and MyD88−/− mice are required to rule out the possibility that B2R-responsive phenotypes of TLR2−/− DCs and TLR4d/d DCs reflect compensatory responses , respectively induced by TLR4 and TLR2 ligands of T . cruzi [20–22] . The intertwined nature of the innate pathways controlling IL-12 production by APCs is illustrated by the recent demonstration [23] that T . cruzi DNA potently induces IL-12 production by mouse macrophages through the activation of TLR9 . Given the evidence that DCs are parasitized by T . cruzi [45] , it will be interesting to determine if endogenous ( BK/LBK ) and exogenous ( T . cruzi DNA ) danger signals may activate their respective sensor receptors , B2R and TLR9 , at distinct temporal stages ( i . e . , early and late ) of intracellular infection . While examining the frequencies of type-1 effectors in extra-lymphoid and lymphoid tissues of wild-type and B2R−/−-infected mice , we became aware that B2R deficiency affected the temporal and spatial distribution of IFN-γ-producing CD4+ and CD8+ T cells . Recall assays performed at day 14 p . i . revealed weakened IFN-γ production by intracardiac CD4+ and CD8+ T cells isolated from B2R−/− mice . However , we found high and comparable frequencies of INF-γ-producing T cells in the spleen of the same B2R−/− and wild-type mice . Since the parasites are scarcely found in the spleens of wild-type and B2R−/− mice , we may infer that activation of the kinin/B2R pathway is dispensable for early induction of type-1 effectors in the spleen . Adoptive cell transfer studies are required to find out if the induction of these early type-1 effector T cells is controlled by MyD88-coupled pathways [24] , such as those triggered by TLR2/TLR9 [23] and/or by IL-1R/IL-18 R [44] . In addition , it is possible that IL-12 induction by the NKT/CD1 pathway [29] may also contribute to early development of type-1 effectors in lymphoid tissues . It is intriguing that intracardiac CD4+ and CD8+ T cells from B2R−/− mice ( 14 d p . i . ) showed impaired production of IFN-γ , despite the fact that the spleen of these mice displayed high frequencies of type-1 effectors . Coincidently , tissue parasite burden is drastically increased in B2R−/− heart , thus showing an inverse correlation between these two parameters at day 14 p . i . Although we cannot a priori assume that Ag specificities of T cells recruited to the heart of wild-type and B2R−/− mice at 14 d p . i . are necessarily the same , independent studies performed with the Brazil [46] and Y strain of T . cruzi [47] converged in appointing cytotoxic CD8+ T cells as the key effectors controlling intracellular parasite outgrowth in cardiac tissues . So far , efforts to characterize the Ag specificity of intracardiac CD8+ T cells in our infection model have been hampered by the findings that Dm28c T . cruzi strain did not present open reading frames for genes coding for ASP-2 antigens [48] , which in other systems provide dominant epitopes recognized by cytotoxic CD8+ T cells [46 , 47] . In spite of these limitations , it is conceivable that immunoregulatory dysfunctions were responsible for the weakened type-1 responses observed in peripheral T cells from B2R−/− mice . For example , it is possible that the migratory competence of effector T cells generated in lymphoid tissues may depend on DC activation via the kinin/B2R pathway . Pertinently , recent analysis of the susceptible phenotype of CCR5−/− mice infected with T . cruzi implicated this chemokine receptor in the recruitment of CD8+ and CD4+ effector T cells into infected heart [13 , 14] . Given these precedent findings , it will be worthwhile investigating if B2R and CCR5 signaling , whether acting separately or in conjunction , might promote the migration of effector T cells to peripheral sites of infection , such as the heart . As the infection advanced ( 14→28 d ) , wild-type mice developed high frequencies of IFN-γ-producing CD4+ and CD8+ effector T cells , both in the spleen and heart . Interestingly , a significant proportion of these Ag-responsive T cells displayed activated ( CD44+ ) phenotypes . In contrast , B2R−/− mice showed negligible frequencies of activated type-1 effectors at day 28 , both in spleen and heart . Of note , we found that the intracardiac CD4+ and CD8+ T populations recovered from the CD3+ pool of B2R−/− mice were significantly contracted ( Figure 5 ) . Considering that B2R−/− mice recovered the capacity to mount protective type-1 responses upon adoptive transfer of wild-type DCs , it is possible that maintenance of T cell homeostasis may depend , at least to some degree , on DC responses elicited by endogenously released kinins . Albeit speculative , this hypothesis is worth exploring in light of independent reports showing that aberrant T cell apoptosis is the primary cause of the immunoregulatory abnormalities underlying host susceptibility to acute infection by the Dm28c strain of T . cruzi [49] . Another intriguing phenotypic characteristic of infected B2R−/− mice emerged when we monitored production of IL-17 and TNF-α in our recall assays . Unexpectedly , we found that the weakened TH1 responses of B2R−/− CD4+ T cells ( whether isolated from the spleen/heart ) at day 28 d p . i . was accompanied by upregulated production of IL-17 and TNF-α , two pro-inflammatory cytokines associated with the effector activity of TH17 cells . Recently characterized as a separate lineage of pro-inflammatory T helper cells distinct from conventional TH1 and TH2 cells [42 , 43] , TH17 cells differentiate from naïve precursors under the critical influence of IL-6 and TGF-β1 [50] . It is also known that committed TH17 cells depend on the IL-23 survival signal to develop their pro-inflammatory function in vivo [51] . Notably , at early stages of infection ( 14 d p . i . ) , there was no significant production of IL-17 and TNF-α by spleen- or heart-derived T cells from infected B2R−/− mice , whether detected by conventional recall assays or polyclonal activation with anti-CD3 antibodies ( unpublished data ) . It is unclear why the TH1/TH17 balance was inverted as the acute infection progressed in B2R−/− mice . Recently , IL-27 was identified as the cytokine that suppresses TH17 differentiation driven by IL-6 and TGF-β via STAT-1 , independently of IFN-γ [50] . Interestingly , T . cruzi–infected WSX-1 mice ( deficient in the IL-27Ra ) [52] develop severe hepatic injury , correlating with overproduction of various pro-inflammatory cytokines , such as IL-6 , TNF-α , and IFN-γ [52] . Although TH17 responses were not evaluated in T . cruzi–infected WSX-1 mice , these animals strongly upregulated TH2 cytokines [52] . However , we were unable to detect IL-4 production or IgG isotype switching in infected B2R−/− mice , indicating that these mice strains do not share the same phenotype . Importantly , the recovery of type-1 responses in DC recipient B2R−/− mice was associated with reduced production of IL-17 and TNF-α . Additional studies are underway to determine if DCs activated by the kinin/B2R pathway may influence TH1/TH17 lineage development in T . cruzi infection via IL-27 , or through alternative mechanisms . Collectively , our results have linked development of acquired resistance to T . cruzi infection to DC functional responses controlled by the kinin/B2R signaling pathway . Our study provides a paradigm for investigations of the innate role of endogenously released kinin “danger” signals in TH1/TH17 development in other infections and inflammatory diseases .
Experiments were done with mouse strains BALB/c , C57BL/6 WT ( B2R+/+ ) , and C57BL/6 B2R−/− [53] . TCT ( Dm28c clone of T . cruzi ) were harvested from the supernatants of infected LLC-MK2 cultures maintained in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 2% FCS . Freshly released parasites were washed 3X with excess PBS before being used in experiments . Epimastigotes ( EPI ) of Dm28c clone of T . cruzi were cultivated in standard liver infusion tryptose medium ( LIT ) containing 10% FCS ( GIBCO ) . Where indicated , TCT were pre-incubated for 20 min at RT with 10 μM of VSPh . Splenic DCs were isolated with anti-CD11c magnetic beads ( Miltenyi Biotec ) and stimulated in vitro with TCT ( 3 × 106/well ) in DMEM/10% fresh FCS for 18 h at 37 °C in the presence or absence of 25 μM lisinopril ( Lis; Sigma ) , an inhibitor of the angiotensin converting enzyme ( ACEi ) and/or 0 . 1 μM HOE-140 , as indicated . In some experiments , DCs were treated with VSPh-TCT . Controls were done with 10 nM BK , 10 ng/ml LPS , or 100 ng/ml CpG . For intracellular staining of IL-12 , 1 × 106 DCs were washed and pre-incubated with 2% of normal mouse serum ( NMS ) supplemented with anti-mouse CD16/CD32 FCγ III/II receptor ( clone 2 . 4G2 ) ( 1 μg/106 cells ) ( BD Biosciences ) . The washed cells were stained with anti-mouse CD11c-FITC ( BD Biosciences ) in PBS/2% NMS . After washing ( 2X PBS ) , the cells were fixed in 2% paraformaldehyde , washed , and permeabilized with 0 . 05% saponin ( Sigma-Aldrich ) . Staining with PE-labeled anti-IL-12 p40/p70 ( BD Biosciences ) was performed in PBS/2% NMS/0 . 5% saponin . Surface expression of co-stimulatory proteins was monitored by incubating DCs with antibodies to CD40 or CD86 ( BD Biosciences ) in the presence of PBS/2% NMS . Isotype-matched control was performed with rat IgG-FITC or IgG1-PE ( BD Biosciences ) . Samples were analyzed by ( FACSCalibur ) ( BD Biosciences ) , and data analyses were done with CELLQuest software ( BD Biosciences ) or Win-MDI software ( TSRI ) . Mice were pre-treated or not with 10 mg/kg intraperitoneally of ACEi ( captopril ) and/or 100 μg/kg subcutaneously of HOE-140 , as indicated , and 1 h later the mice were injected intravenously with 1 × 106 TCT . DCs were isolated from spleen at 18 h p . i . Briefly , pooled lymph node fragments were treated with collagenase D ( Sigma-Aldrich ) , and CD11c+ DCs were positively selected using magnetic beads covered with anti-mouse CD11c ( Miltenyi Biotec; 90% pure ) . CD11c+ DCs ( 106 cells/well ) were incubated for 4 h in RPMI complete medium with 10 μg/ml brefeldin A ( Sigma-Aldrich ) and were stained for CD11c and IL-12 p40/p70 as described above . B2R+/+ and B2R−/− mice were infected by the intraperitoneal route with 6 × 105 TCT . After 28 d , splenocytes were recovered and were stimulated with 25 μg/ml boiled soluble T . cruzi antigen ( EPI-Ag ) . Total CD3+ T cells ( T cell Enrichment column; R&D Systems ) were purified from either spleen or heart of infected B2R+/+ or B2R−/− mice ( 14 d and 28 d p . i . ) . CD4+ and CD8+ T cells were also purified ( 14 d and 28 d p . i . ) from spleen or heart of infected mice with magnetic microbeads conjugated to anti-mouse CD4+ and CD8+ ( Miltenyi Biotec ) and isolated by passing over a MACs LS+ column held in a VarioMACS magnetic separator ( Miltenyi Biotec ) . Positively selected cells were 85%–95% pure , as determined by flow cytometry analysis . Recall assays were performed by co-culturing 1 × 106 CD3+ , CD4+ , or CD8+ T cells with 1 × 104 splenic CD11c+ DCs from B2R+/+ mice as APCs loaded with 25 μg/ml boiled soluble T . cruzi antigen ( EPI-Ag ) . Culture supernatants were collected after 72 h and cytokines ( IFN-γ , IL-17 , TNF-α ) were quantified by ELISA utilizing purified and biotinylated Abs ( R&D Systems ) . Values are presented as pg cytokine/ml ( mean ± SD ) . Statistical differences between mean values were evaluated by ANOVA , and pair-wise comparisons were done by the Tukey test . B2R+/+- and B2R−/−-infected mice were killed at the time points indicated ( 14 d and 28 d p . i . ) and single cell suspensions were prepared from the spleen and heart . Red blood cell–depleted cells were stimulated with 25 μg/ml boiled soluble T . cruzi antigen ( EPI-Ag ) and treated with anti-mouse CD16/CD32 FCγ III/II receptor before staining . Cells were then fixed in 2% paraformaldehyde and stained with FITC-labeled mouse antibody against CD4 or CD8 , and PE-Cy-labeled mouse antibody against CD44 ( BD Biosciences ) . For intracellular staining , stimulated cells were treated with brefeldin A ( BD Biosciences ) and stained with PE-labeled anti-IFN ( XMG1 . 2; eBiosciences ) . Samples were analyzed by FACSCalibur ( BD Biosciences ) , and data analyses were done with CELLQuest software ( BD Biosciences ) . qPCR for parasite quantification was performed as described previously [54] with minor modifications . Briefly , DNA was isolated from spleen and heart tissues of B2R+/+ and B2R−/− mice infected by the intraperitoneal route with 6 × 105 TCT , after digestion with proteinase K , followed by a phenol-chloroform-isoamyl alcohol affinity extraction . q-PCR using 100 ng of total DNA was performed on an ABI PRISM 7900 sequence detection system ( Applied Biosystems ) using SYBR Green PCR Master Mix according to the manufacturer's recommendations . Purified T . cruzi DNA ( American Type Culture Collection ) was sequentially diluted for curve generation in aqueous solution containing equivalent amounts of DNA from uninfected mouse tissues . The equivalence of host DNA between samples was normalized by levels of genomic beta-2 microglobulin ( B2m ) gene in the same samples . The following primers were used for T . cruzi genomic DNA , TCZ , GCTCTTGCCCACACGGGTGC ( forward ) , and CCAAGCAGCGGATAGTTCAGG ( reverse ) ; and for genomic B2m , CTGAGCTCTGTTTTCGTCTG ( forward ) and TATCAGTCTCAGTGGGGGTG ( reverse ) . B2R+/+ and B2R−/− mice were infected with 1 × 104 trypomastigotes of the Brazil strain . Hearts were obtained at 15 d and 30 d p . i . RNA from the tissues was isolated using the Trizol LS reagent following the manufacturer's protocol . Briefly , 5 ng of RNA was reverse-transcribed in a final volume of 20 μl using Superscipt II transcriptase ( Invitrogen ) . The reverse transcription mixture consisted of 1 mM dNTPs ( Pharmacia Biotech ) , 20 mM dithiothreitol , 50 mM Tris HCl ( pH 8 . 3 ) , 75 mM KCl , 3 mM MgCl2 , 2 ng hexamer ( Pharmacia Biotech ) , and 200 U of superscript RT RNase H- reverse transcriptase ( Invitrogen ) . The reaction was incubated for 50 min at 42 °C . The qPCR primers for IFN-γ were 5′ forward GCGGCCTAGCTCTGAGACAA and 5′ reverse GACTGTGCCGTGGCAGTAAC , which amplified the 97-bp IFN-γ gene fragment . qPCR was carried out using magnesium chloride ( 2 mM ) , primers , and the PCR Sybr Green Master Mix ( Roche Applied Science ) in a final volume of 20 μl . The reaction conditions for qPCR used for quantification of IFN-γ have been previously described [55] . A standard curve for the quantification of IFN-γ was developed in the range of 0 . 5 pg pg using the primers at same conditions . The result was normalized using GAPDH mRNA for each sample . The primer sequence and the conditions used for the real-time PCR quantification were the same as previously published [55] . | Antibodies and IFN-γ-producing effector T cells are essential for the immune control of infection by Trypanosoma cruzi , the intracellular protozoa that causes human Chagas disease . Despite the potency of anti-parasite immunity , the parasites are not cleared from their intracellular niches . Instead , a low grade chronic infection prevails , provoking severe immunopathology in the myocardium . Although it is well established that innate sentinel cells sense T . cruzi through receptors for microbial structures , such as Toll-like receptors , it remained unclear whether endogenous inflammatory signals also contribute to the development of adaptive immunity . The present study was motivated by awareness that T . cruzi trypomastigotes ( extracellular infective forms ) are equipped with proteases that liberate the pro-inflammatory bradykinin peptide from an internal segment of kininogens . Here we demonstrate that splenic dendritic cells ( DCs ) , the antigen-presenting cells that coordinate the adaptive branch of immunity in lymphoid tissues , are potently activated via G-protein-coupled bradykinin B2 receptors ( B2R ) . Analysis of the outcome of infection in B2R-knockout mice revealed that the mutant mice developed a typical susceptible phenotype , owing to impaired development of IFN-γ-producing effector T cells . Notably , the immune dysfunction of B2R-knockout mice was corrected upon cell transfer of wild-type DCs , thus linking development of protective T cells to DCs' sensing of endogenous danger signals ( kinins ) released by trypomastigotes . | [
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]
| 2007 | Bradykinin B2 Receptors of Dendritic Cells, Acting as Sensors of Kinins Proteolytically Released by Trypanosoma cruzi, Are Critical for the Development of Protective Type-1 Responses |
The whole genome analysis of two strains of the first intermediately pathogenic leptospiral species to be sequenced ( Leptospira licerasiae strains VAR010 and MMD0835 ) provides insight into their pathogenic potential and deepens our understanding of leptospiral evolution . Comparative analysis of eight leptospiral genomes shows the existence of a core leptospiral genome comprising 1547 genes and 452 conserved genes restricted to infectious species ( including L . licerasiae ) that are likely to be pathogenicity-related . Comparisons of the functional content of the genomes suggests that L . licerasiae retains several proteins related to nitrogen , amino acid and carbohydrate metabolism which might help to explain why these Leptospira grow well in artificial media compared with pathogenic species . L . licerasiae strains VAR010T and MMD0835 possess two prophage elements . While one element is circular and shares homology with LE1 of L . biflexa , the second is cryptic and homologous to a previously identified but unnamed region in L . interrogans serovars Copenhageni and Lai . We also report a unique O-antigen locus in L . licerasiae comprised of a 6-gene cluster that is unexpectedly short compared with L . interrogans in which analogous regions may include >90 such genes . Sequence homology searches suggest that these genes were acquired by lateral gene transfer ( LGT ) . Furthermore , seven putative genomic islands ranging in size from 5 to 36 kb are present also suggestive of antecedent LGT . How Leptospira become naturally competent remains to be determined , but considering the phylogenetic origins of the genes comprising the O-antigen cluster and other putative laterally transferred genes , L . licerasiae must be able to exchange genetic material with non-invasive environmental bacteria . The data presented here demonstrate that L . licerasiae is genetically more closely related to pathogenic than to saprophytic Leptospira and provide insight into the genomic bases for its infectiousness and its unique antigenic characteristics .
Leptospirosis is a globally important tropical infectious disease that takes a disproportionate toll in tropical regions [1] . Caused by more than 250 serovars of spirochetes distributed among nine species of pathogenic Leptospira and at least five known species of intermediate Leptospira [2] , the burden of leptospirosis disease falls predominantly on people living in poverty and under inadequate sanitary conditions [3] . Yet , pathogenic mechanisms in leptospirosis remain poorly understood [2] . Reasons for the varying pathogenic potentials of different varieties of Leptospira to cause human disease have not been explored . Mechanisms of leptospiral tropisms for different mammalian reservoirs hosts are unknown . Lateral transfer of DNA has been observed in Leptospira but mechanisms for such transfer have yet to be defined [4]–[6] . The present study was designed to gain insight into the evolution of intermediate Leptospira with the highest degree of resolution currently possible—using comparative whole genome analysis—and to explore the degree to which evidence might link this leptospiral clade to an evolutionary position between pathogenic and saprophytic Leptospira clades as suggested by phylogenetic analysis of 16S rRNA gene sequences [7]–[9] . DNA-relatedness and phylogenetic analyses have resolved the genus Leptospira into three distinct lineages [8] , [10]–[14] comprising 20 species: nine pathogens , five intermediates and six saprophytes . Pathogenic Leptospira are capable of infecting and causing disease in humans and animals; intermediate Leptospira are able to infect humans and animals and cause a variety of clinical manifestations [8] , [15] , [16] , although less frequently; saprophytic Leptospira are environmental bacteria that do not infect mammals at all . Genome sequencing efforts have so far focused on pathogenic ( L . interrogans [17] , [18] and L . borgpetersenii [19] ) and saprophytic species ( L . biflexa [20] ) . Genomic comparisons indicate that while the L . biflexa genome is relatively stable , the genomes of pathogenic species have undergone considerable insertion sequence-mediated rearrangement [19] , [20] . It has been shown that there is considerable genomic plasticity even within the same species . For example , an ∼54 kb genomic island and a large inversion in Chromosome I differentiate the L . interrogans sv . Lai and Copenhageni genomes [18] , whose coding sequences are ∼99% similar at the amino acid level . A comparison of in vitro growth characteristics also indicates that the third lineage of Leptospira , which includes L . licerasiae , occupies an intermediate position between the pathogenic and saprophytic species . Despite reference to intermediate Leptospira as “saprophytic intermediates , ” [9] convincing clinical data confirm the pathogenicity of these Leptospira [8] , [13] . Knowledge of the genomic content of these intermediate species is necessary to complete our understanding of leptospiral evolution . In this study , we sequenced and annotated genomes of L . licerasiae sv . Varillal strains VAR010 and MMD0835 , the first intermediate species to be sequenced . In view of the range of stresses encountered by pathogenic bacteria during the course of infection , it is becoming apparent that in addition to virulence factors such as hemolysins there are additional proteins or contributory ( pathogenicity-associated ) factors involved in stress management strategies that are essential for successful infection . Genomic comparisons of the infectious species L . licerasiae , L . interrogans , L . borgpetersenii and the non-infectious saprophyte L . biflexa have provided much needed insight into these contributory factors , leptospiral virulence and pathogenicity .
L . licerasiae sv . Varillal type strain VAR010T ( human isolate ) and strain MMD0835 ( Philander isolate ) were originally isolated in Iquitos , Peru [8] . The type strain has been deposited in the American Type Culture Collection ( ATCC BAA-1110T ) . L . licerasiae sv . Varillal str . MMD0835 strain is available through BEI Resources ( http://www . beiresources . org/ ) . Both strains were grown in liquid Ellinghausen-McCullough-Johnson-Harris ( EMJH ) medium under standard culture conditions to a density of ∼108 organisms/mL . Cells were harvested from 10 mL of culture ( 109 Leptospira ) by centrifugation and genomic DNA ( gDNA ) was extracted using TRIzol ( Invitrogen Life Technologies , USA ) following manufacturer's directions . To remove RNA , extracted gDNA was then treated with an RNase cocktail ( Roche , USA ) containing RNase A and H . The genome of L . licerasiae sv . Varillal type strain VAR010T was sequenced using a combination of 454 FLX Titanium and Illumina Solexa Genome Analyzer IIX . Paired-end libraries were constructed with fragment sizes ranging from 2000 to 4000 for 454 and 200 to 300 for Illumina . A total of 2272294 reads ( 1:4 . 26 454:Illumina ) were assembled using the Celera Assembler version 7 . 0beta [21] . The genome assembled into 14 contigs ( 4 scaffolds ) at 58-fold sequence coverage with 99 . 93% of the genome with more than 19-fold coverage . L . licerasiae sv . Varillal str . MMD0835 was sequenced using just the Illumina Genome Analyzer II platform . A single paired-end library with a fragment size between 300–500 bp was constructed . A total of 1112438 reads were used by the CLC bio de novo assembler ( CLC NGS Cell v . 3 . 20 . 50819 , http://www . clcbio . com ) to generate 48 contigs at 25-fold sequence coverage with 78 . 0% of the genome above 19-fold coverage ( 99 . 9% above 4-fold coverage ) . The nucleotide sequences and the corresponding automated annotations for the genomes of L . licerasiae str . VAR010T and MMD0835 were submitted to GenBank , with accession numbers AHOO01000000 and NZ_AFLO00000000 , respectively . Genomes were run through the JCVI automated annotation pipeline v10 . 0 . Ab initio gene predictions were generated using Glimmer3 [22] in an iterative fashion . The initial set of gene predictions was then used to train a second round of Glimmer3 analysis to produce the final set of gene predictions . All predicted genes were subsequently translated into all six reading frames and searched against a non-redundant amino-acid database using BLASTP . Each query protein-coding region was extended by 300 nucleotides in an attempt to extend the alignment through regions of low similarity and through different frames and stop codons using Blast-Extend-Repraze ( BER , http://ber . sourceforge . net/ ) . All putative protein coding sequences ( CDS ) were then searched against Pfam [23] and TIGRFAM [24] protein family models with HMMER3 [25] . Coding sequences that scored well to these models were assumed to share the function modeled by the HMM . All predicted proteins were then searched against the NCBI Protein Clusters Database ( PRK ) [26] . The remaining evidence types used in the automated functional annotation of gene products were SignalP [27] , which detects the presence of putative signal sequences and TmHMM [28] to predict membrane-spanning regions . The autoAnnotate program weighed the evidence obtained from the searches from a ranked list of evidence types to make a preliminary annotation , including name , gene symbol , Enzyme Commission ( EC ) [29] number , JCVI role category [30] , and Gene Ontology ( GO ) [31] terms to each protein in the genome . Each protein was assigned a descriptive common name coming from an HMM name , a JCVI database of experimentally characterized proteins ( CharProtDB ) [32] , or from a best BER match protein . Proteins predicted to encode enzymes were assigned EC numbers , JCVI role categories , GO terms and gene symbols ( e . g . , “recA” ) as appropriate . The autoAnnotate program also employed the Protein Naming Utility ( PNU ) [33] to standardize protein nomenclature . Functional assignments were further enhanced with the Genome Properties [34] system , which records or predicts the presence or absence of metabolic pathways ( e . g . , biotin biosynthesis ) , protein complexes ( e . g . , ATP synthase ) , cellular structures ( e . g . , outer membrane ) and certain genome traits ( e . g . , optimal growth temperature , cell shape , etc . ) . Additional structural features such as tRNAs were identified with the tRNAscanSE [35] . 16S and 23S ribosomal RNA genes were identified directly from BLAST search results . Other structural RNAs were identified from matches to Rfam , a database of non-coding RNA families [36] and Aragorn [37] . Insertion sequence elements were identified using the online tool ISsaga ( http://issaga . biotoul . fr/ISsaga ) with default settings [38] . Genomic islands ( GIs ) were identified using the online tool IslandViewer ( http://www . pathogenomics . sfu . ca/islandviewer ) [39] , which integrates three different genomic island prediction methods: IslandPick [40] , IslandPath-DIMOB [41] , and SIGI-HMM [42]; we report putative GIs predicted by multiple tools . Regions of pairwise synteny between the Leptospira genomes were identified by first finding the maximum unique matches with a minimum length of five amino acids using PROmer [22] , [43] , followed by visualization of the data using MUMmerplot ( http://mummer . sourceforge . net/ ) and Gnuplot 4 . 0 ( http://www . gnuplot . info/ ) as previously described [44] . QuartetS [45] was used to identify orthologous protein sequences among the eight Leptospira genomes used in this study . QuartetS uses an approximate phylogenetic analysis of quartet gene trees to infer the occurrence of duplication events and discriminate paralogous from orthologous genes [45] . The QuartetS pipeline was run with default parameters . To be considered orthologs , the bi-directional best hit pairs had to satisfy the following conditions: ( i ) the alignment region had to cover at least 50% of the length of each sequence and ( ii ) the e-value of the pair-wise alignment had to exceed 1e−5 . To better understand the functional differences between pathogenic , intermediate and saprophytic Leptospira , each of the annotated genomes was uploaded to the RAST ( Rapid Annotation using Subsystem Technology ) server [46] retaining the original gene calls . Subsystems predicted to be active within each genome were then compared . A subsystem is a generalization of the concept of a biochemical pathway , extended to include ancillary components and alternative reactions reflecting functional variants found in various species . Prophages were identified using Phage_Finder [47] version 2 . 0 , which now utilizes HMMER3 [25] , [48] , drastically improving the speed of the HMM searches . Predicted prophage regions were identified using default settings and under strict ( -S ) mode . To facilitate identification of prophages in Leptospira genomes , Bacteriophage LE1 [49] , [50] [51] was added to the BLAST database used for prophage identification . Phage_Finder version 2 . 0 is available at http://sourceforge . net/projects/phage-finder/files/phage_finder_v2 . 0/ under the GNU General Public License . A three-day culture of L . licerasiae str . VAR010 ( ∼108 cells/mL ) was harvested by centrifugation at 4000 rpm for 90 min at room temperature . Cells were washed thrice with 1× PBS then treated with 50% aqueous phenol for 30 min at 65°C with continuous stirring . The cells were immediately immersed in an ice-water bath to reduce the temperature to 10°C , then centrifuged at 4000 rpm for 40 min at 10°C . The top layer ( phenol saturated aqueous layer ) and bottom layer ( water saturated phenol layer ) were removed and dialyzed against ddH2O extensively to remove phenol ( three days with change in water twice per day ) —the phenol layer was analyzed by GC-MS and polyacrylamide gel electrophoresis . The dialyzed lipopolysaccharide ( LPS ) was lyophilized then re-suspended in 500 µL of water; 200 µL was used for sugar composition analysis . For GC-MS , samples were silylated using Trimethylsilyl ( TMS ) . First , samples were methanolyzed using 1 M MeOH-HCl , at 80°C for 16 h , followed by re-N-acetylation and TMS derivatization using Tri-Sil TP reagent ( Thermo Scientific ) according to manufacturer's directions . The derivatives were subjected to GC-MS analysis and the data quantified using an internal inositol standard . LPS isolation and GC-MS analysis were done by the Glycotechnology Core Resource at the University of California , San Diego .
454 and Illumina pyrosequencing of str . VAR010T yielded 2 , 272 , 294 reads that were assembled into 14 contigs ( 4 scaffolds ) with 4 , 211 , 147 high-quality mostly contiguous bases . These contigs had an average length of 300 . 8 kb , an N50 of 522 . 9 kb and a maximum length of 1 . 67 mb . The str . MMD0835 genome was assembled into 48 contigs with 4 , 198 , 811 contiguous bases ( N50 of 463 . 5 kb; max . length of 1 . 07 mb ) . The overall characteristics of the draft L . licerasiae genomes are summarized in Table 1 . G+C content . Gaps in genome coverage were not filled in with manual sequencing given resource constraints . This approach is consistent with de novo sequencing and publication of other pathogen genomes , given that the length of the draft genomes was consistent with other sequenced leptospiral genomes ( Table 1 ) and that the two strains whose genome sequences reported here are vastly similar . Gaps are typically caused by large ( greater than the library “insert” size ) fragments , which tend to be rRNA operons , large mobile elements or duplicated regions and likely do not materially detract from the quality of the data analysis presented here . Prophages can be important drivers of microbial evolution by providing fitness factors for their host [57] , [58] , by facilitating movement of DNA through transduction of the host chromosome or packaging of pathogenicity islands [59] and altering serotype through lysogenic conversion [60] , [61] . To explore any of these possibilities in any of the available Leptospira genomes , Phage_Finder [47] was run under strict ( -S ) mode to identify prophage regions . Phage_Finder identified two prophage regions in the genomes of both L . licerasiae strains . The first region in each strain was located on ∼103 kb contigs ( AHOO02000007 in VAR010 and NZ_AFLO01000023 in MMD0835 ) with best BLASTP matches to bacteriophage LE1 of L . biflexa . LE1 was previously shown to be of circular topology , to form intracellular particles consistent with phage , and to replicate like a plasmid [51] . Given this information and that a large portion of each contig was predicted to be prophage , it was reasonable to believe these phage-like contigs in L . licerasiae also represented linear forms of circular phage genomes like LE1 . There was significant overlap in the sequence of the ends , also suggesting a circular form . To demonstrate circular topology , outward-facing primers were designed and used in PCR reactions . The results of PCR produced 300 bp products , indicating that both LE1-like phages are indeed circular in L . licerasiae strains VAR010 and MMD0835 ( Figure 1 ) . Comparisons between LE1 and these prophages at the protein level indicated that the later three quarters of the L . licerasiae prophage proteins match some portion of LE1 ( Figure 1 ) , albeit at a low percent identity ( average ∼30% identity ) . A comparison between the two LE1-like prophages revealed that they are identical at the amino acid level ( Figure 1 ) . We propose naming the L . licerasiae LE1-like prophages vB-LliZ_VAR010-LE1 and vB-LliZ_MMD0835-LE1 using a previously suggested systematic bacteriophage nomenclature [62] . vB-LliZ_VAR010-LE1 encodes 102 predicted proteins and has a G+C of 37 . 8% which is lower than the average for the entire L . licerasiae genome—41 . 6% . These L . licerasiae prophage elements possess ∼22 kb of unique sequence that LE1 lacks as well as several unique predicted open reading frames interspersed among the LE1 homologs . A comparison of this ∼22 kb region to other Leptospira genomes identified multiple efflux pumps in the infectious L . licerasiae that may function in adaptation to the mammalian host . Further , this amino acid similarity to bacterial efflux pumps suggests phage-mediated gene transfer between the L . licerasiae chromosome and LE1-like prophage . While the presence of these efflux pumps in the genomes of other infectious species would also suggest a role in pathogenicity , BLASTP searches against the non-redundant protein database ( nr ) indicate that these proteins have homologs in the non-pathogen Leptonema illini DSM 21528 . Why L . licerasiae and not L . biflexa have maintained copies of these genes is unclear . Also within this region , the predicted L . licerasiae protein LEP1GSC185_3887 is notable in that it shares homology with a TolC/IS1533 transposase fusion protein . It has been suggested that the mobile genetic element ( MGE ) IS1533 , has mediated LGT resulting in the antigenic switch of sv . Copenhageni to sv . Hardjo [63] . The second prophage region was only partially detected in the L . licerasiae genomes by Phage_Finder ( Figure 2 ) , but is adjacent to a cryptic prophage region expressed in L . interrogans sv . Lai and is presumably associated with pathogenicity [64] . The region detected by Phage_Finder is located at nucleotide position 210203 . . 191954 of VAR010 and 71814 . . 108770 of MMD0835 , but after comparison to the above mentioned unnamed prophage element in L . interrogans sv . Lai , could be extended to include coordinates 210203 . . 171583 of VAR010 and 71814 . . 110434 of MMD0835 ( Figure 2 ) . Presumably the reason this region was truncated by Phage_Finder was due to a lack of sufficient homology in the BLAST database used and/or due to the lack of a head morphogenesis region , which is required by Phage_Finder to label a region as “prophage” under strict mode . Since this region lacks an identifiable head morphogenesis region yet retains tail-like proteins , it may be functionally analogous to phage tail-type bacteriocins , called pyocins in Pseudomonas aeruginosa [65] and monocins in Listeria [66] . L . licerasiae str . VAR010 causes mild disease in humans and has been isolated from peridomestic and wild rodents and marsupials in Peru [8] . Although phenotypic differences between VAR010 and MMD0835 have yet to be described , VAR010 ( 3931 total CDS ) has 185 non-orthologous CDS relative to strain MMD0835 ( 3885 total CDS ) , whereas strain MMD0835 has 140 non-orthologous CDS relative to strain VAR010 reminiscent of another environmental pathogen with a plastic genome , Burkholderia pseudomallei [67] . The majority of these non-orthologous genes encode hypothetical proteins . Both strains share 3 , 745 CDS with an average pair-wise amino acid similarity of 99 . 98% . Of these , 1211 have no orthologs in the other genomes used in this study . A putative function could be assigned to 632 with the remainder comprising hypothetical ( 579 ) proteins ( Table S1 ) . Considering only those genes common to both strains of each species , L . licerasiae shares 2 , 237 ( ∼57% ) with L . interrogans , 2 , 077 ( ∼53% ) with L . borgpetersenii and 1 , 898 ( ∼48% ) with L . biflexa . 1 , 547 orthologs ( ∼39% of the predicted L . licerasiae CDS ) were present in all genomes compared ( Figure 3 ) and likely represent a substantial proportion of the core genome of Leptospira . As shown in Figure 4 , the gene order is more conserved in the intermediate and pathogenic branches . Surprisingly , L . licerasiae had the highest average protein identity with L . interrogans sv . Lai ( 2 , 278 proteins with an average pairwise identity of ∼67% ) . Taken together these observations suggest that L . licerasiae is more closely related to the pathogenic branch of infectious Leptospira than to the saprophyte , L . biflexa . This was unexpected since 16S rRNA phylogeny suggests that L . licerasiae occupies an intermediate position between the pathogens and saprophytes [8] . Table 2 shows the subsystem distribution of predicted CDS in L . licerasiae , L . interrogans , L . borgpetersenii and L . biflexa . Based on these data it would seem that intermediate Leptospira retain several proteins related to nitrogen , amino acid and carbohydrate metabolism that have likely been lost by the pathogenic sub-branch . For example , L . licerasiae ( LEP1GSC185_2652 ) and L . biflexa ( LEPBI_I1590 ) both possess ilvA , which encodes threonine ammonia-lyase an enzyme that catalyzes the conversion of threonine to 2-oxobutanoate; while neither L . borgpetersenii nor L . interrogans appears to do so . That L . licerasiae and perhaps the other intermediates do well in artificial culture media might be related to the retention of these metabolic functions . It is a commonly accepted concept that genes unique to pathogenic microorganisms are likely to be necessary for infection ( pathogenesis ) . To identify potentially pathogenicity-associated genes , we compared the genome content of three infectious leptospiral species , L . licerasiae ( 2 strains ) , L . interrogans ( 2 strains ) and L . borgpetersenii ( 2 strains ) with that of the non-infectious saprophyte , L . biflexa ( 2 strains ) . These comparisons identified 452 conserved pathogen-specific proteins ( Figure 3 ) . Based on domain homology searches , 315 were assigned a putative function ( Table S2 ) . Infectious Leptospira species share a number of proteins predicted to participate in environmental signaling and processing and metabolism ( Table S2 ) . That the infectious species studied here appear to possess a complete vitamin B12 biosynthesis operon and a novel regulatory mechanism is perhaps the most notable metabolic difference between infectious and non-infectious Leptospira . Indeed the absence of these genes from the L . biflexa and recently sequenced Leptonema genomes would indicate that the ability to synthesize B12 was acquired after the speciation event giving rise to the infectious branch of Leptospira predating the separation of the intermediate and pathogenic sub-branches . The genomes of over 100 infectious strains searched so far including the intermediates species , L . inadai and L . broomii , possess at least two copies of the B12 riboswitch ( M . Matthias and J . Vinetz manuscript in preparation ) , supporting the belief that these elements are essential for pathogenicity . As in other bacteria , the availability of different nutrients inside and outside the mammalian host requires changes in the metabolic capacity of Leptospira . Published data have firmly established that Leptospira have an absolute requirement for B12 for growth at 37°C [68] . Much like iron , B12 is sequestered in vivo . Hence , for survival in vivo , leptospiral pathogens need to synthesize B12 de novo or scavenge B12 from the host . Whether leptospires are fully capable of synthesizing the highly complex B12 molecule from simpler precursors de novo is not known . But , cobI ( LEP1GSC185_3345; LIC20129 ) , an enzyme involved in cobalamin biosynthesis , is ∼30-fold up regulated during mammalian infection consistent with a role in vivo in replication and/or pathogenicity ( J . Lehmann , J . Vinetz , and M . Matthias manuscript in preparation ) . In addition , although all leptospiral genomes sequenced to date , including L . biflexa , encode the enzyme cob ( I ) yrinic acid a , c-diamide adenosyltransferase , which catalyzes the first step in the conversion of cobinamide to B12 , all infectious Leptospira , including L . licerasiae , L . interrogans , L . borgpetersenii , L . santarosai , L . noguchii and L . weilii , encode at least one additional homolog . The reason for this is unclear , but it may be that these pathogen-specific homologs are required for B12 biosynthesis in vivo . While L . interrogans , L . borgpetersenii and L . licerasiae , appear to be able to use either l-glutamate or cobinamide to synthesize B12 , it would seem that this is not a universal feature of infectious Leptospira . Leptospira encode four essential B12-dependent enzymes: B12-dependent methionine synthase , two B12-dependent methylmalony-CoA mutase related proteins and a B12-dependent ribonucleotide reductase . Methionine synthase transfers a methyl group from methyl-tetrahydrofolate to homocysteine as the final step in the synthesis of methionine; ribonucleotide reductases generate the deoxyribonucleotides needed for DNA synthesis and allow the production of DNA in the absence of oxygen; methylmalonyl-CoA interconverts ( R ) -methylmalonyl-CoA and succinyl-CoA in the terminal step of β-oxidation of fatty acids/catabolism of cholesterol . A role for B12 in leptospiral pathogenicity has yet to be established . However , B12 synthesis has been linked to fatty acid metabolism and survival of the intracellular pathogen Mycobacterium tuberculosis in vivo [69] . As humans do not synthesize B12 , these genes may represent novel drug targets . The L . licerasiae VAR010 and MMD0835 genomes encode 196 and 198 putative lipoproteins , respectively consistent with the number found in other leptospiral species ( L . interrogans – 184; L . borgpetersenii – 130 and L . biflexa 164 ) [20] . Of these , infectious species share LipL31 ( LEP1GSC185_3242 , LlicsVM_010100016712 ) , LipL32 ( LEP1GSC185_2633 , LlicsVM_010100013757 ) , LipL40 ( LEP1GSC185_1670 , LlicsVM_010100003275 ) , LipL41 ( LEP1GSC185_1838 , LlicsVM_010100002470 ) , LipL46 ( LEP1GSC185_3176 , LlicsVM_010100016407 ) , LigB ( LEP1GSC185_1828; LlicsVM_010100002515 ) , LruA/LipL71 ( LEP1GSC185_0209 , LlicsVM_010100006058 ) and LruB ( LEP1GSC185_0754 , LlicsVM_010100019404 ) . That these genes are absent from the L . biflexa genome suggests a potential role in pathogenicity . The function of LruB is unknown , but serology suggests this protein is expressed in vivo [70] . Much recent work has demonstrated the importance of fibronectin and plasminogen binding proteins in Leptospira [71]–[82] . Fibronectin binding proteins are adhesins that play an important role in certain bacterial infections [83] , [84] . Putative pathogenicity factors LigA and LigB , specific to pathogenic Leptospira are induced at physiological osmolarity and are involved in leptospiral adhesion to extracellular matrix proteins and plasma proteins including collagens I and IV , laminin , fibronectin and fibrinogen [85] . The above mentioned LipL32 and LipL40 are putative plasminogen binding proteins [81] . Apart from LigB , at least three other conserved pathogen-specific outer membrane proteins are predicted to mediate attachment to host cells: a putative fibronectin binding protein , Lfb1 ( LEP1GSC185_0134; LlicsVM_010100012092 ) [86]; Lsa66 , a leptospiral surface adhesin of 66 kDa ( LEP1GSC185_1758; LlicsVM_010100002865 ) shown to bind laminin and plasma fibronectin extracellular matrix molecules [72] , and a protein believed to mediate attachment to host cells ( LEP1GSC185_2102; LlicsVM_010100001165 ) . Previously published immunological data from Peru indicate that the L . licerasiae O-antigen is antigenically unique [8] . Comparative analysis of all extant Leptospira spp . genomic data , including the new data presented here , explains this antigenic uniqueness at a genomic level . In contrast to the complex LPS O-antigen biosynthetic loci found in the published L . interrogans , L . borgpetersenii and L . biflexa genomes , which contain 91 , 76 and 56 genes respectively , the L . licerasiae O-antigen locus we propose is comprised of a modest 6-gene operon , LEP1GSC185_2122–2127 ( Figure 5 , Table 3 ) . The genes in this cluster have no apparent orthologs in the already sequenced L . interrogans , L . borgpetersenii and L . biflexa genomes . We are confident that this operon is the true L . licerasiae O-antigen locus based on the following observations: 1 ) There are only two wzx O-antigen transporter homologs in the genome . One of these ( LEP1GSC185_0029 ) is not in an operon with any other genes of types typically associated with O-antigen biosynthesis . The other , LEP1GSC185_2124 , is part of the proposed O-antigen locus . 2 ) Of the 29 putative polysaccharide glycosyltransferases we could identify in the L . licerasiae genome , while none are orthologs of genes in the O-antigen regions of the other sequenced Leptospira genomes , 22 are bidirectional best hits ( that is , candidate orthologs ) with non-O-antigen related genes from one or more of these genomes . Of the remaining 7 genes , one ( LEP1GSC185_3401 ) is associated with a glycogen-related operon , two ( LEP1GSC185_1696 and _2304 ) are part of short operons with genes of unknown function , and one ( LEP1GSC185_2985 ) is proximal to flagellin genes . The remaining three , LEP1GSC185_2122 , 2123 and 2126 , are clustered together in the proposed O-antigen locus . 3 ) The remaining two genes in the proposed O-antigen locus , LEP1GSC185_2125 and 2127 , encode functions commonly associated with sugar modification in O-antigens , pyruvoylation and acetylation , respectively . Homologs of the latter gene are specifically annotated as O-antigen related . 4 ) This O-antigen region is fully within a single contig . The boundaries of the proposed L . licerasiae O-antigen biosynthesis operon map to three different syntenic regions in L . interrogans , which suggests a complex history of differential genome rearrangement and LGT events in these two species . Indeed , the six genes of the operon do not correspond to any syntenic blocks in any sequenced genome – the most similar genes to each are present in entirely disjoint sets of bacterial and archaeal strains ( Table 3 ) . This would seem to imply either , 1 ) that potential en bloc LGT source genomes with similarly constructed O-antigens have yet to be sequenced , or 2 ) that a series of LGT events from different sources have accumulated these genes in the L . licerasiae lineage to create a novel O-antigen cluster . Since the O-antigen cluster is not predicted to reside on a GI the latter seems more likely . By analogy to extant knowledge of how E . coli O-antigen operons are assembled [87] , we can hypothesize that the L . licerasiae O-antigen consists of a repeating unit with at least 4 sugars ( corresponding to the primer sugar and the products of the three glycosyltransferase enzymes ) , that these sugars are bioavailable from the core Leptospira metabolome ( i . e . glucose , galactose , mannose , etc . , since no blocks of sugar biosynthesis or sugar modification genes are present in the operon ) and at least one of them is modified by ( probably ) pyruvoyl and/or acetyl groups . The chemical composition of the polysaccharide component of leptospiral LPS has been examined in a few serovars [88]–[90] . The proportion of the major component sugars rhamnose , galactose , arabinose and xylose was shown to vary between strains . The composition of the LPS derived from L . licerasiae sv . Varillal is consistent with previously published data . However , our GC-MS analysis indicates that L . licerasiae LPS ( Figure 6 ) is composed primarily of arabinose ( ∼61 . 6% ) , with xylose ( ∼12 . 8% ) , mannose ( ∼11 . 5% ) , rhamnose ( ∼9 . 3% ) , galactose ( ∼4 . 0% ) and glucose ( <1% ) . The relative proportion of arabinose and rhamnose ( 6∶1 ) in the LPS of L . licerasiae is significantly different from that ( 1∶3 ) reported in L . interrogans sv . Copenhageni [89] , which might help to explain why there is absolutely no serological cross-reactivity between sv . Varillal and Copenhageni [8] . The presence of rhamnose in the purified VAR010 LPS is surprising since the genome does not appear to encode a complete pathway for the synthesis of dTDP-l-rhamnose shown to be present in L . interrogans and L . borgpetersenii; the enzymes that catalyze the final two steps on the pathway , rmlC and rmlD , are absent . Because both L . licerasiae genomes are unfinished , it is possible that these genes reside on unsequenced regions of the genome . But , since other intermediate strains sequenced to date , L . broomii and L . inadai and the saprophyte , L . biflexa also seem to lack rmlC and rmlD homologs , it is also biologically plausible that L . licerasiae truly lacks either gene . A TBLASTN search against the L . licerasiae genomes failed to produce any significant alignments , thus it is does not seem that the genes were missed . L . licerasiae does possess the enzymes necessary to synthesize GDP-d-rhamnose from GDP-d-mannose , gmd ( LEP1GSC185_1627; LlicsVM_010100003480 ) and rmd ( LEP1GSC185_1109; LlicsVM_010100011485 ) . Although rare , other pathogens such as Pseudomonas aeruginosa have been shown to produce LPS containing d-rhamnose [91]; therefore , it is possible that L . licerasiae produces d-rhamnose , but this needs to be confirmed experimentally . The polymerase ( wzy ) and chain length determinant ( wzz ) genes are not observed in the proposed O-antigen locus , but may be located elsewhere in the L . licerasiae genome . These genes may be difficult to identify by homology due to their membrane protein nature . There are two identified wzy homologs in L . licerasiae with candidate orthologs in L . interrogans and L . borgpetersenii . There are no obvious wzz homologs in the L . licerasiae genome . The formal possibility exists that this O-antigen consists of only a single repeat , obviating the need for wzy and wzz genes , but this would be unprecedented if true . Seven putative genomic islands in L . licerasiae ranging in size from 5 kb to ∼36 kb ( Table 4 ) were identified , the longest of which coincides with the previously mentioned cryptic prophage in sv . Lai and Copenhageni [64] . In addition , we found 28 putative type II toxin-antitoxin systems ( TASs ) in the VAR010 genome ( Table 5 ) . TASs belong to the prokaryotic mobilome as they are extensively , if not preferentially , spread via plasmid-mediated LGT [92] . Like many , if not most of the mobilome members , the TASs are not simply mobile , but appear to behave like selfish elements . If a mobile genetic element encoding a TAS is lost during cell division , the concentrations of the labile antitoxin rapidly decreases , allowing the toxin , which is more stable , to kill the cell . Thus , TASs contribute to the stable maintenance and dissemination of plasmids and genomic islands in bacterial populations despite the associated fitness cost . In M . tuberculosis , 37% of these systems are located on genomic islands [93] . In L . licerasiae , 36% ( 10/28 ) of the putative type II TASs reside on putative genomic islands , and thus , appear to have been acquired by LGT . Of the L . licerasiae type II TASs , chpK/chpI ( Table 5 ) has been confirmed in L . interrogans [94] and appears to be unique to infectious species [95] . L . interrogans encodes another four TASs [96] . By contrast , L . biflexa str . Ames and Paris possess several TASs ( 22 and 20 TASs , respectively [97] ) much like L . licerasiae . As additional independent evidence of lateral transfer , more than half of the L . licerasiae-specific CDS have no or poor homology with other leptospiral proteins . These include phosphate , chromium and molybdate transport systems . Of these proteins , most have homology with non-invasive environmental bacteria including Sorangium cellulosum [6 proteins] , Bdellovibrio bacteriovirus [6 proteins] and Haliscomenobacter hydrossis [5 proteins] . While IS elements appear to be major contributors to genomic diversification in pathogenic Leptospira , which may possess more than 20 insertion sequence ( IS ) elements [19] , the relative lack of IS elements in the L . licerasiae and L . biflexa genomes would suggest that genomic diversity where it exists is a result of different mechanisms . The phylogenetic origins of the laterally transferred genes , suggest that L . licerasiae is able to exchange genetic material with non-invasive environmental bacteria , whether this species can become naturally competent remains to be determined . This study bridges a major gap in our knowledge of leptospiral biology and addresses a key question in the field regarding the pathogenic potential of the intermediate clade of Leptospira [9] . The data presented here 1 ) demonstrate that L . licerasiae is more closely related to pathogenic than to saprophytic Leptospira; 2 ) provide insight into the genomic bases for its infectiousness and unique antigenic characteristics; and 3 ) support the denomination of the intermediate clade as ‘intermediately pathogenic’ and its consideration as a transitional group between saprophytes and pathogens . Future comparative genomic analysis of the complete set of Leptospira species will provide deeper large-scale insights into the evolution , biology and evolution of virulence of this genus of spirochetes , and guide new experimental directions . | Leptospirosis is one of the most common diseases transmitted by animals worldwide and is important because it is a major cause of febrile illness in tropical areas and also occurs in epidemic form associated with natural disasters and flooding . The mechanisms through which Leptospira cause disease are not well understood . In this study we have sequenced the genomes of two strains of Leptospira licerasiae isolated from a person and a marsupial in the Peruvian Amazon . These strains were thought to be able to cause only mild disease in humans . We have compared these genomes with other leptospires that can cause severe illness and death and another leptospire that does not infect humans or animals . These comparisons have allowed us to demonstrate similarities among the disease-causing Leptospira . Studying genes that are common among infectious strains will allow us to identify genetic factors necessary for infecting , causing disease and determining the severity of disease . We have also found that L . licerasiae seems to be able to uptake and incorporate genetic information from other bacteria found in the environment . This information will allow us to begin to understand how Leptospira species have evolved . | [
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| 2012 | Whole Genome Analysis of Leptospira licerasiae Provides Insight into Leptospiral Evolution and Pathogenicity |
Aneuploidy is a hallmark of tumor cells , and yet the precise relationship between aneuploidy and a cell’s proliferative ability , or cellular fitness , has remained elusive . In this study , we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic , genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness . We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild type . Direct competition experiments confirmed that three out of four aneuploid events isolated from evolved populations were themselves sufficient to improve fitness . To expand the scope beyond this small number of exemplars , we created a genome-wide collection of >1 , 800 diploid yeast strains , each containing a different telomeric amplicon ( Tamp ) , ranging in size from 0 . 4 to 1 , 000 kb . Using pooled competition experiments in nutrient-limited chemostats followed by high-throughput sequencing of strain-identifying barcodes , we determined the fitness effects of these >1 , 800 Tamps under three different conditions . Our data revealed that the fitness landscape explored by telomeric amplifications is much broader than that explored by single-gene amplifications . As also observed in the evolved clones , we found the fitness effects of most Tamps to be condition specific , with a minority showing common effects in all three conditions . By integrating our data with previous work that examined the fitness effects of single-gene amplifications genome-wide , we found that a small number of genes within each Tamp are centrally responsible for each Tamp’s fitness effects . Our genome-wide Tamp screen confirmed that telomeric amplifications identified in laboratory-evolved populations generally increased fitness . Our results show that Tamps are mutations that produce large , typically condition-dependent changes in fitness that are important drivers of increased fitness in asexually evolving populations .
Aneuploidy , a class of mutation infamous for its disruption of development [1] and oncogenic connections [2 , 3] , is a genetic alteration that changes the copy number of many genes with a single mutational event ( reviewed in [4] ) . Despite its close connection to cancer , a phenomenon characterized by unchecked cellular proliferation , aneuploidy has been shown to inhibit cellular growth in a variety of model systems . Both trisomic mouse embryonic fibroblasts and disomic strains of Saccharomyces cerevisiae have increased doubling times when compared to their euploid counterparts [3 , 5] . The fitness cost associated with aneuploidy has been attributed to proteotoxic stress caused by the unbalanced and uncompensated expression of proteins from the regions of altered copy number [6–9] . Despite this general fitness cost , whole-chromosomal aneuploidy and segmental aneusomy , both of which will henceforth be referred to as “aneuploidy” for simplicity , have been commonly observed in the evolution and adaptation of asexually replicating cells [10–20] . Aneuploidy thus has a paradoxical relationship with cellular fitness [21]: while typically decreasing a cell’s fitness , it is nonetheless selected for under a variety of highly selective conditions . By altering the copy number of multiple genes at once , it has been argued that aneuploidy allows a cell to explore a wide fitness landscape [22 , 23] . Aneuploidy , therefore , may commonly be selected for when cells face novel conditions because this mutation type allows an evolving population to rapidly test many divergent phenotypes . However , the specific fitness effects of aneuploid events have been difficult to directly test and , instead , have typically been inferred from their recurrence between or frequency within evolving populations [13 , 14 , 24] . Even in the rare cases in which a fitness advantage is directly associated with a particular aneuploid event , it remains challenging to identify the gene ( s ) within the aneuploid region whose altered copy number is responsible for the fitness effects observed [25 , 26] . However , the gene ( s ) underlying the phenotype ( s ) associated with an aneuploid event have been identified in a small number of cases [17 , 20 , 27] . Aneuploidy’s genetic complexity and the challenges outlined above have made it difficult to draw firm conclusions about the general role aneuploidy plays in fitness , adaptation , and evolution . In this study , we have directly tested the fitness effects of four naturally selected aneuploid events isolated from three laboratory evolution experiments of S . cerevisiae carried out in nutrient-limited chemostats . We have found that while most aneuploid events positively affect fitness , one event actually decreased fitness despite representing a substantial fraction of the population . Unable to draw general conclusions about aneuploidy from the detailed analysis of only a few specific genetically tractable events , we then created a barcoded genomic collection of >1 , 800 clones each containing a telomeric amplification ( Tamp ) of a different size . By tiling across the entire yeast genome , this collection allowed us to test the fitness effects of telomeric amplifications genome-wide . Using pooled competition experiments in glucose- , sulfate- , or phosphate-limited chemostats combined with barcode sequencing [28] , we have uncovered the fitness profile explored by Tamps under these three conditions . Data from this genome-wide Tamp screen revealed that aneuploidy is typically a large-effect mutation , with condition-specific fitness effects and fitness tradeoffs under alternative conditions . By comparing the Tamp screen data to aneuploid events identified in evolution experiments , we found that most aneuploid events identified in evolution experiments positively affect fitness . The discrete fitness breakpoints in the Tamp fitness profile allowed us to identify candidate driver genes that , in the genetic background of amplification of contiguous genes , were responsible for the fitness effects of each Tamp . We discovered that the fitness effects of most aneuploid events from evolved populations are driven by a small number of driver genes essential for their positive effects on competitive growth . These data are an attempt to systematically define the fitness landscape explored by aneuploidy .
Aneuploidy has been commonly observed in laboratory-evolved populations of S . cerevisiae adapting to nutrient limitation [10 , 12 , 29 , 30] . Our group previously reported that at least one aneuploid clone was observed in 13 out of 24 evolution experiments carried out for over 100 generations ( 122–328 generations ) in nutrient-limiting chemostats [10] . The same study showed that all eight of the evolution experiments carried out under sulfate-limiting conditions contained a recurrent amplification surrounding the high-affinity sulfate transporter SUL1 [10]; two sulfate-limited populations contained aneuploid events in addition to the SUL1 amplification . The direct fitness effects and mechanism of formation regarding the SUL1 amplicon have been examined in detail elsewhere [30–32]; in this study our primary focus was to explore the functional importance of the remaining aneuploid events observed in the 24 evolution experiments . As a proxy for their direct fitness effects , we first determined the population frequencies of the aneuploid events observed in the 24 evolution experiments [10] using array comparative genomic hybridization ( aCGH ) of population DNA . We predicted that aneuploid events rising to appreciable population frequencies provided a fitness advantage to the clones carrying them . In the initial description of the evolution experiments examined here , population aCGH was performed on 10 of the 24 evolution experiments [10]; here we performed population aCGH on the remaining 14 evolution experiments ( Fig 1A and S1 Table , see GEO Accession GSE67769 for raw data ) . Given the tandem repeat structure of the SUL1 amplicons [30 , 32] , their clonal copy number was dynamic and prohibited accurate calculation of their population frequency by population aCGH . In order to estimate the SUL1 amplicon population frequency , we defined the SUL1 clonal copy number as the population copy number rounded to the next highest integer . A detailed analysis of the SUL1 amplicon structure and population dynamics has been presented elsewhere [30] . The aneuploid events present in the evolution experiments ranged in size from 5–1 , 000 kb and were present at frequencies ranging from 6% ( our lower limit of detection ) to 96% of the population with an average population frequency of 47% ( Fig 1A ) . We confirmed the accuracy of this approach by performing breakpoint PCR across the translocation event in the supernumerary chromosome present in population S8 . Both aCGH on S8 population DNA and breakpoint PCR on 98 independent clones isolated from S8 determined this supernumerary chromosome to be present at 13% of the population ( see GEO Accession GSE67769 ) . While 11 of the aneuploid events were unique , seven recurred between populations , both within and between conditions , most notably the amplification on the right arm of chromosome V and the amplification on the left arm of chromosome XIV . The amplification on the right arm of chromosome V recurred in three different evolution experiments carried out under three conditions ( Fig 1A ) , while the amplification of the left arm of chromosome XIV was observed in one of the glucose-limited evolution experiments described here and in two additional glucose-limited evolution experiments previously analyzed [12] . The high population frequencies and the recurrence of aneuploid events between populations supported our hypothesis that the aneuploid events examined here were selected for under the conditions of laboratory evolution . We next asked whether aneuploid and euploid evolved clones isolated from the final generation of evolution experiments were more fit than their wild-type ancestors . We determined the relative fitness of each evolved clone through chemostat competition experiments against an appropriate green fluorescent protein ( GFP ) -marked wild-type control clone and under conditions identical to the evolution experiment from which the evolved clone was isolated . Both euploid and aneuploid evolved clones showed a fitness advantage relative to their wild-type ancestor ( Fig 1B and S2 Table ) . Note that clones P3c1 and P3c2 are euploid despite being isolated from an aneuploid population , because the aneuploid events were not fixed in population P3 . The relative fitnesses of the evolved clones ranged from 17% to 61% more fit than the wild-type ancestor . Evolved clones isolated from sulfate-limited evolution experiments ( n = 8 ) had significantly greater fitnesses than clones isolated from glucose or phosphate-limited evolution experiments ( n = 19 ) ( Wilcoxon Rank-Sum test , p-value = 0 . 036 ) . While there was a statistical difference in the relative fitnesses between euploid and aneuploid clones ( Wilcoxon Rank-Sum test , p-value = 0 . 0014 ) , this was driven in part by the high fitness conferred by the SUL1 amplicon in all evolved clones isolated from sulfate-limited evolution experiments . However , the relationship between aneuploidy and fitness held true even when we restricted our examination to the eight clones isolated under glucose-limiting conditions: aneuploid clones ( n = 4 ) had a significantly greater fitness than the euploid clones ( n = 4 ) ( Wilcoxon Rank Sum test , p-value = 0 . 029 ) . Although these data demonstrated that evolved aneuploid clones , just like evolved euploid clones , are more fit than their wild-type ancestor , it did not establish whether the aneuploid events themselves or other mutations , such as single-nucleotide variants ( SNVs ) , contributed to the improved fitness of evolved clones . To provide this direct connection we genetically isolated the aneuploid events and SNVs from three evolved aneuploid clones and determined the direct fitness consequences of both the aneuploid events and the SNVs . To genetically isolate the aneuploid events present in evolved clones we first determined the full repertoire of mutations present in a subset of evolved clones . We chose to study three evolved aneuploid clones: two clones isolated at generations 141 and 217 ( P5c3 and P6c1 ) from phosphate-limited evolution experiments begun with a haploid founder and one clone isolated at generation 250 ( S8c2 ) from a sulfate-limited evolution experiment begun with a diploid founder . P5c3 has two aneuploid events: an extra copy of chromosome XIII and a supernumerary chromosome consisting of the right arm of chromosome VI joined to a telomeric amplicon from the left side of chromosome XVI ( VIR t XVIL ) . P6c1 has a supernumerary chromosome consisting of a telomeric amplicon on the right side of chromosome V joined to the right arm of chromosome VI ( VR t VIR ) and S8c2 contains a supernumerary chromosome consisting of two copies of a telomeric amplicon from the right side of chromosome V flanking a centromeric segmental amplicon from chromosome X ( VR t XCEN ) . These clones were chosen because they did not contain large deletions , thus making them amenable to backcrossing and tetrad dissection . We performed whole-genome sequencing ( WGS ) of these clones to an average mapping coverage of 46–68X ( S3 Table ) . Three to seven SNVs were called in each clone ( Table 1 ) and confirmed by Sanger sequencing . We also sequenced the populations from which clones P5c3 and P6c1 were isolated to an average mapping coverage of 39X and 116X , respectively , and determined that the SNVs identified in these clones ranged in frequency from below detection to 98% ( Table 1 ) . To isolate segregants that had a single evolved SNV or aneuploid event in an otherwise ancestral genetic background , we backcrossed the haploid clones P5c3 and P6c1 to their corresponding wild-type strain and directly sporulated the diploid clone S8c2 . We identified appropriate segregants by genotyping and then used chemostat competition experiments to determine the independent fitness effects of each evolved mutation ( Fig 2 and S2 Table ) . More than half of the mutations examined showed either neutral/near-neutral ( <5% ) fitness increase or negative effects on fitness , agreeing with previous reports that genetic hitchhiking is quite important for the spectrum of mutations observed in asexually evolving populations [33 , 34] . In particular , the supernumerary chromosome isolated from evolved clone S8c2 , despite occupying 13% of the S8 population , actually decreased the fitness of clones carrying it by 10% ( Fig 2 ) . A minority of evolved mutations , including three large-scale aneuploid events , the amplification of SUL1 , and a missense mutation in the high-affinity phosphate transporter PHO84 , all increased fitness in the conditions from which they were isolated . In general , the aneuploid events we examined showed diverse relationships with the overall fitnesses of the evolved clones from which they were isolated . In P6c1 , the fitness effect of the VR t VIR supernumerary chromosome added to a second positive-effect mutation , the missense mutation in PHO84 , to predict the overall fitness of the original evolved clone . In contrast , in P5c3 the positive fitnesses associated with both aneuploid events in that clone were each similar to the overall fitness of the original evolved clone , suggesting epistasis between these two mutations . Finally , the overall fitness of evolved clone S8c2 was quite similar to the fitness effect of the SUL1 amplification alone , suggesting epistasis between the SUL1 amplicon and the 10% fitness cost conferred by the VR t XCEN supernumerary chromosome and the missense mutation in ADR1 in this clone . To confirm that we had identified and genetically isolated all functionally important mutations , for P5c3 and P6c1 we isolated and determined the relative fitness of backcrossed segregants that either had all or , in the case of P5c3 alone , none of the mutations present in the original evolved clone . As expected , the backcrossed segregants with all of the evolved mutations had a relative fitness similar to the original evolved clone , while the P5c3 backcrossed segregant with none of the evolved mutations had neutral fitness ( Fig 2B and 2C ) . We were unable to isolate similar backcrossed clones corresponding to S8c2 . However , given the negative fitness effects of the VR t XCEN supernumerary chromosome in S8c2 , we were particularly interested to see if there was any epistasis , and specifically sign epistasis , between the point mutations and the VR t XCEN supernumerary chromosome in S8c2 . To test this , we determined the relative fitness of a backcrossed clone with all of the evolved mutations except for the SUL1 amplicon ( “No SUL1 amp” in Fig 2A ) . This clone had a fitness of -13% which , given the >5% fitness deficit of the VR t XCEN supernumerary chromosome , the HO mutation , and the YNL181W mutation , indicated epistasis between these mutations , although sign epistasis was not observed . When organisms adapt to a particular environment they may acquire mutations that produce a fitness tradeoff under alternative conditions [35 , 36] . Aneuploid events have previously been proposed to be pleiotropic mutations that , over the course of a population’s adaptation to a novel environment , are eventually replaced by mutations with fewer non-selective effects and correspondingly fewer fitness tradeoffs [37] . With these observations in mind , we determined the growth rates for 20 of the evolved clones in batch culture in rich media and observed a significant decrease in growth rate relative to wild-type for three of the 20 clones ( S1 Fig ) The similar doubling times to wild-type for most of the evolved clones suggested that the majority of evolved clones do not show a fitness tradeoff under typical lab growth conditions . However , comparing monoculture growth rates is an insensitive method to detect small fitness differences between clones . We therefore examined the fitnesses of six evolved aneuploid clones and the four aneuploid events we had previously isolated ( Fig 2 ) using chemostat competition experiments under the two nutrient limitation conditions not previously examined . Each of the four isolated aneuploid events and the SUL1 amplicon showed different fitness effects in the two alternative conditions ( Fig 3A ) . Typically , each aneuploid event decreased or had a small effect ( <5% ) on fitness under the two alternative conditions tested . However , both the VR t XCEN supernumerary chromosome from the sulfate-limited population S8 and the VR t VIR supernumerary chromosome from phosphate-limited population P6 increased fitness under glucose-limited conditions . We next tested evolved aneuploid clones under the two nutrient limitation conditions not previously examined and observed results similar to those achieved with the isolated aneuploid events . The evolved aneuploid clones typically had lower-than-wild-type fitness under the two alternative nutrient conditions , although occasionally they had increased fitness under alternative conditions ( Fig 3B ) . Finally , we compared the pleiotropy , defined as the variance in fitness between conditions , of the four isolated aneuploid events to the pleiotropy of single-gene changes in copy number and found aneuploid events to be significantly more pleiotropic than single-gene changes in copy number ( unpaired , two-tailed t test , p = 0 . 049 , S2A Fig ) These results generally supported previous hypotheses that proposed aneuploidy to be highly pleiotropic [7 , 37]; however , these results also emphasized that aneuploidy does not always lead to negative fitness tradeoffs but can also have unselected fitness benefits under alternative conditions . These detailed analyses of evolved aneuploid clones isolated from laboratory evolution experiments demonstrated the varying impact aneuploidy could exert on cellular fitness and proved that aneuploidy can cause fitness improvements in experimental evolution under nutrient limitation . However , this type of rigorous analysis is not scalable , and the limited number of clones we have examined here precluded any conclusions about the general effects of aneuploidy on fitness , adaptation , and evolution . With the dual goals of ( 1 ) identifying which aneuploid events in the remaining evolved clones increased fitness and ( 2 ) generating sufficient data to approach general questions about aneuploidy’s role in adaptation and evolution , we devised a screen to assay the fitness effects of aneuploidy genome-wide . In designing our screen , we decided to focus on a particular category of aneuploid event: telomeric amplicons ( Tamps ) , which we defined as a segmental amplification that initiates at a given location in the genome and extends to the proximal telomere . Tamps are a mutation type worthy of focused study as they are frequently observed in our evolved clones ( 17/36 aneuploid events are Tamps ) , and Tamps also play a role in human diseases such as cancer and developmental disorders [13 , 14 , 38] . To construct a genome-wide collection of Tamps , we returned to a classic method of genetic analysis: chromosome fragmentation [39] . This method was originally used for mapping the physical location of cloned genes . In our case , we were interested in it as an approach to fragment the yeast genome into a series of differently sized Tamps . By targeting our chromosome fragmentation vector ( CFV ) to the KanMX cassette that replaces each gene in the yeast deletion collection [40] , we were able to generate Tamps initiating at selected genomic locations simply by altering the particular deletion collection strain we chose to transform with our CFV ( Fig 4A and S3 Fig ) . Furthermore , as each deletion collection strain already had a unique DNA barcode identifying the genomic location of the KanMX cassette ( “Tamp BC” in Fig 4A ) , we could simply use barcode sequencing ( barseq ) [28] to determine the location at which the Tamp initiated . The design of our CFV included an additional random 12 base-pair barcode that , upon transformation into the target deletion collection strain , was incorporated into the Tamp and provided a barcode for each independent transformation event of an individual deletion collection strain ( Fig 4A “Replicate BC , ” see Materials and Methods for details ) . The ability to track multiple biological replicates of each Tamp allowed us to determine more accurately the fitness for each Tamp . We chose to build our Tamp pool from the diploid heterozygous deletion collection . We chose this deletion collection for two reasons . First , we wanted to match most closely the diploid background of most of the aneuploid clones isolated from our evolution experiments . Second , we expected there to be fewer suppressor mutations , which are commonly selected for in a homozygous or haploid deletion background to ameliorate the effects of the deleted gene [41 , 42] . Importantly , we were only able to take advantage of the yeast heterozygous deletion collection in this way because our lab had previously determined a set of 2 , 254 strains from this collection that have neutral fitness , with a range of relative fitnesses from -0 . 05 to 0 . 04 , under our standard chemostat growth conditions of sulfate- , glucose- , and phosphate-limitation ( S4 Table ) [34] . Thus , by restricting our method to these 2 , 254 deletion collection strains and the limitations under which they have neutral fitness , we can be reasonably confident that any fitness effects we do measure are due to the Tamp itself and not the underlying genetic background . With the intent of scaling eventually to the entire genome , we first sought to test our method on a small genomic region carrying a known driver gene: specifically , the telomeric 60 kb on the right arm of chromosome II ( chr II ) . We chose to first focus on this region because it contains the high-affinity sulfate transporter SUL1 , which our group had previously shown to be advantageous when amplified under sulfate-limiting conditions [10 , 30] . Furthermore , we demonstrated in the experiments described above that amplification of this region in its native chromosome context is also beneficial ( Fig 2 , “SUL1 amp” ) . We chose 60 kb since that amplicon size is the largest we have observed in diploid sulfate-limited evolution experiments [10] . We hypothesized that only Tamps containing SUL1 would increase fitness under sulfate-limiting conditions . We successfully created 21 Tamp strains , each initiating at a different gene within this 60kb region and extending to the right telomere , by transforming 21 neutral-fitness heterozygous deletion strains with our KanMX-targeted CFV ( see Materials and Methods for additional details ) . Each deletion strain was transformed individually , and the karyotype confirmed by aCGH ( see GEO Accession GSE67769 , see Materials and Methods for additional details ) . Pooled competition of these 21 strains for 9–12 generations followed by sequencing of the deletion collection barcode at five different time points allowed us to track the relative frequencies of each Tamp and infer their relative fitnesses ( Fig 5A and S5 Table ) . As expected , our results demonstrated that Tamps containing SUL1 increased fitness under sulfate-limiting conditions ( Fig 5A ) . In addition to SUL1 , a second driver gene had previously been identified within this 60 kb region: BSD2 amplification increases fitness under sulfate-limiting conditions [34] . Data from this targeted Tamp screen identified both BSD2 and SUL1 as driver genes under sulfate-limiting conditions . Tamps containing both SUL1 and BSD2 had an average fitness increase of 23% , Tamps containing only SUL1 had an average fitness increase of 15% , and Tamps containing neither SUL1 nor BSD2 had an average fitness decrease of 18% . The same procedure was repeated under glucose-limiting and phosphate-limiting conditions and no increase in fitness relative to wild type was observed ( S4 Fig ) , thus demonstrating the condition-specific fitness effects of the Tamps examined here . We noticed that both SUL1 and BSD2 were highlighted in our data by a discrete decrease in Tamp fitness , or “Downstep” ( Fig 5A ) . This was due to the fact that Tamps lacking SUL1 or BSD2 had decreased fitness compared to Tamps containing one or both of those genes . We hypothesized that such Downsteps could be used to identify additional driver genes in our genome-wide screen . Similarly , “Upsteps” could be used to identify genes that increased fitness when no longer amplified on a Tamp . Upsteps , therefore , could be used to identify novel “anti-driver” or growth-inhibiting genes in our genome-wide screen . After we confirmed the validity of our method with the chr II-targeted screen described above , we scaled our approach to the entire genome . The 2 , 254 neutral-fitness deletion collection strains were pooled and transformed with our KanMX-targeted CFV . To ensure each Tamp was represented by multiple independent transformation events , >42 , 000 transformant colonies were collected , guaranteeing approximately 20 unique biological replicates for each Tamp . Barseq of the resulting Tamp pool revealed it to be of adequate complexity: 1 , 802 of 2 , 254 targeted Tamps ( 80% ) were represented by >0 . 005% of the total reads , and each Tamp was represented by , on average , 26 independent transformants marked by unique biological replicate barcodes . We next used our genome-wide pool of Tamps to inoculate three glucose- , phosphate- , and sulfate-limited chemostats , for a total of nine pooled competition experiments . Each competition experiment was carried out for approximately 25 generations , with samples for barseq taken at ten time points throughout ( Fig 4B ) . We were able to track the Tamp frequencies of >100 , 000 unique biological replicates across ten time points under all three conditions . These data allowed us to , after the filtering steps described below , determine the fitnesses of 1 , 631 , 1 , 596 , and 1 , 551 Tamps in glucose- , phosphate- , and sulfate-limited conditions , respectively ( S6 Table ) . Our ability to track independent biological replicates of each Tamp was crucial in obtaining accurate Tamp fitness estimates , as our CFV-based method of generating Tamps had a significant error rate: while 20/25 Tamp strains generated in our chr II-targeted pool had the correct karyotype , only eight of the 16 Tamp strains we tested from our genome-wide pool had the correct karyotype as determined by aCGH ( see GEO Accession GSE67769 ) . The abnormal Tamp karyotypes included , most commonly , amplicons initiating at the correct genomic location but not extending to the proximal telomere and , occasionally , contained other large aneuploid events ( S7 Table ) . To mitigate the effect of biological replicates for which fitness was mismeasured due to incorrect Tamp formation or background mutations , we first filtered out Tamps with highly variable fitness estimates between biological replicates: this excluded approximately 20% of all Tamps from subsequent analysis and , as summarized above , left 1 , 631 , 1 , 596 , and 1 , 551 Tamps in glucose- , phosphate- , and sulfate-limited conditions , respectively , for further analysis ( see S1 Text for additional details ) . Next , we combined data from all biological replicates for a given Tamp to obtain a more accurate estimate of each Tamp’s fitness ( S5 Fig , See Materials and Methods ) . Specifically , for those Tamps with more than 15 biological replicates ( approximately 55% of remaining Tamps ) , we used the mode of the fitness distribution described by all biological replicates as the Tamp fitness; when 15 or fewer biological replicates were available , simply the mean of the biological replicates was used as the Tamp fitness ( See S1 Text ) . We confirmed the overall accuracy of our methods in 24 control experiments competing eight Tamp strains in all three nutrient-limiting conditions in head-to-head competition experiments against an appropriate GFP-marked control strain ( S8 Table ) . We found that the fitnesses determined in our genome-wide screen agreed well with those determined in head-to-head competition experiments of aCGH-validated strains ( S2B Fig , adjusted R2 = 0 . 64 ) . When we plotted the fitnesses of each Tamp across the genome , we noticed that , similar to the chrII-targeted screen , neighboring Tamps typically had similar fitnesses , which defined plateaus bordered by distinct fitness breakpoints ( S6 Fig and S7 Fig ) . As described above , we hypothesized that “Downsteps” in fitness could be used to identify driver genes that , under the condition tested , increased fitness when amplified in the context of a Tamp . Similarly , sharp increases in fitness , or “Upsteps , ” could be used to identify anti-driver genes that , when amplified in the context of a Tamp , decreased fitness under the tested condition . After we observed the stepwise shape of this fitness profile , we used DNAcopy [43] , an analysis program typically applied to aCGH data to identify regions of similar copy number as well as copy number variant ( CNV ) breakpoints via circular binary segmentation , to define fitness plateaus and fitness breakpoints in our Tamp fitness data ( S9 Table , See Materials and Methods ) . Segmenting our genome-wide fitness data in this way generated a summary view of the fitness effects of Tamps . We believed this analysis approach was well suited to our data because , similar to CNVs analyzed by aCGH , we expected our fitness data to be somewhat noisy and for neighboring Tamps to have similar fitnesses . As a good example of our analysis approach , Fig 5B visualizes the results of our genome-wide Tamp screen for chromosome II under sulfate-limiting conditions . The top panel of Fig 5B depicts as blue lines the 122 Tamps spanning chromosome II for which we determined fitnesses; each Tamp initiates at a different location along chromosome II and extends to the proximal telomere . The fitness of each Tamp is plotted in the bottom panel of Fig 5B directly below its corresponding vertical blue line ( see the red arrow for one example ) . Segmenting our genome-wide fitness data using DNAcopy defined fitness breakpoints that are outlined with the yellow and teal stacked boxes: yellow boxes enclose Tamps that increased fitness , while teal boxes enclose Tamps that decreased fitness . Just as we observed in our chromosome II–targeted pool , Tamps that amplified the right arm of chromosome II , where SUL1 is located , increased fitness under sulfate-limiting conditions . Note that the Downstep telomeric of SUL1 we observed in the chrII-targeted pool was not observed in the genome-wide pool because we did not include any Tamps initiating between SUL1 and the telomere in our genome-wide Tamp pool . Although the incorrect karyotypes of individual Tamp biological replicates is an unfortunate by-product of our methodological approach , our analysis pipeline significantly ameliorated this limitation . We are therefore confident that this genome-wide Tamp screen provided an accurate description of the fitness effects of a complex pool of Tamps . As such , our method provides a systematic view of the fitness landscape described by Tamps under multiple selective conditions . Next , we asked whether the fitness effects of Tamps were always condition dependent or if there were some Tamps that commonly increased or decreased fitness across the conditions we examined . Our genome-wide Tamp screen identified a unique list of fitness breakpoints in each of the three conditions we examined . The union of these three lists thus defines the minimum number of regions showing a change in fitness compared to neighboring regions in at least one condition . Specifically , we identified 175 regions with different fitnesses in at least one condition . We compared the fitnesses of these 175 regions between conditions and generally found little correlation between conditions ( Fig 6A–6C ) . However , a few regions had common fitness effects between conditions; four and seven of the 175 regions increased or decreased fitness by >5% , respectively , in all three conditions . As examples of our Tamp dataset , the fitnesses of Tamps from four chromosomes are shown in Fig 6D . Similar to Fig 5B , in this figure we have shown stacked boxes that represent groups of Tamps with similar fitness as defined by our segmentation of the genome-wide fitness profile with DNAcopy . However , in this figure we have not plotted raw Tamp data as we did in Fig 5 . As we were interested in comparing the fitness effects of Tamps between conditions , we summed the fitness effects of each Tamp under all three conditions . To emphasize regions that have common effects between all three conditions , in the “Summary” section of Fig 6D we displayed as stacked boxes only those Tamps with the same fitness effect under all three conditions ( i . e . , >5% fitness advantage or disadvantage ) . The relative fitness of these boxes represents the sum of the fitness effects under all three conditions . Notice that some of the stacked boxes appear to be missing from the”Summary” section of Fig 6D . This is because only a few regions had common fitness effects between all conditions; boxes enclosing regions with different fitness effects under different conditions are excluded from the “Summary” section . While chromosome II and XIV lacked any region with a common fitness effect across all three conditions , chromosomes V and XI both contained regions that were either universally advantageous or detrimental when amplified . For example , amplification of the left arm of chromosome XI decreased fitness under all three conditions ( Fig 6A–6C , grey circles , and Fig 6D ) . Other Tamps showed common fitness effects in two of the three conditions we tested . For example , amplification of the left arm of chromosome XIV increased fitness not only under glucose-limiting conditions but also under phosphate-limiting conditions ( Fig 6B , red arrow , Fig 6D ) . Next , we examined two regions recurrently amplified in the set of evolution experiments examined here and those previously described [12] . The right arm of chromosome V was amplified in three different evolution experiments carried out under the three nutrient-limiting conditions . Similarly , the genome-wide Tamp screen predicted a 51 kb Tamp on the right arm of chromosome V to increase fitness by approximately 6%–7% under all three conditions ( Fig 6D ) . However , the Tamps observed in the evolved populations were actually somewhat larger ( 84–440 kb ) than this 51 kb Tamp . It is notable that the chromosome V amplicon in two of the three evolved populations initiated at the closet Ty element centromeric of this 51 kb high-fitness Tamp . The Tamp screen predicted the chromosome V amplicons observed in the evolved populations to affect fitness by +6% , -3% , and -1% under sulfate- , phosphate- , and glucose-limiting conditions respectively ( S10 Table ) . In summary , while our Tamp screen predicted that amplification of 51 kb on the right arm of chromosome V is commonly advantageous , the precise amplifications observed in our evolution experiments were predicted to be neutral under glucose- and phosphate-limiting conditions and to increase fitness only under sulfate-limiting conditions . The recurrent amplification on the right arm of chromosome XIV has been observed in three independent glucose-limited evolution experiments [10 , 12] . Consistent with these observations , the genome-wide Tamp screen predicted this event to increase fitness by >20% under glucose-limited conditions ( Fig 6D ) . Interestingly , our genome-wide screen predicted a smaller Tamp on the left arm of chromosome XIV to increase fitness under phosphate-limiting conditions; however , no such amplicon has been yet reported in any phosphate-limited evolution experiment . Chromosome XIV left-arm Tamps were predicted to have a nearly neutral effect on fitness under sulfate-limiting conditions ( < 2% fitness increase ) . A similar rearrangement was also previously identified as yeast “chromosome XVII” because of an aberrant karyotype in the original genetic mapping strains , suggesting this amplification may have fitness benefits in other conditions as well [44] . The dataset from our Tamp screen allowed us to ask general questions about the relationship between aneuploidy , specifically telomeric amplicons , and fitness . First , we compared the fitness of each Tamp to its size in base-pairs and found little correlation ( adjusted R2 = 0 . 05 , S2C Fig ) . Although Tamp truncation was not an insignificant problem in our dataset , our analysis approach , by filtering out Tamps with high intra-replicate variation in fitness and using the mode of the biological replicate fitness distribution to estimate fitness , partially ameliorated the effects of incorrectly sized replicates on each Tamp’s fitness estimate . Next , we took advantage of data previously generated by our lab that determined the fitness effects of single-gene amplifications genome-wide under the same conditions explored in our Tamp screen [34] ( see S1 Text ) . We compared the fitness distribution defined by our genome-wide Tamp screen to the fitness distribution defined by single-gene amplifications [34] ( Fig 7A ) . We found that the distribution of Tamp fitnesses was much broader than that defined by single-gene amplifications . Additionally , we noted that distribution of Tamp fitnesses appeared bimodal , with one negative fitness peak and a second positive fitness peak . This result supports the hypothesis that aneuploid events are mutations that have large effects , positive and negative , on organisms’ fitness . Aneuploid events are hypothesized to be highly pleiotropic: a characteristic that may explain their eventual supplantation by more targeted mutation types [37] . To test this hypothesis , we defined pleiotropy as the between-condition variance in fitness . Taking advantage of the same genome-wide single-gene amplification dataset referenced above [34] , we compared the density distributions of variance in fitness of Tamps to those of single gene amplifications . We found that Tamps described a much broader distribution than that described by single gene amplification ( Fig 7B ) . These results support the hypothesis that aneuploid events are pleiotropic . The data from our genome-wide Tamp screen , combined with our lab’s previous data describing the fitness effects of single-gene amplifications genome-wide , also allowed us to explore the genetic basis of aneuploidy’s effects on cellular fitness . First , we asked if the fitness of any given Tamp could be predicted by the average of the fitness effects of all single-gene amplifications within the boundaries of the Tamp . We found that the average of the fitnesses of single-gene amplifications for the genes contained within a Tamp did not predict the fitness of the Tamp itself ( S2D Fig ) Next , we explored the alternative hypothesis that only a few genes within a Tamp are centrally important for effecting the fitness of the entire amplicon . This hypothesis was additionally supported by the stair-step shape of the Tamp fitness landscape: if many genes within a Tamp contributed to the fitness effects observed , one would expect a smooth fitness profile in which the addition or loss of individual genes from the amplicon produced an incremental change in fitness; instead , the fitness profile produced by our Tamp screen often revealed plateaus in fitness bordered by discrete fitness breakpoints . As discussed above , we hypothesized that Downsteps in the Tamp fitness landscape identified driver genes that increased fitness when amplified , while Upsteps identified anti-driver genes that decreased fitness when amplified . Combining fitness data from all three conditions , we identified 181 fitness breakpoints: 77 Downsteps and 104 Upsteps . As our genome-wide Tamp screen did not contain a Tamp for every gene in the genome , each Downstep or Upstep region necessarily overlapped several genes . We averaged the fitness effects of single-gene amplification for genes overlapping each of the 181 fitness breakpoints and found that the average fitness at Downsteps was significantly greater than at Upsteps ( Fig 7C , unpaired , two-tailed t test , p = 0 . 008 , raw data in S17 Table ) . These results supported the hypothesis that one or few gene ( s ) , located at Upsteps and Downsteps , were primarily responsible for effecting the fitness of each Tamp . By identifying driver and anti-driver genes respectively , Downsteps and Upsteps can be used to identify potential genetic targets of adaptation . In fact , under both glucose- and phosphate-limiting conditions , but not under sulfate-limiting conditions , the genes overlapping Upsteps were enriched for genes mutated in populations evolved under the corresponding nutrient limitation ( Fig 7D , Fisher’s exact test , p = 2 . 6 x 10–4 and p = 0 . 027 for glucose- and phosphate-limiting conditions , respectively ) [34] . As we expected Upstep genes to decrease fitness when amplified , we might therefore have expected that lower levels of expression of these same genes would increase fitness . Our results thus agree with the recent observation by Kvitek and Sherlock that the majority of mutations selected in haploid yeast evolved under glucose-limited conditions are loss-of-function mutations [45] . There are additional similarities between the glucose-limited Upstep genes identified in our Tamp screen and the genes mutated in glucose-limited evolution experiments . First , glucose-limited Upstep genes are enriched for Gene Ontology ( GO ) terms closely related to those enriched in the group of recurrently mutated genes identified by Kvitek and Sherlock in glucose-limited evolution experiments ( “intracellular signal transduction , ” Fisher’s exact test , Holm–Bonferroni corrected p = 0 . 01 , and “regulation of intracellular signal transduction , ” Fisher’s exact test , Holm–Bonferroni corrected p = 0 . 015 ) . Second , the genes located at Upsteps in our glucose-limited Tamp screen are enriched for genes observed by Kvitek and Sherlock to be recurrently mutated in glucose-limited evolution experiments and include: HOG1 , IRA2 , LCB3 , PBS2 , PDE2 , and SSK2 ( Fisher’s exact test , p = 6 . 4 x 10–6 ) . Given the large number of genes overlapping several Downsteps ( up to 30 genes ) , we sought to filter the list of Downstep genes and produce a list of high-quality candidate driver genes . We filtered the list of Downstep genes by comparing it to several published datasets: the list of genes commonly up-regulated in clones evolved under glucose- , phosphate- , or sulfate-limiting conditions [10 , 46]; the list of genes that increased fitness when present on a low-copy-number plasmid under glucose- , phosphate- , or sulfate-limiting conditions [34]; and the list of genes mutated in populations evolved under glucose- , phosphate- , or sulfate-limiting conditions [34] . After this filtering , we identified a total of 100 candidate driver genes important for increasing fitness in the context of a telomeric amplicon under the three nutrient-limiting conditions we examined here ( S11 Table ) . Importantly , our filtered list of candidate driver genes still identified at least one driver gene at most Downsteps ( 58 out of 77 , or 75% ) . Although we expected our method to identify driver genes that , when amplified , individually increased fitness , we also expected our method to identify genes that increased fitness only when amplified in the context the Tamp . In fact , 12 of the 73 candidate driver genes identified here have a negative effect on fitness when amplified individually ( S11 Table ) . We hypothesized that these 12 driver genes in particular must synergistically interact with one or more genes coamplified on the Tamp . The synergistic partners of the identified driver genes are probably located between the identified driver gene and the telomere . As these telomeric synergistic partners would only be expected to affect fitness when coordinately amplified with our currently identified driver genes , they would not be expected to produce a step in the fitness profile . Identification of these pairs or groups of synergistically interacting genes remains a target of future research . With the fitness data from the genome-wide Tamp screen and this list of candidate driver genes in hand , we returned our analysis to the aneuploid events observed in our laboratory-evolved populations as well as aneuploid events previously documented in a similar set of evolution experiments [12] . Fitness data from our Tamp screen predicted that 11 of the 16 telomeric amplicons identified in evolved populations increased fitness under their corresponding conditions , while the remaining five telomeric amplicons were likely passenger mutations ( two of these five amplicons represented the chromosome V amplicons observed in sulfate- and phosphate-limited populations and discussed above ) ( S10 Table ) . Importantly , our Tamp screen allowed us to predict the fitnesses of telomeric amplicons that are difficult to test by traditional genetic means , as they are linked to large deletions that rendered any haploid spore intermediary inviable . For each telomeric amplicon observed in an evolved population , we estimated the number of driver genes within its length by counting the number of Downsteps it overlapped ( S10 Table ) . Typically , evolved Tamps overlapped one to three Downsteps , suggesting that only a few driver genes were primarily important for determining the fitness increase associated with these aneuploid events . As mentioned above , we have not yet identified the synergistic partners of these driver genes . The main exception to this statement is the amplification of the left arm of chromosome XIV recurrently observed in populations evolved under glucose-limiting conditions ( Fig 7E ) . This large amplification overlapped six Downsteps and was predicted by our screen to increase fitness by >20% . There were multiple candidate driver genes along this segment , including YNL019C , RPL16B , OCA1 , RAS2 , YNL095C , SKO1 , BNI5 , YNL162W-A , PEX6 , and EGT2 . The data from our Tamp screen proved useful in addressing general questions about the genetic basis for aneuploidy’s effect on cellular fitness and identified potentially novel driver genes that are important for increasing fitness in the context of aneuploidy . Furthermore , we have used data from our Tamp screen to predict the fitness effects of telomeric amplicons observed in evolved populations that are otherwise not amenable to traditional genetic analyses .
Our survey of aneuploid events identified in populations of S . cerevisiae evolved in nutrient-limited chemostats produced circumstantial evidence for aneuploidy’s positive effect on cellular fitness: aneuploid events rose to high population frequencies , and clones isolated with aneuploid karyotypes had fitnesses greater than wild type . In addition , we found that evolved aneuploid clones had a significantly greater relative fitness than evolved euploid clones . However , as the aneuploid and euploid clones were also different with respect to their genetic background , the nutrient-limiting conditions of their evolution experiment , and the number of generations that they were grown in the chemostat [10] , there are several possible confounding explanations for their significant difference in fitness . Three out of four aneuploid events , for which we directly determined the fitness , were sufficient to increase fitness relative to wild type . Each , however , showed a different relationship to the overall fitness of the original corresponding evolved clone , demonstrating that aneuploid events show varying degrees of epistasis with the other mutations acquired over the course of evolution . Interestingly , the VR t XCEN supernumerary chromosome isolated from the sulfate-limited population S8 , despite occupying a substantial proportion of the population ( 13% ) , decreased fitness under sulfate-limiting conditions . Furthermore , this supernumerary chromosome contained a telomeric amplification of the right arm of chromosome V that was recurrently amplified in three different populations evolved under three different nutrient-limited conditions . Both the population frequency of this event as well as its recurrence were strongly suggestive of its selection under sulfate-limiting conditions . However , the S8c2 VR t XCEN supernumerary chromosome actually decreased fitness by 10% under sulfate-limiting conditions . It is possible that this discrepancy can be explained by the non-transitive relationship of fitness that has previously been observed over the course of laboratory evolution [47] . Epistasis may also explain this result , as the SUL1 amplicon alone from clone S8c2 increased fitness to a similar extent as that observed with the original evolved clone; this suggests that the fitness effects of the VR t XCEN supernumerary chromosome were fairly neutral in the context of a SUL1 amplification . These results argue that the VR t XCEN supernumerary chromosome is a passenger mutation . This is consistent with previous findings that showed genetic hitchhiking to be important to the spectrum of mutations observed in populations of asexually dividing cells [33 , 48] . Given the strong effects of epistasis and genetic hitchhiking on mutation frequency , these results should offer a strong cautionary message to the sole reliance on recurrence and population frequency for differentiating driver mutations from passenger mutations . Although the remaining telomeric amplicons observed in the laboratory-evolved populations examined in this study were all concurrent with large deletions , making their genetic isolation difficult using traditional techniques , data from our Tamp screen allowed us to predict that the majority of these laboratory-evolved Tamps increased fitness in the conditions under which they were observed . In contrast , only one out of the 12 non-synonymous mutations we tested , a missense mutation in PHO84 isolated from the phosphate-limited evolved clone P6c1 , increased fitness by more than 5% . Consistent with this observation , we observed a broader range of fitness effects in our Tamp screen than in a genome-wide screen for the fitness effects of single-gene amplifications ( Fig 7A ) [34] . These results show that aneuploid events are important drivers of increased fitness in populations of S . cerevisiae evolving under nutrient limiting conditions . Furthermore , these data are consistent with the hypothesis that aneuploid events allow evolving populations to broadly explore a fitness landscape by prompting large jumps in fitness unattainable by the mutation of single genes [23 , 37] . Aneuploidy is likely particularly important for the adaptation to novel conditions . Fitness data from our Tamp screen and from competition experiments with aneuploid events and evolved aneuploid clones confirmed that Tamps , and aneuploidy more generally , are pleiotropic mutations with typically condition-dependent fitness effects; most aneuploid events and clones had decreased fitness under alternative conditions . Occasionally , as observed in both the Tamp screen and in direct fitness assessments with evolved clones and evolved aneuploid events , similar fitness effects were observed between conditions . Notably , similar fitness effects under different conditions were observed with the VR t VIR supernumerary chromosome isolated from the phosphate-limited population P6 , which increased fitness to a similar extent under both phosphate- and glucose-limiting conditions . Particularly surprising was the observation that the supernumerary chromosome isolated from the sulfate-limited population S8 , which decreased fitness by 10% under sulfate-limiting conditions , actually increased fitness by 11% under glucose-limiting conditions . As all of the competition experiments were carried out under conditions of chemostat growth , it is possible that some of the aneuploid events with common fitness effects across nutrient-limiting conditions affected growth under continuous culture generally . Mutations such as the S8 supernumerary chromosome might contribute to the increased adaptability of aneuploid cells: an aneuploid event acting as a passenger mutation under a cell’s current condition could provide a dramatic increase in fitness under a novel condition . This conversion of a passenger mutation to a driver mutation may be more likely to occur with aneuploid events than with point mutations or single-gene changes in copy number because of the number of genes affected by a single aneuploid event . For example , although not yet observed in phosphate-limited evolution experiments , our Tamp screen predicted the amplification of the left arm of chromosome XIV to increase fitness under both glucose- and phosphate-limiting conditions ( Fig 6D ) . However , only one of the ten candidate driver genes present within this amplicon is predicted to be responsible for the increased fitness of this amplicon under both phosphate- and glucose-limiting conditions . By affecting the copy number of many genes simultaneously , aneuploid events are necessarily pleiotropic . However , while aneuploid events may also show similar fitness effects under different conditions , these fitness effects are likely mediated through the copy-number change of distinct groups of driver genes . These data emphasize the condition-dependent nature of aneuploidy’s effect on cellular fitness and may help address the “aneuploidy paradox”: the observation that while aneuploidy typically decreases a cell’s proliferative ability , it increases fitness under certain conditions [2 , 3 , 5 , 7] . The data from our Tamp screen allowed us to investigate general aspects of the relationship between aneuploidy and cellular fitness . The data presented here are further support for the current hypotheses that aneuploidy is both a large effect-size mutation and that it is more pleiotropic than single-gene changes in copy number . As aneuploidy generally decreases cells’ proliferative ability , one might have expected larger Tamps to increase fitness to a lesser extent than smaller Tamps as the burden of carrying such a large Tamp outweighed any benefit due to amplification of genes along its length . However , our Tamp data show that there is no overall correlation between size and fitness of the Tamps examined in our screen . Although there was no overall correlation between fitness and Tamp size in our data , there was a distinct negative relationship between size and Tamp fitness on the right arm of chromosome II under sulfate-limiting conditions ( slope = -0 . 0087 relative fitness/kb , adjusted R2 = 0 . 87 ) . A more detailed analysis of the fitness data produced by our Tamp screen may reveal a more general relationship between Tamp size and fitness . In addition , as it is likely that Tamps initiating far from a telomere are less likely to complete break-induced-replication ( BIR ) , thus resulting in truncated amplicons , our results here , while ameliorated by our analysis pipeline , likely represent an underestimate of any deficit correlated with size . Our Tamp screen revealed that amplicons with increased fitness could not be differentiated from amplicons with decreased fitness simply by averaging the fitness effects of all single-gene amplifications along their lengths . However , when we focused on fitness breakpoints , we were able to differentiate increases in fitness ( Upsteps ) from decreases in fitness ( Downsteps ) by averaging the fitness effects of all single-gene amplifications overlapping the breakpoint region . These results suggested that a minority of genes were responsible for an amplicon’s fitness effects . In fact , we have previously shown this to be true for Tamps of the right arm of chromosome II and the amplification of SUL1 under sulfate-limiting conditions ( increase in fitness due to SUL1 on a low-copy number plasmid = 23% [30] , increase in fitness due to 60 kb telomeric amplicon overlapping SUL1 = 16% ) . However , the amplification of SUL1 under sulfate-limiting conditions is a clear outlier , in that it increases fitnesses much more than any other single-gene amplification under the three nutrient-limited conditions examined here . Synergistic effects between the driver genes identified in this study and a small number of as-yet-unknown interaction partners located distally along the amplicon are likely responsible for the fitness effects of most amplicons . While the models tested here limited the interaction between genes within an amplicon to be simply additive , we acknowledge that the interactions between genes within aneuploid regions are likely to be much more complex and warrant further study . In fact , we have already tested a method to confirm the identity of driver genes and reveal synergistic partners within a Tamp . Focusing once more on the right arm of chromosome II , we created 21 independent strains that each paired a single 60 kb Tamp with deletion of a different gene along this amplicon . As expected , under sulfate-limiting conditions , deletion of SUL1 eliminated the fitness increase due to this 60 kb Tamp ( S8 Fig ) . Genome-wide application of this method , or focused application to amplicons of particular interest , for example the amplification of the left arm of chromosome XIV under glucose-limited conditions , would further illuminate the types of interactions between genes contained within aneuploid regions . While the data produced by our Tamp screen have allowed us to gain a genome-wide view of the fitness landscape explored by Tamps , it is prudent to highlight some of the limitations of this dataset . First , as noted above , there is a high error rate in the formation of Tamps with the method employed here . Despite our attempts to account for these errors in both our experimental design ( i . e . , incorporating a biological-replicate barcode into each Tamp strain ) and analysis pipeline , future experiments would benefit from an improved experimental approach . One approach would be to identify replicate barcodes that were associated with Tamps of inappropriate sizes and exclude these barcodes from the analysis . This could be accomplished by pairing Pulse-Field Gel Electrophoresis with gel extraction and barseq to determine the actual size of each barcoded Tamp strain . Second , the segmentation approach used to fragment the genome-wide Tamp fitness data into regions of approximately equal fitness may be an oversimplification of these data , and a more detailed examination of the fitness changes across the genome is warranted . Our genome-wide Tamp pool represents all possible amplicons at 3–4 gene resolution , however , previous studies have shown that rearrangements , including those that produce the Tamps studied here , are often mediated by repetitive elements in the S . cerevisiae genome , such as Ty elements [12] . As such , it becomes informative to compare the telomeric amplicons observed in evolution experiments to our Tamp screen and ask if the most advantageous Tamps are selected during the course of laboratory evolution . The recurrent amplicon of the right arm of chromosome V provides a good example of the benefits of this analysis . As described above , the chromosome V amplicon observed in both phosphate- and sulfate-limited evolution experiments is larger than the highest-fitness Tamp on the right arm of chromosome V identified in our screen . This is likely due to the fact that there are no Ty elements or repetitive regions closer to the fitness breakpoint identified by our Tamp screen than the one employed to form the amplicons observed in the evolution experiments . This provides an example where the genomic context likely restricted the formation of the most advantageous amplicon under these conditions . Generally , however , the regions commonly amplified in evolution experiments are the highest-fitness Tamps identified by our screen . The top 54 most advantageous Tamps identified by our screen under sulfate-limiting conditions all overlap the SUL1-containing region on the right arm of chromosome II . Similarly , amplification of the left arm of chromosome XIV has been observed repeatedly under glucose-limiting conditions; 15 of the top 16 most advantageous Tamps identified by our screen under glucose-limiting conditions overlap the left arm of chromosome XIV . Unlike sulfate- and glucose-limited evolution experiments , populations evolved under phosphate-limited conditions show no such obvious recurrent amplification . However , the most fit Tamp predicted by our screen is the amplification of the left arm of chromosome XVI , although of a slightly smaller size than that predicted to be advantageous under glucose-limiting conditions ( Fig 6 ) . Although this amplification has not yet been observed in any phosphate-limited evolution experiment to date , there are several Ty elements in the region where positive-fitness Tamps initiated in our screen , so its absence cannot easily be explained by a genomic context unfavorable to amplification . By combining data from a genome-wide telomeric amplicon screen and detailed analyses of clones and aneuploid events isolated from laboratory evolution experiments , we have provided details about the relationship between aneuploidy and cellular fitness . These data identified new candidate driver genes , the copy number changes of which are important for fitness , contribute to our understanding of how aneuploidy acts at the cellular level , and add to our understanding of aneuploidy’s role in adaptation and evolution . Recent advances in the direct targeting of DNA breaks in human cells [49] , combined with the wealth of information generated from the sequencing of cancer genomes , may allow a similar comparative approach to be applied to the effects of aneuploidy in cellular proliferation and its role in the evolutions of cancers . We also note that the same experimental design could be applied to conditions in which aneuploidy is known to be detrimental as a way to identify critical dosage-sensitive genes .
The strains , plasmids , and primers used in this study are listed in S12 Table , S13 Table , and S14 Table , respectively . Unless specified below , yeast strains were grown at 30°C and standard media recipes were used . We generated population DNA from archived glycerol stocks for the 14 evolution experiments not previously examined and determined the population frequency of aneuploid events by aCGH . We confirmed the accuracy of this approach by comparing the population frequencies of aneuploid events in population P7 , as determined previously in [10] from fresh population DNA samples , to the frequencies determined by the method described here and found similar results . All aCGH data are available at GEO Accession GSE67769 . In addition , we used a PCR assay that amplified the breakpoint of the VR t XCEN translocation event present in population S8 using primers OAS005–0AS0008 . This breakpoint PCR assay identified the VR t XCEN supernumerary chromosome in 13 of 98 total clones tested ( 13% ) ; our population aCGH determined the frequency of VR t XCEN supernumerary chromosome to be 13% . To determine relative fitness , we competed individual clones of test strains against an appropriate control strain with eGFP integrated at the HO locus in nutrient limited chemostats . We used both large volume ( approximately 300 ml ) and small volume ( 20 ml ) [50] chemostats for competition experiments . A single colony of each control or test strain was used to start an overnight culture in the same media in which the competition experiment was to be carried out; the overnight culture was then grown at 30°C for approximately 12–36 h . 1 ml of this overnight was used to inoculate each chemostat , which was then allowed to grow at 30°C without dilution for approximately 30 h , at which point fresh media was added to the culture chamber at a rate of 0 . 17 hour-1 . After achieving steady-state , 50% of a control-strain chemostat was mixed with 50% of a test-strain chemostat , resulting in two chemostat replicates for a single competition experiment . Flow-cytometry using a BD Accuri C6 flow cytometer ( BD Biosciences ) at regular intervals throughout the competition allowed us to track the percentage of GFP-marked control cells over time . The data were plotted with ln[ ( dark cells/GFP+ cells ) ] versus generations , and we defined the slope of this relationship as the relative fitness of the test strain . The number of replicate competition experiments , as well as the appropriate control strain , is detailed for all test strains in S2 Table . Two Tamp strains were constructed individually by direct transformation with a chromosome-fragmentation vector ( CFV ) . 250 bp of homology to the genomic location at which we desired to create a Tamp was cloned into the multiple cloning site of the previously designed CFV YCF4 [39] . The appropriate CFV was then transformed into a haploid FY background strain to create chrIIR-Tamp 1N and chrVR-Tamp 1N and the karyotype confirmed by aCGH ( see GEO Accession GSE67769 ) . These haploid strains were backcrossed to create chrII-Tamp 2N and chrVR-Tamp 2N . DNA samples from evolved clones and populations were prepared for WGS using Illumina Nextera kits according to the provided protocol . Libraries were sequenced on either an Illumina HiSeq or a GAII , generating the number of reads detailed in S3 Table . Reads were aligned with BWA [51] and SNVs were called using samtools [52] after applying standard filters . Population frequency of SNVs from population samples was determined from the allele frequency displayed in Integrative Genome Viewer ( IGV ) [53] . The clones and populations analyzed here ( P6c1 , P6 , P5c3 , P5 , S8c2 , and S8 ) were included in a previous analysis [34] and the raw data are deposited at BioProject ID PRJNA248591 and BioSample numbers SAMN02800460 ( S8c2 ) , SAMN02800438 ( P6c1 ) , SAMN02800436 ( P5c3 , run 1 ) , SAMN02800435 ( P5c3 , run 2 ) , SAMN02800403 ( CEN . PK WT diploid , run 1 ) , and SAMN02800404 ( CEN . PK WT diploid , run 2 ) . To isolate individual mutations ( both SNVs and aneuploid events ) identified by WGS of the evolved clones P6c1 and P5c3 , we backcrossed each evolved clone to an isogenic wild-type strain of the opposite mating type , sporulated , and dissected tetrads using standard sporulation media and protocols . Evolved clone S8c2 , a diploid , was itself sporulated and tetrads dissected using standard sporulation media and protocols . After Sanger-sequencing confirmed the SNVs identified by WGS , tetrads were genotyped by Sanger sequencing and backcrossed repeatedly until each SNV and aneuploid event was isolated into an otherwise wild-type background . Spores isolated from S8c2 with the desired genotype were backcrossed a final time so that each mutation was once again in a diploid background . The karyotypes were confirmed by aCGH for all clones eventually used for relative fitness competition experiments ( see GEO Accession GSE67769 ) . In order to compare the pleiotropic effects of aneuploid events and single-gene changes in copy number we calculated the between-condition variance in relative fitness for each mutation ( aneuploid event or single-gene amplification ) under the three nutrient-imitated conditions examined . Specifically , for each aneuploid event examined in Fig 3 we determined the between-condition variance in fitness . Next , we performed the same calculation for all single-gene amplifications as determined previously [34] . In this study , Payen et al . determined the fitness effects of single-gene amplifications by pooled competition experiments with a genome-wide collection of yeast ORFs cloned into a low-copy-number ( CEN ) plasmid [54] . We compared the distribution of fitness differences defined by single-gene changes in copy number to that observed with the aneuploid events examined in Fig 3 ( S2A Fig ) using an unpaired , two-tailed t test . In order to create Tamps from deletion collection target strains we constructed two unique CFVs to target the KanMX cassettes that replaced Watson and Crick genes , pABS003 and pABS004 , respectively . Primers OAS009 and OAS010 were used to amplify the KanMX cassette region , which was cloned into the BamHI and EcoRI sites of the CFV YCF4 to produce pABS003 . Primers OAS011 and OAS012 were used to amplify the KanMX cassette region , which was cloned into the BamHI and EcoRI sites of the CFV YCF4 to produce pABS004 . We then transformed 26 heterozygous yeast deletion target strains with a version of the appropriate CFV linearized with NotI . Overall , 20 of the 26 heterozygous deletion strains yielded transformants with the expected Tamp . For our subsequent experiments , we chose to pool 21 of the Tamp strains , including the ybr282wΔ/+ Tamp strain , which also carried an extra copy of chromosome II . We added to this pool the yal066wΔ/+ heterozygous deletion collection strain to act as a wild-type fitness control; YAL066W is a pseudogene . To make the final pool that was used in subsequent competition experiments , the 21 Tamp strains plus the surrogate wild-type control strain were inoculated in minimal media , grown for approximately 12 h at 30°C , the cell densities were normalized , and all 22 strains were pooled together . 2 ml glycerol stocks made with 1 ml 50% glycerol plus 1 ml pooled culture were saved at -80°C . To determine the fitness effects of the 21 Tamps in the chrII-targeted pool , we performed chemostat competition experiments with this pool in triplicate under sulfate- , glucose- , and phosphate-limiting conditions . At 5 time points throughout each competition experiment DNA samples were prepared and used to make barseq libraries with the PCR primers OAS013 and OAS014 or OAS029 and OAS030 . After purification , the barseq libraries were pooled and loaded onto an Illumina HiSeq . The 6 bp barcode used for multiplexing the samples onto a single lane are indicate in S15 Table . As these reads were obtained from a run that had been multiplexed with other samples unrelated to this study , we have made available tab-delimited files of the raw sequencing data that contain the multiplexing barcode in the first column and the Tamp BC read in the second column . These files can be found at BioProject ID PRJNA257895 with BioSample IDs SAMN02979479 and SAMN02980022 to SAMN029794825 . To determine the relative fitness of each of the 21 Tamps in this pool we used an analysis approach that has been successfully used by our lab in a previous publication [34] . Briefly , the frequency of each Tamp at each time point was determined from the barseq reads using a custom pipeline . For each Tamp we then plotted the log2 ( frequency at time = t / frequency at time = 0 ) versus generations and the slope of the line was taken as the relative fitness . The relative fitness of the yal066wΔ/+ strain was set at 0 and all the other Tamp fitnesses were normalized to it . The relative fitnesses for all 21 Tamps under all three nutrient-limiting conditions are reported in S5 Table and plotted in S4 Fig . Occasionally , insufficient reads were obtained to calculate the fitness of a particular strain under a particular condition . In this case the fitness is noted as “NA . ” To develop a method that could confirm the identity of driver genes along a Tamp , we tested a method that paired a single large Tamp with single gene deletions along its length . We generated a MATα 60 kb chrII Tamp strain ( chrII-Tamp 1N ) as described above and crossed it to 22 MATa deletion strains corresponding to genes within this 60 kb region . These MATa deletion strains were from a minimally passaged collection derived from the yeast magic marker collection [40] . We pooled these 22 strains and competed them in the three nutrient-limiting conditions in triplicate as described for the chrII-targeted Tamp pool . Similarly , we performed barseq on these samples using the same protocol as described for the chrII-targeted Tamp pool . These barseq libraries were pooled together and sequenced on an Illumina HiSeq ( the 6 bp barcodes used for multiplexing are reported in S15 Table ) and 354 , 545 , 894 reads were obtained . As these reads were obtained from a run that had been multiplexed with other samples unrelated to this study , we have made available tab-delimited files of the raw sequencing data that contain the multiplexing barcode in the first column and the Tamp BC read in the second column . These files can be found at BioProject ID PRJNA257895 with BioSample IDs SAMN02979479 and SAMN02980022 to SAMN029794825 . Fitnesses were determined for each strain as described for the chrII-targeted Tamp pool , except that they were normalized to the fitnesses of the 60 kb chrII amplification alone ( strain “chrII Tamp 2N” ) instead of yal066wΔ/+ and are reported in S5 Table and plotted in S8 Fig . To construct the genome-wide Tamp pool , 2 , 254 neutral fitness strains ( [34]; S4 Table ) from the yeast heterozygous deletion collection ( “Magic Marker” collection , [40] ) were grown in YPD + G418 ( 200μg/ml ) + 0 . 18 μg /ml His ( + 50uM riboflavin when recommended ) for approximately 24 h at 30°C . We separated these deletion collection strains into two pools depending on the orientation of the KanMX cassette ( S3 Fig ) : Watson-strand genes on the Left side of the centromere and Crick-strand genes on the Right side of the centromere ( wlcr pool ) and Watson-strand genes on the Right side of the centromere and Crick-strand genes on the Left side of the centromere ( wrcl pool ) . We designed two CFVs , one for each pool , that were identical except for the orientation of the KanMX cassette: pAS006 ( for the wlcr pool ) and pAS007 ( for the wrcl pool ) . In order to maintain a high complexity of the 12 bp replicate BC , approximately 20 , 000–30 , 000 Escherichia coli colonies transformed with pAS006 and pAS007 , respectively , were scraped and used to prepare plasmid DNA ( Wizard Miniprep ) for yeast transformation . The wlcr and wrcl yeast heterozygous deletion pools were each transformed with their appropriate CFV . The transformation efficiency with CFVs pABS006 and pABS007 was only about 20% ( as determined by a PCR assay ) , so our pool of scraped colonies included both Tamp strains and original heterozygous deletion strains . However , the design of the PCR primers used to generate our barseq libraries ( OAS021 to OAS023 ) only amplified the strain-identifying barcode from successfully formed Tamp strains . The total number of unique transformants collected was approximately 23 , 000 and approximately 20 , 000 for the wlcr and wrcl pools , respectively , and resulted in an average of 26 unique replicates for each Tamp . Given the large number of replicate BCs included in the CFVs pABS006 and pABS007 , each transformant was identifiable by a unique combination of the strain-identifying barcode , as derived from the yeast deletion collection barcode ( Tamp BC ) , and the replicate BC ( Fig 4A ) . To confirm the construction of this pool , we prepared barseq libraries for sequencing using primers OAS021 to OAS023 from aliquots of each pool . These barseq libraries were prepared as described for the chrII-targeted Tamp pool and sequenced on an Illumina MiSeq with sequencing primers OAS024 to OAS027 , generating 4 , 348 , 080 reads . The fastq files for this barseq experiment are at BioProject ID PRJNA257895 with BioSample IDs SAMN02979480 to SAMN029794821 . Additional details about these files are included in S15 Table . Analysis of the barcodes sequenced in this run confirmed that our pool was sufficiently complex to warrant further pooled competition experiments . As revealed in the construction of our chrII-targeted Tamp pool , generating Tamps using CFVs was not an error-free process and variable karyotypes were sometimes produced . Unfortunately , this problem was exacerbated in the construction of the genome-wide Tamp pool with larger Tamps being more likely to have incorrect karyotypes . The most commonly observed incorrect karyotype was one where the Tamp initiated at the correct genomic location but did not extend all the way to the proximal telomere; this problem was most common for larger Tamps ( S7 Table ) . We adjusted our analysis pipeline to try and correct for these variable karyotypes . Similar to the chrII-targeted Tamp competition experiments , we inoculated nine total large volume ( approximately 300 ml ) nutrient-limited chemostats supplemented with 20 mg/L histidine with aliquots of our wlcr and wrcl pools ( both pools were inoculated into a single chemostat ) . We performed pooled competition experiments under the three different nutrient limited conditions ( phosphate- , glucose- , and sulfate-limited ) in triplicate; chemostat inoculation and growth were the same as described for the chrII-targeted Tamp pool competition experiments . We defined each of the triplicate chemostat competition experiments as a technical replicate . For each of the nine chemostats , ten time points were taken throughout the competition experiment . For each time point , DNA was extracted and two barseq PCR reactions were carried out ( one targeting wlcr Tamps and one targeting wrcl Tamps ) using primers OAS021 to OAS023 and resulting in a total of 180 barseq samples . These 180 samples were pooled in equal proportions in two pools of 90 samples each . The pool , 6 bp barcodes used for multiplexing , and generations corresponding to each of the 180 samples are recorded in S15 Table . Each pool was sequenced on three lanes of an Illumina HiSeq , generating a total of 752 , 336 , 013 reads . These fastq files are deposited at BioProject ID PRJNA257895 with BioSample IDs SAMN02979482 to SAMN02980021 . The method we used to determine the fitness of each Tamp in the pools can be found in S1 Text . The relative fitness for each Tamp and its error are plotted for each condition in S7 Fig . When we plotted the fitnesses for each Tamp across the genome , we observed that parts of the fitness landscape had a stair-step appearance , in which fitness plateaus were bordered by sharp fitness breakpoints . In order to segment the genome into regions defined by Tamps of similar fitness , we applied the copy-number variant prediction software , DNAcopy [43] , to our genome-wide fitness data using the following settings: we required a minimum of two adjacent fitness data to define a fitness plateau and a significance of 0 . 05 to call a fitness breakpoint . This segmentation defined a total of 250 fitness segments across the three different nutrient-limiting conditions ( Colored boxes in S7 Fig ) . Previously , our lab determined the fitness effects of single-gene amplifications genome-wide using pooled competition experiments followed by barseq of genome-wide ORF collections on both low-copy-number ( CEN ) and high-copy-number ( 2 μ ) plasmids [34] . We compared these single-gene amplification data to our genome-wide Tamp data in three ways . First , we compared the kernel density estimates for the fitnesses defined by Tamps to the fitnesses defined by single-gene amplifications ( Fig 7A ) . The kernel density estimates were computed in R . Next , we stratified the 250 groups of Tamps defined by DNAcopy as positive or negative and averaged the fitnesses of all single-gene amplifications contained within its length as determined by their low-copy-number ( CEN ) fitness effects ( S2D Fig ) . Finally , we examined the breaks between fitness plateaus as defined by our DNAcopy segmentation analysis and categorized each break as either an Upstep ( i . e . , an increase in fitness moving along the chromosome towards the telomere ) or a Downstep ( i . e . , a decrease in fitness moving along the chromosome towards the telomere ) . We averaged the fitnesses , as determined by their low-copy-number fitness effects , of all single-gene amplifications contained within each breakpoint region plus one gene centromeric of the centromeric border of the breakpoint region ( Fig 7A ) . This extra gene was included simply to compensate for any insensitivity in the DNAcopy segmentation of our fitness data . As described in the main text , we filtered the list of Downstep genes by comparing it to several published datasets: the list of genes commonly up-regulated in clones evolved under glucose- , phosphate- , or sulfate-limiting conditions [10 , 46]; the list of genes that increased fitness when present on a low-copy number plasmid under glucose- , phosphate- or sulfate-limiting conditions [34]; and the list of genes mutated in populations evolved under glucose- , phosphate- , or sulfate-limiting conditions [34] . Specifically , for the comparison with the Payen low-copy-number plasmid fitness data , we compared Downstep genes to Payen et al . ’s list of outlier fitness genes with fitnesses <-0 . 10 or >0 . 10 ( denoted as “CEN outlier” in S10 Table and S11 Table ) and also to the set of genes with fitnesses greater than two standard deviations more than the mean fitness of that dataset ( denoted as “CEN mean + 2SD” in S10 Table and S11 Table ) . CEN mean + 2SD genes still have extreme fitnesses but did not reach the stringent cutoff imposed in the Payen et al . study to be called “outliers . ” For phosphate-limitation this included single-gene amplifications with fitnesses <-0 . 096 or >0 . 097 , and for glucose-limitation this included single-gene amplifications with fitnesses <-0 . 052 or >0 . 050 . The list of “outliers” called by Payen et al . already included all mean + 2SD genes for sulfate-limited conditions . | Aneuploidy ( altered copy number of genomic regions ) is observed in the majority of tumors , but it remains unclear whether aneuploidy is a cause or consequence of cancer . Evidence from the yeast Saccharomyces cerevisiae and mammalian cells has shown that aneuploid cells tend to grow more slowly than normal cells; however , aneuploidy has also been shown to promote tumor formation and microbial adaptation . To address this paradox , we took two approaches to study the relationship between fitness—measured as cellular growth—and aneuploidy . First , we examined aneuploid events isolated from laboratory-evolved populations of S . cerevisiae and found that the majority of such events improve cellular fitness , have a large effect-size , and show diverse fitness effects under different conditions . Second , we developed a method to create thousands of aneuploid strains spanning the yeast genome and used pooled competition experiments followed by barcode sequencing to determine their relative fitnesses . These genome-wide data revealed aneuploidy to have effects that were both large and wide-ranging ( pleiotropic ) . We found that both the positive and negative fitness effects are typically driven by a small number of genes within each aneuploidy event . We conclude that aneuploidy is functionally important in the process of adaptation of yeast during laboratory evolution experiments and propose that it has the potential to play an adaptive role during the evolution of cancers . | [
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| 2015 | The Fitness Consequences of Aneuploidy Are Driven by Condition-Dependent Gene Effects |
To avoid organ dysfunction as a consequence of tissue diminution or tumorous growth , a tight balance between cell proliferation and differentiation is maintained in metazoans . However , cell-intrinsic gene expression mechanisms controlling adult tissue homeostasis remain poorly understood . By focusing on the adult Caenorhabditis elegans reproductive tissue , we show that translational activation of mRNAs is a fundamental mechanism to maintain tissue homeostasis . Our genetic experiments identified the Trf4/5-type cytoplasmic poly ( A ) polymerase ( cytoPAP ) GLD-4 and its enzymatic activator GLS-1 to perform a dual role in regulating the size of the proliferative zone . Consistent with a ubiquitous expression of GLD-4 cytoPAP in proliferative germ cells , its genetic activity is required to maintain a robust proliferative adult germ cell pool , presumably by regulating many mRNA targets encoding proliferation-promoting factors . Based on translational reporters and endogenous protein expression analyses , we found that gld-4 activity promotes GLP-1/Notch receptor expression , an essential factor of continued germ cell proliferation . RNA-protein interaction assays documented also a physical association of the GLD-4/GLS-1 cytoPAP complex with glp-1 mRNA , and ribosomal fractionation studies established that GLD-4 cytoPAP activity facilitates translational efficiency of glp-1 mRNA . Moreover , we found that in proliferative cells the differentiation-promoting factor , GLD-2 cytoPAP , is translationally repressed by the stem cell factor and PUF-type RNA-binding protein , FBF . This suggests that cytoPAP-mediated translational activation of proliferation-promoting factors , paired with PUF-mediated translational repression of differentiation factors , forms a translational control circuit that expands the proliferative germ cell pool . Our additional genetic experiments uncovered that the GLD-4/GLS-1 cytoPAP complex promotes also differentiation , forming a redundant translational circuit with GLD-2 cytoPAP and the translational repressor GLD-1 to restrict proliferation . Together with previous findings , our combined data reveals two interconnected translational activation/repression circuitries of broadly conserved RNA regulators that maintain the balance between adult germ cell proliferation and differentiation .
During development , tissues grow to form functional organs . In adulthood , animal tissues remain constant in size , in part , as a result of the dynamic balance between self-renewal/proliferation and differentiation . Perturbation of this balance affects tissue homeostasis and , consequently , compromises organ function . While excess proliferation contributes to tumorigenesis , a deficit in proliferation leads to tissue degeneration . Hence , tight regulatory mechanisms are in place to control the balance between self-renewal/proliferation and differentiation . One prevalent cell-extrinsic regulatory mechanism of stem cells to self-renew/proliferate is their dependency on supporting niche cells , which trigger established signal transduction pathways that primarily lead to changes at the transcriptional level . However , to fine-tune proper tissue homeostasis and to provide tight feedback controls , additional cell-intrinsic gene expression mechanisms are likely to exist . In recent years , invertebrate germline tissues emerged as powerful in vivo models to investigate the balance between proliferation and differentiation . One influential paradigm is the adult “female” germ line of C . elegans , which depends on a single somatic niche cell and maintains a strict spatio-temporal organization of proliferating and differentiating cells [1] . Undifferentiated germ cells proliferate exclusively in the distal end of the germ line , termed the proliferative zone ( PZ ) [2] , [3] , [4] . The PZ is proposed to contain a distal pool of germline stem cell-like cells ( GSCs ) and a proximal pool of transit amplifying cells that gradually mature to start differentiation at a defined distance from the distal end [1] , [5] , termed the mitosis-to-meiosis boundary . Germ cells crossing this boundary enter meiotic prophase , which is here defined as differentiation onset [2] , [6] , [7] . Germline proliferation relies on the Notch signaling pathway that is instructed by the somatic distal tip cell ( DTC ) [8] , [9] , [10] . Consistent with its continuous requirement for germ cell proliferation in the adult , the inactivation of Notch signaling leads to progressive loss of GSCs , due to differentiation of all germ cells [9] . Conversely , constitutive activation of Notch results in the expansion of the proliferative GSC pool at the expense of differentiation [11] , [12] . In agreement with this , germ cells in the PZ express the Notch receptor GLP-1 , while differentiating cells lose GLP-1 expression [13] . Hence , Notch-mediated transcriptional regulation of mitotic fate-promoting genes is suggested to directly maintain the proliferative fate [14] , [15] , [16] . However , germ cell-intrinsic mechanisms that promote niche-mediated germ cell proliferation are still widely unknown . In nematodes and flies , germ cells also utilize conserved translational repressors to promote the undifferentiated state [17] . In C . elegans , two nearly identical translational repressors of the PUF RNA-binding protein family , FBF-1 and FBF-2 , jointly referred to as FBF , are essential for adult GSCs [18] . FBF recognizes specific sequence elements ( FBEs ) in its mRNA targets and , by translationally repressing numerous meiosis-promoting genes , FBF is critical for maintaining the undifferentiated , proliferative state [7] , [18] , [19] , [20] . Moreover , the fbf-2 locus is a proposed target of Notch-mediated regulation [14] , [21] , thus linking transcriptional activation with translational repression , the two dominant mechanisms used for sustained germ cell proliferation in different organisms [1] . Across species , differentiation onset of germ cells depends on translational control [17] . In nematodes , the STAR-type RNA-binding protein , GLD-1 , inhibits GLP-1 protein accumulation [22] , [23] and recognizes glp-1 mRNA by three GLD-1-binding motifs ( GBMs ) present in its 3′UTR [24] , [25] . Meiotic prophase entry also requires the Nanos protein family member , NOS-3 , a presumed translational repressor of yet unknown mitosis-promoting genes [21] . However , in the absence of GLD-1 , NOS-3 , or both , germ cells enter meiotic prophase in the adult [7] , [26] . The cytoplasmic poly ( A ) polymerase ( cytoPAP ) complex GLD-2/GLD-3 is a proposed translational activator of meiosis-promoting mRNAs , envisioned to extend their poly ( A ) tail lengths . GLD-2 is a non-canonical nucleotidyltransferase , stimulated by the Bicaudal-C family member , GLD-3 [27] , [28] . However , in the absence of GLD-2 , GLD-3 , or both , the PZ is expanded but meiosis is still initiated [7] . This complexity highlights that differentiation onset is in general a multi-pathway-regulated process [17] . In the current model of the core genetic network underlying differentiation onset in C . elegans , the four meiosis-promoting RNA regulators act in two parallel pathways . The two translational repressors ( gld-1 and nos-3 ) form the first pathway; the two translational activators ( gld-2 and gld-3 ) form the second pathway . This genetic redundancy is most apparent in germ cells that lack GLD-3 and NOS-3 , as they do not enter meiotic prophase and continue to proliferate [7] . Importantly , tumorous proliferation of gld-3 nos-3 double mutant germ cells is independent of Notch signaling and dependent on cyclin E activity [4] , [7] . Intriguingly , germ cells lacking GLD-2 and NOS-3 are able to start meiotic prophase [6] , [7] . This suggests that the current pathway assignments are too simplistic and emphasizes that more meiosis-promoting regulators must exist [4] , [6] , [7] . Especially , commitment to female meiotic progression provides precedence for redundant translational activation activities in C . elegans . Here , in addition to GLD-2 cytoPAP-mediated GLD-1 expression [29] , the GLD-4/GLS-1 cytoPAP complex has been identified to translationally activate gld-1 mRNA [30] . As a non-canonical poly ( A ) polymerase , GLD-4 is most similar to the conserved group of Trf4/5-type RNA modifiers that regulate RNA stability in the nucleus [30] , [31] , [32] . However , GLD-4 poly ( A ) polymerase is cytoplasmic and requires for its functions the nematode-specific protein , GLS-1 [30] , [33] . In the absence of GLD-2 , the GLD-4/GLS-1 cytoPAP complex is essential for female meiotic progression into pachytene [30] . In this study , we report that the GLD-4/GLS-1 cytoPAP complex has a dual role in regulating the balance between proliferation and differentiation . We find that the GLD-4/GLS-1 cytoPAP complex is crucial to maintain germ cell proliferation in the adult , in part by promoting robust translation of glp-1 mRNA . Moreover , to ensure that meiosis-promoting factors are inefficiently translated , GLD-2 cytoPAP levels are kept low in the GSC pool by FBF-mediated translational repression . Lastly , we also find that GLD-4/GLS-1 cytoPAP promotes meiotic prophase entry , in parallel to GLD-2 cytoPAP and independently of Notch . Our data suggest that two translational feedback loops limit the size of the proliferative germ cell pool and maintain a healthy balance of germ cell proliferation and differentiation in the adult germ line .
On average , the PZ of adult germ lines extends from the first germ cell row at the distal end further proximally to row 20 , where germ cells start differentiation by entering meiotic prophase ( Figure 1A , B ) . In wild type , the PZ is populated by about 225–250 germ cells ( Figure 1C ) . Since there are no molecular markers for subpopulations of cells in the PZ , like stem cells , transit amplifying cells and cells in pre-meiotic S-phase , the start of meiotic prophase is commonly defined as the onset of differentiation [1] , [34] . Differentiation is revealed by the germ cells' specific nuclear architecture and chromatin morphology , the combinatorial expression and localization of the meiotic cohesin REC-8 , the synaptonemal protein HIM-3 , and phosphorylated nuclear envelope protein pSUN-1 [34] ( Figure 1A , B ) . Germ cells in single mutants of meiosis-promoting genes ( i . e . gld-1 , nos-3 , gld-2 , gld-3 ) initiate meiotic prophase [7] . However , shifts in the position of the mitosis-to-meiosis boundary suggest a role in proliferation or differentiation . For example , in gld-2 single mutants , the PZ is extended and contains more germ cells than wild type [7] ( Figure 1C , D ) , consistent with gld-2's function in promoting meiotic entry [35] . We found that gld-4 and gls-1 single mutants have smaller PZs with fewer germ cells ( Figure 1C , D ) . The strength of the reduction appears to correlate with the reported allelic strengths of the individual mutations [30] , [33] ( Figure 1C ) . As the PZ of gld-4 gls-1 double mutants is similarly reduced ( Figure 1C ) , these results argue for a common role of gld-4 and gls-1 in promoting mitosis . The PZ of the gld-2 gld-4 double mutant is similar to wild type in size and germ cell number ( Figure 1C , D ) . Together , these results suggest that gld-2 and gld-4 have independent and opposing roles to set the mitosis-to-meiosis boundary in adults . The PZ expands during larval development and is maintained during adulthood [8] . We measured the size of the PZ at the last larval stage ( L4 ) , and 24 hours ( h ) , and 48 h later in young adults ( Figure 1E–G ) . The difference between wild type and gld-4 is the smallest in L4 and greatest during adulthood , due to a large relative shrinkage of the PZ in gld-4 young adults ( Figure 1E–G ) . Therefore , gld-4 activity is primarily important for the maintenance but not establishment of the PZ during early adulthood . The documented presence of GLD-4 [30] and GLS-1 [33] in the distal end of the germ line and the single mutant phenotypes argue for a role of gld-4 and gls-1 in promoting germ cell proliferation . CytoPAPs are envisioned to regulate poly ( A ) tail metabolism of target mRNAs in a positive manner [31] . Biochemically , cytoPAPs elongate poly ( A ) tails , which in turn stabilize mRNAs and enhance their translation . We hypothesized that GLD-4 targets mRNAs encoding proteins important for proliferation in the PZ . An obvious , but not exclusive , candidate for this regulation is the Notch receptor-encoding glp-1 mRNA . Notch expression is regulated at multiple levels in C . elegans [13] , [23] , [36] . To uncouple mRNA regulation from protein regulation , we used a translational reporter of GFP::H2B under the control of the glp-1 3′UTR [25] ( Figure 2 ) . The glp-1 3′UTR reporter is driven by a ubiquitous germ cell-specific promoter and encodes a translational fusion product of GFP and histone 2B ( Figure 2A ) . This nuclear GFP signal reflects GLD-1-mediated regulation of the glp-1 mRNA [25] . In a wild-type background , reporter GFP expression is present in all animals analyzed and its pattern is similar to endogenous GLP-1 protein expression [13] , [25] ( Figure 2B ) . To assess whether reporter GFP expression is under the influence of GLD-4 cytoPAP activity , we crossed the glp-1 3′UTR reporter locus into the strong loss-of-function gld-4 ( ef15 ) mutant background ( Figure 2C , D ) . To control for unexpected genetic background influences , we compared heterozygous and homozygous gld-4 siblings from the progeny of a heterozygous mother ( see Materials and Methods ) . In the gld-4 heterozygous mutant , reporter GFP expression is similar to a wild-type background ( compare Figure 2B with C ) . Strikingly , upon gld-4 removal , reporter expression was undetectable in almost all germ lines ( Figure 2D ) . Consistent with a reduction in the GFP signal , we also observed lower GFP protein amounts by immunoblotting . When comparing gld-4 animals to wild-type background , we observed a reduction of >80% in protein abundance ( Figure 2E ) . These results imply that the expression of the glp-1 3′UTR reporter depends on gld-4 cytoPAP activity . GLS-1 and GLD-4 function together in meiotic progression [30] , and in promoting differentiation onset ( Figure 1C ) . Similar to the gld-4 mutant , reporter GFP expression is undetectable in most gls-1 ( ef8 ) mutant germ lines ( ∼87% , n = 220 ) , suggesting that gls-1 promotes glp-1 3′UTR reporter expression similar to gld-4 activity . To investigate whether gld-4 is the only known cytoPAP regulating reporter expression , we assessed GFP expression in gld-2 mutants and detected it in almost all germ lines ( Figure 2F ) . Moreover , the amounts of GFP reach wild-type protein levels and are similar between gld-2 homozygous and heterozygous mutants ( Figure 2E ) . Importantly , reporter GFP expression is still dependent on gld-4 activity in the gld-2 mutant background , as its expression is undetectable in all gld-2 gld-4 homozygous double mutants ( Figure 2G ) . These results suggest that glp-1 3′UTR reporter expression is largely independent of gld-2 cytoPAP activity , and specifically dependent on gld-4 cytoPAP activity . To further investigate at which level GLD-4 cytoPAP may regulate glp-1 3′UTR reporter expression , we made use of GLD-1 , a known translational repressor of glp-1 mRNA [23] . In gld-1 single mutants , reporter GFP is expressed in the PZ and in differentiating germ cells ( 100% , n = 140 ) . To test , whether loss of GLD-1 would de-repress reporter GFP expression in the gld-4 mutant , we analyzed GFP::H2B expression in the gld-1 gld-4 double mutant background . Most germ lines weakly express GFP when compared to gld-4 mutants ( compare Figure 2H with 2D ) . A similar weak de-repression is observed in gld-4 mutant germ lines that contain mutated GLD-1-binding site reporter mRNAs ( glp-1 3′UTR mut ) ( Figure 2I ) . Taken together , these results confirm that the glp-1 3′UTR reporter can be translated in a gld-4 mutant background and that expression of the glp-1 3′UTR reporter is partly dependent on the GLD-4 cytoPAP even when GLD-1-mediated repression is removed . Several mechanisms may account for reduced glp-1 3′UTR reporter expression in the absence of gld-4 . To confirm that the effects on GFP::H2B expression are due to translational and not transcriptional regulation of the glp-1 3′UTR reporter , we examined the mRNA levels of the wild-type glp-1 3′UTR reporter by RT-qPCR ( Figure 2J ) . Compared to wild type , we noticed a reduction of ∼4-fold in both gld-4 and gld-2 mutant backgrounds ( Figure 2J ) , suggesting that glp-1 3′UTR reporter mRNA is less abundant in either cytoPAP mutant . Importantly , the glp-1 3′UTR reporter mRNA levels are similar to each other in both cytoPAP homozygous mutants , yet they give rise to different amounts of reporter protein ( compare Figure 2D with 2F , and Figure 2E ) . Hence , we conclude that the major reduction in reporter GFP expression in gld-4 mutants is primarily at the translational and not at the transcriptional level . To further investigate whether endogenous GLP-1 protein expression is one likely candidate of gld-4-mediated regulation , we measured GLP-1 protein expression in gld-4 mutants and compared it to wild type ( Figure 3 ) . By quantifying GLP-1 intensities in distal germ lines of L4+24 h and L4+48 h animals , we observed a significant decrease in the gld-4 mutant background in the PZ over time ( Figure 3A , B ) . When we measured endogenous glp-1 mRNA levels in L4+24 h animals we observed a mild increase in gld-4 ( ef15 ) mutants compared to wild type ( Figure 3C ) . Together these observations suggest that gld-4 promotes GLP-1 expression post-transcriptionally . A prerequisite for GLD-4/GLS-1-mediated glp-1 mRNA regulation is that they form an mRNP complex . To test for a possible association of GLD-4 and GLS-1 with glp-1 mRNA , we performed several RNA co-immunoprecipitation ( RIP ) experiments , using GLD-4-specific , GLS-1-specific , and non-specific antibodies . Subsequent RT-PCR ( Figure 4A ) and RT-qPCR ( Figure 4B ) analysis of different RIP experiments revealed a specific enrichment of endogenous glp-1 mRNA , which is similar to the positive control , gld-1 mRNA ( Figure 4B ) . These results demonstrate an association of GLD-4/GLS-1 cytoPAP complex with endogenous glp-1 mRNA and establish a potential physical link for glp-1 mRNA translational regulation . Cytoplasmic polyadenylation affects RNA stability and translational efficiency [37] . To test whether ribosomal engagement of the endogenous glp-1 mRNA requires GLD-4 cytoPAP , we performed sucrose gradient sedimentation experiments . In theory , the more ribosomes are attached to an mRNA , the further the mRNA migrates into the gradient during ultra centrifugation . Therefore , efficiently translated mRNAs will be in the heavier , polysome fractions of the gradient , while poorly or non-translated mRNAs tend to sediment to lighter , non-polysomal fractions . Due to the large amounts of material needed , we compared control RNAi and gld-4 ( RNAi ) knockdown worms ( Figure 4C ) , knowing that gld-4 ( RNAi ) efficacy is less robust than using mutants . In extracts of wild type and control RNAi ( Figure 4C ) , about 50% of the endogenous glp-1 mRNA resides in the polysome fraction , suggesting that half of the glp-1 mRNA population is actively translated , consistent with the known germline and embryonic translational repression of glp-1 mRNA [23] , [38] . Upon knockdown of gld-4 , but not in control RNAi , we observed a shift of glp-1 mRNA into lighter fractions of the gradient ( Figure 4D ) . This reflects a specific decrease in translational competence of endogenous glp-1 mRNA as rpl-11 . 1 , a germ line-enriched mRNA that encodes a protein of the large ribosomal subunit [39] , is unaffected ( Figure 4D ) . CytoPAPs modify the 3′ends of RNAs [31] . To investigate whether GLD-4 affects the length of the glp-1 mRNA poly ( A ) tail , we performed a poly ( A ) test ( PAT ) assay [40] , and compared endogenous glp-1 mRNA poly ( A ) tails , using sucrose gradient fractioned mRNA and non-fractionated input as our starting material . To obtain enough RNA material for the PAT assay and to discriminate translationally active from inactive mRNA pools , we combined several samples of the non-polysomal and polysomal fractions . While all three samples show reduced glp-1 poly ( A ) tail lengths in gld-4 ( RNAi ) compared to control RNAi knockdowns , we observe no clear difference between the respective non-polysomal and polysomal fractions ( Figure 4E , F ) . The observed poly ( A ) tail differences are consistent with the contribution of gld-4 activity to gld-1 mRNA [40] . This data suggests that GLD-4 cytoPAP activity has an overall impact on glp-1 poly ( A ) tail status . Together , our combined results suggest that GLD-4 association with endogenous glp-1 mRNA may stimulate its efficient translation . GLD-4 and GLD-2 cytoPAP expression patterns are distinct in “female” germ lines . GLD-4 is expressed equally strong within the entire PZ and in meiosis [30] ( Figure 5A ) . By contrast , GLD-2 is poorly expressed in the distal half of the PZ , becomes more abundant further proximal , and is most abundant in cells that have entered meiosis [28] ( Figure 5A ) . Hence , the differential expression of the two proteins in the PZ may form the basis of GLD-4's unique role in mitosis and GLD-2's role in meiotic entry . Intriguingly , the protein expression pattern of GLD-2 does not match its ubiquitous mRNA expression pattern in the distal PZ [28] . This suggests translational regulation of GLD-2 expression . An obvious translational repressor in this region is FBF , which represses two mRNAs encoding meiosis-promoting regulators , GLD-1 [18] and GLD-3 [7] . To test for FBF-mediated gld-2 mRNA repression , we knocked down fbf by RNAi and assessed GLD-2 protein abundance in the distal-most germ line by indirect immunofluorescence , using GLD-4 as a reference , and quantified the amounts ( Figure 5B , C ) . While GLD-4 levels are not significantly different between fbf ( RNAi ) and control RNAi germ lines , GLD-2 expression levels in the PZ are higher in fbf ( RNAi ) than in wild type ( compare Figure 5A and 5B ) or control RNAi experiments ( Figure S1A–C ) . The GLD-2 protein increase is largely limited to the distal half of the PZ: ∼2 . 2-fold more in cells most distal ( Figure 5C , area 1 ) , compared to ∼1 . 5-fold more in cells most proximal to the PZ ( Figure 5C , area 2 ) . Such a restriction to the proliferative zone is consistent with previous reports on FBF activity [18] , [20] , [41] and suggests that GLD-2 but not GLD-4 is a specific target of FBF regulation . FBF interacts with mRNAs through the conserved FBF-binding element ( FBE ) [18] . We identified five putative FBEs in the 3′UTR of gld-2 mRNA ( Figure 5D ) and tested each element for binding to FBF protein in a yeast 3-hybrid assay . Only FBE4 in its wild-type sequence was consistently and specifically bound by FBF ( Figure 5E ) . Neither element was bound by PUF-5 ( Figure 5E ) , a different C . elegans PUF protein that is abundantly expressed in differentiating female gametes [42] . Intriguingly , the bound FBE sequence is also present in two closely related Caenorhabditis species , suggesting that gld-2 mRNA translational repression may be conserved ( Figure 5D ) . Moreover , RIP experiments of GFP-tagged FBF-2 confirmed a physical association of gld-2 mRNA with FBF in worm lysates , which appears to correlate with the number of active FBEs in the tested mRNAs ( Figure 5F ) ; the positive control , gld-1 mRNA , possesses two functional FBEs and was enriched strongest [18] . Taken together , we conclude that consistent with published FBF-1 RIP-Chip experiments [19] , GLD-2 but not GLD-4 is most likely a direct target of the central mitosis-promoting translational repressor , FBF . Consistent with previous genetic findings [7] , an evolutionary conserved translational repression of GLD-2 cytoPAP in undifferentiated cells might be pivotal for the robustness of the balance between proliferation and differentiation . The current framework of the core regulatory network underlying meiotic entry appears incomplete and a third meiosis-promoting activity is likely to exist ( Figure 6A ) [4] , [6] , [7] , [34] . Even though both meiosis-promoting pathways are inactive in the gld-2; nos-3 double mutant , germ cells enter meiosis [7] , [26] ( Figure 6B , D; Table 1 ) . Intriguingly , GLD-2 and GLD-4 have a combined function during late meiosis when germ cells are past the onset of differentiation [30] . Hence , it seemed plausible that a further biological overlap of those two enzymes may exist at differentiation onset . Indeed , we find that the triple mutant gld-2 gld-4; nos-3 lacks any signs of differentiation and it is tumorous ( Figure 6C , E; Table 1 ) . This demonstrates that gld-4 activity promotes meiotic entry in the absence of gld-2 and nos-3 . GLS-1 stimulates GLD-4 enzymatic activity and the GLD-4/GLS-1 cytoPAP complex promotes late meiosis [30] . To test if gld-4 activity requires gls-1 function for promoting meiotic entry , we generated the gld-2 gls-1; nos-3 triple mutant . Similar to the gld-2 gld-4; nos-3 triple mutant , no meiotic entry was observed ( Figure 6F; Table 1 ) , indicating a shared function of gld-4 and gls-1 . Together this suggests that in addition to a requirement for proliferation , the GLD-4/GLS-1 cytoPAP complex promotes the onset of differentiation in combination with GLD-2 cytoPAP . A prediction of this model is that the function of a single cytoPAP is enough to promote entry into meiosis in the absence of nos-3 . Hence , we generated the gld-4; nos-3 and the gls-1; nos-3 double mutants . In either double mutant , in comparison to the triple mutant with gld-2 , we found robust entry into meiosis ( Figure 6G , H; Table 1 ) . In conclusion , gld-4 and gls-1 promote meiotic entry in parallel to gld-2 and nos-3 , suggesting that gld-4 and gls-1 might be additional pathway components that promote differentiation onset . Moreover , the striking similarity between the gld-3 nos-3 double and gld-2 gld-4; nos-3 triple tumorous germ lines suggest that gld-2 and gld-4 or gls-1 activities are largely equivalent to gld-3 activity with regard to the meiotic entry process . NOS-3 and GLD-1 are assumed to act in a pathway parallel to the GLD-2/GLD-3 cytoPAP pathway ( Figure 6A ) . To complete our analysis of the genetic interactions between the NOS-3/GLD-1 and the GLD-4/GLS-1 cytoPAP pathways , we generated triple mutant strains that had either one of the GLD-4/GLS-1 cytoPAP complex components removed in a gld-2 gld-1 double mutant background ( Figure 7; Figure S2; Table 1 ) . Germ cells , double mutant for gld-2 gld-1 , enter meiosis in the majority of germ lines ( Figure 7A; Figure S2; Table 1 ) . Germ cells , triple mutant for gld-2 gld-1 gld-4 ( Figure 7B ) or gld-2 gld-1 gls-1 ( Figure 7C ) , failed to enter meiosis and all germ lines are tumorous ( Table 1 ) . Importantly , germ cells in the gld-1 gld-4 ( Figure 7D ) and the gld-1 gls-1 ( Figure 7E ) double mutants enter meiosis ( Table 1 ) . Surprisingly , the gld-1 gls-1 double mutant did not stain for HIM-3 ( Figure 7E ) . However , gld-1 gls-1 germ cells entered meiosis , as judged by their nuclear architecture , chromosome morphology , and the expression of pSUN-1 ( Figure 7F ) . Our combined results are consistent with the previous triple mutant results , in which a nos-3 mutant gene replaced gld-1 ( Figure 6 ) , and establish a role of gld-4 and gls-1 in the onset of differentiation , suggesting that both genes operate in parallel to gld-2 , gld-1 and nos-3 . Notch signaling promotes proliferation , upstream of the meiosis-promoting network [35] . To investigate whether proliferation of tumorous triple mutant gld-2 gld-1 gld-4 and gld-2 gld-1 gls-1 germ lines depends on Notch activity , we investigated GLP-1 protein expression and genetically ablated glp-1 function ( Figure S3 ) . In either triple mutant , GLP-1 remains expressed throughout the tumorous germ lines ( Figure S3A , C ) . Consistent with their proliferative activity , dividing cells are scattered throughout the germ line and stain positively for phospho-histone-3 ( PH-3 ) ( Figure S3A , C ) , a marker for cells in prometaphase [43] . Loss of glp-1 in either triple mutant neither abolishes proliferation nor leads to meiotic entry and cells remain undifferentiated ( Figure S3B , D ) . These results suggest that Notch is not required for proliferation in germ cells that are fully compromised in all meiosis-promoting pathways . Germ cell proliferation in gld-3 nos-3 tumorous germ lines is independent of Notch signaling but depends on cyclin E [4] ( Figure 8A; Table 1 ) . In cye-1 RNAi knockdown experiments , we found that also gld-2 gld-1 gld-4 tumorous proliferation requires cyclin E activity ( Figure 8B; Table 1 ) . Moreover , consistent with gld-3 nos-3; glp-1 cye-1 ( RNAi ) germ lines [4] , an additional removal of Notch activity in gld-2 gld-1 gld-4; cye-1 ( RNAi ) animals increases the ability of germ cells to start meiotic prophase more distally ( Figure 8C ) . In either case , however , differentiation onset is aborted immediately after zygotene/very early pachytene and germ cells do not commit to meiosis . Together , these similarities among the gld-3 nos-3 and gld-2 gld-1 gld-4 tumorous germ lines suggest that gld-4 and , most likely gls-1 , are components of a meiosis-promoting pathway that acts on the gld-2 side of both known meiosis-promoting pathways , rather than in a separate , third meiotic entry pathway ( summarized in Figure 9 ) [4] .
The GLD-4/GLS-1 cytoPAP complex has multiple roles in germ cell development [30] , [33] . In this work , we demonstrate that both complex members contribute to the maintenance of the size of the proliferative zone by primarily influencing adult germline proliferation and secondarily differentiation onset . This dual role is consistent with the ubiquitous expression of both proteins in the respective regions of the adult germline tissue [30] , [33] . GLD-4 is the enzymatic component of the GLD-4/GLS-1 cytoPAP complex and is evolutionarily most similar to nuclear Trf4/5-type polymerases , which add short poly ( A ) tails to nonproductive RNA molecules to initiate exosome-mediated RNA degradation [32] . By contrast , GLD-4 and its enzymatic activator GLS-1 are cytoplasmic proteins implicated in translational control [30] , [33] . The notion that translational activation of mRNAs is coupled to cytoplasmic poly ( A ) tail extension or maintenance is primarily shaped by the work on poly ( A ) polymerases , such as members of the conserved GLD-2 family [31] . By analogy , GLD-4 cytoPAP's role in proliferative germ cells may therefore translationally activate mitotic-fate promoting mRNAs . We provided four pieces of evidence that the Notch receptor-encoding glp-1 mRNA is a likely mRNA target of GLD-4/GLS-1 cytoPAP activity: ( 1 ) GLD-4 associates with glp-1 mRNA , and ( 2 ) positively influences its poly ( A ) tail length . ( 3 ) Furthermore , we found that expression of a glp-1 3′UTR translational reporter and that of endogenous GLP-1 protein depends on GLD-4 presence . ( 4 ) Lastly , translational efficiency of endogenous glp-1 mRNA requires gld-4 activity . Therefore , these results combined support the idea that abundant GLP-1 expression is maintained by GLD-4-mediated translational activation . However , the partial reduction in glp-1 poly ( A ) tail length might either reflect an intrinsic enzymatic difference between Trf4-type PAPs and GLD-2 , or suggests that other , yet undiscovered cytoPAPs may work redundantly to GLD-4 . Alternatively , additional poly ( A ) tail-independent mechanisms for GLD-4-mediated translational activation may exist . Regardless of the precise molecular function of GLD-4 , the translational repressor of glp-1 mRNA , GLD-1 protein , starts to accumulate in the proximal part of the PZ , prior to the mitosis-to-meiosis boundary [44] , suggesting that glp-1 mRNA may already be subject to translational repression in the proximal PZ . Therefore , to ensure robust GLP-1 protein expression , GLD-4-mediated translational activation of glp-1 mRNA may help to counteract GLD-1-mediated translational repression to maintain the size of the PZ in the adult ( Figure 9A , B ) . However , glp-1 mRNA is presumably not the only target of the GLD-4/GLS-1 cytoPAP complex , and others are likely to exist . In the balance between proliferation and differentiation , the two translational activators , GLD-4 and GLD-2 , seem to have antagonistic roles that may also constrain their regulation and function in the PZ . A loss of GLD-4 shrinks the PZ and a loss of GLD-2 expands the PZ . Therefore , GLD-2 may promote meiosis at the expense of mitosis in the gld-4 single mutant . Conversely , GLD-4 may be responsible for the expansion of the PZ in the gld-2 single mutant . Importantly , upon loss of both cytoPAP activities , the PZ re-adjusts to an intermediate size , arguing that they form an antagonistic pair . In particular , the distinct expression profile of either cytoPAP presumably reflects and affects their divergent roles in regulating mRNA-specific gene expression . The delay of GLD-2 protein expression in the PZ correlates with its genetic requirement for the onset of differentiation and a putatively required absence in undifferentiated cells . Moreover , its 2–3 fold lower abundance in the distal half of the PZ may selectively favor and functionally constrain GLD-4-mediated germ cell proliferation . Hence , a healthy balance between GLD-2 and GLD-4 functions appears to be perpetuated to maintain the size of the adult PZ . To maintain adult germ cell proliferation and prevent progressive shrinkage of the PZ , gld-2 mRNA translation is delayed by FBF , a dominant translational repressor of several meiosis-promoting genes [7] , [18] , [19] , [45] . We found that GLD-2 but not GLD-4 cytoPAP accumulation in the PZ appears to be inhibited by FBF , and that gld-2 mRNA associates with FBF most likely at least through one FBF-binding site in its 3′UTR . Therefore , a translational repressor ( FBF ) that turns off the activities of mRNAs encoding meiosis-promoting proteins ( e . g . GLD-2 ) is combined with a translational activator ( GLD-4 ) that turns on mRNA activities that encode mitosis-promoting proteins ( e . g . GLP-1 ) to maintain germ cell proliferation ( Figure 9A , B ) . Conversely to germ cell proliferation , the onset of differentiation requires translational repressors ( GLD-1 and NOS-3 ) that presumably turn off mRNA activities encoding mitosis-promoting proteins and translational activators ( GLD-2 , GLD-3 , GLD-4 , and GLS-1 ) that presumably turn on mRNA activities encoding meiosis-promoting proteins [6] , [7] , [26] , [35] ( Figure 9A , C ) . Previous genetic work established two parallel pathways , which either indirectly or directly promote differentiation onset ( Figure 6A ) . However , not all components are equal in their potential to contribute to meiotic prophase entry . In this regard , the synergism of NOS-3 and GLD-3 is of equal strength , as is NOS-3 with both GLD-2 and GLD-4/GLS-1 , or , GLD-1 with both GLD-2 and GLD-4/GLS-1 . Hence , our findings of a dual role for GLD-4 cytoPAP strengthens the role of translational control even further , highlights the importance of translational activation for the balance of proliferation and differentiation , and clarifies the many levels of redundancy within the two , major , parallel pathways of the current genetic circuitry ( Figure 9A ) . Differentiation onset deploys two translational activators of presumed meiosis-promoting mRNAs ( Figure 9C ) . In this regard , GLD-2 cytoPAP performs a more prevalent role in activating meiosis-promoting mRNAs as its combined loss with genes of the first , translational repressor pathway ( i . e . gld-1 gld-2 or gld-2; nos-3 doubles ) causes more germ cell overproliferation than is observed in the respective gld-4 double mutant germ lines . Importantly , germ cells of gld-2; nos-3 or gld-2 gld-1 double mutants enter meiosis in a gld-4- and gls-1-dependent manner , as triple mutant germ cells ( e . g . gld-1 gld-2 gld-4 or gld-2 gld-4; nos-3 ) do not enter meiosis . Consistent with previous findings that germline proliferation in tumorous gld-2 gld-1 or gld-3 nos-3 double mutants is glp-1-independent [4] , [35] , tumorous triple mutant gld-2 nos-3 germ cells that lack in addition either gld-4 or gls-1 do not require GLP-1 activity to remain in mitosis either , arguing for their genetic position downstream of Notch and in parallel to each other for meiotic entry ( Figure 9A ) . Intriguingly , the similarities between the gld-3 nos-3 double and gld-2 gld-4; nos-3 or gld-2 gls-1; nos-3 triple mutants suggest further that gld-3 activity equals the combined activities of gld-2 and gld-4/gls-1 with respect to the loss of nos-3 , which positions gld-4/gls-1 within the second , translational activator pathway at the level of gld-2 ( Figure 9A ) . These genetic behaviors appear to parallel the known molecular protein interactions . The multi-KH domain protein GLD-3 binds directly to GLD-2 cytoPAP and GLS-1 [30] , [33] , illustrating that GLD-3 may serve as an integral regulatory factor for both GLD-2 and GLD-4/GLS-1 cytoPAPs to promote differentiation onset . Redundancy of cytoPAP-mediated translational activation has been previously reported in a later step of meiotic prophase of female germ cells that require abundant GLD-1 expression for meiotic commitment [30] . Intriguingly , gld-2 gld-4 double mutant germ cells enter meiosis [30] , suggesting that the remaining low GLD-1 amounts might be sufficient to promote meiotic entry . Consistent with this idea , gld-2 gld-4 gld-1 triple mutant germ cells never enter meiosis , arguing that in the absence of cytoPAP activity , the remaining gld-1 activity/GLD-1 amount is indeed crucial for meiotic entry . In agreement with previous findings [4] , [46] , [47] , our work suggests that for differentiation onset in gld-2 gld-4 double mutants , cyclin E represents an important target of GLD-1-mediated translational repression . However , we expect additional differentiation onset-promoting mRNA targets to be positively regulated by GLD-2 and GLD-4 , either in a combinatorial manner or separately . Alternatively , other RNA-directed molecular functions , such as miRNA stability described for GLD-2 orthologs in mammals [48] , might be relevant . Future research on the RNA-regulatory repertoire of GLD-2 and GLD-4 will be required to better resolve these issues . We propose that two modules of translational activation and repression , interconnected via their mRNA targets , establish a molecular rheostat that leads to a reciprocal expression of either proliferation or differentiation factors . Together they maintain adult germline proliferation in adult C . elegans animals . Translational repression , in particular , is an established mechanism in Drosophila and C . elegans development . Translational control is also an essential mechanism of the transition from self-renewal/proliferation to differentiation in Drosophila germ cells [49] , [50] . Our work suggests that the regulation of turning translation on is equally important for maintaining a healthy balance between proliferation and differentiation as turning translation off . With this work , we begin to fill this obvious gap in our understanding of adult tissue maintenance .
C . elegans strains were handled according to standard procedures [51] . Worms were grown at 20°C and used for most experiments at an age of 24 hours ( h ) past mid-L4 . Bristol N2 served as the wild-type strain . Mutations used: LGI: gld-2 ( q497 ) , gld-1 ( q485 ) , fer-1 ( b232 ) , gls-1 ( ef4 ) , gls-1 ( ef8 ) , gld-4 ( ef9 ) , gld-4 ( ef15 ) . LGII: gld-3 ( q730 ) , nos-3 ( q650 ) . LGIII: glp-1 ( q175 ) . Transgenes used: rrrSi117[Pmex-5::GFP::H2B::glp-1 ( wt 3′UTR ) unc-119 ( + ) ] II , rrrSi118[Pmex-5::GFP::H2B::glp-1 ( GBM1 , 2 , 3 mut 3′UTR ) unc-119 ( + ) ] II; both are Mos1-mediated single copy gene insertions and their sequences are described in the supplemental text of [25] . JH2929 expresses the LAP-tagged FBF-2 [52] . To generate the new gld-2 ( q497 ) gld-1 ( q485 ) double mutant , we crossed heterozygous gld-2 males with heterozygous gld-1 hermaphrodites . Next , we crossed F1 non-green worms , containing gld-1 and gld-2 , and green siblings , containing the hT2[qIs48] I;III balancer . In the F2 progeny heterozygous balancer animals were screened for a recombination event between the gld-2 and the gld-1 locus by genomic PCR for the q485 deletion and sequencing for the q497 point mutation . Homozygote hT2[qIs48] I;III animals are embryonic lethal and cannot be analyzed as a wild-type sibling control . All other double and triple mutants on LGI were generated in a similar manner and balanced by hT2[qIs48] I;III and are listed in Table 1 . Quadruple mutants containing genes on LGI and LGIII were balanced by hT2[qIs48] I;III and validated by PCR for deletions and by sequencing for gld-2 ( q497 ) and glp1 ( q175 ) . Double and triple mutant combinations on LGI and LGII were maintained by a closely linked GFP transgene ( ccIs4251 ) to unc-15 ( e73 ) on LGI and mIn1[mIs14 dpy-10 ( e128 ) ] on LGII . The presence of all mutations was validated by PCR for deletions or by sequencing . Primer sequences are available on request . RNAi experiments were performed according to published feeding RNAi procedures [53] . The fbf RNAi construct corresponds to fbf-1 ( nts 1040–1845 ) , cye-1 is described elsewhere [54] , and the empty pL4440 vector served as a control . L4-staged N2 animals were placed on RNAi plates and analyzed 24 h later . The efficiency of fbf knockdown was confirmed by a loss of anti-FBF immunoreactivity 24 h past L4 , and after continued feeding at 48 h past the L4 stage by phenotypic changes of the germ lines , i . e . the shrinkage of the PZ and even later the appearance of male-fated germ cells [55] . Antibodies against the following proteins were used as described: anti-HIM-3 1∶200 [56] , anti-pSUN-1 1∶1000 [57] , anti-GLD-4 1∶20 [30] , anti-GLP-1 1∶10 [13] , anti-PH-3 1∶500 ( 9706 , Ser-10 , 6G3 , Cell Signaling ) , anti-FBF-1 1∶100 [55] , anti-GLH-2 1∶200 [58] , anti-GLS-1 1∶20 [33] . Monoclonal anti-REC-8 1∶20 ( mo560-G25-1 , at 10 ng/ul ) and anti-GLD-2 1∶20 ( A4-4 , at 10 ng/ul ) antibodies were generated against recombinant HIS-REC-8 ( aa330–525 ) and GST-GLD-2 ( aa959–1113 ) fusion peptides . The antibodies are specific to the respective proteins; no immunocytochemistry signal was observed in corresponding null mutants and the protein expression patterns in wild type match published ones [28] , [59] . Secondary antibodies ( 1∶500 ) were coupled to FITC , CY3 and CY5 ( Jackson Labs ) . Extruded germ lines were prepared in solution as described [33] . The correct localization and comparable intensities of GLH-2 served as a tissue penetration control for all immunofluorescence experiments . Images were acquired with Axiovision Software ( Zeiss ) on a wide-filed Imager Z1 ( Zeiss ) microscope , equipped with an AxioCam MRm ( Zeiss ) camera . Raw images were processed in Photoshop CS5 ( Adobe ) and assembled in Illustrator CS5 ( Adobe ) . For quantification of immunofluorescent intensities , all images for comparison were taken with identical settings . A median focal plane was chosen where the syncytium was at its maximum width . The pixel intensities were measured in Fiji ( ImageJ ) . To compare GLP-1 intensities , a line scan was performed as is indicated in Figure 3B , ranging from the distal germline tip to the beginning of pachytene . Then all values were binned into the 10 fractions whose positions are displayed in Figure 3B . Averages of those fractions between all analyzed germ lines were calculated and normalized to GLH-2 intensities ( measured in the same way ) . To compare cytoplasmic GLD-2 and GLD-4 intensities , four identical circles were placed over the rachis of the distal arm along the distal-to-proximal axis as indicated in Figure 5C ( five germ cell diameters ( GCD ) proximal of the distal tip , at the end of the PZ , at the beginning of pachytene , and ten GCD into pachytene ) and averaged for all germ lines per genotype . The GLD-2/GLD-4 intensities given are not normalized to the GLH-2 signals , which were in these cytoplasmic regions very low . To ensure equal penetration , we independently measured the peri-nuclear GLH-2 signal in neighboring nuclei and found it very similar among all analyzed germ lines . Immunoblots were performed according to standard procedures with a mixture of two anti-GFP antibodies , at a final dilution of 1∶1000 ( 11814460001 , clones 7 . 1 and 13 . 1 , Roche ) and 1∶200 ( sc-9996 , B-2 , Santa Cruz ) , anti-tubulin 1∶100000 ( T 5168 , clone B-5-1-2 , Sigma ) , and HRP-conjugated anti-mouse secondary antibodies ( Jackson Labs ) . Three-hybrid experiments were performed as described [60] . gld-2 RNA sequences were cloned into the XmaI and SphI sites of the vector pIIIA/MS2-2 , using either PCR-amplified fragment ( FBE4 ) or annealed synthetic oligonucleotides ( remaining FBE sites ) . Their nucleotide positions in relation to the first nucleotide of the gld-2 3′UTR are as follows: FBE1 ( nts 298–335 ) ; FBE2 ( nts 354–394 ) ; FBE3 ( nts 460–493 ) ; FBF4 ( nts 683–763 ) ; FBE5 ( nts 952–986 ) . For binding specificity , a mutation ( UG to AC , see Figure 4D ) was engineered by site-directed mutagenesis using Quikchange ( Stratagene ) . For the sucrose gradient experiments , whole-worm extracts of L1 synchronized adult animals ( L4+24 h ) grown in comparable feeding-RNAi conditions were prepared by pulverizing frozen worms and adding lysis buffer [50 mM HEPES pH 7 . 5 , 125 mM KCl , 5 mM MgCl2 , 1 mM DTT , 0 . 005% NP-40 , 2× Protease Inhibitor Cocktail without EDTA ( Roche ) , 100 U/ml Ribolock ( Fermentas ) , 2 mM PMSF , 4 mM Benzamidine , 2 µg/ml Leupeptin , 2 µg/ml Pepstatin , 0 . 1 µg/ml Pefabloc , 2 mM NaF , 2 mM Na3VO3 and 200 µg/ml Cycloheximide] , followed by a low speed centrifugation to removed insoluble components . The clear supernatant of three biological replicates was layered onto a 17–50% w/v sucrose gradient and processed as previously described [61] with the only exception that the gradients were spun for 210 min . For the mRNA distributions analysis , 10 fmole of a polyadenylated in vitro transcribed luciferase mRNA was added to each fraction prior to RNA isolation as an internal RNA standard for extraction efficiency . The Trizol ( Invitrogen ) isolated RNA from individual fractions was resolved in equal volumes of water and further analyzed by qRT-PCR or pooled for splint-mediated poly ( A ) tests [40] . RIP experiments were performed from mixed-stage animals as described [62] using anti-GFP ( MPI-CBG ) , anti-GLD-4 [30] , or anti-GLS-1 [33] antibodies . Semiquantitative RT-PCR samples of three independent RIP experiments were resolved on ethidium bromide-stained agarose gels . The control samples without Reverse transcriptase were negative and are not shown . For the qRT-PCR experiments , equal volumes of gradient fractionated RNA , total RNA , or RIP material of three biological replicates was used as input material . cDNA synthesis was performed using Revert Aid Premium Transcriptase ( Fermentas ) in combination with oligo dT primers , following the manufactures protocol . qPCRs were performed on a Mx3000P qPCR system ( Stratagene ) using ABsolute qPCR SYBR Green mix ( Thermo ) under standard conditions . For measuring poly ( A ) tail length , we pooled five non-polysome and polysome fractions as indicated in Figure 4 . Together with 4 µg total RNA of the input material , we processed the pooled sucrose gradient experiments by ligating an RNA anchor to the 3′ends , preformed a gene-specific RT-PCR , and resolved the DNA samples on a high-resolution agarose gel , according to [40] . Lane quantifications were performed using Fiji ( ImageJ ) , as described in [40] . | Throughout adulthood , animal tissue homeostasis requires adult stem cell activities . A tight balance between self-renewal and differentiation protects against tissue overgrowth or loss . This balance is strongly influenced by niche-mediated signaling pathways that primarily trigger a transcriptional response in stem cells to promote self-renewal/proliferation . However , the cell-intrinsic mechanisms that modulate signaling pathways to promote proliferation or differentiation are poorly understood . Recently , post-transcriptional mRNA regulation emerged in diverse germline stem cell systems as an important gene expression mechanism , primarily preventing the protein synthesis of factors that promote the switch to differentiation . In the adult C . elegans germ line , this study finds that the evolutionarily conserved cytoplasmic poly ( A ) polymerase , GLD-4 , plays an crucial role in maintaining a healthy balance between proliferation and differentiation forces . This is in part due to translational activation of the mRNA that encodes the germ cell-expressed Notch signaling receptor , an essential regulator of proliferation . Moreover , GLD-4 activity is part of a redundant genetic network downstream of Notch that , together with several other conserved mRNA regulators , promotes differentiation onset . Given the widespread expression of these conserved RNA regulators in metazoans , cell fate balances that are reinforced by translational activation and repression circuitries may therefore be a general mechanism of adult tissue maintenance . | [
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| 2014 | GLD-4-Mediated Translational Activation Regulates the Size of the Proliferative Germ Cell Pool in the Adult C. elegans Germ Line |
The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways . Despite being widely studied , it is not yet completely understood and systems-level analyses have been lacking . In this study , we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network . We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage . The resultant network contained several novel and known regulators of the innate immune response , many of which did not show any observable change in expression at the sampled time points . The response network shows the dominance of genes from specific functional classes during different stages of the immune response . It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response . In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response , time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed . Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response , and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway . The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response , while taking into account the intermediate regulators . We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems .
The innate immune system is the primary host response to invading pathogens . The innate immune response is characterized by germline-encoded pattern-recognition receptors ( PRRs ) that detect and bind to specific microbial components , also known as pathogen-associated molecular patterns ( PAMPs ) . Toll-like receptors ( TLRs ) are a family of PRRs that are conserved from worm to mammals and expressed on different types of immune cells , such as macrophages , dendritic cells ( DCs ) and B cells , as well as non-immune cells , such as fibroblasts and epithelial cells . 10 and 13 TLRs have been identified in human and mouse , respectively , each with distinct microbial ligands . The binding of these ligands to their specific receptors triggers downstream signaling cascades causing the expression of pro-inflammatory cytokines , ultimately leading to systemic inflammation . TLRs primarily function through two pathways – the MyD88-dependent pathway which leads to the expression of proinflammatory cytokines , and the TIR-domain–containing adaptor protein-inducing IFN-β ( TRIF ) -dependent pathway which produces the type I interferons ( IFNs ) [1] , [2] . Though much is known about the pathways activated during the innate immune response , recent perturbation studies have identified previously unknown regulators and transcription factors , highlighting the complexity of the innate immune system and the incompleteness of our current knowledge [3]–[5] . While these studies provide important information about the genes affected on perturbation of a causal gene , they do not explain the cause of the observed expression changes . Additionally , these studies are inherently limited to genes which show changes in expression at the time of observation thus providing an incomplete representation of the activated pathways . The complexity of the innate immune system , the ease of monitoring transcriptional changes , and the availability of large amounts of regulatory and interaction information , all facilitate its analysis using computational methods . An initial computational study mapped all the known interactions associated with the immune response from literature [6] . This study provided a high confidence signaling network and identified the “bow-tie” structure of the immune response . However , it was limited in size and coverage . Li et al . used this signaling map to identify 10 distinct input-output pathways [7] . The resultant modules were further used by Richard et al . to identify a minimum set of genes whose deletion affects the fidelity of the TLR signaling pathways [8] . Though these methods used novel approaches to analyze the TLR signaling pathways , they did not take the temporal changes of the immune response into account . Using a different approach , Seok et al . studied the regulatory networks of 10 transcription factors and their targets using the Network Component Analysis approach [9] . While this study considered the dynamic nature of the immune response through the use of time-course gene expression profiles , it was limited to only 10 transcription factors . Thus , the computational analyses so far performed to study the innate immune response have either been limited by the size of the molecular network used , or by the lack of time-course gene expression profiles . In this study , we perform a comprehensive computational analysis of the dynamic aspects of the innate immune response in the context of a large-scale molecular network . Several methods using condition-specific genetic , transcriptional and epigenomic data in the context of large protein-protein interaction ( PPI ) and protein-DNA interaction ( PDI ) networks have been developed , and have led to the identification of novel regulators and pathways in several cellular systems [10] , [11] . These include Network Component Analysis ( NCA ) [12] , DREM [13] and its recent update SDREM [14] , ResponseNet [15] and SteinerNet [16] , [17] . Data from time-course gene expression profiles is particularly informative in this context since it can capture chronological events in the cellular system . However , some of the methods listed above , like ResponseNet and SteinerNet , are insensitive to the temporal aspect of gene expression , while others like NCA and SDREM use the temporal gene expression information only to identify transcription factors activated at various time points but not to predict active networks . Others have used time-course gene expression profiles either to identify time-specific protein-modules in PPI networks [18]–[21] , or to infer transcription regulatory networks activated over time [12] , [13] , [22] . Though all the methods described so far are relatively successful in identifying network components and modules activated at specific time points , no attempt has been made to identify paths connecting genes expressed at different time points . Such temporal paths can show potential connections between genes expressed at different stages of a response thus providing information about intermediate , transiently expressed regulators that would otherwise have been overlooked . In this work , we studied the innate immune response in dendritic cells ( DCs ) stimulated by lipopolysaccharide ( LPS ) . LPS is a component of the outer membrane of Gram-negative bacteria and specifically binds to the TLR4 receptor , triggering both the downstream MyD88 and TRIF-dependent pathways . We used time-course gene expression profiles collected at 8 time points after LPS stimulation in the context of a high-confidence PPI , PDI and post-translational modifications ( PTM ) network . We grouped the gene expression profiles into three groups – the initial response genes ( greatest fold-change in expression between 0 . 5–1 hour after stimulation ) , the intermediate regulators ( greatest fold-change in expression between 2–4 hours after stimulation ) and the late effectors ( greatest fold-change in expression between 6–8 hours after stimulation ) . We then attempted to identify the most probable paths connecting the initial response genes to the late effectors in the interaction network , while taking into account the intermediate regulators . In order to do this , we used a network flow optimization approach allowing the flow to follow a time-dependent path within the molecular network . Using this method , we were able to identify an optimal gene sub-network for activated DCs . Based on this sub-network , we identified several known core components of the innate immune response , novel down-stream participants and pathways connecting these core components . We were able to identify genes playing an important role in the innate immune response but showing no observable change in expression . We also analyzed time-course gene expression profiles of MyD88-knockout cells and TRIF-knockout cells , and compared their gene sub-networks to that obtained for wild-type DCs in order to identify the components that are independently activated in each pathway . Finally , we identified the distinct functional classes of genes expressed during different stages of the immune response and how their patterns of expression change in MyD88 and TRIF-knockout DCs compared to those in wild-type DCs .
We used a minimum cost flow optimization approach to identify important components of the innate immune response over time on LPS stimulation . A network of PPI and regulatory interactions , including transcription factor-target gene , phosphorylation , dephosphorylation and ubiquitination relationships , was prepared . Network edges were scored based on interaction reliability as obtained from the protein-protein interaction database , HitPredict [23] . Time-course gene expression levels were obtained using RNA-seq from DCs before LPS stimulation and up to 8 hours after LPS stimulation . The genes with significant changes in expression after LPS stimulation were divided into 3 groups based on the time of their greatest change in expression: In order to identify potential paths through the molecular network connecting the genes within the three groups , we formulated the problem as a minimum cost flow optimization problem incorporating the gene expression levels in three stages . Figure 1 shows a schematic representation of the proposed method . We set our source nodes as the initial response genes . The target nodes of the network were the late effector genes . Edges of the network were assigned costs that were inversely proportional to their interaction reliability . Edges were also given a flow capacity proportional to the observed change in expression of the adjacent genes . A constraint was added to the flow optimization problem to force the flow to go through at least one intermediate regulator . We solved the optimization problem to identify the path of minimum cost for the flow to pass through the network using linear programming techniques ( see Materials and methods for the problem formulation ) . The method found the most probable paths in the network between genes expressed in the initial response and those expressed at a later time while taking into account the genes expressed during the intermediate stage . Each edge of the optimal sub-network was assigned a flow signifying its importance . This resulted in a weighted gene sub-network where the edges were scored according to their importance . Flows were calculated for nodes , or genes , as the sum of the flows of their incoming edges . Genes with high flows were considered important due to their connection to high-flow edges . The reliability of the optimal solution was confirmed and statistical significance was calculated for each gene in the optimal sub-network by randomizing the source and target nodes ( see Materials and methods ) . The flow assigned to a gene within the sub-network shared an inverse relationship with its statistical significance , demonstrating that a high flow was a good indicator of reliability ( Figure S1 ) . The genes with the highest flows – Socs3 , Nfκb1 , Jak2 , Jun , Fos , Cxcl10 and Stat1 are well-known components of the innate immune response . Table 1 shows 20 genes with the highest flows in the optimal sub-network for activated wild-type DCs ( See Table S1 for the list of all predicted genes and their statistical significance ) . As shown by the results , the method not only predicted essential genes expressed within each of the 3 groups , but also genes for which no significant change in expression was detected but were connected to others with significant changes in expression over time . In order to evaluate the reliability of the gene network resulting from the paths identified by solving the flow optimization problem , we compared the genes in the optimal sub-network with the experimentally identified regulators of the innate immune response from previous perturbation experiments [3] , [4] . Of the 125 regulators identified by Amit et al . [3] , our sub-network contained 62 ( 49 . 6% ) , all of which had a flow greater than 1 ( Table S2 ) . In a similar study by Chevrier et al . [4] , our sub-network contained 30 of the 43 known or novel regulators identified ( 69 . 8% ) , and 56 of the 102 ( 54 . 9% ) TLR target genes affected by the perturbation of these regulators ( Table S3 ) . The sub-network also contained the gene , Polo-like kinase 2 ( Plk2 ) , which activates a distinct signaling cascade . Thus , our sub-network contained a significant number of the regulators of the innate immune response that were recently experimentally identified . We further confirmed the quality of the predicted gene network through Gene Ontology ( GO ) and KEGG pathway enrichment analysis . The genes having flows greater than 1 in the sub-network , were enriched for the Toll-like receptor signaling pathway ( p = 5 . 10e-41 ) , Jak-STAT signaling pathway ( p = 4 . 88e-45 ) , pathways in cancer ( p = 2 . 50e-41 ) and chemokine signaling pathway ( p = 5 . 16e-40 ) among others ( See Table S4 for full list ) . The association of the predicted genes with the innate immune response is further confirmed by the GO Biological Process terms enriched for these genes . Protein amino acid phosphorylation ( p = 7 . 80e-36 ) , immune response ( p = 1 . 35e-32 ) and regulation of programmed cell death ( p = 1 . 72e-29 ) were some of the most enriched terms ( See Table S5 for full list ) . 49 . 7% of the genes identified in the optimal sub-network did not show significant change in their expression levels on LPS stimulation . In order to confirm that these genes contribute to the enrichment of functional terms associated with the innate immune response , we compared the enrichment of the KEGG pathways and the GO terms in all predicted genes with those that showed differential expression after LPS stimulation ( Table S6 , S7 ) . Including predicted genes lacking differential expression significantly improved the enrichment of the KEGG pathways and the GO terms associated with the innate immune response over that observed for differentially expressed genes only . This further confirmed the association of the genes predicted in optimal sub-network with the innate immune response . Additional analysis of GO term enrichment of genes identified in the sub-network at each time point showed the distinct processes active during different stages of the immune response . Table 2 shows the most significant GO Molecular Function and Cellular Component terms enriched in genes identified at each time point . The most significant term enriched for genes expressed between 0 . 5–1 hour is “transcription regulator activity” ( p = 1 . 18e-09 ) for 20% of the genes indicating an upregulation of transcription factors during the first hour of the immune response . On the other hand , genes predicted at 2–4 hours are enriched for “nucleotide binding” ( p = 9 . 33e-04 , 28 . 5% genes ) and “protein kinase activity” ( p = 1 . 27e-03 , 13% genes ) suggesting a role for signal transducers . Finally , the terms enriched for genes predicted between 6–8 hours are “proteasome complex” ( p = 2 . 98e-11 , 7% ) and “peptidase activity” ( p = 5 . 2e-08 , 13% ) highlighting the activity of the immunoproteasome during this phase of the innate immune response . Finally , genes that were identified in the optimal sub-network but which did not show change in expression during the sampled time points were enriched for GO terms such “protein kinase activity” ( p = 7 . 52e-31 , 16% ) , “cytokine binding” ( p = 5 . 9e-26 , 6% ) and “transcription factor activity” ( p = 1 . 18e-07 , 12% ) ( Table 3 ) . To check the quality of the network paths predicted by the method , we identified all the possible paths predicted in the optimal sub-network that matched a directed path of the same length in a KEGG pathway . Our method was able to predict directed paths of 3 edges or more in 13 KEGG pathways , including the Jak-STAT signaling pathway , the Chemokine signaling pathway , the Toll-like receptor pathway and the MAPK signaling pathway ( Table 4 , Table S8 ) . The longest predicted directed path contained 7 edges and was part of the Jak-STAT signaling pathway . Thus , the method was able to partially recover known pathways in the form of short paths connecting genes expressed at consecutive time points . We also identified all shortest paths up to 3 edges ( i . e . containing 4 nodes at most ) between genes expressed at different stages of the immune response and checked how well they were represented in the same KEGG pathway . We found that 84 . 9% of the predicted paths have at least 2 genes in the same KEGG pathway , while 11 . 6% of the paths have all genes in the same KEGG pathway ( Figure S2 ) . Taken together , these results confirm the reliability of the optimal gene sub-network identified for activated wild-type DCs . To demonstrate the utility of our algorithm , we compared the optimal sub-network identified by our method to that identified using a non-temporal minimum cost flow optimization method , ResponseNet [15] . Using minimum cost flow optimization through our initial network , ResponseNet identified paths from the initial response genes to the late effectors without taking the intermediate regulators into account ( Table S9 ) . Table 5 shows the results of the comparison between the optimal sub-networks predicted by our method and ResponseNet . ResponseNet identified fewer genes and interactions in the predicted sub-network . More significantly , since there was no constraint for the flow to pass through the intermediate regulators , it identified only 49 of these as compared to the 154 by the current method . Our method also identified significantly higher number of known regulators in the innate immune response in addition to longer paths in associated pathways . On the other hand , ResponseNet failed to identify a directed path of 3 or more edges within any KEGG pathway associated with the innate immune response . These results clearly demonstrate that including the intermediate regulators into the problem formulation , as we propose here , improves the ability of the method to predict candidate genes and associated networks using time-course gene expression profiles . The gene predicted with the highest flow in the optimal sub-network was Suppressor of cytokine signaling 3 ( Socs3 ) followed by Nuclear factor κb1 ( Nfκb1 ) . Both genes were significantly upregulated between 2–4 hours and are well-known regulators of the innate immune response . Socs3 , along with Socs1 and Socs2 , is an inhibitor of cytokine signaling pathways . It is a key regulator of interleukins 6 and 10 ( Il6 and Il10 ) [24] . In the identified sub-network , Socs3 is induced by the primary regulators of the immune response such as Nfκb1 and inhibits a large number of proteins , specifically interleukin receptors ( Figure 2a ) . Nfκb1 is induced both in the early and late phase of the innate immune response and is primarily responsible for the expression of inflammatory cytokines . Other genes identified with high flows were the Janus kinase 2 ( Jak2 ) , Rous sarcoma oncogene ( Src ) and phosphoinositide-3-kinase , regulatory subunit 5 ( Pik3r5 ) , all of which have been implicated in the TLR response pathway . Src , a protein tyrosine kinase that modulates a large number of signaling pathways during the innate immune response , was upregulated between 2–4 hours . Along with Src , other tyrosine kinases from the Src family , such as Hck and Lyn , were also identified ( Figure 2b ) . Syk , another protein tyrosine kinase of the Syk-ZAP70 family that is found in innate immune cell types , was also identified as part of the network though no significant change in gene expression levels was detected at the tested time points ( Figure 2c ) . Several other components of the Src signaling pathways like Card9 , Cblb , Fcerγ and various integrins were also identified within the gene sub-network . Among other known regulators , the induction of Ralgds by Ras proteins , and the further upregulation of the Rac genes , was also detected ( Figure 2d ) . Gadd45b , an anti-apoptotic inhibitor induced by Nfκb [25] was also part of the sub-network . Gadd45b was significantly upregulated between 2–4 hours and was predicted to inhibit the cyclins B2 , B3 and CDK ( Figure 2e ) . Another anti-apoptotic inhibitor , the X-linked inhibitor of apoptosis ( Xiap ) was also identified . Xiap is regulated by Nfκb and in turn inhibits Casp3 and Casp7 thus controlling apoptosis ( Figure 2f ) [25] . Another class of proteins identified , were the Akt serine-threonine protein kinases Akt1 , Akt2 and Akt3 , which are downstream effectors of the PI3K pathway ( Figure 2g ) . Expression level change was only observed for Akt1 which was down-regulated at 0 . 5–1 hours followed by an up-regulation at 3 hours . Other predicted components include the Dual specificity phosphatases ( DUSP proteins ) which were significantly upregulated between 0 . 5–1 hour , except Dusp6 . The Dusp proteins regulate the immune response by dephosphorylating the Map kinases and repressing the LPS-induced inflammatory response ( Figure 2h ) . Interestingly , the network indicated that the Dusp genes were expressed within the early stages of the innate immune response suggesting that control of inflammation begins soon after its induction . Many of the genes identified in the network do not show any significant change in expression after activation of the DCs , but are known to be essential for the response . An example is the protein phosphatase 2a catalytic subunit α ( ppp2ca ) which has a high flow in the sub-network . A serine threonine phosphatase required for the dephosphorylation of the 20S proteasome subunits , ppp2ca is known to affect the ability of the proteasome to degrade substrates , along with protein kinase A ( PKA ) [26] . Ppp2ca has also been recently shown to play an important role in the regulation of endotoxin tolerance through the regulation of MyD88 activity [27] . The identified gene sub-network indicated extensive interactions between ppp2ca and the subunits of the immunoproteasome , suggesting a role of ppp2ca in the regulation of the immunoproteasome ( Figure 3 ) . The immunoproteasome is induced by interferons and is central to the regulation of the immune response and in the prevention of auto-inflammatory diseases through its ability to degrade toxic protein aggregates during cytokine-induced oxidative stress [28] . We applied the method described above to time-course gene expression profiles obtained from DCs of MyD88 and TRIF-knockout mice in the context of the comprehensive molecular interaction network . MyD88 and TRIF are essential components of the innate immune response and trigger distinct pathways that result in the activation of early and late phase Nfκb , respectively . Previous studies have shown that Nfκb and Mapk8 ( JNK ) are activated in a delayed manner in MyD88-knockout cells . However , inflammatory cytokines like IL12 or TNFα are not produced [29] . In order to identify the MyD88-independent response network , we used gene expression levels from MyD88-knockout DCs to assign edge capacities , and removed the MyD88 gene and its links within the network prior to solving the minimum cost flow optimization problem ( See Table S10 for identified genes and edges ) . We performed a similar analysis on the data from TRIF-knockout DCs by removing TRIF and its links from the network and predicting a MyD88-dependent response network on LPS stimulation ( See Table S11 for identified genes and edges ) . A comparison of the genes and their flows in the identified sub-networks suggests that the response pathways active in the wild-type and TRIF-knockout sample are similar ( Figure 4a ) . The active sub-networks identified for both these samples are enriched in the KEGG pathways “Cytokine-cytokine receptor interaction” ( p = 1 . 13e-29 ) , “Jak-STAT signaling pathway” ( p = 2 . 34e-15 ) and “Toll-like receptor signaling pathway” ( p = 6 . 07e-11 ) . These findings suggest the dominance of the MyD88 pathway in the wild-type response . Indeed , this dominance has been previously observed during pulmonary infection [30] . On the other hand , the most enriched pathways in the genes exclusively identified in the MyD88-knockout network are the “Circadian rhythm” ( p = 6 . 29e-5 ) and “Ubiquitin mediated proteolysis” ( p = 3 . 2e-4 ) suggesting an association between these pathways and the MyD88-independent , TRIF-dependent pathway ( Table 6 , Tables S12 and S13 ) . In order to identify the dominant changes in the immune response over time , we classified the genes from the optimal sub-networks obtained for the wild-type , MyD88-knockout and TRIF-knockout DCs into functional classes . Global changes in the expression patterns of genes identified as part of the optimal sub-network at each of the 3 stages showed a dominance of functionally distinct groups at different times during the immune response ( Figure 4b ) . In wild-type DCs , transcription factors and enzyme modulators were predominantly expressed during 0 . 5–1 hour after LPS stimulation . On the other hand , kinases and signaling molecules were abundant between 2–4 hours after stimulation . Finally , proteases and defence/immunity proteins along with receptors showed the greatest changes in expression in the late phase of the immune response between 6–8 hours . TRIF-knockout DCs showed similar changes in the expression patterns of genes . However , these patterns were significantly different in the MyD88-knockout DCs . Transcription factors were not as significantly upregulated in the early phase , but more so in the late phase , when the expression of proteases and defence/immunity genes was significantly reduced . Thus , the identified sub-networks suggest a pattern in the global change in gene expression during the different stages of the immune response . The similarity of the patterns of gene expression in the TRIF-knockout DCs and wild-type DCs further support the dominant role of the MyD88-dependent pathway in the innate immune response . An analysis of the functional distribution of the genes predicted in the network , but not showing significant differential expression on activation , illustrates their similarity to the intermediate regulators in the wild-type as well as knockout DCs . Several important components of the innate immune response were identified in both knockout sub-networks , however , with significantly different flows . Nfκb1 , Jak2 and Socs1 were genes with the highest flows ( >40 ) in the TRIF-knockout network . These genes were also identified in the MyD88-knockout network , but with flows just above 1 . This disparity in the flows possibly indicates their changing levels of expression and significance within the two sub-networks . The sub-network associated with MyD88-knockout DCs had different genes with high flows – Akt3 , Casp8 and Stat2 . Interestingly , the kinase Pik3r5 had similar levels of predicted flow in both knockout networks . It was upregulated in both instances but much more so in the MyD88-knockout DCs . Git1 and Cry1 were two of the important candidates identified only in the MyD88-knockout gene network . Git1 ( G-protein coupled receptor kinase interacting protein 1 ) acts in the formation of a scaffold to bring together molecules to form signaling modules and increase the speed of cell migration . Its role in the innate immune response is currently not known . However , it was significantly upregulated in the MyD88-knockout sample and found to interact with Pxn , Arhgef6 and Arhgef7 ( Figure 5a ) . The other important gene identified , Cry1 , is a key component of the circadian core oscillator complex . The role of Cry1 in the negative regulation of the activation of Nfκb and further induction of proinflammatory cytokines has been recently elucidated [31] . Cry1 was significantly upregulated in the MyD88-knockout DCs between 6–8 hours after stimulation and could potentially be regulating the activation of Nfκb signaling . Though Cry1 was part of the gene network associated with the activation of wild-type DCs , it was not identified in the optimal gene sub-network associated with TRIF-knockout DCs , suggesting that the upregulation of Cry1 and its role might be controlled by the MyD88-independent , TRIF-dependent pathway ( Figure 5b ) . The MyD88-knockout associated gene network also contained a number of genes from the E2 and E3 ubiquitin-conjugating enzyme families , including several members of the Trim family , which are known for their role in suppressing the immune response by increasing the ubiquitination and subsequent degradation of regulatory genes [32] . The selective prediction of these ligases in the MyD88-knockout response network suggests that proteolytic degradation might also be predominantly affected by the TRIF-dependent pathway . The response network identified for the TRIF-knockout sample highlights the wild-type MyD88 pathway wherein MyD88 triggers the activation of Nfκb which in turn induces the inflammatory cytokines , further inducing the Jaks and Stats and finally upregulating the Socs genes which repress the inflammatory response ( Figure 5c ) .
The innate immune response is complex and occurs through multiple pathways . The interplay within the activated pathways makes the identification of novel components and their associations difficult . In this study , we addressed this issue by using time-course gene expression profiles of activated dendritic cells in combination with a comprehensive molecular interaction network . We developed a method based on minimum cost flow optimization in a large interaction network to identify paths between genes expressed at different time points of the immune response . Using this method , we identified an optimal gene sub-network activated during the innate immune response . We confirmed the role of several known and novel components in the identified network and suggest a role for the protein ppp2ca in the regulation of the immunoproteasome . A flow value was assigned to each identified gene and interaction within the network indicative of its importance . We also compared the response of the wild-type DCs with DCs from MyD88-knockout mice and TRIF-knockout mice and identified the global changes in expression patterns of genes in distinct functional classes . Our results are consistent with previous studies suggesting the dominant role of the MyD88-dependent pathway . We further showed that genes related to proteasomal degradation and circadian rhythms are primarily associated with the MyD88-independent , TRIF-dependent pathway . The method proposed here is independent of the biological system and can be used to identify time-dependent gene sub-networks with the help of time-course gene expression profiles related to any other cellular conditions . Future work in this area will be aimed at developing methods to accurately predict longer pathways while incorporating time-course gene expression profiles from multiple time points without the necessity of grouping them .
GM-CSF-induced bone marrow-derived dendritic cells ( GM-DCs ) were prepared from C57BL6/J mice ( purchased from Japan Clea Inc . ) as described previously [3] . The cells were stimulated with LPS from Salmonella minessota Re-595 ( purchased from Sigma ) at a concentration of 100 ng/ml . Stimulated cells were harvested at 0 , 0 . 5 , 1 , 2 , 3 , 4 , 6 , 8 , 16 , 24 hours after stimulation . Total RNA was extracted from the cells using TRIzol ( Invitrogen ) according to the manufacturer's instruction . The RNA was subjected to RNA-seq as described in a previous study [35] . Mice deficient in MyD88 or TRIF were prepared as described in an earlier study [36] , [37] . The RNA-seq data is available in the Sequence Read Archive under the accession number DRA001131 . The RNA-Seq data for the wild type , MyD88-knockout and TRIF-knockout DCs at 10 time points , from 0 to 24 hours after LPS stimulation , were obtained in the form of 35 bp single-end reads . The reads were mapped to the RefSeq mm9 mouse reference genome [38] using Bowtie [39] . Exon-exon junctions were found using TopHat [40] with each read having at most 2 mismatches and 20 mappings to the reference genome , and a minimum intron length of 70 bp . For each read , the mapping with the highest alignment score was selected . The mapping statistics are shown in Table S14 . Transcript abundances for all three samples at 10 time points were estimated using Cufflinks and Cuffdiff [41] using the –T option to treat the samples as a time-series . The data from the last two time points , 16 hours and 24 hours , was not used in this study because we were concerned about the effect of their large separation from the prior time points on the quality of the sub-network predicted . Maximum absolute log fold change in expression was calculated for each gene over all time points , as follows: ( 1 ) Where = maximum absolute log fold change for gene i over time j where j = {0 . 5 , 1 , 2 , 3 , 4 , 6 , 8} , Genes with at least 2 fpkm in 50% of the experiments , at least 10 fpkm for 2 or more time points and an absolute fold change greater than 2 for at least one time point in each sample were considered for further analysis . Each selected gene was assigned to one of the following groups depending on the time at which it showed the highest absolute fold change ( Figure 1A ) : The genes and their expression levels are shown in Tables S15 , S16 , S17 . A network of regulatory and physical interactions from mouse was prepared by combining the following datasets: Table S18 shows the counts of the different interaction types included in the network . PPIs were considered as bi-directional edges whereas all other associations ( transcription factor–target gene , functional association , expression regulation , post-translational modification and inhibition ) were considered uni-directional . Genes and their corresponding proteins were represented by a single node in the network . The edges of the network were weighted according to their reliability . Reliability scores provided by HitPredict and TRANSFAC were used . Innatedb core PPIs and interactions from KEGG pathways were uniformly assigned a high reliability score of 999 since these were manually curated . All scores were scaled to values between 0 and 0 . 8 as shown in Table S19 . ( 2 ) Where = scaling functionThe complete network of 103218 interactions among 12856 proteins , or protein complexes , along with the data source , reliability scores and edge weights is given in Table S20 . The network was denoted by a graph G = ( V , E ) with E edges and V nodes ( including the auxiliary source S and the auxiliary sink T ) . The auxiliary source , S , was connected to the set of initial response genes ( GT1 ) , while the auxiliary sink , T , was connected to the late effector genes ( GT3 ) . Direct edges between GT1 and GT3 were excluded . The intermediate regulators ( GT2 ) were also a part of the network but not connected to the S or T nodes . All edges , E , were assigned a capacity and a cost ( See Figure 1B ) . The genes identified as part of the optimal gene sub-network were assigned a statistical significance . This was done by solving the minimum cost flow optimization problem 5000 times using the original molecular interaction network but with randomly selected genes in the GT1 , GT2 and GT3 sets i . e . initial response genes , intermediate regulators and late effectors in numbers equal to those from the real sample . The p-value was calculated as the fraction of solutions in which a gene was identified with an equal or higher flow than that in the optimal sub-network and with at least all the connecting edges in the optimal sub-network . We observed that the predicted flow in the final network increased with decreasing p-value ( Figure S1 ) suggesting that a high flow was a good indicator of high statistical significance and hence greater reliability . The ResponseNet algorithm was implemented as a non-temporal minimum cost flow optimization method . The problem formulation was changed to remove the constraint in equation ( 12 ) thus allowing the flow to go from the initial response genes ( source nodes ) to the late effectors ( target nodes ) without being constrained to pass the intermediate regulators . Additionally , the term involving was also removed from the optimization problem . The algorithm was run on the same network as our method with identical edge costs . The capacities of edges GT1 – S and GT3 – T were set as described in equations ( 3 ) and ( 6 ) . The capacity of all other edges was set to 1 . The optimal solution was calculated for and the identified genes were compared to known regulators and KEGG pathways as described in the Results . All possible paths within the optimal sub-network from the initial response genes ( GT1 ) to the late effectors ( GT3 ) were identified and compared to all KEGG pathways to determine their overlap . Paths were predicted between genes in the groups GT1 , GT2 and GT3 by finding the weighted shortest paths [47] of up to 3 edges in the optimal sub-network . The edges were weighted as per the formula suggested by Opshal et al . [48]: ( 13 ) where = flow assigned to edge ( i , j ) , The shortest weighted paths identified were then compared to all KEGG pathways . An optimal gene sub-network was identified by solving the flow optimization problem using the time-course genes expression profiles from MyD88 and TRIF-knockout DCs in a manner similar to that described above for the wild-type DCs . The MyD88 gene and its interactions were removed from the starting network when the MyD88-knockout gene expression levels were considered . Similarly , during the analysis of the TRIF-knockout sample , TRIF ( Ticam1 ) and its interactions were removed from the network . Enriched Gene Ontology terms and KEGG pathways were obtained using DAVID [49] with all mouse genes used as the background . Networks were prepared and formatted using Cytoscape2 . 7 [50] . Protein functional classes were identified using PANTHER [51] . | The innate immune response is the first level of protection in organisms against invading pathogens . It consists of a large number of proteins functioning in signaling cascades triggered by the binding of fragments from microbes to specific cellular receptors . Disruptions in these pathways can lead to numerous diseases . As such , the innate immune system has been the subject of a large number of studies . However , due to its complexity and the interplay of a large number of pathways , it is not yet completely understood . In this study , we measured transcriptional changes in activated immune cells and used this information in the context of a large network of protein-protein and protein-DNA interactions to identify a smaller network of response genes . We did this by identifying the most probable network paths connecting genes showing large changes in their expression patterns at successive stages of the response . Analysis of this activated gene network revealed the associations between various temporal regulators of the innate immune response . We also identified response networks for immune cells lacking important mediators , MyD88 and TRIF , to clarify the distinct functional modules affected by their associated pathways in the innate immune response . | [
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| 2013 | Linking Transcriptional Changes over Time in Stimulated Dendritic Cells to Identify Gene Networks Activated during the Innate Immune Response |
We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways ( i . e . patterns of sequential acquisition of resistance to individual antibiotics ) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients . Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission , whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance , the latter of which may or may not be associated with lower transmissibility . The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible . We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years .
Tuberculosis ( TB ) is an infectious disease caused by Mycobacterium tuberculosis and is transmitted between hosts through the respiratory route . The appearance of TB resistant to multiple antibiotics threatens global control strategies that depend on the efficacy of standard combinations of these drugs . Drug-resistant TB in communities initially arises as a result of the sporadic appearance and subsequent selection of drug-resistant M . tuberculosis mutants in individuals receiving inadequate treatment . Individuals acquiring drug-resistance as a result of poor TB treatment may then transmit resistant organisms to their respiratory contacts . Figure 1 displays mechanisms leading to drug resistant TB infection in treatment-naïve and treatment-experienced patients . Drug-resistance in treatment-naïve TB patients reflects primary transmission of resistant strains; in contrast , drug-resistance in TB patients who have previously been treated with anti-TB antibiotics may reflect either transmitted resistance or resistance acquired during previous treatment . Resistant strains observed among treatment-naïve TB patients have demonstrated sufficiently high reproductive fitness to have been transmitted and caused disease . By contrast , resistant strains that are observed among treatment-experienced patients arise from either transmission from another host or from within-host selection of sporadically occurring mutants under drug pressure . Drug resistant strains arising as a result of this second mechanism may not be as easily transmitted to secondary hosts as drug strains that have already demonstrated their ability to infect and cause disease in secondary hosts . Determining which strains are sufficiently fit to be transmitted and cause disease can aid in developing effective strategies to combat the spread of resistance . Probabilistic graphical models , e . g . branching tree mixture models , have been used to infer the sequence of several binary events that have occurred in an unknown order [1]–[3] . These models can potentially provide public health benefit as they only require cross-sectional data , often easily and abundantly available , and are applicable to any biological system that follows an ascending Markov process . Past use of these models include describing the order of acquiring copy number aberrations in renal cancer , modeling the development of HIV genetic mutations associated with antiretroviral resistance and characterizing the acquisition of anti-TB drug resistance from phenotypic TB data [1]–[3] . Knowledge regarding these longitudinal processes may be useful in directing research for disease control . Considerable work has been done in defining and fitting branching tree models . The single mutagenetic tree introduced by Desper et al . [1] , describes the progression of a set of events , or pathway , for a population . The model assumes that there are no reversions following an event and that for each event , there is a unique pathway leading to it . To broaden this class of models for settings where the latter assumption does not hold , Beerenwinkel et al . [2] introduced mixture models that allow for the existence of multiple evolutionary pathways leading to the same event . Izu et al . [3] developed a Bayesian approach to identifying a mixture model and estimating the associated parameters . Branching trees are useful in the context of TB because the probability of reverse mutations is very small ( validating the ascending markov assumption ) , and global cross-sectional phenotypic drug resistance data are publicly available [4] . In analyses of genetic data , an event is a specific mutation; whereas in analyses of phenotypic data , sets of genetic mutations are grouped into single events . For example , the event “resistance to isoniazid” would comprise all patterns of genetic mutations which confer isoniazid resistance . Although phenotypic data does not allow examination of the ordering in which such mutations emerge , such data are more readily available and can provide a basis for generating hypotheses that can subsequently be tested with genetic data . Below , we expand the use of these models beyond their previous application for describing the progression of events in a single population . This paper develops a Bayesian approach to compare pathways in two different populations using branching trees in which some tree parameters are prespecified . We apply these methods to investigate the relationship between drug resistance in treatment-naïve and in treatment-experienced patients . By comparing branching trees from these two groups of patients , we gain insight into the evolution of highly drug-resistant strains that remain capable of being transmitted and causing secondary disease .
We follow Desper et al . in our notation for branching tree models . A branching tree , denoted by , is a special Bayesian network that consists of a set of nodes or vertices , a root , a set of edges connecting the vertices , and edge weights . Vertices represent the event of a binary random variable and the root represents the binary random variable indicating whether none or at least one of the events characterized by the vertices have occurred . The edges connecting the nodes have weights equal to the conditional probability of the child event given the prior occurrence of the parent event . As the branching trees described here do not take time into account , the edge weights are not informative about the times to occurrences of events . An example is provided by the two trees in Figure 2 with edge weights and . From these , we infer that prevalence of is higher but not that resistance to it occurs faster in the latter compared with the former tree . For more details on timed branching tree used in oncogenesis see Desper et al . Branching trees model the joint distributions of events and impose constraints on the dependencies among events and on the order in which they can occur . Let be the set of nodes for which is the root; denote the edge directed from node pointing towards node ;and be the probability mapping such that . A path from to is a sequence of edges and is an ancestor of . The path is a cycle if . A branching tree imposes the restriction that there be no cycles and that each edge must be directed toward a different node . A node with no offspring is called a leaf . One particular branching tree to define is a star tree . In this paper , the nodes represent the acquisition of drug resistance to one or more drugs and the root represents a wild type state ( i . e . full sensitivity to all anti-TB drugs ) . The edges connecting the nodes signify that the event represented by the offspring ( child ) node can only occur given the prior occurrence of the event represented by the parent node . The edge weights are the conditional probabilities of these events . Because a branching tree requires that each edge be directed toward a different node , single branching trees may not be sufficient to describe the underlying processes of interest . Beerenwinkel et al . [2] introduced mixture models in order to accommodate the existence of multiple evolutionary pathways leading to the same node . A -tree mixture model is comprised of branching trees , , and their respective tree weights , , where is the marginal probability that a random individual follows a pathway represented by the tree . Let denote the probability that the individual follows a pathway represented by ( Beerenwinkel et al . [2] referred to this probability as the responsibility of ) . We refer to a tree structure as the graph of the mixture model without the edge weights , i . e . the collection of trees , . Mixture models often contain a special noise component or star tree , in which all nodes originate in the root . Figure 2 provides an example of a mixture model , in which the first tree is the star tree . Mixture models which include a star tree ensure that every possible multinomial state has probability greater than zero . We adapt the two-step process introduced in Izu et al . to estimate mixture models , in which aspects are prespecifiedwhere and is treated as known for of the K trees . The first step estimates the structures of the remaining trees . To accomplish this , we adjust the EM-like algorithm in Beerenwinkel et al . [2] to account for the prespecified portion of the model . This involves iterating between estimating the responsibilities for each individual and reconstructing the remaining trees using the data weighted by the responsibilities . Given an estimate of the responsibility of the tree for the sample is estimated ( E step ) byFollowing this step , for is reconstructed by using the maximum branching algorithm ( M step ) found in Desper et al . with the following adjusted joint and marginal probabilitiesAs discussed in Izu et al . we can also compose a set of candidate models that include similar , but different , structures for the unspecified trees and then use a given criteria to choose the best model . In certain settings , it may be reasonable to assume the structure of all trees in the model thereby avoiding the need for the first step . Given the structure of the trees , the second step uses Bayesian methods to estimate the parameters associated with the partially-known mixture model . Let represent a multinomial random variable whose outcomes are determined by the pattern of events for the set of binary random variables or vertices . There are possible outcomes , where n is the number of vertices . Let be the corresponding probabilities of each outcome associated with the tree . For example , for the mixture model shown in Figure 2 there are possible outcomes for the multinomial distribution . If corresponds to the event resistance to but not or , the probability of this outcome isLet . We place non-informative priors on the tree weights , , and the parameters associated with . The posterior distribution of these parameters can be obtained from an MCMC implementation in WinBUGS . To use mixture models to compare two populations , A and B , we include trees derived from data on population A as prespecified elements in our mixture model for population B . Tree weights associated with these trees provide a measure of the similarity between the two populations , which we define below . The mixture model for population B iswhere of the trees describe pathways that are also seen in population A , and the remaining trees describe pathways seen only in population B . We define the measure of similarity as . From our definition of above , the measure of similarity is the probability that an individual from population B follows any of the pathways resulting from the model describing population A . Using the bayesian methods described above , we can obtain a posterior distribution for this quantity . The data we analyze are obtained from Anti-Tuberculosis Drug Resistance in the World , Fourth Global Report [4] . These data arise from surveillance in countries where all notified culture-positive TB cases received drug susceptibility testing ( DST ) and from population-representative surveys in countries where not all TB cases routinely receive DST . Between 1994 and 2007 , DST results were collected from patients from 138 settings in 114 countries and 2 Special Administrative Regions ( SARs ) of China . The anti-TB drugs reported include isoniazid ( H ) , rifampin ( R ) , ethambutol ( E ) and streptomycin ( S ) . Twenty-nine settings were excluded because data were either only reported for treatment-naïve patients or combined for naïve and treatment-experienced patients , leaving a total of 85 , 672 samples from treatment-naïve patients and 18 , 619 samples from treatment-experienced patients . Seven different regions were considered ( AFR = African region , AMR = region of the Americas , EMR = Eastern Mediterranean region , FSU = Former Soviet Union region , NFSU-EUR = Non-Former Soviet Union European region , SEAR = South-East Asian region , WPR = Western Pacific region ) as shown in Table 1 . Originally , all European countries were included in a single region . However , the prevalence of resistance to any anti-TB drug is significantly higher in countries of the former Soviet Union than in other European countries: 39% ( 95% CI: 38–40 ) and 8 . 2% ( 95% CI: 7 . 8 , 8 . 5 ) , respectively , among treatment-naïve cases and 71% ( 95% CI: 70–72 ) versus 20% ( 95% CI: 18 , 22 ) among treatment experienced cases . Because of this large difference , we split the European region into two sub-regions . Resistance pathways may vary between regions , both because of geographic heterogeneity in strain lineage and because of differential selective pressure due to different historic usage of anti-tuberculosis drugs [5] . As a consequence , we analyze data from each region separately . Methods described in Izu et al . are used to analyze the data from the treatment-naïve patients . The resulting tree structures and their corresponding edge weights comprise the prespecified components in the mixture model fit to data from treatment-experienced patients .
In the resulting mixture models for treatment-naïve patients , models from all seven regions contain two trees . The non-star tree for the models describing the AMR , EMR , FSU , SEAR and WPR is shown in Figure 3 ( a ) -these are all trees with a single leaf . Izu et al . used a simulation study to show that these methods perform well when the underlying data generating tree structure has a single leaf . The non-star tree from the models describing AFR and NFSU-EUR is shown in Figure 3 ( b ) . For each region , estimates for the tree and edge weight parameters are shown in Table 2 . is an estimate of the proportion of the population following the tree . The four columns following represent the edges and corresponding edge weights associated with tree . The edge weight is the conditional probability of resistance to the drug indicated by the child node given resistance to the drug indicated by the parent node . If the parent node is the root ( WT ) , the edge weight is the marginal probability of resistance to the drug indicated by the child node . For example , in the AFR , 16% of all TB strains follow pathways described by the first tree which has the set of edges E = {WTH , WTR , WTE , WTS} . 84% of TB strains follow pathways to resistance described by the second tree with the set of edges E = {WTH , HR , HE , RS} . In the latter , the conditional probability of resistance to rifampin given resistance to isoniazid is 0 . 86 . The weights on the star tree found in the first column of Table 2 , range from 0 . 09 ( SEAR ) to 0 . 18 ( FSU ) and all standard errors are less than 0 . 026 . With the exception of the FSU , the probabilities associated with edges beginning at the root in the non-star tree are all less than 0 . 10 ( s . e . 0 . 015 ) , reflecting the relatively low prevalence of resistance observed among treatment-naïve patients . In contrast , for the FSU , the probability associated with the edge from the root is 0 . 27 ( s . e . = 0 . 007 ) . A prespecified mixture model was fit to the data on treatment-experienced patients with the non-star trees from the fit to data on naïve patients as specified components ( Figure 3 ) . The number of unspecified trees was obtained from fitting a fully specified mixture model to the data from treatment-experienced patients . The trees represented exclusively in the model for treatment-experienced patients describe pathways for resistance that are unique to this population ( i . e . not observed among the treatment-naïve ) . Models for each region , with the exception of SEAR , contain two unspecified trees , one of which is the star tree , and the other of which is shown in Figure 4 . The model describing the SEAR contains three unspecified trees: the star tree , and the trees shown in Figure 4 ( a ) and 4 ( c ) . Each of the three different non-star tree structures , contain the edge HR . The non-star tree for the EMR and SEAR , is the only structure in which streptomycin , not isoniazid , is the child node of the root . The analysis of resistance patterns from treatment-naïve and experienced patients produces identical tree structures for the AFR , EMR , NFSU-EUR and SEAR . The results of analyses are shown in Table 3 . Because there is only one prespecified tree , the measures of similarity is the weight for the unspecified tree shown in the first column of Table 3 . In our application , the measure of similarity is the probability that a treatment-experienced patient follows a pathway identical to that seen in treatment-naïve patients . It ranges from 0 . 29 to 0 . 71 and all standard errors are less than 0 . 18 . The breakdown for each region is as follows: 0 . 52 ( AFR , s . e . = 0 . 18 ) , 0 . 71 ( AMR , s . e . = 0 . 03 ) , 0 . 36 ( EMR , s . e . = 0 . 12 ) , 0 . 33 ( FSU , s . e . = 0 . 03 ) , 0 . 48 ( NFSU-EUR , s . e . = 0 . 18 ) , 0 . 29 ( SEAR , s . e . = 0 . 12 ) , and 0 . 51 ( WPR , s . e . = 0 . 06 ) . As shown in Izu et al . , bootstrap methods provide information regarding the stability of these tree structures . For each region , a set of candidate tree structures are obtained for naïve and treatment-experienced patient from fitting 30 bootstrap samples . The program Mtreemix [6] was used to fit Beerenwinkel's mixture model to data from naïve patients and an adaption to the Mtreemix program was used to fit our prespecified mixture model to data from treatment-experienced patients . All candidate sets contain fewer than four structures with the exception of the NFSU-EUR and SEAR for treatment-experienced patients ( five and eight structures , respectively ) . Results of Izu et al . imply that estimates from models where more structures occur in the set of candidate trees are less stable . Results provided in Table 3 show that the standard deviations of the posterior distribution for the branching tree parameters in these regions are relatively high . In analyses described above , we prespecified a single tree in our mixture model . This section presents the results of simulations to gauge the accuracy of our methods . Data are simulated from the seven resulting mixture models from the treatment-experienced data . In each of the models , labeled simulations 1–7 , one tree structure and its edge weights are prespecified and treated as known . We estimate the structure and corresponding edge weights for the remaining unspecified portion of the model as well as all tree weights . Table 4 shows how often the correct tree structure is chosen . The agreement between these results and those from the bootstrap analyses ( Table 5 ) are generally high , with some notable exceptions . The results from AFR , EMR , and WPR appear to be stable in both analyses and the results for SEAR are particularly unstable in both . In the AMR , FSU and NFSU-EUR , the results from the simulation samples differ from the results from the bootstrap samples . The NFSU-EUR shows the largest difference . The correct tree structure is chosen in 84% of the simulations , but in only 3% of the bootstrap samples . The tree structure chosen in 83% of the bootstrap samples is similar to the correct tree except for the non-star tree in the unspecified portion of the model . The set of edges for the non-star tree is: E = {WTH , HR , HE , HS} . We compared the distribution of the bootstrap samples that resulted in this alternative tree and the simulation samples resulting in the correct tree . Eight of the sixteen multinomial parameters show different distributions in the bootstrap compared to the simulation samples . We believe that these differences constitute the main driver of this discrepancy . Such differences could make it difficult for the data to distinguish between closely related trees ( e . g . those that differ by a single edge ) that explain the data equally well . The results from fitting the models are shown in Table 6 , which provides the coverage for each parameter estimated in the seven models . Coverage is defined as the percentage of time the 95% credible intervals contain the true parameter , given the simulation resulted in the correct tree structure . Of all seven simulations , all parameter estimates have coverage higher than 90% . Our simulations show that when the tree structure is correct , the mixture model parameters are well estimated .
This paper describes methods to estimate partially prespecified mixture models which can be used to compare two populations . Our model is applied to investigate patterns of resistance amongst treatment naïve and experienced patients . Trees from treatment-naïve data ( Figure 3 ) reflect pathways from strains which have demonstrated the ability to be transmitted and cause disease . Trees from treatment-experienced patients ( Figure 4 ) describe pathways from a combination of transmitable and reproducible strains and those which may have suffered some cost in terms of their ability to transmit . There are different explanations for the patterns we observe in the two populations and these methods cannot definitively differentiate among them . Below , we review our results and use them to generate hypotheses about underlying mechanisms of TB resistance which may be worthy of further testing . In the AFR , EMR , NFSU-EUR and SEAR the same tree structure arises from both treatment-naïve and experienced patients , implying the pathways to multi-drug resistance are similar in both populations . One possible explanation is that in these regions , all pathways result in transmissible resistant TB strains . Factors that are region specific provide other possible explanations . For example , there is a high prevalence of HIV in the AFR . Patients with suppressed immune systems may be more susceptible to strains that have lower overall reproductive fitness , thereby permitting all pathways observed among re-treatment cases to be also seen in naïve cases [7] . The NFSU-EUR has the lowest prevalence of drug resistance among all regions ( Table 1 ) . For both naïve and experienced patients in this region , much highly resistant disease is observed among immigrants from areas where the prevalence of drug resistance is high [8] . One potential explanation is that the majority of highly resistant disease in this region results from transmission with only minimal contribution of acquired resistance . In contrast , analysis of AMR , FSU , SEAR and WPR resulted in branching trees which differ among treatment-naïve and experienced patients . This tends to imply that some pathways to resistance produce strains that are relatively less transmissible and cause disease in secondary hosts . Alternatively , it may be that new resistance pathways appearing first among re-treatment cases through acquisition may not have had enough time to be observed among new cases . Among treatment-naïve patients , the pathway of the most common tree begins with streptomycin; however , in treatment-experienced patients , the majority of the trees , it begins with isoniazid . This difference may reflect the history of TB treatment . Streptomycin was the first anti-TB drug in general use followed by isoniazid and then rifampin . It is also possible that in some settings ( and with some resistance-conferring mutations ) , resistance to isoniazid is associated with a reproductive fitness cost that decreases the microbes transmissibility or ability to cause disease [9]–[11] . It is unlikely that this ordering of mutations reflects current sequencing of drug use since in most settings the vast majority of cases will be treated simultaneously with four drugs ( rifampin , isoniazid , ethambutol and pyrazinamide ) [12] . Only in rare settings is streptomycin ( the only antibiotic of the four reported here that requires injection ) used in first-line regimens for treating tuberculosis . Each non-star tree describing both treatment-naïve and experienced patients contains the edge HR . This important edge defines the development of multidrug resistant TB ( MDR-TB ) . Given that a strain follows a pathway associated with the tree under study , the weight corresponding to the edge HR is the conditional probability of the strain being MDR given that it is resistant to isoniazid ( INH ) . This edge weight in the trees for naïve patients provides insight into the probability of MDR-TB given INH resistance in strains that are being transmitted . Except for the AFR , the HR edge weight is lower in trees associated with treatment-naïve patients , suggesting in these regions , the conditional probability of MDR-TB given INH resistance may be lower among transmitted strains . The measure of similarity provides a quantitative measure of the degree of similarity of two populations . We note that it does not directly provide information regarding the process of acquiring resistance in the two populations . Resistance pathways seen in the sample of treatment- naïve patients may not actually represent every possible pathway associated with this population . In addition , patients presenting for re-treatment who were originally infected with resistant strains may also have acquired additional resistance [13] . Therefore , comparison of tree structures from treatment-naïve and treatment-experienced patients cannot serve as a basis for estimating the proportion of the latter who were originally infected with resistance strains . Nonetheless , the proportion of drug-resistant and MDR TB attributable to transmission found in several molecular epidemiologic studies , 38% to 53% , and 64% respectively are similar to the weights associated with trees observed in treatment-naïve patients [14]–[17] . The large amount of data from treatment-naïve patients allows us to estimate reliably the prespecified portion of the model . In some settings , it may not be appropriate to assume that branching trees are known for a portion of the model . The Bayesian approach permits incorporation of uncertainty by placing a prior distribution on the parameters of the prespecified trees; the methods of Szabo and Boucher [18] that permit incorporation of measurement error into the mixture model can also be used . We would have included this approach in our analysis had such measures been available in the settings where the data were collected . In other settings , it may be preferable to avoid prespecification of model components and estimate all model parameters completely from available data . To aid in such endeavors , our model could be naturally extended to include other covariates , such as indicator variables for different populations . Izu et al . discuss the possibility that multiple structures may describe data equally well as was possibly the case in the NFSU-EUR . The authors recommend using bootstrap methods and simulation to assess reliability of results . In such situations , examining the similarities among the different plausible tree structures provides insight regarding resistance pathways . In the results described above , all of the trees resulting from the bootstrap samples shared many of the same properties . The most notable similarity was the role of E as the child node to R in 96 . 5% and 72 . 9% of the resulting structures from the bootstrap samples across all regions for naïve and experienced patients , respectively . 92 . 7% of the bootstrap samples across all regions for both groups of patients resulted in a structure with H as an ancestor to R , implying resistance to isoniazid precedes resistance to rifampin–a finding that has also been previously described . In summary , the proposed methods permit investigation of pathways to resistance in treatment-naïve and treatment-experienced patients , subject to limitations describe above . These results are useful for formulating questions regarding the biology and epidemiology of drug resistant tuberculosis and can help generate testable hypotheses about which pathways to multiple drug resistance may be most likely to generate fit strains capable of being successfully transmitted . The analyses presented here are limited by the fact that only phenotypic resistance data were available . As discussed in Izu et al . , genotypic data that permit inference regarding the pathways by which specific drug-resistance conferring mutations accumulate would allow for refinement of hypotheses that can be tested . Although the focus of this paper is on tuberculosis , our methods can be generalized to any biological process for which the assumption of an ascending markov process applies . | Drug-resistant tuberculosis ( TB ) initially arises as a result of the sporadic appearance and subsequent selection of drug-resistant M . tuberculosis mutants . Such strains may or may not be associated with fitness costs affecting their ability to transmit and cause disease . Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission . Those collected from treatment-experienced patients reflect strains that may or may not be associated with lower transmissibility . Determining which strains are sufficiently fit to be transmitted and cause disease can aid in developing effective strategies to combat the spread of resistance . Branching trees are graphical models used to infer the sequence of several binary events ( i . e . a pathway ) that have occurred in an unknown order . We propose a novel method using branching trees with prespecified components to compare evolutionary pathways among different populations . We apply our model to understand if there are unique drug-resistant pathways found only amongst treatment experienced patients that might reflect acquired resistant disease associated with fitness costs that limits its ability to transmit . Our methods can be generalized to any biological process for which the assumption of an ascending markov process applies . | [
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"Introduction",
"Methods",
"Results",
"Discussion"
]
| [
"mathematics",
"statistics",
"biostatistics",
"statistical",
"methods"
]
| 2013 | Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases |
The eukaryotic genome is organized in a three-dimensional structure called chromatin , constituted by DNA and associated proteins , the majority of which are histones . Post-translational modifications of histone proteins greatly influence chromatin structure and regulate many DNA-based biological processes . Methylation of lysine 36 of histone 3 ( H3K36 ) is a post-translational modification functionally relevant during early steps of DNA damage repair . Here , we show that the JMJD-5 regulates H3K36 di-methylation and it is required at late stages of double strand break repair mediated by homologous recombination . Loss of jmjd-5 results in hypersensitivity to ionizing radiation and in meiotic defects , and it is associated with aberrant retention of RAD-51 at sites of double strand breaks . Analyses of jmjd-5 genetic interactions with genes required for resolving recombination intermediates ( rtel-1 ) or promoting the resolution of RAD-51 double stranded DNA filaments ( rfs-1 and helq-1 ) suggest that jmjd-5 prevents the formation of stalled postsynaptic recombination intermediates and favors RAD-51 removal . As these phenotypes are all recapitulated by a catalytically inactive jmjd-5 mutant , we propose a novel role for H3K36me2 regulation during late steps of homologous recombination critical to preserve genome integrity .
Cells have developed several pathways to promptly remove DNA lesions of different types in order to prevent dangerous outcomes such as cell death , DNA deletions and chromosomal rearrangements that can contribute to human diseases [1–3] . Double strand breaks ( DSBs ) , involving both DNA strands , can be repaired by homologous recombination ( HR ) , which uses the sister chromatid or the homologous chromosome as template to direct an error-free repair . Presynaptic steps of HR include the generation of ssDNA overhangs by DNA end resection and recruitment of the strand exchange protein RAD51 to the break site . This is followed by strand invasion of the ssDNA overhang into the DNA template ( synapsis ) , formation of displacement loops ( D-loops ) , and DNA synthesis [1] . Postsynaptic events of DSBs repair can proceed either with second DSB end capture and formation of Hollyday junctions ( by the double Hollyday Junction pathway dHJ ) , or with the displacement of the invading strand and its ligation with the second end ( by the SDSA pathway ) [4] . Alternatively , DSBs can be repaired by the error-prone non-homologous end-joining pathway ( NHEJ ) [5] . NHEJ is based on protection of DNA ends mediated by the recruitment of Ku70/80 heterodimers and ligation of the ends by the DNA ligase 4/XLF/XRCC4 complex [6] . Though DSBs are generally deleterious , specific cell types self-inflict DSBs in particular conditions . This is the case in budding yeast during mating-type switching , in lymphocytes during V ( D ) J recombination or in germ cells during meiosis , in which DSB formation is a required step in the formation of crossovers between homologous chromosomes . After DNA damage , cells activate a series of events , generally called DNA damage response , that includes the sensing of the damage , signaling and repair . In eukaryotes , the response to damage occurs in the context of chromatin in the cell nucleus , in which the DNA is wrapped around histone proteins [7] . Several studies highlight the relevance of chromatin components to properly execute DNA damage response and achieve successful repair . Over the last years , the role of histone variants , heterochromatic proteins and ATP-dependent chromatin remodeling factors in DNA damage response has been investigated in detail [8 , 9] . Similarly , several histone modifications have been functionally linked to DNA damage repair [10–15] . In particular , the methylation state of histone H3 lysine 36 ( lysines can be mono- , di- and tri-methylated ) is emerging as a modification with key roles in the early phases of DNA repair . In human , tri-methylation of histone H3 lysine 36 ( H3K36me3 ) is required for DNA damage sensing and DSB repair through HR , by promoting the recruitment of early components required for the formation of RAD51 foci [16–18] . Loss of the methyltransferase SETD2 , responsible for H3K36me3 deposition , and overexpression of the H3K36me3-specific demethylase JMJD2A/KDM4A , inhibit DSB repair by HR . SETD2 and KDM4A members have also been implicated in DNA mismatch repair , in microsatellite instability , and in increased spontaneous mutation frequency , further indicating the relevance of this mark in genome stability [19 , 20] . Likewise , di-methylation of H3K36 ( H3K36me2 ) has been associated with early steps of the NHEJ repair pathway . After damage , H3K36 is rapidly di-methylated by the methyltransferase Metnase , which promotes NHEJ by recruiting early DNA repair components such as NBS1 and Ku70 . Loss of Metnase and ectopic expression of JHDM1A , a KDM2 family member with specificity for H3K36me2 , impair DSB repair by NHEJ [21 , 22] . It is currently unknown if H3K36me2 also promotes HR-based repair . Furthermore , whether the modulation of H3K36 methylation is required after the initiation of DNA repair and for the progression of the repair process has not been investigated . Considering the complexity and the dynamic nature of histone post-translational modifications , it is of crucial importance to systematically investigate the contribution of enzymes regulating histone post-translational modifications to DNA repair processes . C . elegans is an excellent model organism to uncover factors involved in induced-DNA damage repair , both in somatic and germ cells , by genetic screening approaches [23–25] . The mechanisms of DSBs formation and repair that occur physiologically in meiotic cells have been also studied in detail in the germline of C . elegans [26] . Importantly , DNA damage repair pathways are well conserved in the nematode and many of the components required in mammalian cells to ensure genome stability have also been identified in C . elegans as key molecules participating in responses to DNA damage [27] . In a screen to uncover potential roles of histone demethylases in the response to DSBs , we identified jmjd-5 as a gene required for protecting germ cells from DSBs . JMJD-5 shares homology with the mammalian JMJD5/KDM8 , a JmjC-containing protein essential for mouse embryonic development , cancer growth and mitotic division [28–32] . To date , the catalytic activity of JMJD5/KDM8 is debated , with reports supporting a role of this protein as a demethylase and others as a hydroxylase [29 , 33–36] . Here we show that , in C . elegans , JMJD-5 regulates the level of H3K36me2 and it is required for DSB repair by the HR pathway . Animals carrying a deletion of jmjd-5 are hypersensitive to IR and show prolonged retention of RAD-51 at DSBs , both after IR and during meiotic recombination . We found that jmjd-5 genetically interacts with rtel-1 and helq-1 , which encode DNA helicases required for the resolution of postsynaptic recombination intermediates and the removal of RAD-51 from dsDNA-RAD-51 filaments , respectively [37 , 38] . This suggests a postsynaptic role for JMJD-5 in regulating HR by promoting RAD-51 eviction and the progression of DNA repair . Importantly , the phenotypes observed in jmjd-5 mutant animals are recapitulated in a catalytic inactive mutant , suggesting a novel and critical role for H3K36me2 regulation in late steps of DNA damage repair and in safeguarding genome integrity .
To identify potential histone demethylases involved in DSB repair , we performed a candidate screen of existing mutants of genes encoding JmjC-containing proteins . We identified jmjd-5 as a gene required for normal response to ionizing radiation ( IR ) , a source of DSBs . jmjd-5 encodes for a 578 amino acid long protein and it is a member of the KDM8 family of proteins ( Fig 1A and S1 Fig ) . The jmjd-5 ( tm3735 ) allele used in the screen carries a deletion of 621 bp plus an insertion of 2 bp in the jmjd-5 gene ( Fig 1A ) , which results in a significant decrease of the associated transcript level ( Fig 1B ) . Analyses under standard growth conditions ( 20°C ) revealed that jmjd-5 ( tm3735 ) animals are phenotypically wild-type , with only a slightly reduced brood size [average +/- sem: wild type ( N2 ) : 289+/-4 , n = 25 , jmjd-5 ( tm3735 ) : 231+/-19 , n = 19] and very low level of embryonic lethality ( Table 1 ) . However , when exposed to different doses of IR , adult jmjd-5 ( tm3735 ) animals showed increased embryonic lethality of their progeny ( Fig 1C ) , increased chromosomal abnormalities in diakinesis ( N2: 28 . 5% , n = 63 , jmjd-5: 54 . 7% , n = 53 , 80Gy ) and reduced fertility , compared to N2 animals ( Fig 1D ) . Moreover , the surviving jmjd-5 F1 animals generated after the irradiation of adult animals , showed an increased rate of sterility ( Fig 1E ) . Hypersensitivity to IR was also observed when we irradiated jmjd-5 ( tm3735 ) L1 larvae and measured their ability to produce viable progeny ( Fig 1F ) . In contrast , the response to exposure to UV-C , known to generate lesions in only one DNA strand [39] , was unaffected in jmjd-5 ( tm3735 ) animals , as the embryonic mortality rate was similar to that of N2 animals ( Fig 1G ) . Overall , these results suggest that jmjd-5 plays a protective role both in adult and larval germ cells towards double strand DNA damage . As the C . elegans germ cells favor HR-mediated repair , our data also support a role for jmjd-5 in HR-mediated repair . To address whether jmjd-5 also functions in NHEJ , an efficient but error-prone repair pathway activated mainly in somatic cells [40] , we irradiated late embryos and tested their rate of growth . Irradiation of jmjd-5 late embryos did not result in larval arrest , in contrast to animals lacking the NHEJ gene cku-70 , suggesting that jmjd-5 is not involved in NHEJ repair ( Fig 1H ) . It should be noted , however , that the cku-70; jmjd-5 double mutant has a low but significant larval arrest under normal conditions , suggesting that jmjd-5 may have a protective role in somatic cells that is revealed when the NHEJ pathway is abrogated . In support of this possibility , jmjd-5 was previously identified in a genome-wide RNA interference screen as required to protect genome stability in somatic cells [24] . In summary , loss of jmjd-5 results in increased embryonic lethality and in decreased brood size after gamma irradiation , suggesting a role of jmjd-5 in the HR-mediated repair process that protects germ cells from the consequences of DSBs generated by IR treatment . After DNA damage , germ cells activate protective processes , including a block of mitotic cell division to allow DNA repair before replication , and the promotion of apoptosis to eliminate damaged cells . After IR , we observed a reduction in mitotic cell number in jmjd-5 ( tm3735 ) comparable to that detected in N2 animals ( Fig 2A ) . This suggests that the checkpoint response to DNA damage in mitotic cells takes place normally in this mutant . Indeed , enlarged mitotic nuclei , generally regarded as a sign of mitotic arrest , are observed both in N2 and jmjd-5 ( tm3735 ) germlines after irradiation ( Fig 2B ) . Likewise , apoptosis is activated in jmjd-5 ( tm3735 ) animals , as demonstrated by the presence of syto12-positive cells ( Fig 2C ) and increased transcript levels for the pro-apoptotic cep-1/p53-dependent genes egl-1 and ced-13 after IR ( Fig 2D ) . Of note , jmjd-5 mutant animals show increased apoptosis compared to wild-type animals not only after DNA damage induction by irradiation but also in untreated conditions ( Fig 2C ) . Consistently , removal of cep-1 , required for eliminating damaged germ cells , in jmjd-5 ( tm3735 ) background is accompanied by a partial , but significant , restoration of fertility , by increased embryonic lethality and reduction of apoptosis ( S2A–S2C Fig and Fig 2C and 2D ) . These results indicate that checkpoint responses to IR are not defective in jmjd-5 mutants , suggesting that the hypersensitivity to IR may be due to compromised DNA damage repair . DSB repair by HR is initiated by DNA-end resection followed by the recruitment of members of the recombinase family RecA/RAD-51 to the ssDNA . RAD-51 is a commonly used marker of DSBs whose recruitment at DNA breaks can be visualized as distinct bright foci by immunofluorescence microscopy . RAD-51 foci show a characteristic localization pattern in N2 germlines , as schematically shown in Fig 3A . Under physiological conditions , RAD-51 identifies DSBs required for meiotic recombination generated by spo-11 [41] . RAD-51 foci arise in the transition zone ( TZ , zone 3 ) , where recombinant intermediates start to be coated by RAD-51 , peak at early/mid pachytene ( zone 4 and 5 ) and disappear in late pachytene ( zone 6 and 7 ) [41 , 42] . We first analyzed the pattern of RAD-51 staining in N2 and jmjd-5 ( tm3735 ) animals under normal conditions ( Fig 3B ) . We found that the onset of RAD-51 staining was normal in jmjd-5 ( tm3735 ) animals and , despite a small decrease in the absolute number of RAD-51 foci in mid-pachytene ( zone 5 ) , the RAD-51 staining clearly arose in early and mid pachytene germ nuclei of jmjd-5 ( tm3735 ) . This suggests that the initial recruitment of RAD-51 on DSBs is unaffected in the mutant ( Fig 3B ) . However , we observed that jmjd-5 ( tm3735 ) animals showed a small but consistent increase in the size of the region containing RAD-51 foci , with some foci persisting until the very end of pachytene , where they are normally absent in N2 ( Fig 3B and 3C ) ( zone 6 p< 0 . 0001 , zone 7 , p<0 . 005 , Student’s t-test , unpaired ) , suggesting that the jmjd-5 mutant animals could encounter spo-11-independent unscheduled damage or have impaired repair of DSBs . To distinguish between these two possibilities , we analysed the staining of RAD-51 in jmjd-5 ( tm3735 ) ;spo-11 ( me44 ) genetic background . A dramatic decrease of RAD-51 staining was observed in both strains and no significant difference in the number of RAD-51 foci was observed in the double mutant compared to spo-11 mutant ( p>0 . 1 , Student’s t-test , unpaired ) ( S3A Fig ) , indicating that the increase of RAD-51 foci observed in jmjd-5 mutant is most likely related to defective DNA damage repair . Similarly , the increase of apoptotic cells observed in jmjd-5 mutant animals ( Fig 2C ) was spo-11-dependent ( S3B Fig ) . We repeated the RAD-51 analysis after IR and , as expected , both N2 and jmjd-5 ( tm3735 ) animals showed a large increase of RAD-51 staining and foci were observed in late pachytene ( zone 6 and 7 ) also in N2 . However , in jmjd-5 ( tm3735 ) animals , the average number of RAD-51 foci per nucleus in this region was significantly increased , compared to N2 ( Fig 3C ) . We were unable to determine the exact number of RAD-51 foci in early/mid pachytene in this condition , due to the massive levels of RAD-51 staining . Overall , these experiments show that jmjd-5 is likely not required at early steps of DNA repair , as RAD-51 is normally recruited to DSBs . Instead , jmjd-5 mutant animals show a persistence of RAD-51 staining in late pachytene , rather supporting a role of jmjd-5 in successfully completing DSB repair . Furthermore , the fact that aberrant persistence of spo-11-dependent RAD-51 foci is detected under normal conditions suggests a possible role for jmjd-5 in HR occurring during meiosis . In addition to repair of DNA damage , HR is also required during meiosis . In this process , HR contributes to the formation of chiasmata , the structure that holds together the homologous chromosomes and ensures proper chromosome segregation in meiosis I . Failure to recombine results in chromosome misaggregation and aneuploid gametes , leading to embryonic lethality , as well as an increased number of males , generated by impaired X chromosome disjunction [43 , 44] . We analyzed jmjd-5 ( tm3735 ) animals grown at 20°C and at 25°C , the latter being a temperature known to challenge the recombination process [45] . We did not observe signs of aberrant HR during meiosis at 20°C ( Table 1 ) , however , when animals were grown at 25°C for several generations ( the data reported refer to the analysis of N2 and jmjd-5 ( tm3735 ) after five generations at 25°C ) , we noted decreased fertility ( average +/- sem: N2: 185+/- 8 , n = 12 , jmjd-5 ( tm3735 ) : 78+/-13 , n = 11 ) and a mild but significantly increased embryonic lethality and male production rates in the jmjd-5 ( tm3735 ) genetic background , compared to N2 ( Table 1 ) . In line with these latter phenotypes , we observed oocytes with aberrant numbers of paired chromosomes and , in some cases , with fragmented or compacted chromosomes ( Fig 4A ) . The fact that these phenotypes appear after several generations at 25°C strongly indicates that they are not related to an intrinsic temperature sensitive property of the hypothetical mutated JMJD-5 protein . Instead , this observation suggests a protective role of jmjd-5 in the germ cells at high temperature , which prompted us to visualize the pattern of RAD-51 staining at 25°C . In N2 , the pattern of RAD-51 staining was qualitatively very similar to that observed at 20°C , with a clear reduction of RAD-51 foci in late pachytene ( zone 6 and 7 , compare Fig 3B and Fig 4B ) . We did observe a minor increase in the percentage of mid/late pachytene nuclei with higher numbers of RAD-51 foci ( zone 5 and 6 , p<0 . 005 , Student’s t-test , unpaired ) , which could reflect the increased level of recombination occurring at this temperature , [45] . In contrast , in jmjd-5 ( tm3735 ) animals grown at 25°C the percentage of nuclei with high numbers of RAD-51 foci was increased compared to N2 cultivated at the same temperature ( Fig 4B and 4C ) ( zones 4–7 , p<0 . 0001 , Student’s t-test , unpaired ) and to jmjd-5 ( tm3735 ) grown at 20°C ( compare Fig 3B and Fig 4B ) ( zone 4 , p<0 . 005 , zones 5–7 , p<0 . 0001 , Student’s t-test , unpaired ) . Furthermore , similarly to what we observed at 20°C , RAD-51 staining was also observed in a large percentage of nuclei in the late pachytene region of mutant germlines ( Fig 4B and 4C ) . Persistent lesions derived by defects in meiotic DSB repair are reported to activate a meiotic checkpoint resulting in increased apoptosis [27 , 46] . We therefore analyzed the rate of apoptosis in animals grown at 25°C and observed increased apoptosis in jmjd-5 ( tm3735 ) animals ( S4 Fig ) compared to wild type N2 in the same conditions , further confirming a role for jmjd-5 in DSB repair . The observation that RAD-51 foci persist to late pachytene in jmjd-5 mutant animals at any temperature tested and after DNA damage , indicates a role for jmjd-5 in steps occurring after RAD-51 loading and suggests that loss of jmjd-5 results in HR intermediates loaded with RAD-51 that cannot be properly resolved , leading to increased apoptosis and chromosomal instability . To gain information regarding the site of JMJD-5 action , we used genetic approaches . JMJD-5 may act presynaptically , after RAD-51 recruitment on ssDNA , or postsynaptically , after D-loop formation . We postulated that if removal of jmjd-5 results in increased stalled postsynaptic HR intermediates , the concomitant lost of rtel-1 , a gene required for their resolution [37] , should result in a worsening of the jmjd-5 phenotypes . As jmjd-5 ( tm3735 ) shows embryonic lethality after some generations at 25°C , we measured the embryonic lethality of jmjd-5;rtel-1 double mutants at this temperature . We found that , after only one generation at 25°C , embryonic viability was significantly affected , in comparison to single mutants ( Fig 5A ) , indicating that loss of jmjd-5 results in the formation of stalled HR intermediates that require rtel-1 to be resolved . Considering that rtel-1 disassembles RAD-51 double stranded DNA filaments ( RAD-51-dsDNA ) and has no activity on single stranded DNA-RAD-51 filaments ( RAD-51-ssDNA ) [37] , our result also suggests that jmjd-5 acts in a post-strand invasion step of HR . The completion of HR events requires the eviction of RAD-51 from dsDNA , a process promoted , in C . elegans , by at least by two genes: helq-1 , a helicase of the family of MUS308 , and rfs-1 , a paralog of RAD-51 [38] . rfs-1 and helq-1 act in two parallel and redundant pathways to remove RAD-51 from dsDNA filaments , as suggested by the previously reported evidence that the low level of embryonic lethality observed in single helq-1 and rfs-1 mutants dramatically increases in the helq-1;rfs-1 double mutant [38] . The embryonic lethality observed in helq-1;rfs-1 mutants is associated with stalled recombination intermediates , persistent RAD-51 foci in late pachytene , and defective chromosomal structures in diakinesis [38] . As these phenotypes are similar to the ones observed in jmjd-5 ( tm3735 ) animals raised at 25°C for several generations , we hypothesized that jmjd-5 may act together with or in parallel to helq-1 and rfs-1 to promote RAD-51 removal . To test this hypothesis we generated jmjd-5;helq-1 and jmjd-5;rsf-1 double mutants and analyzed the level of embryonic lethality . We noted a significant increase of embryonic lethality in jmjd-5;rfs-1 double mutants grown at 25°C for one generation , compared to the single mutants ( Fig 5B ) . In contrast , jmjd-5;helq-1 double mutants did not show an increase in embryonic lethality in this condition ( Fig 5B ) , suggesting that jmjd-5 acts in the same pathway as helq-1 and in parallel to rfs-1 . Furthermore , similar to jmjd-5 mutants , animals carrying rfs-1 and helq-1 deletion alleles were previously found to be IR hypersensitive [47–49] . We therefore tested the jmjd-5;helq-1 and jmjd-5;rsf-1 double mutants for IR sensitivity . Again , loss of rfs-1 acted synergistically with jmjd-5 , as jmjd-5;rfs-1 double mutants showed a remarkably increased sensitivity to IR , compared to single mutants . No additive effect was measured in jmjd-5;helq-1 double mutant animals ( Fig 5C ) . This further suggests that jmjd-5 acts in the same pathway as helq-1 and redundantly to rfs-1 in protecting germ cells from IR . Overall , these results suggest that jmjd-5 acts postsynaptically , together with helq-1 and in parallel to rfs-1 , to facilitate RAD-51 removal from dsDNA , preventing stalled HR intermediate formation and therefore promoting successful completion of HR and DSBs repair . To investigate the function of jmjd-5 in postsynaptic events , we analyzed its enzymatic activity . jmjd-5 encodes a protein of 578 aa with a C-terminal JmjC domain , present in almost all known proteins with histone demethylase activity [50] . The specificity of the catalytic activity of the mammalian homologue , JMJD5/KDM8 , is controversial , with several studies reporting specific H3K36me2 demethylase activity [29 , 30 , 34 , 51] , and others reporting a possible function as a protein hydroxylase [33 , 36 , 52] . We therefore tested if JMJD-5 is active towards H3K36me2 by measuring the level of this mark by western blot on lysates derived from adult jmjd-5 ( tm3735 ) animals . As shown in Fig 6A , loss of jmjd-5 results in increased levels of H3K36me2 , but not of H3K36me1/3 . Other marks tested appear unchanged in the mutant ( S5 Fig ) . As the jmjd-5 ( tm3735 ) phenotypes reported here are related to germ cell viability , we also analyzed the level of H3K36me2 in the germline using immunofluorescence ( IF ) . As previously reported , in N2 germlines H3K36me2 is present in mitotic and meiotic germ cells with a dotted staining ( Fig 6B ) , with a visible reduction in a region corresponding to the X chromosome [53] . Quantitative analysis revealed an increased level of H3K36me2 in mitotic and meiotic germ cells of jmjd-5 ( tm3735 ) animals ( Fig 6B and 6C ) . Of note , the level of H3K36me2 in the somatic distal tip cell ( Fig 6B ) is not apparently increased , supporting a role for jmjd-5 in regulating the H3K36me2 mark predominantly in germ cells . In addition , a region depleted of H3K36me2 staining that we assume to be , as in wild type N2 animals , the X chromosome , is still evident in the mutant , suggesting that JMJD-5 is most likely not contributing to the control of the H3K36me2 level on the X chromosome . The JmjC domain contains a pocket that is required for catalytic activity and point mutations of residues located in this region are sufficient to abolish the enzymatic activity [54 , 55] . Using CRISPR/Cas9-mediated genome engineering , we generated a mutant , termed jmjd-5 ( DD ) , encoding a mutated protein in which two highly conserved amino acids of the pocket have been mutagenized ( His484Asn and Asp486Ala ) ( Fig 1A ) . The point mutations generated did not affect the expression level of the gene , as judged by qPCR ( Fig 1B ) . By immunofluorescence analysis , the level of H3K36me2 in jmjd-5 ( DD ) appears increased compared to N2 , further supporting a role of JMJD-5 in regulating the level of H3K36me2 ( Fig 6B ) . To test whether the phenotypes observed in jmjd-5 ( tm3735 ) mutants are due solely to a lack of catalytic activity , we examined if jmjd-5 ( DD ) animals show similar defects . We found that jmjd-5 ( DD ) animals are hypersensitive to IR , as shown by increased embryonic lethality of F1 progeny of irradiated adults , compared to the control ( Fig 6D ) . Furthermore , we observed a persistence of RAD-51 foci in late pachytene in the jmjd-5 ( DD ) mutants compared to wild-type animals ( Fig 6E , left ) ( zones 6–7 , p<0 . 0001 ) . Thus , the defects in RAD-51 staining observed in jmjd-5 ( tm3735 ) mutants are reproduced in jmjd-5 ( DD ) animals . Strikingly , the RAD-51 defects were enhanced when jmjd-5 ( DD ) animals were grown at 25°C for five generations ( Fig 6E , right ) ( zone 4–7 , p<0 . 0001 , compared to wild-type in the same condition ) and , consistently , jmjd-5 ( DD ) animals cultivated at 25°C have increased level of males , of embryonic lethality ( Table 1 ) and apoptosis ( S4 Fig ) , indicating that the HR events occurring in meiotic cells are defective in the absence of the jmjd-5 catalytic activity . Overall these results suggest that JMJD-5 regulates H3K36me2 level and that the HR defects observed in jmjd-5 ( tm3735 ) mutant animals , both in physiological conditions and after irradiation , are strictly depending on the JmjC catalytic activity .
Here , we report a novel role for JMJD-5 in the process of HR , in response to DSBs occurring both under physiological conditions during meiosis , and after IR-induced DNA damage . Our data suggest that jmjd-5 is not required to mount DNA damage checkpoints , as mutant animals can properly elicit mitotic arrest and apoptosis when exposed to DNA damage . Similarly , in the absence of jmjd-5 , RAD-51 is correctly loaded on DSBs , indicating that early steps of the DNA damage response ( e . g . , damage sensing , DNA resection and RAD-51 recruitment on ssDNA ) are largely unaffected in the mutant strain . Our results point , instead , to a role for jmjd-5 after RAD-51 recruitment that we characterize using genetic approaches . The observation that in jmjd-5 mutant animals RAD-51 foci persist in late pachytene , together with the genetic interaction with rtel-1 , which is required to resolve recombination intermediates , indicates that jmjd-5 prevents the occurrence of stalled HR intermediates . Importantly , as rtel-1 has no activity against RAD-51-ssDNA filaments , which resemble presynaptic HR substrates , but can unwind RAD-51-dsDNA filaments , this result also implies that jmjd-5 acts in a postsynaptic step of HR . The genetic interaction of jmjd-5 with helq-1 , a gene required for the resolution of dsDNA RAD-51 filaments , further indicates that jmjd-5 acts postsynatically , probably contributing to RAD-51 eviction and to the progression of the HR process , specifically acting in the helq-1 pathway . Our results show that the catalytic activity of JMJD-5 is critical during HR . Even though we cannot exclude the contribution of other unidentified targets , the upregulation of the levels of H3K36me2 in jmjd-5 mutants indicates that the DNA damage-related functions of jmjd-5 are directly correlated with the modulation of H3K36me2 . It is possible that the jmjd-5-associated reduction of H3K36me2 facilitates RAD-51 eviction and the progression of DNA damage repair by favoring , directly or indirectly , the recruitment of HELQ-1 . Alternatively , H3K36 methylation may compete with other modifications on H3K36 or on neighboring residues required for the completion of HR and DNA damage repair . In this context it is interesting to note that JMJD-5 carries at its N-terminal portion a GNAT ( Gcn5-related N-acetyltransferase ) domain , suggesting that JMJD-5 may be able to coordinate the levels of histone acetylation and methylation , favoring a more open chromatin environment that facilitates , for example , the helicase activity of HELQ-1 or DNA synthesis . As we failed to generate a mutant allele affecting specifically the GNAT domain , its activity , targets and implication in HR are at the moment unknown . We can , however , exclude that the increased H3K36me2 level observed after jmjd-5 loss results in a DNA structure that is more sensitive to damage . Indeed , analysis of the level of RAD-51 foci one hour after irradiation , prior to DNA repair , resulted in a similar number of RAD-51 foci in N2 and jmjd-5 mutant animals ( S6A Fig ) . Similarly , loss of jmjd-5 and the consequent increased level of H3K36me2 do not lead to remarkable changes in gene transcription , as suggested by the transcriptome analysis of jmjd-5 ( 3735 ) , at 20°C and 25°C , obtained by RNA deep sequencing ( S1 appendix ) , suggesting that the changes in H3K36me2 level do not largely impact transcription activity at global levels . Despite we could not appreciate a global change of H3K36me2 level after irradiation ( S6B Fig ) , a role of H3K36 methylation regulation in genome stability in C . elegans is also supported by studies on jmjd-2 , an H3K9/K36me3 histone demethylase that has also been implicated in DNA damage response [55 , 56] . Reduction of jmjd-2 increases p53-dependent apoptosis and RAD-51 foci , in mid but not late pachytene [55] , suggestive of a function of jmjd-2 distinct from that of jmjd-5 . The evidence that the mammalian homologue , JMJD2A/KDM4A , is also implicated in HR [18] , suggests that the functions of H3K36 methylation regulation in DNA repair are evolutionary conserved and it is therefore tempting to speculate that the role of jmjd-5 in late steps of HR that we identified in C . elegans is maintained in higher organisms . While direct evidence for such a role will require further studies in mammalian cell-culture systems , some indications that JMJD5/KDM8 is important for genome stability in mammals have been reported . JMJD5 was initially identified as a mutator gene in a screen using Blmm3 mice , carrying a hypomorphic mutation in the ReqQ-like gene 3 DNA helicase gene found in Bloom syndrome patients [31] . More recently , the catalytic activity of JMJD5 towards H3K36 has been suggested to ensure genome stability by preventing the formation of multipolar spindles in human cells [30] . It is noteworthy that some phenotypes of jmjd-5 mutant animals appear after exposure to high temperature for several generations . Interestingly , as described for other chromatin factor mutants [57–59] , jmjd-5 mutant animals show progressive reduction of the brood size and could not be maintained at 25°C for many generations . Further analyses are required to understand the implication of defective DNA damage repair in this phenotype , nevertheless , these observations suggest that chromatin organization may be susceptible to temperature changes , which is also supported by studies in Arabidopsis and Drosophila [60–62] . They also suggest that the effects of aberrant histone modulation in germ cells can be transmitted to the progeny , leading to phenotypes that progress over generations . In conclusion , our analysis provides novel information on JMJD-5 functions in HR and , by suggesting a previously uncharacterized role of H3K36 methylation in late steps of DNA damage repair , emphasizes the relevance of proper H3K36 methylation in the control of genome integrity .
C . elegans strains were grown at 20°C , unless stated otherwise , on NGM plates seeded with OP50 E . coli bacteria under standard conditions [63] . Strains used in this study were as follows: jmjd-5 ( tm3735 ) , NG4370: zdIs5 I; pig-1 ( gm344 ) IV , MT12833: lin-61 ( n3809 ) I , RB864: xpa-1 ( ok698 ) I , TJ1: cep-1 ( gk138 ) I , RB1279: rfs-1 ( ok1372 ) III , helq-1 ( tm2134 ) , FX1524: cku-70 ( tm1524 ) III , VC381: atm-1 ( gk186 ) I , GE24: pha-1 ( e2123 ) III , rtel-1 ( tm1866 ) I , ZR950: jmjd-5 ( DD ) V , AV157:spo-11 ( me44 ) /nT1 IV;V . Double mutants were generated by standard crossing procedures . All experiments were conducted at 20°C , unless stated otherwise . The experimental protocols used in this work do not require an ethic statement . jmjd-5 ( tm3735 ) was identified as hypersensitive to irradiation in a screen where mutants in JmjC-containing protein were tested . Briefly , synchronized young adult worms ( 24 hours post L4 ) of the strains indicated below were irradiated with 80Gy and the embryonic lethality of the F1 progeny deposited after 18–24 hours from the treatment was analysed . The following mutant alleles were tested: rbr-2 ( tm3141 ) , rbr-2 ( ok2994 ) , jmjd-5 ( tm3735 ) , jmjd-4 ( tm965 ) , jhdm-1 ( tm2828 ) , jmjd-2 ( tm2966 ) , jmjd-1 . 2 ( tm3713 ) , jmjd-1 . 1 ( ba1083 ) , jmjd-3 . 1 ( gk387 ) ;jmjd-3 . 2 ( tm3121 ) ;jmjd-3 . 3 ( tm3197 ) . N2 , pig-1 ( gm344 ) and lin-61 ( n3809 ) were used as controls in the screen . The screen was repeated three times . Synchronized young adult worms ( 24 hours post L4 ) of the indicated genotypes were treated with different doses of IR and UV . After treatments worms were allowed to recover for 18 hours . Embryonic lethality was assessed for the time period of 18–24 hours following treatment , by plating 3 worms per plate , in triplicate , per experiment . The number of dead embryos was assessed 24 hours later . The L1 survival assay was adapted from [64] . Briefly , L1 larvae of the indicated genotypes were irradiated and placed onto single plates ( five worms per plate ) . The total number of living worms ( post the L1 stage ) present in the F1 generation was counted using a dissection microscope . Only plates in which all five irradiated P0 animals were alive were analyzed . For brood size measurement after different doses of IR , we counted the number of progeny ( including dead embryos ) produced after the irradiation time of synchronized young adult worms ( 24 hours post L4 ) . Chromosome number and appearance in oocytes were analysed in DAPI stained animals after 18 hours of irradiation . At least three biological replicates were conducted for each experiment . In these experiments pig-1 ( gm344 ) [65] , lin-61 ( n3809 ) [66] , atm-1 ( gk186 ) [67] , cku-70 ( tm1524 ) [68] and xpa-1 ( ok698 ) [69] were selected in base of their reported phenotypes and used , when indicated , as controls . Adult animals ( 24 hours post L4 ) were irradiated and , after 24 hours were incubated in a 33 mM aqueous solution of SYTO12 , for 4 hours at room temperature . After 45 minutes of recovery on OP50 plates , worms were then scored within 1 hour for the presence of fluorescent apoptotic cells . Only properly stained germlines were quantified . Acridine Orange staining ( used in S3 Fig ) was performed as previously described [70] . Germlines of synchronized worms ( 24 hours post L4 ) were dissected after IR and stained with DAPI . Total number of mitotic cells was quantified in optically bisected gonads by fluorescence microscopy . At least 6 gonads were quantified per genotype and treatment dose . Embryos ( P0 ) from animals of indicated genotypes were collected from adult animals grown at 20°C and plated and maintained at 25°C . Fertility , embryonic lethality and male production were estimated at the indicated generations . Gonads of adult hermaphrodites ( 24 hours post L4 ) were dissected and stained as previously described [71] . Briefly , excised germlines were fixed for 10 minutes with 2% formaldehyde ( Sigma Aldrich ) in K2HPO4 ( pH 7 . 2 ) . Germlines were then freeze-cracked on dry ice and placed in cold methanol ( Merck ) for 5 minutes . Blocking was performed for 30 minutes in 1% BSA in PBST . Slides were incubated overnight at 4°C in a humid chamber with primary antibodies , washed 3 times for 10 minutes in PBST and incubated 2 hours with the secondary antibodies at room temperature . Slides were then washed 3 times for 10 minutes in PBST before being mounted on coverslips with Vectashield . In the second wash DAPI was added at a concentration of 100 ng/ml . Primary and secondary antibodies were diluted in blocking solution as follows: anti-RAD51 ( Novus Biologicals; 29480002 ) , 1:10000; anti-H3K36me2 ( Active Motif; 61019 ) 1:200; donkey anti-mouse Alexa 488 ( Life Technologies; A21202 ) 1:200 , goat anti-rabbit Alexa 568 ( Invitrogene; A11036 ) 1:200 . Immunofluorescence images were collected at 0 . 2 μm intervals using a Delta vision platform ( GE Healthcare ) with a IX-71 Olympus microscope Plan Apochromat , LED based 7 colour fluorescence illumination module ( Lumencor ) as light source , CoolSnap HQ2 camera ( Photometrics ) , quadruple filter sets for DAPI , FITC , TRITC , and Cy5 . Images were collected using a 100X oil objective ( Olympus , 1 . 4NA ) and three-dimensional data sets were subjected to deconvolution using softWoRx 6 . 5 . 2 . Software Suite ( Applied Precision ) and then projected onto one dimension using ImageJ [72] . Exposure conditions were kept constant for each experiment . Entire germlines were reconstructed by stitching sequential images with ImageJ ( ImageJ , National Institute of Health , Bethesda , MD ) ( Fiji ) . Quantitative analysis of RAD-51 foci was performed as described in [46] . The number of RAD-51 foci/nucleus in the seven zones is divided in classes and reported in the x-axes of the histograms with a color code . The y-axis indicates the percentage of nuclei falling into each class . At least 5 germlines were scored for each condition . The total number of nuclei scored per zone is indicated in the histograms . Statistic analyses were performed using two tailed unpaired t test . Quantification of the average intensity of H3K36me2/nucleus was performed by measuring the intensity of selected regions in the germlines with ImageJ ( Fiji ) and by normalizing to the number of nuclei in that region . At least 10 gonads were quantified per genotype . Western blots were performed as described in [73] . The following antibodies were used: anti-H3K36me1 ( Abcam; ab9048 ) 1:1000 , anti-H3K36me2 ( Abcam; ab9049 ) , 1:400; anti-H3K36me3 ( Active Motif; 61102 ) , 1:1000; anti-H3K27me3 ( Millipore; 07–449 ) , 1:2500; anti-H3K4me1 ( Abcam; ab8895 ) 1:500 , anti-H3K4me2 ( Millipore; 07–030 ) , 1:2000; anti-H3K4me3 ( Abcam; ab8580 ) 1: 1000 , anti-H3K9me3 ( Abcam; ab8898 ) 1: 1000 , anti-H3K27me2 ( Abcam; ab24684 ) 1:2000 , anti-H3 ( Abcam; 1791 ) , 1:30000 . Quantification of intensity was performed using ImageJ software ( ImageJ , National Institute of Health , Bethesda , MD ) . Young adult hermaphrodites cultured at the indicated temperatures and generations were flash-frozen in liquid nitrogen and stored at -80°C before RNA extraction . RNA was isolated from two independent cultures using TRIzol reagent ( Life Technologies ) and RNeasy Minikit ( Qiagen ) . RNA amplification and sequencing were performed by the Beijing Genomics Institute ( BGI ) . The samples were sequenced as 49 bp length single-end non-stranded reads on Illumina HiSeq2000 by BGI . The raw reads were mapped to the C . elegans ce10 genome assembly using the STAR alignment tool [http://bioinformatics . oxfordjournals . org/content/early/2012/10/25/bioinformatics . bts635] ( v2 . 3 . 0 ) with default parameters ( except: outFilterMismatchNoverLmax = 0 . 1 , outFilterMatchNmin = 16 ) . Uniquely mapped reads were assigned RefSeq genes by htseq-count [https://www . ncbi . nlm . nih . gov/pubmed/ ? term=25260700] ( parameters: -a 30-s no -m intersection-nonempty ) . The RefSeq gene database was downloaded from UCSC ( data stamped March 17 2013 ) , ambiguously mapped genes removed and then filtered to keep only the longest transcript variant of a given gene . Statistical Wald tests ( lfcThreshold = 0 . 5 ) were performed in R with the DESeq2 package ( v1 . 12 . 4 ) [http://genomebiology . biomedcentral . com/articles/10 . 1186/s13059-014-0550-8] after first removing genes with no assigned reads . Differentially expressed genes were defined as having p-values corrected for multiple testing ( Bonferroni ) less than 0 . 1 . Altogether slightly more conservative parameter setting than normal was chosen due to the limited number of biological replicates ( two per group ) . Deregulated genes are listed in S1 appendix . Data are available in https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE93086 . Total RNA was isolated using TRIzol reagent ( Life Technologies ) and the cDNA was synthesized using TaqMan Reverse Transcription kit ( Applied Biosystems ) . qPCR was performed as described in [74] . Reactions were performed in triplicate , in at least two independent experiments . Oligonucleotide sequences are available upon request . The housekeeping genes rpl-26 and act-1 were used as internal controls . The jmjd-5 ( DD ) strain was generated by microinjection of young adult pha-1 ( e2123 ) worms using the co-CRISPR approach described in [75] . The injection mix contained plasmid pJW1285 that drives expression of Cas9 and pha-1 ( e2123 ) sgRNA ( 60ng/μL , Addgene#61252 ) , the pha-1 ( e2123 ) PAGE-purified 80mer single-stranded oligodeoxynucleotide ( ssODN ) HR template ( 50ng/μL; IDT ) , two plasmids expressing sgRNAs targeting the jmjd-5 locus ( 50ng/μL each ) , and an 100mer ssODN HR template to introduce the desired mutations in jmjd-5 ( 100ng/μL; IDT ) . To generate sgRNA plasmids targeting the jmjd-5 locus annealed oligo pairs were ligated into BbsI-digested pJJR50 ( Addgene#75026 ) . To identify worms with edited jmjd-5 alleles surviving F1 progeny of injected animals was singled and allowed to lay eggs followed by lysis with 120ug/mL Proteinase K ( Macherey-Nagel ) . Lysates were used as input in PCR reactions using the DD F/R primer pair and results were confirmed by Sanger sequencing of the DD seq F/DD R amplicon . Progeny of positive integrants was backcrossed once with N2 males to remove the remaining pha-1 ( e2123 ) allele , which was confirmed by Sanger sequencing of the pha-1 F1/R1 amplicon . Sequences of the oligonucleotides used for the generation of jmjd-5 ( DD ) are reported in S1 Table . | DNA damage repair occurs in the context of chromatin and it is influenced by post-translational modifications of histone proteins . The level of methylation at lysine 36 of histone 3 ( H3K36 ) plays an important role in early phases of DNA damage repair by recruiting early repair factors at the site breaks and regulating the formation of RAD-51 foci . Here , we suggest that the regulation of H3K36me2 levels by JMJD-5 is also relevant at later stages of DNA repair . By using C . elegans as model system , we show that loss of jmjd-5 , resulting in increased levels of H3K36me2 , impairs the resolution of recombination intermediates and the release of RAD-51 , with consequences in the DNA repair process after ionizing radiation and in meiotic recombination . Thus , our work provides further insights into the mechanism regulating H3K36 methylation and its central role in repair of double strand breaks and in genome stability . | [
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| 2017 | JMJD-5/KDM8 regulates H3K36me2 and is required for late steps of homologous recombination and genome integrity |
Host factors are recruited into viral replicase complexes to aid replication of plus-strand RNA viruses . In this paper , we show that deletion of eukaryotic translation elongation factor 1Bgamma ( eEF1Bγ ) reduces Tomato bushy stunt virus ( TBSV ) replication in yeast host . Also , knock down of eEF1Bγ level in plant host decreases TBSV accumulation . eEF1Bγ binds to the viral RNA and is one of the resident host proteins in the tombusvirus replicase complex . Additional in vitro assays with whole cell extracts prepared from yeast strains lacking eEF1Bγ demonstrated its role in minus-strand synthesis by opening of the structured 3′ end of the viral RNA and reducing the possibility of re-utilization of ( + ) -strand templates for repeated ( - ) -strand synthesis within the replicase . We also show that eEF1Bγ plays a synergistic role with eukaryotic translation elongation factor 1A in tombusvirus replication , possibly via stimulation of the proper positioning of the viral RNA-dependent RNA polymerase over the promoter region in the viral RNA template . These roles for translation factors during TBSV replication are separate from their canonical roles in host and viral protein translation .
Plus-stranded ( + ) RNA viruses recruit numerous host proteins to facilitate their replication and spread [1] , [2] . Among the identified host proteins are RNA-binding proteins ( RBPs ) , such as ribosomal proteins , translation factors and RNA-modifying enzymes [1]–[5] . The subverted host proteins likely affect several steps in viral RNA replication , including the assembly of the replicase complex and initiation of RNA synthesis . However , the detailed functions of recruited host RBPs in ( + ) RNA virus replication are known only for a small number of host factors [2] , [6]–[8] . Tomato bushy stunt virus ( TBSV ) is model plant RNA virus coding for two replication proteins , p33 and p92pol , which are sufficient to support TBSV replicon ( rep ) RNA replication in a yeast ( Saccharomyces cerevisiae ) model host [9] , [10] . p33 and p92pol are components of the membrane-bound viral replicase complex , which also contains the tombusviral repRNA serving not only as a template for replication , but also as a platform for the assembly of the viral replicase complex [11]–[13] . Recent genome-wide screens and global proteomics approaches with TBSV and a yeast host revealed a large number of host factors interacting with viral components or affecting TBSV replication . The identified host proteins are involved in various cellular processes , such as translation , RNA metabolism , protein modifications and intracellular transport or membrane modifications [14]–[17] . Various proteomics analyses of the highly purified tombusvirus replicase has revealed at least five permanent resident host proteins in the complex , including the heat shock protein 70 chaperones ( Hsp70 ) [18]–[21] , glyceraldehyde-3-phosphate dehydrogenase [4] , pyruvate decarboxylase [21] , Cdc34p E2 ubiquitin conjugating enzyme [4] , [21] , [22] , eukaryotic translation elongation factor 1A ( eEF1A ) [23] , [24] and two temporary resident proteins , Pex19p shuttle protein [25] and the Vps23p adaptor ESCRT protein [24] , [26] , [27] . The functions of several of these proteins have been studied in some detail [4] , [17] , [18] , [19] , [20] . The emerging picture from systems biology approaches is that eukaroyotic translation elongation factors ( eEFs ) , such as eEF1A , play several roles during TBSV replication . Accordingly , eEF1A has been shown to facilitate the assembly of the viral replicase complex and stimulate the initiation of minus-strand synthesis by the viral RNA-dependent RNA polymerase ( RdRp ) [23] , [24] . Another translation elongation factor identified in our genome-wide screens with TBSV is eukaryotic elongation factor 1Bgamma ( eEF1Bγ ) [15] . eEF1Bγ is an abundant , but not essential cellular protein , which is part of the eukaryotic translation elongation factor 1B complex also containing the eEF1Bα subunit in yeast and the eEF1Bα and eEF1Bδ subunits in metazoans [28] . The eEF1B complex is the guanine nucleotide exchange factor for eEF1A , which binds and delivers aminoacyl-tRNA in the GTP-bound form to the elongating ribosome . Additional roles have been ascribed to eEF1Bγ in vesicle-mediated intracellular protein transport , RNA-binding , vacuolar protein degradation , oxidative stress , intermediate filament interactions and calcium-dependent membrane-binding [29] , [30] , [31] . In this paper , we characterize the function of eEF1Bγ in TBSV replication . Our approaches based on yeast and in vitro replication assays reveal that eEF1Bγ is a component of the tombusvirus replicase and binds to the 3′-end of the viral RNA . Using a cell-free replication assay , we define that eEF1Bγ plays a role by enhancing minus-strand synthesis by the viral replicase . The obtained data support the model that eEF1Bγ opens up a ‘closed’ structure at the 3′-end of the TBSV ( + ) RNA , rendering the RNA compatible for initiation of ( - ) -strand synthesis . Moreover , we find that eEF1Bγ and eEF1A play nonoverlapping functions to enhance ( - ) -strand synthesis . Altogether , the two translation factors regulate TBSV replication synergistically by interacting with different portions of the viral ( + ) RNA and the replication proteins .
eEF1Bγ is coded by TEF3 and TEF4 nonessential genes in yeast [32] , [33] . Single deletion of TEF3 ( CAM1 ) or TEF4 reduced TBSV repRNA accumulation to ∼25% ( Figure 1A , lanes 3–8 ) , while deletion of both genes resulted in even more inhibition , supporting TBSV repRNA accumulation only at 15% level ( lanes 9–11 ) . Expression of eEF1Bγ ( Tef4p ) in tef4Δ yeast increased TBSV replication to ∼80% , demonstrating that the defect in TBSV repRNA replication in tef4Δ yeast can be complemented . Altogether , these data established that eEF1Bγ plays an important stimulatory role in TBSV replication . To obtain direct evidence on the involvement of eEF1Bγ in TBSV replication , we prepared cell-free extracts ( CFE ) from a yeast strain lacking the TEF4 gene or from wt yeast . These yeast extracts contained comparable amount of total proteins ( Figure 1C , right panel ) . The CFE extracts were programmed with the TBSV ( + ) repRNA and purified recombinant p33 and p92pol obtained from E . coli . Under these conditions , the CFE supports the in vitro assembly of the viral replicase , followed by a single cycle of complete TBSV replication , resulting in both ( - ) -stranded repRNA and excess amount of ( + ) -stranded progeny [20] , [34] . Importantly in the case of a translation factor , this assay uncouples the translation of the viral proteins from viral replication , which are interdependent during ( + ) RNA virus infections . CFE obtained from tef4Δ yeast supported only 29% of TBSV repRNA replication when compared with the extract obtained from wt yeast ( Figure 1C , lane 2 versus 4 ) . These data demonstrate that Tef4p plays an important role in the activity of the viral replicase complex . To test if the decrease in TBSV repRNA replication in vitro was due to reduced ( + ) or ( - ) -strand synthesis , we measured the replication products under non-denaturing versus denaturing conditions ( Figure 1C ) . We found that the amount of dsRNA [representing the newly-synthesized 32P-labeled ( - ) RNA product hybridized with the input ( + ) RNA; lane 1 , Figure 1C , see also ref . [23]] and the newly-synthesized ( + ) RNA both decreased by ∼3-fold in CFE obtained from tef4Δ yeast in comparison with those products in the wt CFE ( lane 3 ) . Since the ratio of dsRNA and ssRNA did not change much in the CFEs ( Figure 1C ) , the obtained data are consistent with the model that Tef4p ( eEF1Bγ ) affects the level of ( - ) RNA production , which then leads to proportionately lower level of ( + ) RNA progeny . Adding purified recombinant eEF1Bγ to CFE from tef4Δ yeast supported TBSV repRNA replication to similar extent as the CFE from wt yeast ( i . e . , containing wt eEF1Bγ , Figure 1D , lanes 3–6 versus 1–2 ) , indicating that the recombinant eEF1Bγ can complement the missing Tef4p in vitro , when the same amount of p33 and p92pol was provided . Using large amount of eEF1Bγ in the CFE-based assay did not further increase TBSV repRNA replication ( Figure 1D , lanes 3–4 ) , suggesting that eEF1Bγ should be present in optimal amount during TBSV replication . To obtain additional evidence if eEF1Bγ could stimulate RNA synthesis by the viral RdRp , we used the E . coli-expressed recombinant p88Cpol RdRp protein of Turnip crinkle virus ( TCV ) . The TCV RdRp , unlike the E . coli-expressed TBSV p92pol or the closely-related Cucumber necrosis virus ( CNV ) p92pol RdRps , does not need the yeast CFE to be functional in vitro [35] , [36] . Importantly , the template specificity of the recombinant TCV RdRp with TBSV RNAs is similar to the closely-related tombusvirus replicase purified from yeast or infected plants [10] , [36] , [37] , [38] . The recombinant TCV RdRp preparation lacks co-purified eEF1Bγ ( E . coli does not have a homolog ) , unlike the yeast or plant-derived tombusvirus replicase preparations , facilitating studies on the role of eEF1Bγ on the template activity of a viral RdRp . When we added various amounts of the highly purified recombinant eEF1Bγ to the TCV RdRp assay programmed with TBSV-derived SL3-2-1 ( + ) RNA template , which is used by the TCV RdRp in vitro to produce the complementary ( - ) RNA product [37] , we observed a ∼2-to-4-fold increase in ( - ) RNA synthesis by the TCV RdRp ( Figure 2A , lanes 3–5 ) . eEF1Bγ in the absence of the TCV RdRp did not give a 32P-labeled RNA product , excluding that our eEF1Bγ preparation contained RdRp activity ( not shown ) . Altogether , our data suggest that eEF1Bγ can stimulate in vitro activity of TCV RdRp on a TBSV ( + ) RNA template , confirming a direct role for eEF1Bγ in viral ( - ) RNA synthesis by a viral RdRp . To test if the stimulating activity of eEF1Bγ on the in vitro RdRp activity was due to binding of eEF1Bγ to the ( + ) RNA template and/or to the TCV RdRp protein , we performed assays , in which the recombinant eEF1Bγ was pre-incubated with the TCV RdRp or the ( + ) RNA template prior to the RdRp assay . These experiments revealed that pre-incubation of the purified eEF1Bγ with the TBSV-derived SL3-2-1 ( + ) RNA template prior to the RdRp assay led to a ∼4 . 5-fold increase in ( - ) RNA products ( Figure 2B , lanes 1–2 ) . In contrast , pre-incubation of the TCV RdRp with the ( + ) RNA template ( Figure 2B , lanes 3–4 ) or eEF1Bγ with the TCV RdRp ( Figure 2B , lanes 7–8 ) prior to the RdRp assay did not result in increase in ( - ) RNA synthesis . Overall , data shown in Figure 2B imply that eEF1Bγ can stimulate ( - ) RNA synthesis only when eEF1Bγ binds to the ( + ) RNA template before the RdRp binding to the template . To further test the stimulatory effect of eEF1Bγ , we also tested the RdRp activity in the presence of eEF1Bγ using a mutated ( + ) RNA template . The mutation [SL3-2-1m ( + ) ] opens up the closed structure in the promoter region that leads to increased template activity [39] . The mutated template showed only ∼2-fold increased RNA products in the RdRp assay with eEF1Bγ ( Figure 2C , lanes 3–4 versus 1–2 ) . In contrast , eEF1Bγ did not stimulate RNA products when the negative-stranded RI-III ( - ) RNA was used as a template in the TCV RdRp assay ( Figure 2C , lanes 9–10 versus 7–8 ) . Thus , these data support the model that eEF1Bγ can mainly stimulate ( - ) -strand synthesis by the RdRp on the wt 3′ TBSV sequence , while it is not effective on the ( - ) RNA template . To test if eEF1Bγ directly binds to a particular region within the TBSV repRNA , we performed electrophoretic mobility shift ( EMSA ) experiments with purified eEF1Bγ and 32P-labeled regions of ( + ) repRNA that included known cis-acting elements involved in ( - ) RNA synthesis [39] , [40] , [41] . These experiments revealed that eEF1Bγ bound efficiently to the 3′-end of the TBSV ( + ) repRNA ( construct SL3-2-1 , carrying the terminal 3 stem-loop structures , Figure S1 ) . Template competition experiments confirmed that SL3-2-1 RNA bound competitively to eEF1Bγ in vitro ( Figure S1B ) . To further define what sequence within SL3-2-1 is bound by eEF1Bγ , we used complementary DNA oligos to partially convert portions of SL3-2-1 into duplexes ( RNA/DNA hybrids ) as shown in Figure 3A . EMSA assay with purified recombinant eEF1Bγ revealed that the very 3′-terminal SL1 region had to be “free” ( not part of the duplex ) for eEF1Bγ to bind efficiently to the SL3-2-1 RNA ( compare lane 1 with lane 5 in Figure 3A ) . Since eEF1Bγ is known to bind to A-rich single-stranded sequences [32] , we mutagenized the tetraloop ( GAAA ) sequence to either CUUG or GUUU tetraloop sequences ( Figure 3B ) that are expected to maintain the stability of the double-stranded stem . EMSA analysis showed that neither RNAs with the new tetraloop sequences bound efficiently to eEF1Bγ ( Figure 3B , lanes 5–7 and 11–13 ) . Based on the EMSA data , we conclude that the GAAA tetraloop region of SL1 is an efficient binding site for eEF1Bγ in vitro . However , we cannot exclude that eEF1Bγ binding may be dependent on stabilizing effects of the GNRA tetraloop on the stem structure . The loop nucleotides may or may not be involved in protein-RNA contacts . To examine if binding of eEF1Bγ to SL1 is important for stimulation of ( - ) -strand RNA synthesis by the viral RdRp , we performed an in vitro RNA synthesis assay using a mutated SL3-2-1 carrying the ‘CUUG’ tetraloop instead of the wt ‘GAAA’ tetraloop sequence ( Figure 4A ) . Unlike for the wt SL3-2-1 RNA , eEF1Bγ could not stimulate complementary RNA synthesis by the viral RdRp on the SL3-2-1cuug ( + ) template ( Figure 4A , lanes 7–10 versus 1–4 ) . These data suggest that binding of eEF1Bγ to the ‘GAAA’ tetraloop sequence of SL1 is important to stimulate ( - ) -strand synthesis by the viral RdRp in vitro . To test if eEF1Bγ is a component of the tombusvirus replicase , we purified the His6-Flag-tagged p33 ( HF-p33 ) replication protein via Flag-affinity purification from the detergent-solubilized membrane fraction of yeast [10] . We detected both p33 and eEF1Bγ in the purified preparation ( Figure 5A , lane 1 ) , suggesting that eEF1Bγ is likely part of the replicase complex [21] . Importantly , eEF1Bγ was not found in the control samples containing the His6-tagged p33 ( H-p33 ) that were also purified via the Flag-affinity procedure ( Figure 5A , lane 2 ) . Since eEF1Bγ does not seem to bind to p33 or p92 replication proteins ( data not shown ) , it is likely that eEF1Bγ was co-purified with p33 via the viral RNA template in the viral replicase complex . To demonstrate that eEF1Bγ can indeed bind to the TBSV ( + ) repRNA in cells , we Flag-affinity-purified His6-Flag-tagged eEF1Bγ from the detergent-solubilized membrane fraction and also from the soluble ( cytosolic ) fraction of yeast . Interestingly , the viral RNA was co-purified with eEF1Bγ from both fractions ( Figure 5B , lanes 3 and 7 ) . These data confirmed that eEF1Bγ binds to the viral RNA in yeast . Since eEF1Bγ was found in association with the TBSV repRNA in the cytosolic fraction of yeast , it is possible that eEF1Bγ might affect the viral RNA recruitment from the cytosol into replication that takes place on the peroxisomal or ER membrane surfaces [42] , [43] . Therefore , we tested the recruitment of the TBSV ( + ) repRNA to the membrane fraction in our CFE assay [23] . We found that eEF1Bγ did not facilitate the association of the TBSV ( + ) repRNA with the membrane when applied in the absence of p33/p92 replication proteins ( Figure S2 ) . Moreover , eEF1Bγ did not further increase the amount of TBSV ( + ) repRNA bound to the membrane in the presence of p33/p92 replication proteins , which are needed for RNA recruitment ( Figure S2 , lanes 3–4 and 8–10 ) [24] . Therefore , we conclude that eEF1Bγ is unlikely to promote the recruitment of the TBSV ( + ) repRNA to the membrane . Since both eEF1Bγ and eEF1A bind to the 3′-terminal region of the TBSV ( + ) RNA ( Figure 3 ) and ref: [23] , [24] , it is possible that they could affect each other's functions during replication . To test the mutual effect of eEF1Bγ and eEF1A on the ( - ) -strand RNA production of the viral RdRp , we performed in vitro RdRp assays with purified eEF1A and recombinant eEF1Bγ as shown in Figure 6 . Based on previous experiments , eEF1Bγ was known to stimulate ( - ) -strand synthesis the most when pre-incubated with the template ( + ) RNA ( Figure 2B ) . In contrast , pre-incubation of eEF1A with the viral RdRp was more effective than pre-incubation of eEF1A with the template RNA [23] . Therefore , we performed the pre-incubation experiments prior to the RdRp assay as shown in Figure 6 . We found the largest stimulation of ( - ) -strand synthesis by the viral RdRp in a dual pre-incubation assay , when eEF1Bγ was pre-incubated with the viral RNA template , while eEF1A was separately pre-incubated with the viral RdRp ( Figure 6 , lanes 3–4 ) . Pre-incubation of eEF1Bγ with the viral RNA template ( lanes 5–6 ) or pre-incubation of eEF1A with the viral RdRp ( lanes 7–8 ) were about half as efficient in stimulation of ( - ) -strand synthesis than the dual pre-incubation assay ( lanes 3–4 ) . Therefore , these data support the model that eEF1Bγ and eEF1A both promote ( - ) -strand synthesis and their effect is synergistic , likely involving separate mechanisms ( see Discussion ) . To obtain evidence on the importance of eEF1Bγ in TBSV replication in the natural plant hosts , we knocked down the expression of the eEF1Bγ gene in Nicotiana bethamiana leaves via VIGS ( virus-induced gene silencing ) . Efficient knocking down of eEF1Bγ mRNA level in N . benthamiana ( Figure 7B ) only resulted in slightly reduced growth of the plants without other phenotypic effects ( Figure 7A ) . The accumulation of TBSV genomic RNA , however , was dramatically reduced in both inoculated ( Figure 7B , lanes 1–5 ) and the systemically-infected young leaves ( Figure 7C , lanes 1–4 ) when compared with the control plants infected with the ‘empty’ Tobacco rattle virus ( TRV ) vector . The lethal necrotic symptoms caused by TBSV in N . benthamiana were also greatly attenuated in the eEF1Bγ knock-down plants ( Figure 7A ) . Therefore , we conclude that eEF1Bγ is essential for TBSV genomic RNA accumulation in N . bethamiana . To test if eEF1Bγ is also needed for the replication of other plant RNA viruses , we infected eEF1Bγ-silenced N . benthamiana leaves with the unrelated Tobacco mosaic virus ( TMV ) RNA ( Figure 8A ) . We found that the severe symptoms caused by TMV were greatly ameliorated in eEF1Bγ knock-down plants ( Figure 8A ) . Accumulation of TMV genomic RNA was also dramatically reduced in both inoculated ( Figure 8B ) and systemically-infected ( Figure 8C ) leaves of the eEF1Bγ knock-down plants . Based on these data , eEF1Bγ seems to be needed for TMV replication and/or spread in plants . Thus , our data have revealed new functions for eEF1Bγ in plant RNA virus replication and spread .
We confirmed a direct role for eEF1Bγ in RNA synthesis in vitro by using a cell-free extract prepared from tef4Δ yeast that supported ( - ) -strand RNA synthesis ∼3-fold less efficiently than CFE from wt yeast ( Figure 1 ) . Moreover , in vitro assays with highly purified eEF1Bγ and the recombinant TCV RdRp , which is closely homologous with the TBSV p92pol , also revealed that eEF1Bγ stimulates ( - ) -strand synthesis by binding to the viral ( + ) RNA template ( Figure 3 ) . Accordingly , pre-incubation of eEF1Bγ and the TBSV-derived template RNA prior to the RdRp assay led to the highest level of stimulation of ( - ) RNA synthesis ( Figure 2 ) . On the other hand , eEF1Bγ does not stimulate the RdRp activity directly , since pre-incubation of eEF1Bγ with the RdRp did not lead to more efficient ( - ) -strand RNA synthesis in vitro ( Figure 2 ) . We propose that eEF1Bγ modifies the structure of the ( + ) -strand template prior to initiation of ( - ) -strand synthesis that leads to more efficient RNA synthesis as described below . In vitro initiation of ( - ) -strand synthesis by the viral RdRp requires the gPR promoter consisting of a short 3′-terminal single-stranded tail and a stem-loop ( SL1 ) sequence [39] , [50] . However , the gPR region is present in a ‘closed’ structure in the TBSV ( + ) RNA due to base-pairing of a portion of the gPR with the RSE present in SL3 as shown in Figure 9 . This interaction makes the TBSV ( + ) RNA poor template in the in vitro assay due to the difficulty for the viral RdRp to recognize and/or open the ‘closed’ structure [39] . Our current work with eEF1Bγ , however , suggests that eEF1Bγ can bind to the tetraloop region of SL1 ( and to an A-rich sequence in SL2 ) that leads to melting of the base-paired structure and opening the stem of SL1 and the RSE-gPR base-pairing as shown schematically in Figure 9B . We propose that the open structure can be recognized efficiently by the viral replicase leading to efficient initiation of ( - ) -strand synthesis ( Figure 9B ) . This model is supported by several pieces of evidence presented in this paper , including ( i ) stimulation of ( - ) -strand synthesis by eEF1Bγ when the wt SL1 is present in the template; ( ii ) lack of stimulation of ( - ) -strand synthesis by eEF1Bγ when a mutated SL1 ( tetraloop mutant ) , which does not bind efficiently to eEF1Bγ , was used as a template in the in vitro assay; ( iii ) stimulation of ( - ) -strand synthesis when eEF1Bγ was pre-incubated with the ( + ) -strand template , but not when eEF1Bγ was pre-incubated with the viral RdRp ( Figure 2 ) ; and ( iv ) the lack of stimulation of ( + ) -strand synthesis on a ( - ) -strand template by eEF1Bγ ( Figure 2 ) . In addition , eEF1Bγ stimulated ( - ) -strand synthesis by the viral RdRp when a partially complementary RNA oligo was hybridized with the SL1 region ( Figure 4B ) . However , eEF1Bγ could not efficiently bind to the 3′-end of the TBSV RNA when it formed a hybrid ( duplex ) with a perfectly complementary DNA oligo ( Figure 3A ) , suggesting that eEF1Bγ can melt only the local secondary structure , but cannot unwind more extended duplex regions . An alternative possibility is that eEF1Bγ protein stabilizes the unpaired structure ( when the SL1 structure is kinetically pairing/unpairing ) , rather than implying that it actively "opens" the structure . An intriguing aspect of our model is the possible regulation of the “open” and “closed” structure of the 3′ UTR by eEF1Bγ . Displacement of eEF1Bγ bound to the 3′-end by the viral replicase during ( - ) -strand synthesis could make the 3′-terminus of the ( + ) -strand RNA fold back into a ‘closed’ structure . This could prevent efficient re-utilization of the original ( + ) -strand template during TBSV replication , and the switch to efficient ( + ) -strand synthesis on the ( - ) RNA intermediate ( Figure 9B ) . This model can also explain why the newly made ( + ) -strand RNA progeny will not enter the replication cycle in the absence of bound eEF1Bγ within the originally-formed replicase complexes as observed previously in the CFE assay [20] . We propose that the new ( + ) RNA progeny need to leave the replicase complex , then bind to eEF1Bγ in the cytosol and assemble new replicase complexes , followed by a new round of viral RNA replication . Thus , this model suggests that eEF1Bγ plays a key role in regulation of the use of ( + ) -strand RNAs in TBSV replication ( Figure 9B ) . Our finding of TBSV RNA binding by eEF1Bγ adds to the growing list of RNAs bound by eEF1Bγ . For example , the 3′ UTR of vimentin mRNA is bound by eEF1Bγ [51] , which led the authors to suggest that eEF1Bγ plays a role in vimentin mRNA subcellular localization by also binding to cytoskeleton or membranes . eEF1Bγ also binds to the tRNA-like structure at the 3′ UTR of BMV , albeit the relevance of this binding is currently unclear [51] . Also , the actual role of eEF1Bγ in the VSV replicase is currently not defined [31] . Translation elongation factors seem to be important for replication of many RNA viruses . For example , EF-Tu and EF-Ts play a role in replication of bacteriophage Qbeta [52] , [53] . The eukaryotic homolog of EF-Tu , eEF1A was found to bind to viral RNAs , such as TBSV , Turnip yellow mosaic virus ( TYMV ) [54] , West Nile virus ( WNV ) , Dengue virus , hepatitis delta virus , TMV , Brome mosaic virus , and Turnip mosaic virus [55] , [56] , [57] , [58] , [59] , [60] and to viroid RNAs [61] . Therefore , it is highly probable that many ( + ) -strand RNA viruses recruit translation elongation factors to facilitate and regulate their replication in infected cells . The emerging picture on the functions of eEF1Bγ and eEF1A is that these translation elongation factors play different , yet complementary roles in TBSV replication as suggested in Figure 9B . While eEF1Bγ binds to SL1 , eEF1A has been shown to bind to both p92pol RdRp and the SL3 region of TBSV ( + ) repRNA [23] , [24] . The binding of the RNA by eEF1Bγ promotes the opening of the closed 3′-terminal structure , whereas eEF1A facilitates the proper and efficient binding of the RdRp to the 3′ terminal RSE sequence of the viral RNA , which is required for the assembly of the viral replicase complex [11] , [39] , prior to initiation of ( - ) -strand synthesis ( Figure 9 ) [23] , [24] . The binding of eEF1A-RdRp complex to the RSE might lead to proper positioning of the RdRp over the 3′-terminal gPR promoter sequence opened up by eEF1Bγ , thus facilitating the initiation of ( - ) RNA synthesis starting from the 3′-terminal cytosine ( Figure 9B ) . Altogether , the two translation factors facilitate the efficient initiation of ( - ) -strand synthesis in addition to reducing the possibility of re-utilization of the ( + ) -strand template for additional rounds of ( - ) -strand synthesis . This regulation of RNA synthesis by the co-opted host factors shows the specialized use of host components to serve the need of viral replication . The current work also provides evidence that eEF1Bγ is a key factor in TBSV replication in yeast ( Figure 1 ) and in N . benthamiana ( Figure 7 ) . Since eEF1Bγ is a highly conserved protein in all eukaryotes [32] , it is not surprising that yeast eEF1Bγ , similar to the plant eEF1Bγ , can be co-opted for TBSV replication . Interestingly , deletion of either TEF3 or TEF4 genes reduced TBSV repRNA accumulation in yeast , suggesting that eEF1Bγ is present in limiting amount or eEF1Bγ is present in not easily accessible forms ( in protein complexes ) and/or locations in yeast cells . Silencing of eEF1Bγ in N . bethamiana showed even more inhibition of TBSV RNA accumulation than deletion of eEF1Bγ genes in yeast . This is likely due to the robust antiviral response ( i . e . , induced gene silencing ) of the plant host , which could result in degradation of the small amount of viral RNA produced by the less efficient viral RNA replication in the presence of limited eEF1Bγ in the knock-down plants . Silencing of eEF1Bγ expression in N . benthamiana also reduced the accumulation of the unrelated TMV ( Figure 8 ) , which belongs to the alphavirus-like supergroup . These data suggest that eEF1Bγ is likely involved in TMV replication , which also contains a highly structured 3′- end [54] . Therefore , it is possible that eEF1Bγ is co-opted by different plant RNA viruses , and possibly other RNA viruses as well . Overall , the current work suggests three major functions for eEF1Bγ in TBSV replication ( Figure 9 ) : ( i ) enhancement of the minus-strand synthesis by opening the ‘closed’ 3′-end of the template RNA; ( ii ) reducing the possibility of re-utilization of ( + ) -strand templates for repeated ( - ) -strand synthesis; and ( iii ) in coordination with eEF1A , stimulation of the proper positioning of the viral RdRp over the promoter region in the viral RNA template . These roles for eEF1Bγ and eEF1A are separate from their canonical roles in host and viral protein translation .
Saccharomyces cerevisiae strain BY4741 ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) and the single-gene deletion strain of the TEF4-encoded form of eEF1Bγ ( tef4Δ ) were obtained from Open Biosystems ( Huntville , AL ) . TKY680 strain in which both yeast encoded eEF1Bγ , TEF4 and TEF3 were deleted ( MATa ura3-52 leu2Δ1 his3Δ200 trp1Δ101 lys2-801 tef3::LEU2 tef4::TRP1 ) and its isogenic wild type TKY677 ( MATa ura3-52 leu2Δ1 his3Δ200 trp1Δ101 lys2-801 ) as well as the isogenic single deletion mutant strains , TKY678 ( MATa ura3-52 leu2Δ1 his3Δ200 trp1Δ101 lys2-801 tef3::LEU2 ) and TKY 679 ( MATa ura3-52 leu2Δ1 his3Δ200 trp1Δ101 lys2-801 tef4::TRP1 ) were published previously [30] . The following plasmids pESC-GAL1-Hisp33/GAL10-DI-72 , pGAD-CUP1-p92 pYES-GAL1-p92 , pCM189-TET-His92 were described earlier [21] , [22] . URA3 based pGBK-ADH- Hisp33/GAL1-DI72 , pGBK-CUP1-HisFLAGp33/GAL1-DI-72 , and pGBK-CUP1- Hisp33/GAL1-DI-72 plasmids were constructed by Daniel Barajas ( unpublished result ) . The URA3 based , low copy-number plasmid , pYC-GAL1-Tef4 expressing non-tagged full-length Tef4 protein was constructed as follows: pYC/NT-C plasmid was digested with BamHI and XhoI restriction enzymes and then PCR product of the TEF4 gene was generated with primers #2089 ( ccgcGGATCCATGTCCCAAGGTACTTTATAC ) and #2320 ( CGCCTCGAGTTATTTCAAAACCTTACCGTCAACAATTTCC ) and digested with the same restriction enzymes , followed by ligation . The plasmid pYES-NTC2-GAL1-HisTef4 expressing His6-tagged Tef4p protein was created with the same restriction enzymes using pYES-NT-C2 . HIS3-based pEsc-His/Cup-FLAG plasmid [20] was digested with BamHI and XhoI restriction enzymes and then PCR product of the TEF4 gene was generated with primers #2089 and #2320 and digested with the same restriction enzymes , followed by ligationto obtain pEsc-His/Cup-FLAG-TEF4 . HIS3 based pESC-GAL1-His33/GAL10-DI-72 and LEU2 based pGAD-CUP1-Hisp92 plasmids were transformed into tef4Δ strain . In the in vivo complementation assay , non-tagged Tef4p protein was expressed from URA3 plasmid pYC-GAL1-Tef4 and TEF4 mRNA was detected with a specific probe generated by the T7 transcription of the PCR product obtained with primers #2089 and #3788 ( TAATACGACTCACTATAGGATTATTTCAAAACCTTACCGTCAACAATTTCC ) . TKY680 ( tef3Δ/tef4Δ ) , the isogenic TKY679 ( tef4Δ ) , TKY678 ( tef3Δ ) and wild type TKY677 yeast were transformed with plasmids pESC-GAL1-His33/GAL10-DI-72 and pCM189-TET-His92 . Yeast was pre-grown at 23°C overnight in 3 ml synthetic complete dropout medium lacking the relevant amino acids containing 2% glucose and 1 mg/ml doxycyclin to suppress p92 expression by the inhibition of TET promoter and then TBSV replication was launched by replacing the media with 2% galactose without doxycycline . Cells were harvested at 48 h time point . Total RNA extraction from yeast cells and Northern blotting and Western blotting were done as previously described [15] , [24] . pEsc-His/Cup-FLAG-TEF4 plasmid was transformed into tef4Δ strain . Yeast was pre-grown overnight at 29°C in 2 ml synthetic complete dropout medium lacking histidine ( SC-H- medium ) containing 2% glucose . The volume of the media was increased up to 100 ml 16 h later and copper sulfate was added to a final concentration of 50 µM for induction of protein expression . Yeast was grown to 0 . 8 OD600 ( ∼4–6 h ) . Then , yeast cells were harvested and broken by glass beads in a FastPrep cell disruptor followed by Flag-affinity purification of FLAG-Tef4p protein [34] . The bacterial heterologous expression and purification of His6-tagged Tef3 protein from plasmid pTKB523 was performed as described in ref: [62] using only the Ni affinity column step . Yeast extract capable of supporting TBSV replication in vitro was prepared as described [20] . The newly synthesized 32P-labeled RNA products were separated by electrophoresis in a 5% polyacrylamide gel ( PAGE ) containing 0 . 5x Tris-borate-EDTA ( TBE ) buffer with 8 M urea . To detect the double-stranded RNA ( dsRNA ) in the cell-free replication assay , the 32P-labeled RNA samples were divided into two aliquotes: one half was loaded onto the gel without heat treatment in the presence of 25% formamide , while the other half was heat denatured at 85°C for 5 min in the presence of 50% formamide [20] . To test the in vitro activity of Tef4p , different concentrations ( 26 and 13 pmol ) of purified FLAG/His6-Tef4p was added to 0 . 25 µg ( 4 pmol ) DI-72 ( + ) repRNA transcript and incubated in the presence of yeast cell-free extract and reaction buffer for 10 minutes at RT followed by the addition of MBP-p33 and MBP-p92 along with the rest of the reaction components . The reaction was performed at 25°C for 3 h and analyzed as above . The TCV RdRp reactions were carried out as previously described for 2 h at 25°C [36] , except using 7 pmol template RNA and 2 pmol affinity-purified MBP-p88C . Different concentrations of eEF1Bγ ( 6xHis-affinity purified recombinant Tef3p obtained from E . coli or Flag-affinity purified HF-Tef4p obtained from yeast ) were added to the reaction at the beginning or as indicated in the text and Figure 2 . legend . The 32P-labeled RNA products were analyzed by electrophoresis in a 5% PAGE/8 M urea gel [63] . The 86-nt 3′ noncoding region of TBSV genomic RNA and its mutants were used as the template in the RdRp assay [24] , [36] . RNA templates were generated with T7 transcription using PCR products obtained with the following primers: #1662 ( TAATACGACTCACTATAGGACACGGTTGATCTCACCCTTC ) and #1190 ( GGGCTGCATTTCTGCAATG ) for SL3-2-1 ( + ) , #1662 and #4390 ( GGGCTGCACAAGTGCAATGTTCCGGTTGTCCGGT ) for SL3-2-1cuug ( + ) . SL3-2-1m ( + ) RNA was generated with T7 transcription on PCR products amplified with primers #1662 and #1190 , on a plasmid template harboring GGGCU nucleotide-deletion in SL3 region as described [39] . A duplex RNA was generated by hybridizing SL3-2-1 ( + ) and SL3-2-ds1 ( - ) made by T7 transcription of the PCR product using primers #4361 ( GTAATACGACTCACTATAGGGCTACTTCCGGTTGTCCGGTAGTGCTTCC ) and # 4362 ( CGGTTGATCTGACCCTTCGG ) . For hybridization , equal amounts of both RNAs were mixed in 1X STE buffer [0 . 1 M NaCl 10 mM Tris-HCl ( pH 8 . 0 ) 1 mM EDTA ( pH 8 . 0 ) ] followed by treatments: 94°C for 15 s , 70 cycles with gradually lowering the temperature by 1°C at each cycle for 30 s and finally 20°C for 30s . For EMSA , 6xHis-Flag tagged Tef4p was purified from a yeast tef4Δ strain with anti-FLAG M2-agarose affinity resin . Different concentrations ( 0 . 6 , 0 . 5 and 0 . 4 pmol ) of HF-Tef4p protein was used for incubation with 0 . 2 pmol of 32P-labeled SL3/2/1 ( + ) RNA or mutated RNAs at 25°C in a binding buffer [50 mM Tris-HCl ( pH 8 . 2 ) , 10 mM MgCl2 , 10 mM DTT , 10% glycerol , 2 U of RNase inhibitor ( Ambion ) ] . Samples were incubated at 25°C for 15 min , then resolved in 4% nondenaturing polyacrylamide gel [23] . Similar experiments were also performed with 6xHis-affinity purified recombinant Tef3p obtained from E . coli ( not shown ) . For the co-purification of TBSV DI-72 repRNA and eEF1Bγ protein , the yeast tef4Δ strain was co-transformed with pGBK-ADH-Hisp33/GAL1-DI72 , pGAD-CUP1-Hisp92 and pESC-CUP1-HisFLAG-Tef4 . The pESC-CUP1-FLAGHis-Tef4 plasmid was replaced with the pESC plasmid in the control experiment . Yeast was pre-grown overnight at 29°C in 2 ml SCULH- medium containing 2% glucose and 5 µM copper sulfate . The volume of the media was increased to 20 ml after 16 h for an additional 10 h ( OD600 of ∼0 . 8 ) , then the cultures were transferred to 20 ml SCULH- medium containing 2% galactose to induce TBSV DI-72 RNA transcription at 23°C . The transcription of DI-72 RNA was stopped by changing to the media containing 2% glucose after 8 h . The cultures were diluted to 200 ml and copper sulfate was added to a final concentration of 50 µM to induce the expression of Flag-tagged Tef4 protein . After incubation at 23°C for 24 h , the samples were centrifuged at 3000 rpm for 4 min . Cells ( ∼1 g ) were re-suspended in 2 ml TG Buffer ( 50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , and 10 mM KCl ) supplemented with 0 . 5 M NaCl and 1% [V/V] YPIC yeast protease inhibitor cocktail ( Sigma ) and RNase inhibitor ( Ambion ) . Yeast cells were broken by glass beads in a FastPrep cell disruptor ( MP Biomedicals ) 4 times for 20 sec each at speed 5 . 5 . Samples were removed and incubated 1 min in an ice-water bath after each treatment . The samples were centrifuged at 500 ×g for 5 min at 4°C to remove glass beads , unbroken cells and debris then supernatant was moved into fresh pre-chilled tubes . After being centrifuged again at 500 ×g for 5 min at 4°C supernatant transferred into fresh pre-chilled tubes and soluble ( SU ) and membrane ( ME ) fractions containing the viral replicase complex were separated with centrifugation at 35 , 000 ×g for 15 min at 4°C . The SU fraction was applied on 0 . 1 ml anti-FLAG M2-agarose affinity resin ( Sigma ) and Tef4 protein tagged with 6xHis- and FLAG affinity tags was purified . Before applying ME fraction on the anti-FLAG M2 resin , solubilization of the membrane-bound replicase was performed in 1 ml TG buffer with 0 . 5 M NaCl , 1% [V/V] YPIC yeast protease inhibitor cocktail ( Sigma ) , and 2% Triton X-100 via rotation for 2 hours at 4 °C . The solubilized membrane fraction was centrifuged at 35 , 000 ×g at 4°C for 15 min and the supernatant was added to the resin pre-equilibrated with TG buffer supplemented with 0 . 5 M NaCl and 0 . 5% Triton X-100 , followed by gentle rotation for 2 h at 4°C . The unbound proteins were removed by gravity flow , and the resin was washed two times with 1 ml TG buffer supplemented with 0 . 5 M NaCl , 0 . 5% Triton X-100 and once with 1 ml TG buffer , 0 . 5% Triton without NaCl . The bound proteins were eluted with 150 µl TG buffer without NaCl , 0 . 5% Triton X-100 , supplemented with 150 µg/ml flag peptide and 1% yeast protease inhibitor cocktail via gentle tapping the column occasionally for 2 h at 4°C . After centrifugation at 600 ×g 2 min at 4°C , semi-quantitative RT-PCR was performed to detect TBSV repRNA co-purified with eEF1Bγ using primers , #359 ( GTAATACGACTCACTATAGGAAATTCTCCAGGATTTC ) and #1190 , amplifying full length ( + ) repRNA . To test if eEF1Bγ is present in the viral replicase , yeast tef4Δ strain was transformed with pGBK-CUP1-HisFLAGp33/GAL1-DI-72 , pGAD-CUP1-Hisp92 and pYES-GAL1-HisTef4 . In the control experiment , 6xHisp33was expressed from pGBK-CUP1-Hisp33/GAL1-DI-72 . Yeast cultures were grown in SC-ULH- media containing 1% raffinose and 1% galactose with 5 µM copper-sulfate for 4 days with increasing the volume of the culture from 2 ml to 100 ml to a final OD600 of∼ 1 . 0 . After harvesting of cells , co-purification of 6xHis-tagged Tef4p with HF-p33 ( part of the viral replicase ) was conducted by using anti-FLAG M2-agarose affinity resin as described above ( in the section: FLAG-affinity purification of eEF1Bγ-TBSV repRNA complex ) , with the exception that only solubilized ME fraction was loaded on the column . Proteins bound to affinity resin were eluted by incubation with 150 µl buffer containing FLAG peptide and precipitated with Trichloroacetic acid ( TCA ) [64] . Samples were analyzed by SDS-PAGE and Western blotting . Virus-induced gene silencing ( VIGS ) in N . benthamiana was done as described [65] , [66] . To generate the VIGS vector ( pTRV2- eEF1BγNt ) , a 314-bp cDNA fragment of NteEF1Bγ was RT-PCR amplified from a total RNA extract of N . benthamiana using the following pair of primers: #2993 ( CGCGGATCCAAAGGTTTCTGGGACATGTATGA ) and #2994 ( CGCCTCGAGACACGCTCCTTCTGTGATTCATC ) and inserted into the corresponding ( BamHI/XhoI ) restriction sites of pTRV2 plasmid . The sequence of the N . tabacum eEF1Bγ gene ( GenBank: ACB72462 . 1 ) was derived via a BLASTP search based on the C- terminal ( translation elongation factor ) domain ( aa 252–412 ) of the Saccharomyces cerevisie Tef4 protein . The selected sequence ( TC64920 ) from the Solanaceae Genomics Resource ( www . tigr . org ) gave 98% identity with N . tabacum EF1Bγ -like gene ( GB#: EU580435 . 1 ) . To confirm the silencing of the EF1Bγ gene in N . benthamiana , we performed RT-PCR amplification with primer pairs: #2952 ( CGCGGATCCGGAAAGGTTCCTGTGCTTGA ) and #2992 ( CGCCTCGAGGTCCAGAAGTATCTCTCTACATGTGG ) on total RNA extract of pTRV2- EF1BγNt and pTRV2empty agro-infiltrated N benthamiana plants . PCR conditions were as follows: 27 cycles of 94°C 20sec , 60°C 30sec , 68°C 30 sec with HiFi Taq polymerase . Tubulin mRNA control from the same total RNA samples was detected by RT-PCR using primers #2859 ( TAATACGACTCACTATAGgaACCAAATCATTCATGTTGCTCTC ) and #2860 ( TAGTGTATGTGATATCCCACCAA ) [65] . The leaves of VIGS-treated plants were sap inoculated with TBSV , or TMV on the 9th day after silencing [65] . Total RNA was extracted 3 or 5 days post inoculation [65] . For Northern blot analysis of the viral RNA level , we prepared 32P-labeled complementary RNA probes specific for the 3′-ends of the viral genomic RNAs based on T7 transcription . To obtain the PCR templates for the probes , we used the following primers for TBSV: #1165 ( AGCGAGTAAGACAGACTCTTCA ) and #22; for TMV: #2890 ( TCTGGTTTGGTTTGGACCTC ) and #2889 ( GTAATACGACTCACTATAGGGATTCGAACCCCTCGCTTTAT ) . The TBSV viral RNA is recruited to the membrane from the soluble fraction with the help of TBSV replication proteins and host factors present in the yeast CFE . The in vitro RNA recruitment reaction was performed according to [20] , [23] , except that 32P-labeled DI-72 ( + ) repRNA were used and rCTP , rUTP , 32P-labeled UTP , and Actinomycin D were omitted from the assay . As a negative control , p33 and p92 were omitted from the reaction to detect DI-72 binding nonselectively to host proteins present in the membrane . | RNA viruses recruit numerous host proteins to facilitate their replication and spread . Among the identified host proteins are RNA-binding proteins ( RBPs ) , such as ribosomal proteins , translation factors and RNA-modifying enzymes . In this paper , the authors show that deletion of eukaryotic translation elongation factor 1Bgamma ( eEF1Bγ ) reduces Tomato bushy stunt virus ( TBSV ) replication in a yeast model host . Knock down of eEF1Bγ level in plant host also decreases TBSV accumulation . Moreover , the authors demonstrate that eEF1Bγ binds to the viral RNA and is present in the tombusvirus replicase complex . Functional studies revealed that eEF1Bγ promotes minus-strand synthesis by serving as an RNA chaperone . The authors also show that eEF1Bγ and eukaryotic translation elongation factor 1A , another host factor , function together to promote tombusvirus replication . | [
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| 2011 | Synergistic Roles of Eukaryotic Translation Elongation Factors 1Bγ and 1A in Stimulation of Tombusvirus Minus-Strand Synthesis |
Due to the stringent population bottleneck that occurs during sexual HIV-1 transmission , systemic infection is typically established by a limited number of founder viruses . Elucidation of the precise forces influencing the selection of founder viruses may reveal key vulnerabilities that could aid in the development of a vaccine or other clinical interventions . Here , we utilize deep sequencing data and apply a genetic distance-based method to investigate whether the mode of sexual transmission shapes the nascent founder viral genome . Analysis of 74 acute and early HIV-1 infected subjects revealed that 83% of men who have sex with men ( MSM ) exhibit a single founder virus , levels similar to those previously observed in heterosexual ( HSX ) transmission . In a metadata analysis of a total of 354 subjects , including HSX , MSM and injecting drug users ( IDU ) , we also observed no significant differences in the frequency of single founder virus infections between HSX and MSM transmissions . However , comparison of HIV-1 envelope sequences revealed that HSX founder viruses exhibited a greater number of codon sites under positive selection , as well as stronger transmission indices possibly reflective of higher fitness variants . Moreover , specific genetic “signatures” within MSM and HSX founder viruses were identified , with single polymorphisms within gp41 enriched among HSX viruses while more complex patterns , including clustered polymorphisms surrounding the CD4 binding site , were enriched in MSM viruses . While our findings do not support an influence of the mode of sexual transmission on the number of founder viruses , they do demonstrate that there are marked differences in the selection bottleneck that can significantly shape their genetic composition . This study illustrates the complex dynamics of the transmission bottleneck and reveals that distinct genetic bottleneck processes exist dependent upon the mode of HIV-1 transmission .
The global spread of HIV-1 has been fueled predominantly by HSX transmission , with MSM representing a second major risk group [1] . As was first established over two decades ago , HIV-1 undergoes a severe population bottleneck upon transmission with only a limited number of variants from the diverse pool of strains in the source establishing productive infection in the recipient individual [2–6] . While the biological mechanisms underlying this genetic bottleneck remain poorly understood , recent studies support a combination of host factors , including the effective physical barrier of the mucosa [7] , availability of target cells [8] and levels of immune activation and genital inflammation that may enhance HIV-1 transmission [9–11] . Recently , in a cohort of transmission pairs it was also demonstrated that factors associated with an increased risk of HSX transmission can mitigate this process and reduce the strength of selection of transmission [12] . The application of single-genome amplification and sequencing ( SGA/S ) to subjects sampled during acute and early infection has allowed for the inference of the founder virus [13 , 14] . In 80% of HSX transmissions a single founder virus is responsible for productive clinical infection [7 , 14–18] , whereas among MSM the incidence of multi-variant transmission is reported to be higher with up to 40% of infections established by multiple viral variants [14 , 19] . These data , coupled with epidemiological data illustrating differential risks of infection based on the route of exposure [20] , suggest that the mode of transmission additionally influences the selection of the viral variant ( s ) establishing systemic infection . A number of genetic , immunologic and phenotypic signatures of founder viruses , predominantly located within the envelope glycoprotein ( Env ) , have been identified that affect HIV-1 entry [16 , 21–28] . In particular HIV-1 clade C founder viruses appear to favor shorter , less glycosylated Envs [7 , 21] that are closer to ancestral sequences than their contemporary chronic Env counterparts [29 , 30] , though this does not appear to be the case in subtype B [31–33] . Korber and colleagues extended these earlier findings and identified a number of Env signature sites enriched upon subtype B transmission [34] , many of which may affect Env expression resulting in higher Env incorporation within the budding HIV-1 virion [35] . Aside from these genetic features no dominant phenotypic correlate has been associated with viral transmission other than the preference for CCR5 and CD4+ T cell tropism [4 , 14 , 25 , 36 , 37] . Recent studies have shown that transmitted viruses appear to be more resistant to inhibition by interferon-α ( IFN-α ) than viruses derived from chronic infection [27 , 38] . However , it remains unclear whether IFN-α makes a major contribution to the HIV-1 transmission bottleneck as not all studies have observed this property in founder viruses [39 , 40] . To date , attributes particular to HIV-1 founder viruses have been identified by comparing these sequences to larger datasets of contemporaneous chronic viruses [23 , 25–27] , with smaller studies examining epidemiologically linked transmission pairs [7 , 21 , 41–46] . It is unclear , however , what genetic constraints the mode of sexual transmission imposes on the nascent founder virus . Importantly no studies have directly compared HIV-1 founder viruses specific to MSM versus HSX infections . In this study we used whole-genome amplification and 454 deep sequencing to characterize HIV-1 intra-host genetic diversity in acutely infected subjects and demonstrate , using a genetic distance-based approach , that contrary to previous findings [14 , 19] , only a single founder virus is detectable in the majority of MSM infections . Moreover comparative analyses of MSM and HSX founder viruses suggests that the mode of transmission imposes differential selection pressures , with HSX viruses experiencing broader , modest selection while MSM viruses exhibit stronger selection but at fewer sites . Furthermore , we identify a number of sites within Env that are differentially enriched upon MSM and/or HSX transmission , suggesting that HIV-1 founder viruses specifically evolve or are selected to overcome the different mucosal barriers imposed by the route of transmission . This study provides important insights into how the mode of transmission shapes the HIV-1 founder virus , as well as into the differing selective pressures between MSM and HSX infection , and may facilitate the design of a more effective HIV-1 vaccine or other therapeutic and prevention strategies .
To adapt the application of more sensitive and higher-throughput next-generation deep sequencing data , where the shorter sequencing reads makes the inference of haplotype reconstruction difficult , we implemented a Hamming distance-based genetic approach , termed here the average ( over read pairs ) pairwise hamming distance ( APHD ) . This method calculates the number of mismatches between reads in a sliding window of a defined size to analyze the level of genetic diversity within the viral quasispecies . To validate this approach we applied it to previously published SGA/S-derived env founder virus sequences from 127 subjects ( Dataset 1 ) for which the multiplicity of infection had previously been determined [14 , 19] ( Fig 1A and Supplementary Materials and Methods in S1 Text ) . A classifier based on a logistic regression clearly segregated the subjects into those previously identified to have exhibited infection by a single virus ( n = 98 ) versus those infected by two or more viruses ( n = 29; Fig 1B ) , with the model correctly classifying 97% of the subjects . Cross-validated prediction errors and area under the receiver operating characteristic curve ( AUC ) were used to assess model performance on data not used to build the model . The prediction error based on 10-fold cross-validated AUC was estimated to be 3 . 95% and the 10-fold cross-validated AUC was shown to be approximately 0 . 993 with the corresponding 95% confidence intervals [0 . 981 , 1 . 00] . For each subject , ART_454 software was used to simulate in silico sequencing reads with multiple replicates generated resulting in a dataset of 7 . 9 million env reads . Calculation of the APHD score for each subject from each replicate demonstrated a lack of significant difference between the simulated data and actual data suggesting that modeling sample variation and incorporating the error profiles associated with 454 sequencing has not had an undue effect on the classification of subjects . We conclude , therefore , that the APHD approach is suitable for discriminating between homogeneous and heterogeneous infections , and represents an alternative approach to discriminate between single and multiple founder viruses . To validate application of the APHD approach for deep sequencing data we performed both SGA/S and Roche 454 pyrosequencing of env ( see Methods and S1 Text ) in 6 individuals from a range of Fiebig stages ( II/III to V ) . In subject 571373 ( Fiebig stage II/III ) application of the APHD approach to the 454 sequencing data suggested infection by a single founder virus . A codon-diversity heat map illustrates only minor variation ( <10% ) present in the viral population ( Fig 2A ) , with a low calculated mean APHD of 0 . 063 ( Fig 2B ) placing it inside the 75% percentile of APHD scores obtained for the single founder virus group as calculated above . Comparison of over a dozen SGA/S sequences supported infection by a single founder virus with each lineage containing a unique set of near identical sequences ( Fig 2C ) in agreement with a star-like phylogeny and Poisson distributed hamming distance that conform to a mathematical model of random evolution ( Fig 2D ) . In contrast , subject 654207 demonstrated a high level of diversity inconsistent with a single virus transmission ( Fig 3A ) , resulting in a mean APHD score of 0 . 752 ( Fig 3B ) . SGA/S supported infection by at least 4 founder lineages along with extensive interlineage recombination ( Fig 3C ) while a mathematical model of evolution demonstrated that the SGA/S did not conform to a Poisson distribution ( mean Hamming distance per base of 0 . 005 ) . Notably , however , splitting of variants into their respective sub-lineages did demonstrate conformity to the Poisson distribution and star-like phylogeny resulting in most common recent ancestors ( MRCAs ) in agreement with clinical estimates ( Fig 3D ) . In next three subjects 882283 ( S1 Fig ) , 702865 ( S2 Fig ) , and 574194 ( S3 Fig ) the APHD approach again suggested infection by three or more distantly related viruses with high APHD scores of 0 . 267 , 0 . 680 and 1 . 580 that was confirmed by SGA/S ( which also indicated interlineage recombination ) . Finally , in subject 1051 , deep sequencing demonstrated a high APHD score ( 0 . 490 ) reflective of infection by multiple viruses while our SGA/S data supported at least three founder viruses ( S4 Fig ) . Coincidentally , this subject was also previously examined in detail by SGA/S [14] where at least 4 founder viruses were found ( S4 Fig ) . Therefore , application of our APHD approach to 454 deep sequencing data successfully distinguished between single and multiple founder viruses as validated by parallel SGA/S . To further explore the use of deep sequencing data to examine the multiplicity of infection during the acute phase we next assembled a broader cohort of 74 subjects who had recently acquired HIV-1 either through sexual contact via MSM ( n = 64 ) or HSX ( n = 2 ) exposure including source plasma donors ( SPD ) ( n = 6 ) , or through percutaneous exposure ( n = 1 ) or injecting drug use ( IDU , n = 1 ) , ( see S1 Text and S1 Table ) . To reduce the potential of cohort-induced bias , subjects were predominantly selected from two distinct HIV-1 acute cohorts in Massachusetts and Germany . The majority of subjects ( 88% ) were captured very early after infection , with 12 subjects in Fiebig stage I , 53 in Fiebig stage II/III , 5 in Fiebig stage IV and 4 in Fiebig V . All but two subjects were infected with HIV-1 subtype B , and all subjects exhibited high viral loads typical of acute infection with a median of 923 , 000 copies/ml ( IQR: 270 , 378–4 , 100 , 000 ) and a median CD4+ T cell count of 413 cells/ml ( IQR: 310–552 ) . To provide a more comprehensive and informative genome-wide analysis of multiple founder viruses , we PCR amplified the entire protein-coding region of HIV-1 in conjunction with 454 deep sequencing . While sequence coverage varied between samples and amplicons there was no significant difference in sequence coverage between amplicons with a median sequencing depth of 501 for gag ( IQR: 285–794 ) , 402 for pol ( IQR: 203–679 ) and 600 for the 3′ genomic half ( IQR: 359–862 ) . Only 4 subjects had a 3’ genomic half sequencing depth < 200-fold with the remainder of subjects harboring sufficient sequence coverage to detect variants at 1% . Utilizing env sequences the APHD scores derived from the 74 acute HIV-1 infected subjects ranged from 0 . 0002 to 1 . 580 across different Fiebig stages ( Fig 4A ) . The logistic classifier assigned 63 of these individuals to the single founder virus class , with the remaining 11 subjects categorized as infected by multiple variants ( Fig 4B ) . Notably , 6 subjects were previously examined by Keele and colleagues [14] and our approach correctly assigned 5 of these as founded by a single genetic lineage and 1 by multiple lineages . Thus , among the 74 subjects studied the majority ( 85% ) displayed low env diversity consistent with single variant transmission . Moreover , given that our cohort was predominantly MSM , this data indicates that the majority of these MSM infections ( 83% ) were established by a single founder virus . Given that our findings were contrary to the prevailing understanding that higher risk infections such as MSM transmission frequently exhibit multiple founder viruses and that the results of such studies exploring the multiplicity of infection in HSX , MSM and IDU vary widely ( Table 1 ) [7 , 14 , 15 , 19 , 44 , 47–49] , we undertook a meta-analysis of the multiplicity of HIV-1 transmission from a number of studies . We limited our analysis to the 354 subjects ( Dataset 2 ) for whom full-length envelope sequences had been generated , encompassing MSM , HSX and IDU transmissions ( the set included the 74 subjects sequenced during this study ) [7 , 14 , 15 , 19 , 34 , 44 , 47 , 49 , 50] . Using the previously determined infection outcome ( single or multiple ) from these studies , we found the frequencies of multi-variant transmissions were comparable between HSX , MSM and IDU transmission ( P = 0 . 167 , Chi-Square test; Table 1 ) . Recognizing that 6 of these subjects were counted twice between Keele and our study a re-analysis counting these subjects once still demonstrated no significant difference ( P = 0 . 177 , Chi-Square test ) . Taken together , these findings compiled from a number of different studies indicate that at the time of sampling there are no detectable differences in terms of multiplicity of infection between different modes of transmission . Given that we observed no differences in the number of founder viruses between MSM and HSX infection we sought to investigate whether other differences in the founder viruses may exist between the two modes of transmission . To do so we assembled a collection of 131 founder viruses ( Dataset 3 ) derived from acute clade B HIV-1 infected subjects with a defined HSX ( 55 subjects ) or MSM ( 76 subjects ) exposure [14 , 16 , 19 , 34 , 44] , of which 52 were newly generated by this study ( S2 Table ) . We restricted our analysis to subjects sampled early ( Fiebig stages I-III ) and those classified as having a single variant infection . To distinguish the pattern of selection between MSM and HSX founder viruses we used the RELAX test [51] on a multiple sequence alignment of inferred founder strains , without variable loops ( which are difficult to align , and could introduce false signal for selection ) . RELAX is a comparative codon-based phylogenetic framework implemented in HyPhy that formally tests whether selective pressures are intensified or relaxed in one subset of branches ( “test” ) relative to a “reference” subset of branches , while allowing the strength of selection to vary from site to site in the alignment [51] . Traditionally , the intensity of selection is measured by estimating the ratio ( ω ) of rates at which nonsynonymous ( dN ) and synonymous substitutions ( dS ) are fixed [52] , with an excess of dN ( ω > 1 ) an indicator of diversifying positive selection [53] . Here , the point estimates of ω distributions for MSM and HSX branch sets under the Partitioned Exploratory model assigned more codon sites in the HSX lineages to the positively selected category ( 5 . 4% [5 . 0–6 . 4%] in HSX vs 2 . 6% [2 . 3–2 . 9%] in MSM ) , although inferred that selection on these sites was stronger in MSM ( ω = 15 . 8 [14 . 4–17 . 5] in MSM vs ω = 9 . 2 [8 . 2–9 . 6] in HSX . Therefore HSX founder sequences are subject to broader , albeit weaker , diversifying selective pressure than their MSM counterparts . To determine whether other differences exist between HSX and MSM founder viruses we next compared their ‘transmission index’ , which was shown to be predictive of which sequence in the donor will establish infection in the recipient [12] . In this recent study examining 137 heterosexual transmission pairs Carlson et al . revealed the preferential selection of viruses exhibiting a more wild-type or consensus-like sequence , perhaps reflective of an optimal HIV-1 genome or one exhibiting higher replicative fitness [12] . We hypothesized that the elevated risk of infection among MSM compared to HSX would result in reduced transmission selection bias , or as such a lower transmission index , upon MSM transmission . Using model weights taken from Carlson et al . [12] , we indeed observed significantly lower transmission indices among MSM founder viruses compared to HSX founder viruses ( P = 3 x 10−5 , Fig 5 ) . These data indicate that founder viruses from HSX are more closely related to a clade B consensus sequence , are likely to exhibit higher transmission fitness , and are more likely to have undergone proteome-wide selection at the transmission bottleneck as compared to their MSM counterparts , results consistent with the RELAX analysis . Taken together , these data suggest that viral populations from HSX and MSM infections are exposed to distinct selective pressures upon transmission . Recently , Gnanakaran and colleagues reported on a number of sequence motifs in HIV-1 Env associated with founder viruses [34] . In particular , the presence of a histidine at position H12 and the absence of a potential N-linked glycosylation site ( PNGS ) at position N415 were found to be selected in acute versus chronic viruses . Throughout this study we used the convention of “ ! ” to express the loss of an amino acid at that position . For instance , mutating away from Asn at position 415 would be expressed as ! N415 . We investigated whether any of these 30 previously identified signature sites were enriched in HSX or MSM founder viruses in our dataset of 131 subtype B sequences . We applied a phylogenetically corrected logistical-regression model [54 , 55] , and employed a false-discovery rate approach ( FDR ) to account for multiple comparisons [56] . Adopting a q-value cutoff is critical in this study as thousands of tests were conducted . We generally chose a relatively high q-value cut off in our initial analysis; thus we expect approximately 20% of our sites from our first round of analysis to be by chance . From this analysis , 3 of the 30 previously identified sites in Env were found significantly enriched in HSX founder viruses: R192 and N362 with a q value <0 . 2 and R633 with a q value <0 . 3 ( Table 2; nomenclature denotes cohort consensus residue and HXB2 numbering ) . More specifically , for residues R192 and N362 we observed selection for maintenance of a consensus residue in HSX founder viruses and at residues N362 and R633 we observed selection away from the non-consensus residue lysine ( K ) in HSX founder viruses . R192 is located at the base of the V2 loop and Gnanakaran et al . previously reported a loss of arginine ( R→ ! R ) in chronic viruses [34] . Similarly , they observed N362 mutating away from an asparagine ( N→ ! N ) while at residue R633 the pattern of R→ ! R was found enriched during chronic infection [34] . This analysis demonstrates that some of the previously found signature patterns of founder viruses may be primarily driven by HSX transmission events . Given the RELAX results , which identified a large number of sites under weak positive selection in the HSX group but a small number of sites under strong positive selection in the MSM group , we next conducted an unbiased search under a phylogenetic corrected framework to identify additional signature sites positively or negatively associated with MSM or HSX transmission . Although all associations identified below using one variable ( i . e . HSX ) were significant at P<0 . 05 when using the opposite variable ( i . e . MSM ) , the models are nonetheless distinct and did identify different associations at the q-value cutoffs . From this analysis 7 sites ( 8 residues ) were statistically associated with HSX founder viruses at a q of <0 . 2 ( Q389 , P724 , A823 , V832 , R845 and A854 ) or <0 . 4 ( K617 , Table 3 ) . All of these sites were found within the gp41 domain with the exception of Q389 . Using MSM as the predictor variable revealed 16 sites ( 17 residues ) that were statistically associated with MSM founder viruses at a q of <0 . 2 ( T283 , K343 , Q389 , E429 , P724 , E735 , F752 , R770 , A823 , H842 , R845 ) or <0 . 4 ( I165 , N362 , T465 , G471 , M518; Table 3 ) . From these MSM-associated sites , 8 are within gp120 while the remaining 8 are within the gp41 domain . Four of these sites ( Q389 , P724 , A823 and R845 ) were found to overlap in both analyses and thus were found to be under significant , but opposing , selection in both HSX and MSM founder viruses . For example , at position Q389 , the presence of proline is positively associated with HSX founder viruses while conversely the absence of proline is associated with MSM founder viruses . Specifically , only 1% ( 1 subject ) of MSM sequences contain a proline at position Q389 while 20% ( 11 subjects ) of HSX sequences contain this proline substitution . This Q389 signature site was only seen as significant when taken into account its association with a strongly covarying and highly conserved proline at residue P417 ( q = 0 . 086; Table 3 ) . Residue Q389 is located in the α4 helix of the V4 loop in relatively close proximity to the CD4-binding loop [57] , and also neighbors the lectin DC-SIGN binding site ( N386-T388 ) . Selection here for the bulky hydrophobic residue proline might result in conformational changes in the pocket of the V4 loop and affect virus entry . Notably , this site has previously been found to be under positive selection , with Q389P variants specifically found to reduce HIV-1 replication capacity as well as increase neutralization resistance 15-fold to both b12 and sCD4 [58] . Within the sites identified with MSM as the predictor variable we observed a cluster of sites spatially concentrated ( <15 Å ) around the CD4 binding loop with at least 6 of the 8 MSM signature sites identified in gp120 . Namely I165 , T283 , N362 , Q389 , E429 , and G471 , have previously been shown to alter HIV-1 replication , CD4 dependence and/or lie proximal to the CD4 biding site [57–69] . The identification of signature sites specific to either mode of sexual transmission , further supports that HSX and MSM transmissions are undergoing distinct selection pressures . Finally , we sought to determine whether the aforementioned HSX and MSM signature sites described in Table 3 might simply reflect residue frequency differences in the respective HSX and MSM chronic ‘donor’ populations . To assess this we compared our acute sequence data to a panel of chronic viruses comprising over 1300 SGA/S sequences derived from 59 subjects with known HSX or MSM modes of transmission [34] ( described as Dataset 4 in Methods ) . From the newly described signatures we found 12 sites ( 16 residues ) ( K343 , N362 , Q389 , E429 , T465 , K617 , E735 , A823 , V832 , H842 , R845 , A854 ) that could be influenced by similar residue frequency differences between HSX and MSM chronically infected individuals ( S5A Fig ) . In each of these cases the observed trend in founder viruses is mirrored at chronic infection . For instance , K343E we previously found to be associated with MSM where the presence of glutamic acid ( E ) is significantly higher in MSM founder viruses compared to HSX founder viruses . However , examination of chronic viruses also revealed the same significant trend where K343E is higher in MSM viruses compared to HSX viruses ( P = 0 . 026 , Chi-Square test ) . Thus , the higher frequency of K343E could be driven in part by its higher frequency in chronic circulating strains . Conversely , for 8 of the signature sites ( 9 residues ) ( I165 , T283 , G471 , M518 , P724 , F752 , R770 and R845 ) there was no evidence supporting an influence of differences in chronic residue frequencies impacting the transmission of these variants ( S5B Fig ) . For example , at position P724 in gp41 , there is clear selection for a proline ( P ) in HSX founder viruses while in circulating chronic HSX viruses the frequency of proline was significantly lower than in MSM viruses ( P < . 0001 , Chi-Square test ) . Therefore , the presence of proline appears to be strongly selected for at transmission during HSX infection . Although it is difficult to decipher the origin of these selection events , since higher chronic frequencies could be similarly driven by differences selected at the time of transmission , at least 9 residues are strongly supportive of selection occurring upon transmission followed by regression during chronic infection . Thus , taken together we hypothesize that some of these variants may modestly improve overall fitness and hence be selected for during transmission and conversely selected against ( or are neutral ) over the course of ensuing chronic infection .
In the present study we build upon a large body of work characterizing acute HIV-1 infection by shedding new light on the genetic properties of founder viruses distinguished by the two primary risk groups responsible for driving the global HIV-1 pandemic . Development of an average pairwise Hamming distance ( APHD ) approach , benchmarked to SGA/S data as the gold standard method , enabled us to distinguish between single and multiple founder virus infections using deep sequencing data . Application of this approach can also be extended to other genomic regions other than env ( such as gag and pol ) to assess the complexity of founder virus populations with the caveat that the sensitivity may vary . More importantly , these data demonstrated that the majority ( 83% ) of MSM infections in our cohort exhibited a single founder virus—levels similar to those typically characteristic of HSX infections . Further characterization of these data demonstrated that HSX founder viruses do , however , appear to be under different selective pressures than MSM founder viruses , with HSX founder viruses subject to broader , albeit weaker , diversifying selective pressure than their MSM counterparts . Distinct genetic footprints were also found to be specific to HSX and MSM founder virus populations , supporting discrete selection pressures exhibited by each mode of sexual transmission . The increasing use of next-generation sequencing has led to the development of specialized computational tools to reconstruct the viral haplotypes that constitute the quasispecies within a single host [70–74] , and other methodological approaches have been used to screen for dual infection from deep sequencing data [75] . Although such tools improve our ability to probe the viral diversity they still suffer from a high rate of false positives [76] . As such , our method while based on a sliding window approach exhibits a low-error rate and is easily integrated into our existing data analysis pipeline [77] . While our estimate of the rate of multiple-variant infections in MSM ( 17% ) is lower than the upper range of literature estimates ( 36–41% ) [14 , 19] it is not dissimilar from other published reports . Comparative full-length Env analyses have reported frequencies of only 11–14% of multiple founder viruses in MSM [44 , 48] , with 25% of MSMs in the STEP trial exhibiting multiple founder virus infections [78] . Additional reports using partial regions of Env demonstrated rates of only between 7–9% [79 , 80] . One possible source of rate estimate discrepancy could be the inclusion of subjects sampled following peak viremia since immune-induced adaptations , including CD8+ T cell viral escape mutations , APOBEC-induced mutations or recombinants , selected during this window may distort the Poisson distribution model [81] . In our study of 74 subjects we were able to limit the majority of subjects ( 88% ) to the earliest stages of HIV-1 infection ( Fiebig I-III ) as compared to rates as low as 60% of subjects in previous studies [14 , 19] . Consistent with this hypothesis we found the odds of observing a multivariant infection in MSM was almost four times higher at later Fiebig stages IV-VI than in subjects sampled during earlier Fiebig I-III stages of infection ( odds ratio , 3 . 86; 95% CI , 1 . 64 to 9 . 08; Fisher’s exact test , P = 0 . 003 ) . Importantly , although differences in the number of founder viruses could be attributable to compartmentalization in the source , examination of viruses in a cohort of Zambian transmission pairs found no evidence for preferential selection in the donor genital tract [41] . Thus , our findings support that although the risk of HIV-1 acquisition is significantly greater in MSM , this increased risk is not reflected by the transmission of an increased number of founder viruses . While it is clear that various bottlenecks can limit the number of HIV-1 founder viruses successfully transmitted from a diverse , chronically infected donor ( as recently reviewed in [82] ) , factors determining what viruses survive the bottleneck are not well understood . A recent study by Carlson et al . examining 137 heterosexual transmission pairs revealed the preferential selection of viruses exhibiting a more wild-type or consensus-like sequence , perhaps reflective of an optimal HIV-1 genome or one exhibiting higher replicative fitness [12] . Termed here a ‘transmission index’ , this effect was more pronounced in female-to-male transmission compared to male-to-female transmission , and the effect could be attenuated by donor viral load and presentation of genital ulcers or inflammation ( GUI ) . In our current study we also observed that HSX founder viruses exhibit significantly higher transmission indices than MSM founder viruses . In the absence of any donor sequence information , these data support a model in which there exists stronger selection forces , or increased opportunity for selection , upon the incoming viral quasispecies during HSX versus MSM transmission to optimize for wild-type or high-fitness variants for successful dissemination . Thus , this data fits with the prediction of modeling transmission as a binomial mixture process in which infection risk is inversely correlated with the strength of selection [12] . Given the elevated risk of infection in MSM compared to HSX we expected that the selection bias experienced by MSM founder viruses to be less stringent than that observed for HSX transmission . Such an overall reduced selective bias may make infection more conducive to even subtly weaker viruses . However , these viruses may need to optimize for enhanced CD4 binding in order to gain an advantage and successfully disseminate . Hence , the MSM-selected cluster of sites around the CD4 binding site may be evidence for such a scenario . Moreover , the differences in the selection bias at the transmission bottleneck may transcend to differences in clinical outcomes with reduced bias resulting in increased virulence with faster rates of reversion leading to higher fitness viruses emerging . On the other hand increasing the transmission selection bias may incite founder viruses that are optimized for increased fitness resulting in higher viral loads and poorer clinical outcomes in the newly infected individual . Given that the majority of our HSX subjects were men ( 79% of our 131 founder virus dataset ) , and that within MSM the risk from unprotected receptive anal intercourse is >10 fold higher than for insertive anal intercourse [20] , our study is effectively comparing penile ( HSX ) versus rectal ( MSM ) receptive routes of HIV-1 transmission . As such , our data would suggest that the rectal route may exert less selection pressure upon the incoming viral quasispecies than transmission through penile exposure , consistent with model predictions [12] . In the rhesus macaque model , even after penile exposures to a high dose SIV inoculum only a single variant founder population establishes infection [83 , 84] , while high dose intra rectal exposures are associated with greater numbers of founder viruses [85] . The rectal compartment is highly vulnerable to HIV-1 transmission with a single more fragile layer of columnar epithelium separating the lumen from the lamina propria as compared to the stratified squamous epithelium found in the ectocervix and vagina or the inner foreskin and the glans epithelia of the penis in uncircumcised men [86] . This , coupled with the density of HIV-1 target cells populating the rectum such as activated CD4+ T cells , macrophages and dendritic cells , may contribute to the greater risk of HIV-1 transmission associated with men who have receptive anal sex with men compared with HSX transmission in men or women [87 , 88] , but may also result in relaxation of the selective pressures upon the incoming quasispecies . Indeed , a briefer and narrower eclipse phase has been observed for HIV-1 infections acquired rectally compared to those acquired through the vaginal or penile tissues [83 , 89 , 90] where local viral expansion is necessary before the dissemination of infection to the bloodstream . Thus , MSM viruses may not need to undergo the same level of selection that HSX viruses must endure for successful replication and systemic dissemination . Given previous studies have espoused differences in variable loop length and potential N-linked glycosylation site count in the transmitted virus for HIV-1 subtypes A and C [21 , 24 , 32] , although less clear in subtype B infections [31–33] , we searched for any association between variable loop diversity and mode of transmission . Detailed analyses of the variable loops revealed only one such putative association with MSM founder viruses encoding a more compact V2 loop compared to HSX founder viruses ( mean of 40 . 9 residues for MSM and 42 . 5 for HSX ) although such an effect did not reach statistical significance after correcting for multiple comparisons . While one study has identified through the comparison of acute versus chronic HIV-1 sequences signature mutations associated with founder viruses [34] , our study extends these findings by identifying an array of genetic signatures that may be distinct between MSM and HSX risk groups . Interestingly , the majority of residues found to be associated with HSX risk were located within the gp41 domain , and in particular within the cytoplasmic tail . At residue K617 we observed maintenance of a consensus lysine residue where mutations at this position of the gp41 fusion domain have been shown to significantly reduce viral entry [91] . This unusually long and highly conserved domain of approximately 150 amino acids modulates a diverse array of functions , including viral replication , Env incorporation into virions , and intracellular trafficking and endocytosis to regulate levels of Env surface expression ( reviewed in [92] ) . The C-terminal half of the cytoplasmic domain is characterized by the presence of three structurally conserved α-helices designated lentivirus lytic peptide 1 ( LLP-1 ) , LLP-2 , and LLP-3 [93–95] . Notably , three of the HSX signature residues ( V832 , R845 and A854 ) , where we saw selection for the consensus residue in HSX , are located within the LLP-1 region , which is associated with Env incorporation into virions [96 , 97] . Thus , these specific signature sites within gp41 may influence Env virion incorporation levels and viral entry , thus increasing transmissibility . It is also conceivable that these specific mutations may alter the conformation of the envelope trimer in such a manner that is favored for initial infection . Notably , many of the strongest transmission signature sites observed by Gnanakaran et al were also in the cytoplasmic domain in addition to enrichment for histidine at residue H12 in the signal peptide [34] , the later of which has been demonstrated to increase Env incorporation and infectious titers [35] . Regardless of the precise mechanism , these data support a role for selection upon the cytoplasmic domain of HIV-1 gp41 during transmission . In contrast , nearly half of the residues associated with MSM risk were located in gp120 with six residues ( T283 , N362 , Q389 , E429 , T465 , G471 ) clustered around the CD4-binding pocket with the potential to influence CD4 binding ( Fig 6 ) . In addition to residue Q389 described earlier , which is located in close proximity to the CD4-binding loop [57] , position T283 has been shown to affect CD4 binding site exposure and CD4 binding of gp120s derived from brain and other tissues [60] . Similarly , presence of the N362 PNLG site in the C3 region has been shown to enhance CD4 binding to gp120 as well as cell-cell fusion [68 , 98] , potentially reducing CD4 dependence by stabilizing the CD4-bound confirmation of gp120 [68] . Meanwhile , at residue E429 located in the C4 domain of gp120 we observed selection for glutamine ( E429Q ) where prior work has identified this residue as being critically important for the binding of CD4-blocking MAbs [65] and implicated in altering resistance to the entry inhibitors BMS-806 and #155 [63] , as well as enhancing HIV-1 replication in vitro [59] . Within the V5 loop , residue T465 has also been associated with a neutralization-resistant phenotype [99] , while finally at residue G471 where we observed selection for an alanine ( G471A ) the variants G471R/E have been shown to impart resistance towards CD4 mimetic compounds [61] . Thus , many of the signature sites identified in MSM in gp120 may influence gp120-CD4 interactions for enhanced interactions with CD4 . A limitation of this study is the inclusion of subjects designated as source plasma donors ( SPD ) . These subjects had limited behavioral information available but as part of routine blood-banking practice underwent extensive questioning for HIV-1 risk behaviors and denied having sex for money , homosexual activity or i . v . drug use . Nevertheless , self-reporting of risk behaviors among paid plasma donors is imperfect and it is plausible that some subjects whom were designated as belonging to the HSX risk group as previously categorized [19 , 34] may have additional risk behaviors . However , the exclusion of all SPD subjects from our comparative analysis indicates that the frequency of multi-variant transmission between HSX and MSM transmission remained the same ( 22% vs . 25% , odds ratio , 1 . 16; 95% CI , 0 . 66 to 2 . 06; Fisher’s exact test; P = 0 . 66 ) . The reported higher transmission indices for HSX founder viruses also continued to be significant when compared with MSM founder viruses ( P = 0 . 0007 , Fisher’s exact test ) . Thus , the overall study findings are unlikely to have been influenced by the inclusion of this subject group alone . While HIV-1 acquisition in MSM may be immunologically and virologically distinct from that of heterosexual exposure , we observe no differences in the number of HIV-1 founder viruses . Although the severe HIV-1 transmission bottleneck has a stochastic component in which any reasonably fit CCR5-tropic virus may be capable of establishing productive infection , our data do argue that any selection bias may be comparatively relaxed with ano-rectal MSM transmission , potentially due to the greater frequency of target cells at the site of transmission and the distinct kinetics of virus dissemination [87–90] . Conversely , upon heterosexual exposure we observe an increased bias for consensus-like viruses with a potentially higher replicative fitness that must undergo local viral replication prior to systemic dissemination . In the era of new therapeutic approaches such as AAV delivered HIV-1 inhibitors [100] and effective pre-exposure prophylaxis [101] , it remains to be seen whether breakthrough infections will lead to higher fitness viruses being selected for resulting in more severe clinical outcomes , akin to what was observed during the CAPRISA 004 vaginal microbicide gel trial [102] . More accurate estimation of the frequency of multi-variant infection will also aid in evaluating the clinical impact that infection with multiple variants has on disease progression as a number of studies have reported associations with increased viral load [103–105] and faster CD4+ T-cell decline [106 , 107] resulting in a shorter time to AIDS . Finally , given the recent application of discerning the number of founder viruses as a measurement of relative protection from infection [108 , 109] , more critical delineation of the genetic make-up and complexity of the founder virus population may be important towards the development of an effective HIV-1 vaccine .
Plasma samples were obtained from subjects with acute or early HIV-1 infection enrolled in HIV-1 cohorts in Berlin , Germany , and Massachusetts , California , North Carolina , and South Carolina , USA . The clinical and sociodemographic characteristics of the study participants can be found in S1 Table and Fiebig staging criteria are described in Supplementary Materials and Methods in S1 Text . All study subjects gave written informed consent and plasma collections were performed with local institutional review board and other regulatory approvals . This study was approved by the Institutional Review Board of Massachusetts General Hospital . HIV-1 was PCR amplified and 454 sequenced using a nested RT-PCR with 3 amplicons overlapping the genome . Briefly , viral RNA was isolated from 1ml of plasma using the QiAmp Viral RNA Mini Kit ( Qiagen , Valencia , CA ) and RT-PCR of near full-length HIV-1 genomes performed using nested-PCR primers specific for gag , pol and 3′ half of the viral genome ( see S1 Text and S3 Table ) . Pooled PCR products were prepared for sequencing on the 454 Genome Sequencer Junior ( Roche ) using the Nextera DNA Sample Prep Kit and data processed performed using our previously published sequence analysis pipeline [77 , 110] ( see S1 Text ) . Following alignment of cleaned reads to the consensus assembly sequence a sliding window approach was used to collect all reads that covered a window of 120bp with a step size of 21bp . We then calculated the pairwise Hamming distance ( HD ) ( defined as the number of base positions at which the genomes differ , excluding gaps ) for all reads and averaged this value over all windows to obtain the average pairwise Hamming distance ( APHD ) . To formalize the criteria and evaluate the APHD approach for its discriminative ability we obtained SGA/S sequence data from studies where each sample had been previously designated as infection by a single virus or infection by multiple viruses [14 , 19] . To introduce sequencing errors , a synthetic read dataset was simulated from the SGA/S data using the 454 sequencing error profile from ART a next-generation read simulator [111] . Reads were simulated with varying degrees of coverage in order to achieve the same representative coverage obtained from our deep sequencing data with multiple replicates performed to rigorously assess the uncertainty due to 454-like sampling . To differentiate between single and multiple founder viruses using 454 sequencing reads intra-patient HIV-1 genetic diversity was first characterized as previously described , with nucleotide-phasing information used to distinguish true variants from sequencing errors ( see S1 Text and S6 Fig ) . The mean APHD is calculated using the sliding window approach as previously discussed . Reads were then carefully inspected to rule out factors that might compromise the amount of diversity such as the emergence of CTL escape mutations or early reversions whereby diversity would be restricted to narrow windows specific to known CD8+ T cell epitopes specific to the subject’s HLA and would appear as distinct peaks on the APHD landscape . More specifically , for each individual the optimal “A-list” of CTL epitopes restricted by a subject’s HLA alleles was generated and local haplotype windows were reconstructed across each of the epitopes to assess for any evidence of putative CTL escape mutations . Any windows in which CTL escape mutations were found were further examined and the contribution of this window to the overall APHD score was evaluated to unsure that it did not unduly influence any subject designation as infected by multiple viruses . cDNA was serially diluted and amplified as previously described [14 , 17 , 112] , and the amplified products sequenced using a 454 GS Junior ( see S1 Text ) . A number of previous published datasets were used throughout this study and are numbered accordingly . Briefly , Dataset 1 comprising the SGA/S data collected from 127 acute individuals sampled at varying times post-infection and clinically staged as described by Fiebig et al [113] were used [14 , 19] to test the performance of the APHD approach . To explore the relationship between multiplicity of infection and mode of transmission , Dataset 2 encompassing 354 subjects , for whom full-length SGA/S envelope sequences had been generated , encompassing MSM , HSX and IDU transmissions ( including the 74 subjects newly deep sequenced during this study ) [7 , 14 , 15 , 19 , 34 , 44 , 47 , 49 , 50] were obtained . From the total of 354 subjects a subset of 131 HIV-1 founder viruses from subjects reporting a sexual exposure were selected [14 , 16 , 19 , 34 , 44] ( Dataset 3 ) . The criteria for subject selection was restricted to clade B infections sampled early ( Fiebig stages I-III ) and included only subjects previously classified as being infected with a single virus ( subjects listed in S2 Table ) . Dataset 4 included chronic samples obtained from Gnanakaran et al [34] . This dataset contained over 1300 SGA/S sequences derived from 59 subjects with known exposure status defined as HSX or MSM . These sequences were from individuals who were not on anti-retroviral therapy , and infected for a minimum of two years and represented clade B infections predominantly collected in the United States . PoissonFitter was used to test the hypothesis that a single virus establishes infection [81] . PoissonFitter performs two tests: one test is based on the fit of the Poisson model to the frequency distribution of the Hamming distance observed in each sample; the other is a topological test to verify that observed frequencies are distributed according to a star-like phylogeny ( for this test , no formal statistic is available and consequently no p-value is obtained ) . In this model the main assumption is that a single founder virus evolves under neutral evolution , generating a star-like phylogeny , with a distribution of mutations conforming to a Poisson distribution [13 , 14] . We used the phylogenetically corrected logistic regression to identify sites positively or negatively associated with MSM or HSX state [54 , 55] . Briefly , this approach uses standard logistic regression , with the modification that information from the phylogeny is used to inform the bias parameters . Rather than assuming the sequences are independent and from the same distribution , the phylogenetically corrected logistic regression model assumes the sequences are drawn from a known phylogenetic structure . Using this structure separate phylogenetically corrected logistic regression models were learned from each amino acid at each site . Phyml v3 . 0 [114] was used to infer the phylogenetic structure , using all sequences available in this study . Using this structure , separate phylogenetically corrected logistic regression models were learned for each amino acid at each site . For “indirect” models , only a single feature representing MSM or HSX status was used . For “direct” models , forward selection was used to learn a model that possibly included covariation from other sites in addition to MSM or HSX status . Note that although MSM = 1-HSX , the models are distinct and may identify different associations . In our case , while the association’s differed when using q-value cutoff , all associations identified using one variable were significant at P<0 . 05 when using the opposite variable . The q-value is the minimal false discovery rate that adjusts for multiple tests [56] . The appropriate choice of q-value threshold is context specific and depends on how the results will be interpreted . In the present study , we typically report all tests where q is <0 . 2 ( implying that we expect 20% of reported tests to be false positives ) but sometimes report higher q values to include sites in a hypothesis-raising framework . The associations identified in this study are referred to as adapted and nonadapted forms . Adapted forms ( commonly referred in many studies as escape variants ) are amino acids significantly enriched for in the presence of the risk behavior in question . Nonadapted forms ( also commonly called wild-type or susceptible forms ) are amino acids significantly depleted in the presence of the risk behavior . To distinguish the pattern of selection between MSM and HSX founder viruses we used a comparative codon-based phylogenetic framework test implemented in HyPhy that formally tests whether selective pressures are intensified or relaxed relative to a subset of branches [51] . In this case we assessed whether selective strength on the test subset of branches is compressed toward or repelled away from neutrality , relative to the reference subset of branches . Under this analysis we labeled HSX viruses as reference branches while MSM viruses were labeled as test branches . Internal branches were labeled as MSM or HSX if all of their descendants were also labeled MSM or HSX , respectively . The Null model , which forces HSX and MSM to share the same selective regime can be rejected in favor of the Partitioned Exploratory model ( p = 0 . 009 , Likelihood Ratio Test ) . The Partitioned Exploratory model is merely the model where the “test” and “reference” branches in the tree are endowed with completely independent discrete distributions of omega parameters . The Alternative model , which is a restriction of the Partitioned Exploratory model forces the proportions of sites under different types of selection to be the same can be similarly rejected ( p = 0 . 008 ) . All confidence intervals listed are 95% profile likelihood approximations . In the context of heterosexual linked transmission pairs , we previously trained a model that estimates the probability that any particular amino acid will be transmitted from a donor to a recipient [12] . Although this model was trained grouping together all residues at all sites using a generalized linear mixed model , we showed that a simple extension to full sequences , which we called the “Transmission Index” was predictive of which sequence would establish infection . Here we computed the transmission index of each founder virus in MSM vs HSX founder viruses using logistic regression , with model weights taken from Table 2 of [12] . Amino acid conservation and covariation was taken from the clade B envelope sequence data of [115] . All statistical analysis was performed using JMP Pro , version 12 ( SAS Institute ) . Descriptive measures were used to summarize the data . Continuous variables were summarized using median and inter quartile range ( IQR ) ; categorical variables were summarized using frequency and percent ( % ) . Chi-square and Mann-Whitney tests were used to compare categorical and continuous variables between the study groups , respectively . | While the global spread of HIV-1 has been fueled by sexual transmission the genetic determinants underlying the transmission bottleneck remains poorly understood . Here we characterized founder virus population diversity from next generation sequencing data in a cohort of 74 acute and early HIV-1 infected individuals . We observe that the risk of multi-variant infection in men-who-have-sex-with-men ( MSM ) is not greater than that observed for heterosexuals ( HSX ) , contrary to reports of higher rates of multiple founder virus infections in higher-risk MSM transmissions . These findings were further supported through a metadata analysis of 354 acute and early HIV-1 subjects . We did , however , observe differences between HSM and MSM founder viruses , including a higher selection barrier in HSX transmission with founder viruses being more cohort consensus-like that may be reflective of increased replicative fitness . We also identified a number of residues within Envelope that behave in a risk-dependent manner and could be key for HIV-1 transmission . These novel insights improve our understanding of the HIV-1 transmission bottleneck and underscore the differential selective pressures that founder viruses within the two major transmission risk groups are subjected to . | [
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| 2016 | Differences in the Selection Bottleneck between Modes of Sexual Transmission Influence the Genetic Composition of the HIV-1 Founder Virus |
CLEC5A/MDL-1 , a member of the myeloid C-type lectin family expressed on macrophages and neutrophils , is critical for dengue virus ( DV ) -induced hemorrhagic fever and shock syndrome in Stat1−/− mice and ConA-treated wild type mice . However , whether CLEC5A is involved in the pathogenesis of viral encephalitis has not yet been investigated . To investigate the role of CLEC5A to regulate JEV-induced neuroinflammation , antagonistic anti-CLEC5A mAb and CLEC5A-deficient mice were generated . We find that Japanese encephalitis virus ( JEV ) directly interacts with CLEC5A and induces DAP12 phosphorylation in macrophages . In addition , JEV activates macrophages to secrete proinflammatory cytokines and chemokines , which are dramatically reduced in JEV-infected Clec5a−/− macrophages . Although blockade of CLEC5A cannot inhibit JEV infection of neurons and astrocytes , anti-CLEC5A mAb inhibits JEV-induced proinflammatory cytokine release from microglia and prevents bystander damage to neuronal cells . Moreover , JEV causes blood-brain barrier ( BBB ) disintegrity and lethality in STAT1-deficient ( Stat1−/− ) mice , whereas peripheral administration of anti-CLEC5A mAb reduces infiltration of virus-harboring leukocytes into the central nervous system ( CNS ) , restores BBB integrity , attenuates neuroinflammation , and protects mice from JEV-induced lethality . Moreover , all surviving mice develop protective humoral and cellular immunity against JEV infection . These observations demonstrate the critical role of CLEC5A in the pathogenesis of Japanese encephalitis , and identify CLEC5A as a target for the development of new treatments to reduce virus-induced brain damage .
The Flavivirus genus includes the mosquito-borne dengue , Japanese encephalitis and yellow fever viruses [1] , infections of which can result in clinical syndromes such as hemorrhagic fever and encephalitis . There are four serotypes of dengue virus ( DV ) , which can give rise to severe hemorrhagic syndrome ( dengue hemorrhagic fever/DHF ) and capillary leakage induced-hypovolemic shock ( dengue shock syndrome/DSS ) [2] . On the other hand , the Japanese encephalitis virus ( JEV ) serological group , which includes West Nile virus ( WNV ) and St . Louis encephalitis virus , is a major contributor to the occurrence of viral encephalitis worldwide [3] , with 50 , 000 new cases and 15 , 000 deaths per annum [4] . JEV is the most prevalent cause of encephalitis and although both inactivated [5] and live-attenuated [6] JEV vaccines have been used in Asia for decades , these are not completely effective against all the clinical isolates [7] , and there are still ∼35 , 000 reported cases of Japanese encephalitis ( JE ) resulting in 10 , 000 deaths each year [8] . Unlike DHF and DSS , JE victims experience permanent neuropsychiatric sequelae , including persistent motor defects and severe cognitive and language impairments [9] . However , the molecular pathogenesis of JEV infection is still unclear . JEV-specific infiltrating T lymphocytes and JEV-neutralizing IgM and IgG are believed to play major roles in the recovery and clearance of the virus , while microglia were shown to secret massive amounts of cytokines following JEV infection [10] . While JEV infects and kills neuron directly [11] , viral replication within microglia/glia leads indirect neuronal killing via secretion of cytokines ( such as TNF-α ) and soluble mediators to cause neuronal death [11] . One of the key factors in indirect neuronal cell death during JE is the uncontrolled overactivation of microglia cells [12] . However , the molecular mechanism of JEV-induced microglia activation is unclear , thus we are interested to identify the key molecule to regulate JEV-induced proinflammatory cytokine release from microglia . This information may help in the development of specific treatments for JEV-induced neuroinflammation . CLEC5A ( also known as myeloid DAP12-associating lectin ( MDL-1 ) [13] ) contains a C-type lectin-like fold similar to the natural-killer T-cell C-type lectin domains , and associates with a 12-kDa DNAX-activating protein ( DAP12 ) [14] on myeloid cells such as monocytes , macrophages and neutrophils , but not monocyte-derived dendritic cells . Moreover , we have shown dengue virus ( DV ) can bind and activate CLEC5A and induce the phosphorylation of DAP12 [15] , which is responsible for CLEC5A/MDL-1-mediated signaling [13] . Unlike conventional C-type lectin receptors ( CLRs ) , such as DC-SIGN/CLEC4L , DC-SIGNR/CLEC4M , and mannose receptor/CLEC13D/CD206 [16] , which are all involved in dengue virus ( DV ) entry into target cells , CLEC5A regulates virus-induced proinflammatory cytokine release from macrophages [15] . In addition , blockade of CLEC5A can prevent autoimmune inflammation in collagen-induced arthritis via downregulating osteoclast activation , suppressing cell infiltration of joints , and attenuating proinflammatory cytokine release [17] . These observations indicate that CLEC5A is a critical molecule to regulate inflammatory reactions triggered by pathogens and autoantigens . We thereby went on to determine whether CLEC5A is involved in JEV-induced proinflammatory cytokine release from microglia and bystander neuronal damage . Here , we demonstrate that JEV infects and replicates in peripheral macrophages and microglia . Moreover , blockade of CLEC5A dramatically reduces bystander neuronal damage and JEV-induced proinflammatory cytokine secretion from macrophages and microglia . Furthermore , peripheral administration of anti-CLEC5A mAb attenuates neuronal cell death , inhibits JEV-bearing infiltrating cells into CNS , and restores the expression of tight junction proteins and BBB integrity . These results suggest that CLEC5A is a promising therapeutic target to control neuroinflammation during viral encephalitis .
Reverse-transcription PCR ( RT-PCR ) using cDNA templates from the human microglial cell line CHME3 , macrophage-like cell line U937 , and CD14+-derived macrophages ( MoM ) revealed the presence of a dominant transcript ( CLEC5A ) and an alternatively spliced variant ( CLEC5A_S ) lacking 23 amino acid ( aa ) residues within the stalk region of CLEC5A ( Figure S1A & S1B ) . Similarly , murine CLEC5A_S ( mCLEC5A_S ) lacks 25 aa from the corresponding region of murine CLEC5A ( mCLEC5A ) [18] . ELISA showed that human CLEC5A and mCLEC5A , but not the alternatively spliced variants or structurally related members of the CLEC family , are able to interact with JEV ( Figure 1A ) . The differential abilities of CLEC5A and CLEC5A_S to bind JEV were further confirmed by immunoprecipitation ( Figure S1C ) . This observation demonstrates that the stalk region of CLEC5A plays a critical role in binding to JEV . Incubation of macrophages with JEV was shown to induce the phosphorylation of DAP12 ( Figure S1D ) , a CLEC5A-associated adaptor protein with an ITAM motif contributing to signal transduction . While UV-inactivated JEV-induced DAP12 phosphorylation lasted for only 2 h , DAP12 phosphorylation was detectable for at least 24 h following infection with live JEV , indicating DAP12 phosphorylation is enhanced by JEV replication ( Figure 1B ) . Compared to live JEV , UV-inactivated JEV only induced transient DAP12 phosphorylation and low amounts ( less than 50 pg/ml ) of TNF-α and IL-6 secretion ( data not shown ) . This indicates that virus particles released from JEV-infected macrophages can continually activate DAP12 and induce cytokine release . Furthermore , knockdown of CLEC5A using the short hairpin RNA ( shRNA ) pLL3 . 7/CLEC5A [15] abolished DAP12 phosphorylation ( Figure S1E ) , suggesting that JEV-triggered DAP12 phosphorylation is mediated via CLEC5A . We further investigated whether JEV could also infect and activate human CD14+-monocyte derived macrophages ( MoM ) . Although JEV was found to be less efficient than DV in infecting macrophages , only JEV infected the human neuroblastoma cell line HTB11 ( Figure S2A ) , suggesting that JEV is both myelotropic and neurotropic . Moreover , JEV activates MoM to secret proinflammatory cytokines , and anti-CLEC5A mAb blocked the release of TNF-α and MCP-1 but not IFN-α , from JEV-infected MoM ( Figure 1C ) and murine bone marrow-derived macrophages ( BMM ) from Stat1−/− mice ( Figure S2B&S2C ) . The critical role of CLEC5A in JEV infection was further confirmed by incubating JEV with BMM derived from Stat1−/−/Clec5a−/− mice ( Figure S3 ) . Compared to Stat1−/−/Clec5a+/+ mice , BMM from Stat1−/−/Clec5a−/− mice secreted significantly less TNF-α and MCP-1 in response to JEV infection , while levels of IFN-α secretion were very similar ( Figure 1D ) . These findings further confirm the critical role of CLEC5A in the JEV-induced inflammatory reaction . Since astrocytes ( glial cells ) and microglia ( cerebral residential macrophages ) are major sources of proinflammatory cytokines during cerebral inflammation [11] , [12] , [19] , we went on to investigate the role of CLEC5A in JEV-induced neuroinflammation in Stat1−/− microglia and mixed glia cells . We found that CLEC5A is expressed on microglia ( Figure 2A ) as determined by immunohistochemistry staining . We further isolated the mononuclear cells ( including hematopoietic and non-hematopoietic cells ) by Percoll-gradient centrifugation [20] from naïve mice to determine cell lineages expressing CLEC5A . We found CLEC5A is expressed on the surface of F4/80+ tissue macrophages ( 90% ) , CD11b+ myeloid cells ( 65% ) , and CD45+ cells ( 50%; predominantly in CD45low hematopoeitic ) ( Figure 2B ) . Due to the resistance of wild type mice to JEV infection , previous studies in relation to JEV-induced brain damage have utilized intracranial ( i . c . ) injection of JEV to deliver virus directly to the CNS [21] , [22] . However , this approach causes mechanical damage to the BBB and thus cannot be used to address the mechanisms of JEV-induced changes in BBB permeability and the associated entry of virus into the CNS . Since Stat1−/− mice are sensitive to JEV infection even without i . c . puncture , and are able to develop protective immunity after JEV challenge , these animals provide a useful model to test the protective effects of vaccines against JEV and other pathogens which are unable to infect wild type mice . We set up microglia ( 95% purity ) and mixed glial cell ( approximately 85% astrocytes and 10% microglia ) cultures from Stat1−/− mice to assess the potential involvement of CLEC5A in regulating cerebral inflammation and neuronal death after JEV infection ( Figure S4 ) . Immuno-staining with a mAb to JEV nonstructural protein 3 ( NS3 ) showed that ∼30% of mixed glial cells and ∼10% of microglia were infected with JEV . However , anti-CLEC5A mAb was unable to inhibit either NS3 expression ( Figure 2C ) or virus replication ( Figure 2D ) , suggesting that CLEC5A was not involved in JEV entry into these cells . It has been reported that both JEV-infected astrocytes and microglia release multiple bio-active factors , thereby giving rise to secondary glial activation and neuronal injury [11] , [12] . We therefore investigated the release of proinflammatory cytokines from JEV-infected neurons , mixed glia and microglia cultures . Cells were preincubated with anti-CLEC5A mAb for 1 h , followed by incubation with JEV at 37°C for 1 h , and supernatants were harvested to determine cytokine release at 24 h post-infection . We found that TNF-α and MCP-1 were produced primarily by microglia , and abundant IL-6 was released by JEV-infected mixed glia ( Figure 2E ) , while JEV-infected neurons only released trace amount of these cytokines ( Figure 2E ) . This observation is in accord with a previous report that JEV-infected astrocytes produce IL-6 , but not TNF-α [11] . In our study , pre-incubation with the anti-CLEC5A mAb mediated significant reductions in cytokine release ( Figure 2E ) ; we therefore investigated whether anti-CLEC5A mAb could prevent neuronal death during JEV infection . Direct incubation of neurons with JEV caused 40% cell damage ( live cell count reduced from 171±23 to 93±27 ) ( Figure 2F ) , while UV-irradiated conditioned media ( in the absence of anti-CLEC5A mAb ) from JEV-infected mixed glia ( UV-JEVCM; from Figure 2E ) caused 80% cell damage ( from 162±19 to 27±7 live cells ) ( Figure 2G ) . This indicated that soluble mediators released from JEV-infected mixed glia are more toxic than JEV per se to neurons . In addition , anti-CLEC5A mAb protected approximately 50% of neurons from UV-JEVCM-induced cell damage ( from 162±19 to 76±12 live cells ) , but was ineffective in protecting neurons from direct JEV infection ( Figure 2F ) . Previous studies demonstrated that TNF-α released by microglia plays a critical role in JEV-associated neurotoxicity [11] , while MCP-1 secreted by microglia can recruit inflammatory cells to cause neuronal damage [12] . Therefore , the anti-CLEC5A mAb-mediated protective effect may occur via blocking the release of TNF-α , MCP-1 , and other yet-defined bio-active factors from JEV-infected mixed glial cells and microglia . While wild type mice only respond to high dose JEV challenge ( 1×105 pfu/mouse ) with intracranial ( i . c . ) puncture ( Figure S5A ) , Stat1−/− mice are sensitive to low dose JEV-induced lethality without the need for i . c . puncture ( Figure S5B ) . Thus Stat1−/− mice were used as an in vivo model system to test whether anti-CLEC5A mAb could maintain BBB integrity , which is known to be damaged by virus-induced neuroinflammation [23] , [24] . Changes in BBB integrity over time following JEV challenge , as revealed by 99mTc-DTPA brain SPECT/CT imaging , showed that JEV infection gave rise to increased BBB permeability from day 3 to day 7 post infection ( Figure 3A&B ) . This effect was reduced ( during day 3 to day 5 ) and normal BBB permeability was restored on day 7 in response to treatment with anti-CLEC5A mAb . The integrity of the BBB after anti-CLEC5A mAb treatment on day 7 was further confirmed by the Evans blue assay ( Figure 3C ) . Since tight junctions are critical in the regulation of BBB permeability , and their disruption is a hallmark of CNS abnormalities [25] , we measured the expression of tight junction proteins ( such as ZO-1 , Occludin and Claudins ) and of adhesion molecules ( such as JAM-1 and ICAM-1 ) known to be important for recruitment of inflammatory cells . In JEV-infected mice ( treated with isotype mAb ) , the expression of ZO-1 , Occludin , Claudin-1 , and Claudin-5 was downregulated , while the expression of ICAM-1 was upregulated . However , the expression of junctional adhesion molecule ( JAM-1 ) was not changed after JEV infection ( Figure 3D ) . Interestingly , anti-CLEC5A mAb restored the expression of tight junction proteins and suppressed JEV-induced upregulation ICAM-1 ( Figure 3D ) . Moreover , the increased BBB permeability in JEV-infected mice was accompanied by perivascular cuffing , while anti-CLEC5A mAb reduced the numbers of infiltrating foci ( Figure 3E ) . This demonstrates that peripheral administration of anti-CLEC5A mAb is able to restore BBB integrity to prevent cell infiltration . We further isolated mononuclear cells ( MNCs ) from the JEV-infected Stat1−/− mice brain by Percoll gradient [20] to analyze the cell lineages . At day 5 after JEV infection , most of the infiltrating cells are F4/80+CD11b+ myeloid cells ( left column , Figure 4A ) . Peripheral administration of anti-CLEC5A mAb efficiently inhibits the infiltration of F4/80+ CD11b+ myeloid cells ( right column , Figure 4A ) . It has been shown that CD11b+/CD45+ cells contribute to the pathogenesis of West Nile virus ( WNV ) encephalitis [26] , thus triple-color staining ( CD11b/CD11c/CD45 ) was used to gate the CD11c− population to distinguish residential microglia ( R1 ) and infiltrating myeloid cells ( R1 ) ( Figure 4B ) . To determine the source of CD45+CD11b+ myeloid cells , carboxyfluorescein succinimidyl ester ( CFSE ) was injected into peritoneum ( i . p . ) at day 2 post JEV infection to trace the migration of CFSE+ cells ( Figure S6A ) . All the peripheral blood cells were labeled with CFSE at day 5 post infection whether treated with isotype or anti-CLEC5A mAb ( Figure S6B ) . In addition , intraperitoneal injection of CFSE was unable to label CNS MNCs without intracranial ( i . c . ) puncture ( mode III , Figure S7B ) , nor when i . c . puncture was performed at 2 days after i . p . injection of CFSE ( mode II , Figure S7B ) , even though simultaneous CFSE i . p . injection and i . c . puncture ( mode I , Figure S7B ) can label intracranial MNCs weakly . This indicates that CFSE is either degraded or excreted within 48 hours . We then analyzed the intracranial MNCs isolated from JEV-infected Stat1−/− mice ( Figure 4B ) . We found that approximately 5×104 and 1 . 5×103 CD45+ cells were found in Percoll-gradient purified MNCs from mice treated with isotype and anti-CLEC5A mAb , respectively . Moreover , anti-CLEC5A mAb was able to suppress the infiltration of CD11b+CD45hi cells ( R1 ) into CNS , as well as the proliferation of CD11b+CD45low cells ( R2 ) after JEV infection ( upper row , Figure 4B ) . It is interesting to note that all the CD11b+CD45low cells are CFSE negative , while most of the CD11b+CD45hi cells are CFSE positive ( lower row , Figure 4B ) . These results suggested that the CD11b+CD11c−CD45hi population ( R1 ) are the infiltrating inflammatory myeloid cells from peripheral blood , while the CD11b+CD11c−CD45low ( R2 ) are the resident microglia . It has been demonstrated that CD45+CD11b+Ly6Chi cells have properties of inflammatory monocytes [27] , and CLEC5A+CD11b+Gr1+ cells are responsible for DV-induced septic shock in ConA-primed mice [28] , thus we further characterized CD45+ populations by detecting the expression of CLEC5A and Gr1 ( comprising Ly6C and Ly6G ) . We found that all the CD45hiCD11b+ population expresses Ly6C , while 15% of the CD45hiCD11b+ population also expresses Ly6G ( Figure 4C ) . Moreover , CLEC5A is highly expressed in CD45hiCD11b+ infiltrating inflammatory myeloid cells ( 45% ) , while only 5% of CD45lowCD11b+ cells express CLEC5A ( Figure 4D ) . This observation suggests that the majority of the infiltrating inflammatory cells are CD45hiCD11b+Ly6C+CLEC5A+ , while peripheral administration of anti-CLEC5A mAb efficiently reduces the numbers of inflammatory myeloid cells and suppresses the proliferation of resident macrophages in the CNS ( R2 ) . To determine whether anti-CLEC5A mAb can suppress brain inflammation , viral load was measured in the sera and tissue extracts collected from mice at day 3 and day 5–7 post JEV infection . Short term viremia was observed at day 3 post infection , with all the viruses being cleared from peripheral blood at day 5–7 post infection ( Figure 5A ) . In contrast , JEV titers in spleen and brain were maintained at a high level from day 3 to day 7 post infection . Anti-CLEC5A mAb reduced viral load in the brain at day 5–7 ( p = 0 . 009 ) but was ineffective in spleen ( Figure 5A ) , correlating with reduced NS3 expression in the brain at day 5 post infection ( Figure 5B ) . While JEV infection increased the numbers of MNCs in brain ( from 0 . 8–1×104 to 1–2×105/brain ) , anti-CLEC5A mAb reduced the numbers of MNCs ( 2–3×104/brain ) and NS3-bearing infiltrating myeloid cells ( R1 ) and microglia ( R2 ) ( Figure 5C ) . To further confirm the replication of JEV in R1 and R2 populations , reverse-transcription PCR was used to quantitate the JEV copy numbers in each population after sorting by FACS ( Figure 5D ) . As shown in Figure 5D , inflammatory myeloid cells ( R1 ) bear higher copies of viral RNA than resident microglia ( R2 ) . This result suggests that anti-CLEC5A mAb is able to reduce neuroinflammation by suppressing the infiltration of JEV-positive myeloid inflammatory cells into CNS . We also found that JEV induced the release of TNF-α , IL-6 , MCP-1 and IL-18 into the peripheral blood and CSF at day 5–7 post infection , whereas anti-CLEC5A mAb caused significant suppression of cytokine levels ( Figure 5E&F ) . Since TNF-α and IL-18 are responsible for the cytotoxic and inflammatory responses in a variety of neuropathological conditions [12] , [19] , [29] , [30] , and MCP-1 is a potent chemoattractant for monocytes and dendritic cells [31] , reduced cytokine levels in the CNS following anti-CLEC5A mAb treatment would be expected to limit neuronal damage . We found that the populations of TNF-α+ ( Figure 5G , upper panel ) and IL-6+ ( data not shown ) CD11b+/F4/80+ cells ( infiltrated monocytes and resident microglia ) , as well as IL-6+ ( Figure 5G , lower panel ) and TNF-α+ ( data not shown ) CD11b+/CD45+ ( R1 and R2 ) cells were also reduced in JEV-infected mice following anti-CLEC5A mAb treatment at day 5–7 post infection . Thus , even though peripheral administration of anti-CLEC5A mAb is unable to inhibit viral replication , it can reduce viral load and attenuate inflammation in the CNS via inhibition of cellular infiltration and proinflammatory cytokine secretion . At day 5 post JEV infection , ischemic , shrunken and damaged neurons and Purkinje cells were observed in the cortical region of the cerebrum ( Figure 6A , middle upper panel ) and cerebellum ( Figure 6B , middle lower panel ) , respectively . The JEV-induced pathological changes were inhibited by anti-CLEC5A mAb ( Figure 6A , right upper & lower panels ) . Moreover , JEV infection caused astrocytosis , an abnormal increase in the number of astrocytes due to the destruction of nearby neurons in the CNS , in cerebrum and cerebellum as determined by glial fibrillary acidic protein ( GFAP ) staining ( Figure 6B ) . Upregulation of GFAP and increased astrocyte proliferation in cerebrum ( Figure 6B; middle upper panel ) and cerebellum ( Figure 6B; middle lower panel ) were observed in mice treated with an isotype-matched control antibody , while anti-CLEC5A mAb downregulated reactive astrocytosis substantially ( Figure 6B , right upper & lower panels ) . Thus , peripheral administration of anti-CLEC5A not only suppresses neuroinflammation , but also increases the survival of neuronal and Purkinje cells . Investigation of the ability of anti-CLEC5A mAb to protect Stat1−/− mice from JEV-induced lethality revealed that 50% of Stat1−/− mice that succumbed to JEV infection died in the early stages ( 6 days post infection ) , and all the mice died within 9 days post infection . In contrast , administration of anti-CLEC5A mAb from day 0 ( 150 µg/mouse on days 0 , 2 , 4 , 6 , and 8 ) protected mice from early lethality ( 80% survival ) , and ∼50% mice survived for at least 16 days post infection ( Figure 7A ) . We went on to determine whether administration of anti-CLEC5A mAb inhibited adaptive immunity against JEV . All the mice that survived JEV-induced lethality were found to have sero-conversion from day 12 post infection ( Figure 7B ) , suggesting that suppression of JEV-induced inflammation by anti-CLEC5A mAb did not prevent development of humoral immunity against the virus . This was further confirmed by plaque reduction neutralization tests ( PRNT ) , where serum from surviving mice efficiently neutralized JEV infection ( Figure 7C ) . In addition , the proinflammatory cytokine levels at day 7–9 post infection were much lower in the sera of asymptomatic mice ( Figure 7D ) . These mice also exhibited JEV-specific T-cell responses; high levels of IFN-γ were released from JEV-challenged splenocytes , while IFN-γ was almost undetectable in cells from uninfected mice under the same conditions ( Figure 7E ) . It is interesting to note that virus was cleared from the spleen and CNS at day 21 post infection in surviving mice ( Figure 7F ) , suggesting that the host can eradicate JEV after anti-CLEC5A mAb treatment . Thus , blockade of CLEC5A can suppress proinflammatory reactions without interfering with the development of anti-JEV immunity , making CLEC5A a promising target for the treatment of flaviviral infections in the future .
There is growing evidence that microglial cell activation contributes to neuronal damage and neurodegenerative diseases [32] . Following pathogen invasion , these cells play a role in the clearance of cell debris from damaged tissue , but also secret inflammatory cytokines , which are key mediators of the neurodegeneration associated with both acute and chronic CNS pathologies [33] . Our observation that blockade of CLEC5A on microglia attenuates the neuronal damage caused by the supernatants from mixed glia culture in vitro further demonstrates the contribution of microglia to JEV-induced neuronal death . Moreover , peripheral administration of anti-CLEC5A mAb was found to preserve BBB integrity , inhibit cellular infiltration into the brain , and reduce neuronal death in vivo . This may be attributed the fact that the anti-CLEC5A mAb can enter the CNS when the permeability of BBB is increased during the acute stage of infection , where intracranial anti-CLEC5A mAb can inhibit microglia activation and attenuate neuroinflammation . Although JEV can directly infect neurons and cause cell damage , the soluble mediators from CLEC5A+-activated microglia and infiltrating myeloid cells seem to be the major cause of neuron damage ( bystander neuronal damage ) , since the inhibition of JEV-induced neuroinflammation by anti-CLEC5A mAb does not arise from interactions with cells , such as astrocytes and gangliocytes in the CNS , or from prevention of neuron damage caused by direct JEV infection . Even though anti-CLEC5A mAb has relatively mild inhibitory effects on cytokine release in JEV-infected macrophages ( Figure 1 ) and microglia ( Figure 2 ) , it can inhibit the permeability change of BBB ( Figure 3 ) , reduce cell infiltration into the CNS ( Figure 3 & 4 ) and protect mice from JEV-induced lethality . This may due to the blocking effect of anti-CLEC5A mAb to inhibit the release of yet-discovered soluble mediators which are critical to control BBB permeability and neuronal death . The alternative is that anti-CLEC5A mAb can inhibit the infiltration of CLEC5A+ MNCs which are pathogenic to CNS , thus suppressing neuronal inflammation and reducing lethality . The possibility of a direct effect on MNCs is supported by the observation that DV can activate CLEC5A+ CD11b+Gr-1+ immature myeloid cells to induce shock in ConA-treated mice [28] . Finally , anti-CLEC5A mAb may block the interaction of CLEC5A+ cells with endogenous ligands to reduce neuroinflammation . It has been shown that CLEC5A is critical for collagen-induced autoimmune arthritis ( CIA ) , and CLEC5A-deficient mice are resistant to CIA . This observation suggests that the yet-characterized CLEC5A endogenous ligand ( s ) is ( are ) able to activate CLEC5A+ cells to induce inflammatory reactions [17] . Therefore , the peripheral administration of anti-CLEC5A mAb may inhibit JEV-induced inflammation via blocking multiple pathways . It is interesting to note that anti-CLEC5A mAb is able to inhibit the secretion of IL-18 , which is one of the key factors responsible for neuroinflammation and neurodegeneration [29] . IL-18 enhances caspase-1 expression and induces the production of matrix metalloproteinase ( MMPs ) and other proinflammatory cytokines such as TNF-α and IL-1β [34] , [35] . Upregulation of IL-18 was detected in the sera and CNS of JEV-infected mice , thus the potent protective effects of anti-CLEC5A mAb in JEV-induced neuroinflammation can be attributed , at least in part , to inhibition of IL-18 secretion in the periphery and in the CNS; i . e . , CLEC5A is a critical to regulator of neuroinflammation during JEV infection . After anti-CLEC5A mAb treatment , approximately 50% of JEV-infected Stat1−/− mice became asymptomatic and survived for at least 21 days post infection , while the others remained weak and died gradually within 12 days . While the titers of the neutralizing antibody were similar in the weak and asymptomatic groups ( Figure 7C ) , the proinflammatory cytokine levels at 7–9 days post infection were much lower in the sera of asymptomatic mice ( Figure 7D ) . This indicates that JEV-induced neuroinflammation is the key parameter in predicting the outcome of JEV infection [19] , and that effective suppression of neuroinflammation in the acute stage is critical to increasing survival and preventing neurological sequelae . This observation also suggests that administration of anti-JEV antiserum to patients during the acute stage of infection may be futile , and this argument is supported by the observation that injection of a neutralizing cross-reactive mAb against JEV E protein causes early death in JEV-infected mice [36] . Even though the direct relevance of this Stat1−/− mouse model in relation to human infection with JEV needs to be further confirmed , data for in vivo protection of CLEC5A mAbs still shed light on its therapeutic potential for blocking neuroinflammation . Previous studies have shown that CLEC5A can interact with all the four serotypes of DV [15] , [37] , and here we have demonstrated that CLEC5A also interacts with JEV ( Figure 1A ) , but not with other viruses such as influenza virus and EV-71 ( Figure S8 ) . In addition , anti-CLEC5A mAb can inhibit inflammatory reactions in human macrophages in response to the structurally related West Nile virus ( WNV ) ( Figure S9 ) . Thus , CLEC5A may play a critical role in the pathogenesis of all the flaviviral infections . This study has clearly demonstrated that infiltration of inflammatory cells into CNS correlates with increased BBB permeability , suggesting that BBB integrity has a critical role in limiting JEV-induced CNS inflammation . CLEC5A+ microglia and infiltrating myeloid cells appear to be essential for JEV-induced inflammation of the CNS and neurological sequelae , and anti-CLEC5A mAb efficiently suppressed the release of proinflammatory cytokines from microglia and macrophages , restored BBB integrity and increased survival after JEV infection in mice . The critical role of CLEC5A in JEV infection was further confirmed by using Stat1−/−Clec5a−/− mice , which are resistant to JEV challenge ( Figure S10 ) . Together with our previous observations that anti-CLEC5A mAb is able to prevent DV-induced systemic vascular permeability and protect mice against DV-induced lethal diseases [15]_ENREF_5 , the data presented here indicate that CLEC5A plays critical roles in the pathogenesis of flaviviral infections via regulating vascular permeability and suppressing myeloid cell activation . Like most members of C-type lectin , their exogenous and endogenous ligands are not identified yet . Since crystal structure of CLEC5A reveals that CLEC5A forms homodimer on cell surface [37] , thus the binding of JEV to CLEC5A . Fc may similar to JEV to CLEC5A on cell surface , though cells may also expressed other C-type lectin to increase binding affinity . Recently , we have developed an innate immunity receptor-based ELISA ( IIR-EIA ) to determine the polysaccharide profiling [38] and dengue receptor [15] based on C-type lectin . Fc fusion proteins , thus the identification of CLEC5A as a JEV recognition receptor further suggests that IIR-EIA is an useful tool to identify lectin ligands in the future .
Human monocytes were obtained from healthy donors at the Taipei Blood Center of the Taiwan Blood Services Foundation , under a protocol ( PM-99-TP-075 ) approved by the IRB of the Clinical Center of the Department of Health , Taiwan . Written informed consent was obtained from all donors . All animal studies were performed according to the animal study protocol approved by the Animal Experimental Committee of National Yang-Ming University , and in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Taiwanese Council of Agriculture . The animal protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) of National Yang-Ming University ( IACUC #1000519 ) . All surgery was performed under sodium pentobarbital anesthesia , and every effort was made to minimize suffering . Chemical reagents were purchased from Sigma , culture media/supplements from Invitrogen GIBCO and growth factors from R&D Systems . Anti-CD14 microbeads were from Miltenyi Biotec GmbH , while antibodies for FACS analysis were from BD Pharmingen . Other antibodies were from Upstate Biotechnology ( anti-phosphotyrosine , clone 4G10 ) , Santa Cruz ( anti-human DAP12 & anti-F4/80 ) , Millipore ( anti-tubulin β III isoform ( TU-20 ) mAb ) , and Cell Signaling ( anti-glial fibrillary acidic protein ) . The neurovirulent ( RP-9 ) strain of Japanese encephalitis virus ( JEV ) , used for both in vitro and in vivo studies , was generated from a Taiwanese isolate , NT109 [39] . Aedes albopictus C6/36 cells and the baby hamster kidney fibroblast cell line BHK-21 were used for viral propagation and viral titer determination , respectively . Viral particles were purified from JEV-infected C6/36 cell supernatant by sucrose gradient centrifugation ( 1/10 volume of the 35% ( v/v ) sucrose buffer ( 10 mM Tris-HCL pH 7 . 5 , 100 mM NaCl and 1 mM EDTA ) , followed by ultra-centrifuging at 32 , 000 rpm for 3 . 5 hr , and resuspending the viral pellet in 0 . 5 ml PBS after removing the supernatant thoroughly . Human and murine pcDNA3 . 1 CLEC5A . Fc DNA constructs were transfected into 293 FreeStyle cells ( Invitrogen ) , and the recombinant proteins were purified with Protein A beads . To determine the CLEC5A-JEV interaction by ELISA , sucrose-cushion-purified JEV particles ( 5×106 pfu ) were coated on microtiter plates , and bound CLEC5A . Fc fusion proteins ( 0 . 05 mg/mL; 100 µL/well ) were detected with HRP-conjugated anti-human IgG ( Fc ) ( Jackson Immunoresearch ) using 3 , 39 , 5 , 59-tetramethylbenzidine ( TMB ) ( BD Pharmingen ) as substrate . For immunoprecipitation , 5 µg of CLEC5A . Fc fusion protein was incubated with purified JEV particles ( 5×106 pfu ) at 4°C for 4 h and then with 10 µ1 of protein A–Sepharose beads for 2 h . The immunocomplex was washed gently before fractionation on SDS–PAGE , followed by transfer onto a nitrocellulose membrane and probing with a mouse mAb specific for JEV envelope protein ( E ) [39] . Immunoblots were developed with HRP-conjugated anti-mouse IgG ( Fab ) ′2 ( ab5887 , Abcam ) , followed by enhanced chemiluminescence detection reagents ( Amersham ) . Macrophages ( 1×106 ) were stimulated with JEV ( m . o . i . = 2 ) for 2 h , followed by resuspension in lysis buffer ( 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1% ( v/v ) Triton X-100 , 0 . 1% ( w/v ) SDS , 5 mM EDTA , 10 mM NaF , 1 mM sodium orthovanadate , and proteinase inhibitor cocktail ( Roche ) ) . Total cell extracts ( 100 µg ) were incubated with rabbit anti-DAP12 polyclonal antibody at 4°C for 4 h and then with Protein A–Sepharose for 2 h . The immunocomplex was washed and fractionated on SDS–PAGE , followed by transfer onto a nitrocellulose membrane and probing with anti-phosphotyrosine antibody . Immunoblots were developed with HRP-conjugated anti-mouse IgG ( Cat . AP181P; Chemicon ) followed by enhanced chemiluminescence detection reagents ( Immobilon Western; Millipore ) . To determine the total amount of DAP12 present on the blot , the membrane was stripped with Re-Blot Plus Strong solution ( Cat . 2504; Chemicon ) , and probed with rabbit anti-DAP12 antibody . For human macrophage preparation , peripheral blood mononuclear cells ( PBMCs ) were isolated from the whole blood of healthy human donors by standard density-gradient centrifugation with Ficoll-Paque ( Amersham Biosciences ) . After centrifugation the buffy coat , containing leukocytes ( PBMC ) and platelets , was further washed with PBS , and CD14+ cells were purified using the VarioMACS technique with anti-CD14 microbeads ( Miltenyi Biotec GmbH ) . Cells were then cultured in complete RPMI 1640 medium supplemented with 10 ng/ml human M-CSF ( R&D Systems ) for 6 days [15] . For preparation of murine bone marrow-derived macrophages , bone marrow cells were isolated from femurs and tibias and cultured in RPMI 1640 complete medium supplemented with 10% ( v/v ) fetal calf serum ( FCS ) and 10 ng /ml of recombinant mouse M-CSF ( R&D Systems ) for 6–8 days . At day 7 , the expression of F4/80 ( murine macrophage marker ) was examined by fluorescence-activated cell sorting; >90% of cells were F4/80+ under these culture conditions . Primary neuron , mixed glia and microglia cultures were prepared from the cerebral cortexes of 1-day-old wild-type or Stat1−/− mice ( C57BL/6 background ) as previously described [11] , [40] . In brief , pooled dissected cortexes were digested in papain solution ( 1 . 5 mM CaCl2 , 0 . 5 mM EDTA , 0 . 6 mg/ml papain , 0 . 05 mg/ml DNase I , and 0 . 2 mg/ml cysteine in Hanks' balanced salt solution ) at 37°C for 20 min to dissociate the cells . After centrifugation at 200×g for 5 min , cells were plated on poly-L-lysine coated ( 20 µg/ml ) dishes . For cortical neuron cultures , cells were plated in minimal essential medium supplemented with 10% ( v/v ) FBS and 10% ( v/v ) horse serum . One day after seeding , the culture medium was replaced with neurobasal medium supplemented with B27 , followed by addition of cytosine arabinoside ( 10 µM ) on the third and fourth days to inhibit non-neuronal cell division in vitro . The neuron cultures were used for subsequent experiments after 10–12 days . For mixed glia culture , cells were maintained in DMEM/F12 supplemented with 10% ( v/v ) FCS; medium was replenished 4 days after plating and changed every 3 days . Cells usually reached confluence within 12–14 days after plating . For microglia cultures , the confluent mixed glial cells were incubated with mild trypsin solution ( 0 . 25% ( w/v ) trypsin , 1 mM EDTA ) diluted 1∶4 in DMEM/F12 for 30 min at 37°C to detach the upper layer of astrocytes; 10% ( v/v ) FCS-containing DMEM/F12 medium was then added to inactivate trypsin and the detached astrocytes were aspirated from the mixed glia cultures . The remaining cells ( i . e . , microglia ) were harvested by incubation with 0 . 25% ( w/v ) trypsin solution with vigorous pipetting for 5 min and then were resuspended in DMEM-F12 with 10% ( v/v ) FCS for at least 2 weeks . Cell identity was determined by immunocytochemical staining using antibodies against tubulin β III isoform , ( TU20; Millipore ) for neurons , glial fibrillary acidic protein ( GFAP; Cell Signaling ) for astrocytes , and F4/80 for microglia ( Santa Cruz Biotechnology ) . Mixed glial cultures contained ∼85% astrocytes and ∼10% microglia . Neurons and microglia were >95% pure ( Figure S5 ) . Human CD14+-monocyte derived macrophages ( MoM ) , murine bone-marrow derived macrophages ( BMoM ) , murine mixed glia and microglial cells were mock-infected or infected with JEV at a multiplicity of infection 5 ( m . o . i . = 5 ) . Culture supernatants were harvested to determine virus titer and cytokine secretion by plaque assay and ELISA ( R&D Systems ) , respectively . Conditioned media ( CM ) from mock or JEV-infected mixed glia were collected and UV-irradiated ( 254 nm ) to inactivate virus . UV-irradiated CM ( UV-JEVCM ) was mixed with fresh neurobasal medium supplemented with B27 , before incubation with neuron cells prior to cytotoxicity assays . Breeder mice ( BALB/c strain ) were maintained in the standard animal facility of the National Yang-Ming University . For the production of mAbs , mice were immunized with purified recombinant CLEC5A . Fc fusion protein ( five doses of 50 µg per mouse ) . Lymphocytes from the spleens of immunized mice were fused with mouse myeloma NS-1 cells in the presence of 50% ( v/v ) polyethylene glycol ( PEG1450; Sigma ) . Fused cells were cultured in HAT selection medium and the medium was refreshed after one week . About 2 weeks after fusion , culture supernatants were screened by ELISA to identify the candidate clones for further analysis by limiting dilution . Anti-CLEC5A mAbs were selected by ELISA-based differential screening , and only those recognizing recombinant CLEC5A . Fc , but not human IgG1 , were regarded as positive clones . A similar strategy was used to generate anti-murine ( m ) CLEC5A mAbs . To select antagonistic mAbs against human CLEC5A and murine CLEC5A , mAbs were incubated with human macrophages and murine bone marrow-derived macrophages ( Stat1−/− ) , respectively , in 96-well plates ( 6×104 cells per well ) for 30 min at 37°C , before the addition of JEV ( m . o . i . = 5 ) and incubation at 37°C for 2 h . After washing , cells were incubated for a further 36 h before harvesting the supernatants to measure TNF-α production by ELISA . mAbs capable of inhibiting cytokine release from JEV-infected macrophages were used as antagonistic antibodies for in vitro and in vivo assays . To detect JEV replication in macrophages , cells were incubated with JEV ( m . o . i . = 5 ) for 1 h , then fixed with 1% ( v/v ) paraformaldehyde and permeabilized with 0 . 1% ( w/v ) saponin , followed by addition of anti-NS3 mAb or isotype-matched control ( mIgG1; Sigma ) and then FITC-conjugated goat F ( ab ) ′ anti-mouse IgG . Emitted fluorescence was detected by flow cytometry ( FACSCalibur platform with CellQuest software ( Becton Dickinson ) ) . To determine JEV replication , murine neuronal cells were fixed with 4% ( v/v ) paraformaldehyde , permeabilized with 0 . 5% ( v/v ) Triton X-100 in PBS for 10 min and incubated with blocking buffer ( 10% ( w/v ) BSA in PBS ) before addition of anti-NS3 mAb ( 20 µg/ml ) . After washing , cells were incubated with Cy3-conjugated donkey anti-mouse IgG ( Jackson Immuno ) and Hochest 33342 to detect NS3 and nuclei , respectively . Cover slips were mounted and observed using an FV-1000 laser scanning microscope ( Olympus ) . Cells cultured on coverslips were washed twice with PBS , followed by incubation with 4% ( v/v ) paraformaldehyde for 2 h , and then permeabilized with 0 . 5% ( v/v ) Triton X-100 for 15 min . After washing with PBS , cells were subjected to immunocytochemical staining with HISTOMOUSE-SP KIT ( Zymed ) according to the supplier's instructions . Briefly , cells were incubated with primary antibodies ( including anti-TU20 ( 1∶300 ) , anti-GFAP ( 1∶300 ) or anti-F4/80 ( 1∶500 ) antibodies for 2 h at room temperature , followed by incubation with biotinylated secondary antibody and peroxidase-conjugated streptavidin . Cells were then incubated with chromogen , and counterstained with hematoxylin before observation under a light microscope ( Nikon ) . Mice were transcardially perfused with PBS and brains were removed and placed in 4% ( w/v ) paraformaldehyde overnight at 4°C , followed by embedding in paraffin wax and processing to generate 5-micron sagittal sections for H&E staining . Before immunohistochemical ( IHC ) staining , paraffin sections were subjected to antigen retrieval at 100°C for 20 min in 10 mM citric acid ( pH 6 . 0 ) , and endogenous peroxidase activity was quenched with 3% ( v/v ) H2O2 for 15 min at room temperature . Immunohistochemical staining was performed using a HISTOMOUSE-SP KIT ( AEC ) ( Zymed ) together with anti-GFAP ( 1∶300 ) Ab or mouse JEV-NS3 ascites fluid ( 1∶1000 ) . All images were digitized with Nikon scientific imaging software . Adult C57BL/6- Stat1−/− mice were used in all experimental procedures . Groups of 8-week-old adult mice were inoculated intraperitoneally with JEV ( strain RP-9; 100 pfu per mouse ) [22] . Anti-CLEC5A mAb or isotype control ( 150 µg per mouse ) were administrated intraperitoneally ( i . p . ) on days 0 , 2 , 4 , 6 and 8 after JEV infection , and mice were monitored daily for 16 days to assess morbidity and mortality . All experiments were performed according to the animal study protocol approved by the Animal Experimental Committee of Yang-Ming University . Mononuclear cells ( MNCs ) were isolated from the brains of mock- or JEV-infected mice as previously described [41] . Briefly , PBS-perfused brains were homogenized in HBSS containing 1% ( v/v ) FCS and minced with a scalpel before being passed through an 18-gauge needle . Homogenates were suspended in 37% ( w/v ) Percale ( 4 ml per brain ) and overlaid onto 70% ( w/v ) Percoll ( 4 ml ) in 15 mL conical tubes; 30% ( w/v ) Percoll ( 4 ml ) and HBSS ( 2 ml ) were added prior to centrifugation at 200 g for 40 min at 20°C . After centrifugation , cells were harvested and washed with PBS to remove residual Percoll . The isolated MNCs were resuspended in FACS buffer , followed by incubation with fluorochrome–conjugated anti-CD4 , CD8 , B220 , CD11b , CD45 , F4/80 and CLEC5A mAbs . For intracellular cytokine staining , cells were fixed with 1% ( v/v ) paraformaldehyde and permeabilized with 0 . 1% ( w/v ) saponin , before incubation with specific antibodies . Fluorescence was detected using a FACSCalibur platform with CellQuest software ( Becton Dickinson ) . BBB integrity was evaluated using the Evans blue assay [24] . Mice were injected intravenously with 100 µl of 1% ( w/v ) Evans blue in PBS . One hour later , mice were sacrificed and transcardially perfused with 20 ml of normal saline . Brains were removed and photographed . Total RNA was extracted from whole mouse brains using an RNeasy extraction kit ( Qiagen ) and complementary DNA ( cDNA ) was synthesized by using RevertAid First Strand cDNA Synthesis Kit ( Fermentas , Life Science ) according the vendor's suggestions . Quantitative real-time PCR analysis was performed using the LightCycler System SW 3 . 5 . 3 ( Roche Applied Science ) ( Fermentas , Life Science ) , and the level of mRNA expression level was normalized with β-2 microglobulin . Primer sequences for tight junction proteins and adhesion molecule: ZO-1 , Occludin , Clauidn-1 , Clauidn-5 , JAM and ICAM-1 were synthesized as described previously [24] , [42] . To quantify the viral copy numbers , a standard curve was generated using pCR3 . 1/JEV-3′UTR plasmid as template ( ranging from 32 pg/L to 32 mg/L; the dilution range is equivalent to the copy number 1×10 to 1×107 copies ) . JEV specific primer: forward 5′-AAGTTGAAGGACCAACG-3′ ( nucleotide 10603–10619 ) ; reverse 5′-GCATGTTGTTGTTTCCAC-3′ ( nucleotide 10789–10772 ) [43] . A FLEX Triumph preclinical imaging system ( Gamma Medica-Ideas , Inc . ) was used for the small-animal SPECT/CT scan acquisition . Each mouse was injected intravenously with 18 . 5 MBq ( 0 . 5 mCi ) /0 . 5 cm3 of 99mTc-DTPA and images were acquired 30 min after injection . The mice were scanned first by CT for anatomic coregistration and then by a dynamic SPECT sequence comprising 8 frames . The images were viewed and quantified using AMIDE software ( free software provided by SourceForge ) . A quantitative index of BBB breakdown was defined as the ratio of the mean counts/pixel in the region with disruption of BBB compared with the mean counts/pixel in the neck muscle . To determine the titers of anti-JEV IgM and IgG , serial dilution of JEV-infected mouse sera were added to immobilized JEV particles ( 4×106 pfu ) on microtiter plates , and the bound anti-JEV specific antibodies were detected with HRP-conjugated anti-mouse IgM or anti-mouse IgG secondary antibody using TMB as substrate . The neutralizing activities of mouse sera containing JEV-specific antibodies were determined using a plaque reduction neutralization test . Briefly , 100 pfu of JEV were incubated with serial dilutions of serum samples ( 20-fold to 200-fold ) at 37°C for 1 h , followed by overlaying the virus-serum complex onto BHK21 monolayer . After 1-h adsorption , the virus was removed and BHK21 cells were overlaid with 1% ( w/v ) agarose in RPMI-1640 and incubated at 37°C for 72 h . Cells were then fixed with 10% ( w/v ) formaldehyde and stained with crystal violet . The neutralization effect was expressed as the percentage reduction in plaque numbers in the presence of an anti-JEV serum compared to plaque numbers following infection with JEV ( 100 pfu ) alone [39] . Total splenocytes from the mock-infected mice and the mice that recovered from JEV infection ( day 21 after JEV infection ) were isolated and seeded in 96-well ( 5×105 /well ) . JEV ( PFU = 10 and 30 ) and UV-inactivated JEV were incubated with total splenocytes for 72 hr , followed by collecting supernatants for IFN-γ measurement . The ability of IFN-γ-secreting T cells was verified by immobilized anti-CD3 mAb to activate T cells . Mouse Clec5a genomic DNA , 30 . 7 kb in length ( Ensembl Genomics database ) containing exons 1–7 , was isolated from a 129/Sv genomic DNA BAC library ( CITB mouse; Invitrogen , San Diego , CA ) . In order to generate a targeting vector , the Clec5a genomic DNA fragment was inserted into the pL253 plasmid containing a neomycin resistance gene allowing antibiotic selection in ES cells . In the targeting vector , exons 3 to 5 of Clec5a were flanked by loxP sequences , which could then be excised upon introduction of a Cre recombinase plasmid into the ES cells . Excision of exons 3–5 was confirmed by Southern blot analysis of ES cell genomic DNA , and this was followed by blastocyst injection to generate Clec5a chimeric mice . Genotyping was performed by PCR using the following primers: CU , 5-CCCCAGCATCTTTGTTGTTT-3; FD , 5-CCAGCTAGTGGCTCAGTTCC3; and JD , 5-TTTTCTTCCCCATCCTCTGA-3 , which generated a 687-bp WT and a 798-bp knockout ( KO ) product , respectively . To obtain Stat1and Clec5a double-KO mice , we crossed Clec5a KO mice with Stat1 KO mice [44] . Interbreeding of Clec5a+/−Stat1+/− mice ( F1 ) generated Clec5a+/+Stat1−/− as well as Clec5a−/−Stat1−/− double KO mice , both of which were used in this study . ( Figure S4 ) The in situ labeling method was modified from that previously described [45] . The labeling solution ( 30 µl of 20 of mM CFSE ( Molecular Probes , Eugene , OR ) ) was mixed thoroughly with 140 µl of 100% ethyl alcohol and 250 µl pyogen-free PBS to avoid precipitation . Typically , 8 µl of labeling was injected per gram of mouse body weight ( approximately 3 µg CFSE/g ) up to a maximum of 300 µl of solution . To track leukocyte migration , mice were injected intraperitoneally with CFSE solution on day 2 after JEV infection . Blood samples were collected from CFSE-labeled mice to assess the labeling efficiency , and the MNCs in brain were also harvested from perfused mice to analyze and distinguish the infiltrating and resident cells in brain on day 5 after JEV infection . The experimental procedures and labeling control are shown in supplementary Fig . 6 & 7 . Standard errors of the mean ( s . e . m . ) were calculated and data was analyzed using unpaired Student's t-tests . Survival curve comparisons were performed using log-rank tests ( Prism software ) . | Japanese encephalitis ( JE ) is one of the most common forms of viral encephalitis worldwide , and the common complication post viral encephalitis is permanent neuropsychiatric sequelae resulting from severe neuroinflammation . However , specific treatment to inhibit JEV-induced neuroinflammation is not available . We found that JEV interacts directly with CLEC5A , a C-type lectin expressed on the myeloid cell surface . This observation led to two major findings; first , we demonstrate that JEV activates macrophages and microglia via CLEC5A , and blockade of CLEC5A reduces bystander neuronal damage and JEV-induced proinflammatory cytokine secretion from macrophages and microglia . Second , peripheral administration of anti-CLEC5A mAb does not only inhibit JEV-induced BBB permeability , but also reduces the numbers of activated microglia and cell infiltration into the CNS . The attenuation of neuronal damage and reduced viral load correlate with the suppression of inflammatory cytokines TNF-α , IL-6 , IL-18 , and MCP-1 in the CNS . Our studies provide new insights into the molecular mechanism of neuroinflammation , and reveal a possible strategy to control neuroinflammation during viral encephalitis . | [
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| 2012 | CLEC5A Regulates Japanese Encephalitis Virus-Induced Neuroinflammation and Lethality |
The world is witnessing mass displacement of populations which could impact global efforts to eliminate neglected tropical diseases such as trachoma . On the African continent , South Sudan has experienced high levels of population displacement . Population based baseline trachoma surveys were conducted among refugee camps in two Sudanese localities hosting South Sudanese refugee populations to determine whether the SAFE strategy was warranted . Cross-sectional , multi-stage , cluster-random surveys were conducted within refugee camps in each of two Sudanese localities , Al Salam and Al Jabalain . For survey sampling , multiple camps within each locality were combined to form the sampling frame for that locality . Household water , sanitation and hygiene indicators were assessed , and trachoma signs were graded by trained and certified graders . The prevalence of trachomatous inflammation-follicular ( TF ) in children aged one to nine years was 15 . 7% ( 95%CI: 12 . 1–20 . 2 ) in Al Salam and 10 . 6% ( 95%CI: 7 . 9–14 . 0 ) in Al Jabalain . The prevalence of trachomatous trichiasis ( TT ) in those 15 years above was 2 . 9% ( 95%CI: 1 . 8–4 . 8 ) in Al Salam and 5 . 0% ( 95%CI: 3 . 8–6 . 6 ) in Al Jabalain . The presence of water and sanitation was high in both survey units . Sudan has made progress in reducing the prevalence of trachoma within the country; however , the presence of over one million refugees from a neighboring trachoma hyper-endemic country could impact this progress . These surveys were the first step in addressing this important issue . The results demonstrate that at least three years of mass drug administration with azithromycin and tetracycline is needed in addition to the provision of TT surgical services . Additionally , it highlights that non-endemic or formerly endemic localities may have to adopt new strategies to provide services for refugee populations originating from hyper-endemic regions to ensure elimination of trachoma as a public health problem for all populations .
Trachoma , the leading infectious cause of blindness , is caused by infection with the bacterium Chlamydia trachomatis [1] . Infection occurs through personal contact via hands , clothes , and flies that have been in contact with the infected discharge from the eyes or nose of an infected person . Repeated episodes of infection over many years can cause scarring of the inner eye lid and eventually lead to the eyelids turning inward and eyelashes rubbing the surface of the eye until blindness occurs . Blindness from trachoma is irreversible . The “SAFE” strategy: Surgery to correct misdirected eyelashes , Antibiotics to treat the infection within the body , Facial cleanliness to remove the discharge from the eyes and nose , and Environmental improvement through the building and use of latrines , is used by country programs to assist in eliminating trachoma as a public health problem [2] . Despite the progress that has been made by endemic countries to eliminate trachoma as a public health problem [3] , the increasing displacement of large populations from trachoma endemic areas could impact global progress . The United Nations High Commissioner for Refugees ( UNHCR ) has stated that the world is currently witnessing the highest levels of displacement on record , with 68 . 5 million people categorized as forcibly displaced in 2017 [4] . This figure includes 40 million internally displaced people ( IDPs ) and 25 . 4 million refugees . Over 50% of current refugees are from three countries: Afghanistan , Syria , and South Sudan . Of the world’s refugees , 85% are hosted in countries designated as least developed countries . This displacement can impact national neglected tropical disease ( NTD ) programs , particularly trachoma , as ministries of health work to care for not only their own citizens but refugees as well . Ministries , supporting agencies and donors must determine how to include refugees when implementing trachoma interventions . In January 2005 , fighting between the Government of Sudan and the southern-based Sudan People’s Liberation Army ended after a Comprehensive Peace Agreement was signed . Following an election , South Sudan became an independent country in July 2011 . On December 15 , 2013 fighting between factions within the Government of South Sudan resulted in a protracted conflict , with the greatest military confrontations in Jonglei , Upper Nile and Unity states [5] . The conflict eventually led to increased food insecurity and caused much of the population to need food aid . Since December 2013 , this complex emergency has resulted in 1 . 91 million IDPs and over 2 . 46 million refugees , making the South Sudanese refugee crisis the largest and fastest growing on the African continent [6 , 7] . Though efforts have been made at peace , the UNHCR continues to document new refugees arriving in neighboring countries such as Sudan [8] . Both Sudan and South Sudan are endemic for trachoma [9–16] . Over one million South Sudanese fled to neighboring Sudan as refugees , with the majority settling in White Nile and Khartoum states [8] . Baseline surveys in White Nile state in 2009 showed that all localities ( equivalent of a district ) had below 5% trachomatous inflammation-follicular ( TF ) in children aged one to nine years , except for Al Jabalain locality which had a 6 . 4% TF prevalence [15] . Trachomatous trichiasis ( TT ) was at or below 0 . 2% in those 15 years and above in all but three localities ( Al Getina , Al Salam , and Al Jabalain ) . A subsequent impact survey conducted in Al Jabalain in 2015 and a surveillance survey in 2017 documented that the locality [17] had attained the World Health Organization’s ( WHO ) trachoma elimination threshold of TF less than 5% in children one to nine years and TT less than 0 . 2% in those 15 years and above [18] . Although all localities within White Nile state were considered non-endemic for trachoma as of 2015 , given that many of the South Sudanese refugees were from endemic regions of South Sudan , the Federal Ministry of Health ( FMOH ) conducted baseline surveys in November 2017 in refugee camps located in Al Jabalain and Al Salam localities ( Fig 1 ) . The aim of these surveys was to estimate the prevalence of key trachoma indicators within South Sudanese refugee camps located in two Sudanese localities to determine whether SAFE strategy interventions were needed .
Ethical clearance was received from the Sudan Federal Ministry of Health and the Emory University Internal Review Board ( IRB #079–2006 ) . Due to high illiteracy among the population , different languages of respondents and logistical constraints of written consent forms , IRB approval was obtained for verbal consent to be collected from all participants and recorded electronically . Verbal consent from a parent or guardian was required for those under 16 years of age . Participants were free to withdraw consent at any time without consequence . Most of the refugees in White Nile state are living in official refugee camps established by local authorities and international aid groups , while thousands of other displaced people live in informal settings [19] . Residents in the refugee camps are allowed to move outside the camps for work . Refugee camps located in Al Jabalain and Al Salam localities predominantly serve South Sudanese refugees and are managed by Sudan’s Commission of Refugees and White Nile State governmental departments , with support from non-governmental organizations . Official refugee camps within these two localities were surveyed to determine prevalence of trachoma . Informal camps and settlements were not included in the sampling frame as it would have been difficult to clearly separate from host community villages . To estimate a TF prevalence of 3% in children aged one to nine years with a precision of 2% , given an assumed design effect of 3 . 0 and a 95% confidence level , a total sample size of 837 children was needed for each enumeration unit . Adjusting for an assumed non-response rate of 20% yields a target population size of 1 , 004 children per locality . Assuming 4 . 7 individuals per household and estimating that children aged one to nine years make up 35% of the population , a total of 611 households were targeted . Trachoma graders in Sudan are experienced Ophthalmic Medical Assistants . Prior to the surveys , graders participated in a three-day training on trachoma grading using the WHO simplified grading scheme [20] . This training consisted of classroom and field-based practice . All trainees were first required to pass a slide test of 50 standard trachoma images . Those that passed the slide test moved on to the field reliability exam . To pass the field reliability exam the trainees graded 50 eyes for trachoma signs at a field location . Trainees were then compared to the consensus grade of three expert graders including one designated master grader . Those trainees that achieved an 84% agreement or higher and a kappa of >0 . 7 when assessing the presence or absence of TF were allowed to move into the field for the survey . All data recorders underwent a two-day training on how to use electronic tablets and survey software to collect data , conduct interviews , and properly select households and individuals to be interviewed following the standard protocol . All data recorders were required to pass an examination on their data collection skills to participate on the survey team . Data collection occurred electronically on Samsung tablets using SWIFT Insights , a custom built application based on Open Data Kit [21] . After data collection finished , the survey data was transferred from the tablets’ micro SD cards to a computer via USB connection as deemed appropriate by Sudan’s national standards at the time . Teams were comprised of a trachoma grader , a data recorder , and a driver . One supervisor was responsible for two teams . The same five survey teams worked in each refugee EU . All residents of selected households were enumerated regardless of their presence and/or willingness to be examined . All present and consented children aged one to nine years were examined for TF , trachomatous inflammation-intense ( TI ) , trachomatous scarring ( TS ) and TT , as defined by the WHO simplified grading scheme , using a 2 . 5X loupe and a flashlight [20] . All present and consented individuals ages 10 and older were examined for TT and corneal opacity ( CO ) . Individuals determined to have TT had their eyelids flipped , if possible , and graded for signs of TS . A case of TT unknown to the health system was defined as an individual with at least one eye that has TT for which surgery has not been refused nor received . All children aged one to nine years were observed for an unclean face , defined as the presence of ocular or nasal discharge [22] . At the end of the day , survey teams made one follow-up attempt to return to homes to examine children aged one to nine years who were absent during the examination process . Households that were empty were not replaced by another household . Any child found to have TF or TI was provided with the antibiotic Zithromax in addition to the rest of the household . Those with TT were registered and counseled to have TT surgery during the next scheduled surgical campaign in their locality or camp . Prior to the survey , the questionnaire was translated into Arabic and then back translated into English to ensure consistency of language . The structured interviews with adult household respondents were then conducted in Arabic at each selected household to assess demographic and household characteristics . In cases where the respondent did not speak or understand Arabic , a local translator from the camp was used . Adult females were given special preference for interviews as they are often the primary child caregivers and responsible for water collection and household chores . An improved water source was defined according to the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation and included protected dug well , protected spring , public tap , borehole , or piped water into dwelling [23] . Survey teams directly observed the presence of the latrines , and public and shared latrines were also considered household latrines . Questions also assessed face washing , time to fetch water ( <30 minutes , 30–60 minutes , >60 minutes ) , level of adult education and household possessions , location of infant feces disposal , trachoma knowledge , access to electricity and mobile phones , and home state within South Sudan . Sampling weights were calculated as the inverse of the probability of selection at both stages of sampling . Variance estimates and confidence intervals were calculated using Taylor linearization through svy survey procedures in Stata 14 . 1 ( STATA Corporation , College Station TX , USA ) . Maps were created using ArcGIS 10 . 6 ( ESRI , Redlands CA , USA ) . All reported percentages with confidence intervals are weighted . The absence of males during the survey visit was perceived to be an issue because women carry an increased burden of TT compared to men [24] . Post-stratification weighting using five-year age-sex bands from the survey census population was used when estimating the prevalence of TT among those 15 years and older to account for the systematic absence among older males in this population .
From the 511 visited households in Al Salam refugee EU , 1 , 921 ( 81 . 6% ) individuals of all ages were examined out of 2 , 355 individuals enumerated . Among children aged one to nine years , 916 ( 92 . 5% ) individuals were examined out of 990 enumerated . Of the 397 households selected in Al Jabalain refugee EU , the response rate was 80 . 3% ( 1 , 430 individuals examined /1 , 781 enumerated ) among all ages and 89 . 8% among children aged one to nine years ( 685 examined / 763 enumerated ) . In both refugee EU surveys , females were more likely than males to be examined for trachoma . Children aged one to nine years made up nearly 48% of the examined population ( Table 1 ) . In regards to water , sanitation and hygiene indicators , nearly all households had access to an improved water source and could collect water within 30 minutes or less roundtrip ( Table 2 ) . The great majority of adult household respondents reported that the children had their face washed at least once a day . Upon direct observation at the time of the survey , facial cleanliness among children aged one to nine years was nearly 73% in both camps . The observed prevalence of a latrine was considerably higher in Al Salam refugee EU , 99 . 7% ( 95%CI: 97 . 3–100 . 0 ) , compared to Al Jabalain refugee EU , 70 . 9% ( 95%CI: 51 . 4–84 . 9 ) . Perhaps because of this difference , household respondents reported that they disposed of their children’s excretion in a latrine more often in Al Salam than in Al Jabalain . Although household electricity was scarce or nonexistent in the camps , a majority of households in both camps reported having a mobile phone . Trachoma knowledge was low in both camps , but among those who knew of trachoma , facial and environmental cleanliness were the most commonly reported ways to prevent trachoma ( Table 3 ) . A very small percentage of household respondents reported knowing that surgery can protect someone from trachoma . The prevalence of TF in children aged one to nine years was 15 . 7% ( 95%CI: 12 . 1–20 . 2 ) in Al Salam refugee EU and 10 . 6% ( 95%CI: 7 . 9–14 . 0 ) in Al Jabalain refugee EU ( Table 4 ) . Within each individual camp , the prevalence of TF was above the 5% elimination threshold , with most having a prevalence of 10% or greater ( Fig 3 ) . In Al Salam refugee EU and Al Jabalain refugee EU , the prevalence of TI among children aged one to nine years was very low , and no TS was observed in this age group . The prevalence of TT in those aged 15 years and above exceeded the elimination threshold of 0 . 2% in both EUs ( 5 . 0% ( 95%CI: 3 . 8–6 . 6 ) in Al Jabalain and 2 . 9% ( 95%CI: 1 . 8–4 . 8 ) in Al Salam ) . The prevalence of CO was rare in both refugee EUs . Participants in these two refugee EUs reported coming from four states in South Sudan . The most commonly reported state was Upper Nile state for both Al Salam ( 72 . 4% ) and Al Jabalain ( 77 . 4% ) refugee EUs . A considerable proportion of individuals reported coming from Unity and Jonglei states , and just a few in Al Jabalain refugee EU reported Western Bahr el Ghazal as their home state ( Fig 4 ) .
The results of these population-based surveys demonstrated endemic levels of trachoma in refugee camps located within host localities considered to have eliminated trachoma as a public health problem . Within both refugee EUs , the prevalence of TF among children aged one to nine years was greater than 10% and the prevalence of TT in those 15 years and older was considerably greater than 0 . 2% . The FMOH of Sudan should conduct SAFE interventions in all surveyed camps that include three rounds of MDA and the provision of surgical services to avoid TT-related blindness . The current global trachoma elimination effort coincides with a period of record level human displacement . Trachoma control programs serving areas which host displaced populations should consider the possibility that timely elimination of trachoma as a public health problem may be threatened if these populations do not receive quality SAFE interventions . The South Sudanese populations surveyed in these refugee camps came predominately from Upper Nile , Unity , and Jonglei states of South Sudan . Though baseline mapping in South Sudan is incomplete , what has been surveyed showed that Upper Nile , Unity , and Jonglei states were hyper-endemic for trachoma [9 , 10 , 13] with one district having a prevalence as high as 80 . 1% TF in children and 14 . 6% TT in adults 15 years and above [10] . Many of the districts surveyed had not received SAFE interventions since baseline surveys were conducted . Due to insecurity , mass displacement of populations and a lack of funding , all other districts that were implementing the SAFE program had to suspend activities [25] . SAFE activities have yet to resume in the three states as of December 2018 . Not only have activities been suspended in areas that are known to be endemic , but it has been difficult to finish baseline surveys in other suspected endemic districts in the region . Given the suspended program in the northern region of South Sudan , the known level of trachoma endemicity there , and that refugee camp residents originated from these three states , it is unsurprising that the documented trachoma prevalence levels were greater than 10% TF in children aged one to nine years and TT levels greater than 2 . 9% in adults 15 years and above within the camp surveys . Changing population dynamics associated with displaced populations , whether IDPs or refugees , is an important issue for countries working to document their success in achieving WHO trachoma elimination targets . In these situations , country programs must determine how to address both the displaced populations and the hosting communities . Although there is sparse research on the effect of human displacement on trachoma control , it is clear that civil conflict and poverty can lead to increased transmission of infectious diseases including NTDs [26] . Furthermore , it has been demonstrated that forced or voluntary migration can also lead to the reintroduction of NTDs , including the reintroduction of Chlamydial infection , into communities where the prevalence was previously low [27 , 28] . It is still to be determined , however , if reintroduction of trachoma infection locally can result in a non-endemic district developing sustained trachoma levels above the elimination threshold [29] . In White Nile State , all localities were either non-endemic for trachoma or had reached the elimination target following sustained SAFE interventions . Refugee camps , however , were not included in the sampling frame of those earlier surveys in Al Jabalain or Al Salam localities . In the case of Al Salam locality , refugee camps house over 125 , 000 residents , a population figure that is greater than some locality population sizes in Sudan . This refugee population now has a demonstrated prevalence of 15 . 7% TF . Depending on the degree of mixing with the population outside the camp , there is a concern of reemerging Chlamydial infection in Al Salam locality more broadly . Comprehensive SAFE interventions , as well as the documented WASH indicators in the camps themselves may help contain and eliminate trachoma from these camps before it becomes a larger problem . Displaced populations , like permanent residents living in NTD endemic locations , often have multiple health challenges that need to be addressed . The objectives of humanitarian action include saving lives , alleviating suffering and maintaining human dignity [30] . Populations living in trachoma endemic refugee and IDP camps are at risk of continued infection with trachoma which could ultimately lead to blindness . Those who have trachoma have the right to be provided with treatment ( whether through MDA or surgery ) and education on how to prevent the disease . Providing SAFE services to refugees and IDPs while they are living in the camps not only reduces their risk of going blind but can provide other positive health outcomes . Azithromycin has been shown to decrease mortality among children under five years of age , reduce diarrhea and is the recommended line of therapy for pediatric patients with cholera [31–33] . Additionally , an increased focus on WASH related behaviors such as latrine use and personal hygiene can contribute to reduction in other non-trachoma diseases [34] . Implementing NTD programs within refugee and IDP settings is not without challenges . Often barriers to providing NTD services to refugees and IDPs exist: national NTD programs have limited resources for the host population , methods for obtaining MDA drugs are unclear , refugee care may fall under a separate ministry or legal institutions , and NTD supporting organizations may have difficulty gaining access to refugees because they are outside of their normal village-based programming [26] . Additionally , population dynamics within the camps vary . Displaced populations are increasingly composed of more children , elderly and people with disabilities [35] . A report from UNHCR reported that the majority ( 82% ) of South Sudanese refugees in Sudan are women and children , a finding similar to that reported in our surveys [7] . Furthermore , these surveys showed that there were more children per household ( 48% ) than typically found in non-refugee settings within Sudan ( 35% ) . This may be due to parents sending their children out of harm’s way or orphans staying with relatives residing in camps . These child-related dynamics must be taken into consideration by drug donation programs when determining the percentage of drug needed by children . These challenges highlight the need for governments and supporting organizations to document and share lessons learned . Preferred practices should also be developed in consultation with governments , UN agencies and implementing organizations , both from the NTD and humanitarian response sectors . Recognizing the challenges of providing NTD programs to refugees and IDPs , it is therefore important to more closely align public health activities , such as trachoma elimination , with the humanitarian agenda aimed at these populations . In the case of South Sudanese refugees in Sudan this need for alignment is especially important as the states where the refugees originate are still largely insecure and inaccessible to the Republic of South Sudan NTD program . WHO’s Expanded Special Project for Elimination of NTDs ( ESPEN ) considers South Sudan one of the five priority countries in the African region with the highest burden of NTDs [36] . Taking advantage of opportunities to provide NTD related services to South Sudanese known to be affected with NTDs , regardless of their physical location , helps the global program achieve its targets . By providing services to refugees where they are , while waiting to gain access to where they came from , ensures affected populations are not neglected while they wait to return home . As demonstrated by an earlier trachoma survey conducted in Ethiopia , the ‘block’ nature of the camps allowed for easy selection and compact household locations made logistics easier and took less time to survey [37] . Applying these methods , we have shown that it is possible to conduct trachoma prevalence surveys within refugee camp settings in Sudan and that the information can be used to plan for SAFE strategy interventions . Though planning and implementing a survey within camps required different levels of engagement with the locality , state , and administrative units that govern the camps , once access was granted , the structured nature of the camps made it easier than a typical district survey . Safety was not a concern . Though the structure of the camps made some aspects of surveys easier , there were also challenges and limitations . Camp-specific daily and monthly travel patterns should be considered when planning surveys as this can impact the number of people home at the time of the survey . Al Jabalain refugee EU survey team did not reach its targeted number of children , possibly due to timing of the survey where households were empty as residents were attending food distributions or assisting in food cultivation in farms . This may have reduced the precision of the TF estimate in that EU . Not all camp residents spoke Arabic; therefore , in some cases , a local guide would have to translate the questions and answers into the respondent’s native language . Data on how long camp residents had been in the camp prior to the survey was not collected . This information could have been useful in better gauging whether clinical signs of trachoma was due to recent exposure in South Sudan or newly acquired from Sudan . It is therefore suggested this information is collected in future surveys if possible . As Sudan continues to make progress in reducing trachoma in formerly endemic localities such as Al Jabalain , the FMOH has shown forethought in anticipating the possible impact of hosting large communities of refugees originating from trachoma endemic countries . By beginning the process of conducting baseline surveys in the camps now , before the country begins working on the WHO dossier to validate trachoma elimination as a public health problem , the trachoma control program has been able to better understand prevalence dynamics and plan program interventions . Following these 2017 refugee baseline surveys , in 2018 the FMOH conducted TT surgical outreaches within the camps . During the outreach , health education about how to prevent and treat trachoma was provided . Moving forward , the Sudan FMOH should conduct three rounds of MDA in addition to surgical campaigns and health education within the surveyed camps . Following three years of intervention , an impact survey should be conducted to monitor progress in reducing TF and TT within the population . Additionally , follow-up surveys should be considered for Al Salam and Al Jabalain at the locality level to confirm that the presence of the refugee population has not impacted the prevalence of trachoma within the locality . Given the findings of these surveys in White Nile state , the FMOH should also consider conducting baseline mapping in other South Sudanese refugee camps [8] where residents are suspected to have originated from trachoma endemic regions of South Sudan . A recently published systematic review of visual health in refugees concluded that there were no studies assessing the state of eye health in refugee groups from recent or current conflicts [38] . Governments and supporting organizations are beginning to fill this knowledge gap in countries such as Ethiopia , Uganda and Sudan , all countries hosting South Sudanese refugees [37 , 39–41] . The global community cannot expect to achieve the goals set out in the 2012 London Declaration on NTDs if it does not take into consideration the impact of conflict on programs and the increasing displacement of populations from endemic regions into endemic and non-endemic regions [42 , 43] . We found that there were refugee EUs with trachoma that require SAFE interventions in two non-endemic Sudanese localities . These groups must be accounted for during routine planning , implementation and surveillance activities if countries aim to be validated as eliminating trachoma as a public health problem within their borders . | Ministries of health in multiple countries have made progress in reducing the prevalence of trachoma , the leading cause of infectious blindness . With the increase in displaced populations throughout the world , the work of these national programs could be put at risk as formerly endemic or non-endemic districts now host large numbers of refugees from trachoma endemic regions . To properly respond , national programs must first assess the extent of the disease . We conducted baseline surveys in South Sudanese refugee camps located in two Sudanese localities to determine the prevalence of trachoma . These surveys showed that conducting prevalence surveys in refugee camp settings was possible . Trachoma was found to be present within the refugee camp population and programmatic interventions are required . The global community cannot expect to eliminate trachoma in the near future if displaced populations within countries and across country borders are not addressed . | [
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| 2019 | Prevalence of trachoma within refugee camps serving South Sudanese refugees in White Nile State, Sudan: Results from population-based surveys |
Leptospirosis is a major public health concern in New Caledonia ( NC ) and in other tropical countries . Severe manifestations of the disease are estimated to occur in 5–15% of all human infections worldwide and factors associated with these forms are poorly understood . Our objectives were to identify risk factors and predictors of severe forms of leptospirosis in adults . We conducted a retrospective case-control study of inpatients with laboratory-confirmed leptospirosis who were admitted to two public hospitals in NC in 2008–2011 . Cases were patients with fatal or severe leptospirosis , as determined by clinical criteria . This approach was meant to be pragmatic and to reflect the routine medical management of patients . Controls were defined as patients hospitalized for milder leptospirosis . Risk and prognostic factors were identified by multivariate logistic regression . Among the 176 patients enrolled in the study , 71 had criteria of severity including 10 deaths ( Case Fatality Rate = 14 . 1% ) . Three risk factors were independently associated with severe leptospirosis: current cigarette smoking ( OR = 2 . 94 [CI 1 . 45–5 . 96] ) ; delays >2 days between the onset of symptoms and the initiation of antibiotherapy ( OR = 2 . 78 [CI 1 . 31–5 . 91] ) ; and Leptospira interrogans serogroup Icterohaemorrhagiae as the infecting strain ( OR = 2 . 79 [CI 1 . 26–6 . 18] ) . The following post-admission laboratory results correlated with poor prognoses: platelet count ≤50 , 000/µL ( OR = 6 . 36 [CI 1 . 79–22 . 62] ) , serum creatinine >200 mM ( OR = 5 . 86 [CI 1 . 61–21 . 27] ) , serum lactate >2 . 5 mM ( OR = 5 . 14 [CI 1 . 57–16 . 87] ) , serum amylase >250 UI/L ( OR = 4 . 66 [CI 1 . 39–15 . 69] ) and leptospiremia >1000 leptospires/mL ( OR = 4 . 31 [CI 1 . 17–15 . 92] ) . To assess the risk of developing severe leptospirosis , our study illustrates the benefit for clinicians to have: i ) the identification of the infective strain , ii ) a critical threshold of qPCR-determined leptospiremia and iii ) early laboratory results . In New Caledonia , preventative measures should focus on early presumptive antibacterial therapy and on rodent ( reservoir of Icterohaemorrhagiae serogroup ) control .
Leptospirosis is a major threat to public health but little is known about the actual disease burden , and consequently , the disease has been neglected . Nevertheless , it is recognized as the most widespread zoonosis worldwide and an important and possibly emerging infectious disease . It occurs mostly in tropical and subtropical areas where conditions for transmission are favorable but is also known to occur in temperate climates [1] , [2] . Leptospirosis has also emerged as a disease of the adventure traveler , especially those participating in water sports [3] . According to estimates from the World Health Organization , more than 500 , 000 severe cases occur every year worldwide . In New Caledonia ( NC ) and other French overseas tropical or sub-tropical territories ( French Caribbean , French Guyana , Polynesia and Reunion Island ) , leptospirosis is a significant public health concern [4] . In NC , leptospirosis is known to be endemic with epidemic bursts occurring during hot rainy periods ( from December to March ) [5] , [6] . The average annual incidence is 45 cases per 100 , 000 inhabitants ( 2006–2009 ) but can reach 150 per 100 , 000 inhabitants during the rainiest months . The spectrum of symptoms is extremely broad and leptospirosis shares common clinical signs with many acute febrile diseases , such as influenza , dengue fever or malaria . Severe manifestations occur in 5–15% of human infections and are typified as: i ) Weil's syndrome ( a triad of jaundice , hemorrhage and acute renal failure ) , which has a 10–15% case fatality rate; and ii ) severe pulmonary hemorrhage syndrome ( SPHS ) , which may present as acute respiratory distress and was associated with case fatality rates >50% in several studies [7]–[10] . Prompt triage of high-risk patients is critical because complications require intensive care , specific treatment and monitoring . Although laboratory diagnosis is complex and requires specialized techniques such as microscopic agglutination technique ( MAT ) or real-time PCR , it is essential for biological confirmation . Factors responsible for the manifestation of severe forms have not been clearly established [8] , [11]–[18] . However , pathogen- as well as host-related factors are believed to play a role in the development of severe leptospirosis . This study aimed to determine the risk and prognostic factors independently associated with severe leptospirosis in laboratory-confirmed cases in adults in NC .
NC is an archipelago in the South Pacific with 249 , 000 inhabitants ( Census 2009 , New Caledonian Institute for Statistics and Economics , ISEE ) and located approximately 1 , 200 km east of Australia and 1 , 500 km northwest of New Zealand . It comprises a main island ( Grande Terre ) , the Loyalty Islands , the Isle of Pines , and several smaller islands . Fifty-two percent of the population lives in Noumea and its suburbs , 35% live in others districts on the main island and 13% live on the other islands . We carried out an observational retrospective case-control study to explore the factors associated with severe leptospirosis among patients who were hospitalized with a biologically confirmed or probable leptospirosis between January 2008 and June 2011 in either of two public hospitals ( Centre Hospitalier Territorial , Noumea and Centre Hospitalier du Nord , Koumac ) . All patients hospitalized with a laboratory diagnosis of leptospirosis were retrospectively included . The enrollment criteria excluded patients who had no history of hospitalization , who were not residents of the study area and who were under 18 years old . Biological diagnoses were performed at the Institut Pasteur of New Caledonia ( IPNC ) . The MAT panel used for leptospirosis serology in New Caledonia was adapted to the local epidemiology and includes 11 pathogenic serovars and the Patoc strain , corresponding to the “local panel” described by Berlioz-Arthaud et al . , 2007 [5] . Leptospirosis was categorized as either confirmed ( patients who had: a positive PCR , a seroconversion from negative to a MAT titer ≥400 in paired serum samples or a fourfold increase between an acute serum sample and a convalescent serum sample using the reference MAT ) or probable ( patients who had both a clinical presentation of leptospirosis and a single MAT titer >400 ) . Routine real-time PCR allowed the target organism to be quantified [19] . The sequence polymorphism of the lfb1 gene diagnostic PCR products was used to identify the infecting strains [20] , as this method was formerly demonstrated to allow identification of several different lfb1 sequence cluster types circulating in New Caledonia . MAT results in convalescent sera were also used to putatively identify the Leptospira serogroup [21] . Cases were hospitalized patients who met the clinical definition for severe leptospirosis . Severe leptospirosis was defined by the presence of at least one of the following criteria: acute renal failure requiring dialysis , shock treated with vasoactive drugs , alveolar hemorrhage , bleeding requiring blood transfusion , respiratory insufficiency needing mechanical ventilation or death during hospitalization . Controls were defined as patients hospitalized for milder forms of leptospirosis that presented none of these clinical complications , i . e . neither requiring dialysis , mechanical ventilation , blood transfusion or vasoactive drugs nor presenting an alveolar hemorrhage . For each study participant , a standardized form was retrospectively completed . Clinical manifestations and medical history , including current cigarette smoking , chronic alcoholism , respiratory insufficiency , diabetes mellitus and chronic hypertension were collected as mentioned in medical records . Demographic data and laboratory results were extracted from electronic records . All data were recorded with EPI Data 3 . 5 . The study was approved by the Clinical Research Committee of Institut Pasteur Paris and the research committee of Centre Hospitalier Territorial , Noumea . The study was also approved by the French consultative committee for the data processing in health research ( Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le Domaine de la Santé , CCTIRS ) and was authorised by the French Data Protection Authority ( CNIL , Commission Nationale de l'Informatique et des Libertés ) . Informed oral consent was obtained from patients . The use of oral consent was approved by the CCTIRS because it was thought to be the most appropriate mode for this retrospective observational study . Oral consent was witnessed and documented on a form . All data analyzed were anonymized . Statistical analysis was performed using Stata 11 ( StataCorp LP , College Station , TX , USA ) . Categorical variables were summarized using percentages and compared using the Chi-square test or Fisher's exact test . Continuous variables were summarized using means ± standard deviation ( SD ) and compared using Student's t-test or the Mann-Whitney test as appropriate . Logistic regression was used to identify factors associated with severe leptospirosis and to estimate odds ratios ( ORs ) and 95% confidence intervals ( CIs ) for the associations between exposure variables and severe cases . Variables with p values<0 . 20 were introduced in the multivariate logistic regression model . A manual backward stepwise approach was used to remove non-significant variables; only variables with p values<0 . 05 were retained in the final model . Interactions were sought by introducing interaction terms in the logistic regression model and testing for their significance at the 0 . 05 level . The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of various models . The biological variables that were included in the multivariate predictor model were comprised of missing values and resulted in losses of power and precision , complicated data handling and analysis , and potentially biased ORs due to differences between observed and unobserved data . We used multiple imputation ( MI ) to include all participants with missing responses in the analysis . Missing data were imputed using chained equations ( ICE ) . The multiple imputation prediction model included all variables in the conceptual framework . Thirty imputed data sets were created and analyzed together . The critical thresholds we determined in this model were levels above or below those in which treatment decisions were usually made . Standard logistic regression models were fitted using STATA 11 . The imputed data sets were analyzed in STATA 11 using the ICE suite of commands . In addition , given the limitations imposed by the missing data , we performed a series of sensitivity analyses to assess the robustness of the analysis . In each case , we assumed a specific extreme scenario and apportioned missing values accordingly .
A total of 306 patients hospitalized with leptospirosis were diagnosed by IPNC between January 2008 and June 2011 . One hundred and fifty of these cases were excluded from further analysis because they were not hospitalized in one of the two participating centers ( n = 55 ) , resided overseas ( n = 4 ) , were under 18 years old ( n = 45 ) or could not be traced ( n = 26 ) . Of the 176 patients included in the study , 152 ( 86 . 4% ) were classified as confirmed leptospirosis and 24 ( 13 . 6% ) were classified as probable leptospirosis ( i . e . , they had a clinical presentation of leptospirosis and a single MAT titer >400 ) . The majority of study subjects were men ( 62 . 5% ) with a mean age of 42 . 2±17 . 1 years . Subjects were mostly Melanesian ( 88 . 6% ) living in tribes in rural areas ( 88 . 5% ) . The frequency of clinical symptoms and complications are presented in Table 1 . Jaundice , oliguria and conjonctival suffusion were more frequent among cases whereas the incidence of fever , myalgia and headache were not significantly different between cases and controls . Seventy-one patients ( 40 . 3% ) met our clinical definition for severe leptospirosis . Among them , 23 ( 32 . 4% ) had acute renal failure requiring dialysis . Pulmonary involvement associated with mechanical ventilation was identified in 29 ( 41 . 4% ) patients . Shock treated with vasoactive drug was documented in 62 ( 88 . 6% ) patients , 23 ( 32 . 4% ) patients received packed erythrocytes transfusion and 39 ( 55 . 7% ) patients had alveolar hemorrhage . Among severe cases , ten patients died and all had been admitted or transferred to the only intensive care unit in Noumea . The case fatality rate of the severe forms was 14 . 1% and the median time between admission and death was one day . Among the nine fatal cases for whom the serogroup was identified , seven were Icterohaemorrhagiae . Both methods ( MAT and PCR product sequence polymorphism ) were used to identify the serogroup or the putative serogroup of the infecting strain in 161 patients ( 91 . 5% ) . Using MAT results , the serogroup involved was identified in 51 patients . Among these , 42 were confirmed cases with an acute and a convalescent serum and 9 had a single MAT titer ≥1600 . The most common serogroups were Icterohaemorrhagiae observed in 37 patients , Australis in 6 , Pyrogenes in 5 patients , Pomona in 1 , Tarassovi in 1 , Panama in 1 . The sequence polymorphism of lfb1 PCR products was used to identify the sequence cluster type of the infecting strains in 110 patients . The most common sequence cluster type identified was linked to serogroup Icterohaemorrhagiae in 76 patients , Pyrogenes in 21 patients , Ballum in 8 patients , Australis in 3 , Pomona in 1 and Bataviae in 1 . For the 15 remaining cases , identification of the serogroup was not possible . At the time of admission , 11 patients had acute co-infections with diseases such as laboratory confirmed dengue ( n = 5 ) , syphilis ( n = 2 ) , Ascaris lumbricoides infection ( n = 1 ) , Chlamydia trachomatis infection ( n = 1 ) , A ( H1N1 ) pandemic influenza ( n = 1 ) or tuberculosis ( n = 1 ) . All patients tested positive for dengue were diagnosed by RT-PCR or NS1 antigen ELISA . Moreover , all 11 patients with co-infections were confirmed cases: the diagnosis of leptospirosis was confirmed by PCR in 10 and a seroconversion was ascertained in paired sera using the microagglutination test in one . The factors associated with severe leptospirosis in the univariate analysis are presented in Tables 2 and 3 . In a logistic regression model used to assess independent factors , current cigarette smoking ( p = 0 . 003 ) , L . interrogans from the Icterohaemorrhagiae serogroup ( p = 0 . 011 ) and a delay of >2 days between the onset of symptoms and the initiation of antibiotherapy ( p = 0 . 013 ) were independently associated with severe leptospirosis ( Table 4 ) . The laboratory parameters at admission that were associated with severe leptospirosis are presented in Table 5 ( before and after the MI procedure ) . In multivariate analysis , the following variables were linked with poor prognosis at referral: leptospiremia >103 leptospires/mL before initiating treatment; platelet count ≤50×109/L; serum creatinine >200 mM , serum lactate >2 . 5 mM and serum amylase >250 UI/L ( Table 6 ) . To explore the effects of potential biases related to the missing values in the model including all the patients , we tested the sensitivity of our multivariate model to a series of assumptions about the missing values . Analysis of the associations between most of these parameters and severe leptospirosis resulted in similar OR estimates regardless of the hypothesis used to account for the missing values .
This retrospective case-control study of leptospirosis in NC allowed us to identify risk factors associated with severe forms of leptospirosis in adults . In parallel , we also identified biological variables evident at the time of admission that were predictors of poor outcomes . Our main findings indicated that the L . interrogans serogroup Icterohaemorrhagiae was significantly associated with severe forms of leptospirosis . Multivariate analysis identified other risk factors linked to the host or to the initial management of the disease: smoking was an independent factor of severity , particularly in the pulmonary involvement of the disease . Also , if antibiotics were started later than two days after symptom onset , the risk of developing a severe form was considerably higher than if antibiotics were started earlier . The first laboratory findings available after admission revealed certain parameters that correlated strongly with severe outcomes . Leptospiremia higher than 103 leptospires/mL before treatment was initiated was associated with a higher risk of developing severe leptospirosis . Acute renal failure , acute pancreatitis , a low platelet count and a high serum lactate level at admission were also indicators of a poor prognosis . We classified the severity of leptospirosis based on clinical criteria , which was a pragmatic approach meant to reflect the routine management of patients . We focused on hospitalized patients that had a laboratory diagnosis of leptospirosis . Nevertheless , determining the case definition was difficult , since there is no consensus for severe forms in leptospirosis . Classically , the known severe forms are characterized by Weil's syndrome . The emergence of SPHS has been described more recently [8]–[10] . Thus , some studies using similar methodologies have defined severe cases as those involving patients with SPHS as opposed to patients with non-severe pulmonary forms [8] , [12] , [22] . Other authors restricted the definition of severe forms to fatal outcomes during hospitalization [14] , [15] , [23] . Another option was to classify patients hospitalized for leptospirosis in intensive care units as severe cases and patients hospitalized for leptospirosis in other hospital departments as controls [12] . However , this definition does not take into account the patient's clinical condition at admission and could have been biased because of the patient's distance to the only ICU in Noumea . Similar to our classification , Herrmann-Storck et al . [11] used treatment-related criteria , such as the need for hemodialysis or mechanical ventilation , to define severe cases in Guadeloupe and Spichler et al . also used clinical signs to define severity [24] . One major finding of our study was the independent association between the Icterohaemorrhagiae serogroup and severe leptospirosis in the multivariate analysis . Though Icterohaemorrhagiae has been previously suspected to cause more severe forms of the disease , this relationship has been seldom demonstrated in a significant number of patients [21] . Additionally , sequence-derived identification of the infecting strain provides a higher degree of confidence than MAT-derived putative identification of the serogroup , especially at the individual level [21] . Our identification of the infecting strain in patients diagnosed by PCR increased the statistical power of our analysis and provided a larger set of data . Several hypotheses regarding leptospirosis severity are based on host genetic susceptibility factors [25] , [26] and/or on bacterial virulence [27] , although the virulence mechanisms are poorly understood and are probably multifactorial [28] . In NC , rodents are the main reservoir of leptospirosis . Three rat species ( Rattus rattus , Rattus exulans and Rattus norvegicus ) introduced during different periods of settlement contribute to the maintenance and transmission of the Icterohaemorrhagiae serogroup [3] , [29] , [30] . Our results demonstrating this strain's high pathogenicity highlight the importance of controlling rodent populations , not only to lower the risk of transmission , but also to limit the number of severe forms . In a retrospective study in 17 patients with pulmonary involvement , Martinez Garcia et al . found that smoking was a risk factor for respiratory involvement in human leptospirosis [31] . Our multivariate analysis is in accordance with this finding: smokers were three times more likely to develop severe leptospirosis than non-smokers , and the risks of alveolar hemorrhage and respiratory distress in smokers were especially high . It is suggested that some tobacco components can favor the development of pulmonary haemorrhage by increasing the permeability of lung capillaries , damaging alveolar basement membrane and increasing the local inflammatory response [32] . This association with smoking has also been found for varicella pneumonia [33] . Another noteworthy finding was that the delay before antibacterial therapy had a major impact on outcome . This finding , which agrees with previous reports [11] , [16] , illustrates the need for early initiation of antimicrobial therapy to reduce disease severity . Presumptive treatment based on clinical and epidemiological evidence therefore appears justified while waiting for the laboratory results . In addition , leptospires are usually susceptible to most common antibiotics [34] and the daily treatment costs for standard regimens of beta-lactam/cephalosporins or tetracyclines are low . Few reports have described underlying conditions that are significantly associated with the severity of acute leptospirosis . Although chronic alcoholism and hypertension have been associated with severity [11] , we did not confirm this in our multivariate analysis . Consistent with some previous studies , our results suggest that there is no relationship between gender and the severity of the disease [11] , [14]–[16] , [22] , [23] . Previous reports have been controversial about this factor: only two studies have shown that the male sex was independently linked to severe disease [8] , [35] . The sex ratio in our study population was 1 . 7 male/female , which agrees with the leptospirosis incidence typically found in NC [5] . Numerous studies have described age as a predictor of death [14] , [15] , [23] , [24] , [35] . Our study , in accordance with Gouveia et al . did not identify age as a risk factor [8] . In agreement with previous reports [11] , [12] , [15] , we identified laboratory parameters at the time of admission that were predictive of severe forms . The critical thresholds we determined in this model were above or below the levels at which treatment decisions are usually made . Early determination of these parameters could provide an alert to guide physicians in their management of patients at high risks . Quantification of leptospires in clinical samples could have a prognostic value if performed routinely . Segura et al . described high levels of leptospires in the lung , liver , muscle and kidney tissues of patients who died and found that the presence of at least 104 leptospires/mL of blood can be considered a critical threshold for fatality [36] . These findings agreed with those of Truccolo et al . [37] . We considered it relevant to define a leptospiremia threshold associated with disease severity . In our study we determined the leptospiremia in patients who had been diagnosed by quantitative PCR , but we only kept the values for blood samples taken before antibacterial therapy was started . This forced us to exclude data if the exact date of sampling was not available , and led to a substantial number of missing values in our model . We therefore performed a MI analysis , which is a well-established method for analyzing data sets with missing values , and showed by multivariate analysis that a threshold of 103 leptospires/mL of serum strongly correlated with severe forms . In a recent study , leptospiremia did not correlate with clinical manifestations of outcome [38] . To our knowledge , our study is the first one that clearly delineates an association between leptospiremia levels and disease severity . One limitation of the present study is its retrospective design . Indeed , it was sometimes difficult to estimate longitudinal parameters , such as the delay between the blood sampling for laboratory diagnosis and the initiation of the antibacterial therapy . We therefore used MI to account for the missing values and assumed that the data were not missing completely at random . Additionally , we performed a sensitivity analysis to explore which effects might have biased our results . Even under the most extreme hypothesis in the sensitivity analysis , the same trends were evident . We also distinguished the risk factors and the prognostic factors associated with severe leptospirosis , as presenting them in two separate models may be informative . Physicians cannot intervene with risk factors directly but they can identify high-risk patients . On the other hand , prognostic factors occur secondary to the infection , so taking them into account early and initiating appropriate treatments may limit the development of severe leptospirosis . In conclusion , leptospirosis is responsible for a high number of hospitalizations due to severe forms of the disease in NC . To prevent and control this public health threat , some recommendations may be inferred from our results . The predominance of the Icterohaemorrhagiae serogroup , which independently associated with severe forms of leptospirosis , highlights the possible benefits of rodent control measures . We support the advantages of early antimicrobial therapy initiation in suggestive epidemiological and clinical contexts , even before laboratory confirmation of leptospirosis . As reported previously , cigarette smoking was more common in severe leptospirosis than in mild forms . Physicians should focus on the pulmonary function of their patients , especially if they are smokers . To improve the early management of high-risk patients , some laboratory criteria could be used at the time of admission . High creatinine , lactate and amylase levels and low platelet counts were associated with severe leptospirosis . Our data illustrate the benefit of using a critical threshold of qPCR-determined leptospiremia to assess the risk of developing severe leptospirosis . Leptospirosis remains a major medical challenge , especially in tropical areas and particularly for severe forms that can progress rapidly to multiple organ failure . Physicians need to be aware of the factors associated with severe leptospirosis to reduce severity and mortality through the timely management of patients . | Leptospirosis is a neglected tropical disease and a public health concern worldwide . Factors responsible for the progression towards severe forms have not been clearly established . However , pathogen- as well as host-related factors are both believed to play a role in the development of severe leptospirosis . This study aimed to determine risk and prognostic factors independently associated with severe leptospirosis in laboratory-confirmed cases in adults in New Caledonia . Our study provides important results on these factors . One major finding was the independent association between the serogroup Icterohaemorrhagiae and severe leptospirosis in the multivariate analysis . Though empirically recognized , we think that this association between this highly prevalent serogroup and most severe forms of the disease was seldom ( if ever ) clearly demonstrated . Our data also illustrate the benefit of using a critical threshold of qPCR-determined leptospiremia to assess the risk of developing severe leptospirosis in patients after their admission to hospital . | [
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| 2013 | Risk Factors and Predictors of Severe Leptospirosis in New Caledonia |
Circadian clocks impose daily periodicities to animal behavior and physiology . At their core , circadian rhythms are produced by intracellular transcriptional/translational feedback loops ( TTFL ) . TTFLs may be altered by extracellular signals whose actions are mediated intracellularly by calcium and cAMP . In mammals these messengers act directly on TTFLs via the calcium/cAMP-dependent transcription factor , CREB . In the fruit fly , Drosophila melanogaster , calcium and cAMP also regulate the periodicity of circadian locomotor activity rhythmicity , but whether this is due to direct actions on the TTFLs themselves or are a consequence of changes induced to the complex interrelationship between different classes of central pacemaker neurons is unclear . Here we investigated this question focusing on the peripheral clock housed in the non-neuronal prothoracic gland ( PG ) , which , together with the central pacemaker in the brain , controls the timing of adult emergence . We show that genetic manipulations that increased and decreased the levels of calcium and cAMP in the PG caused , respectively , a shortening and a lengthening of the periodicity of emergence . Importantly , knockdown of CREB in the PG caused an arrhythmic pattern of eclosion . Interestingly , the same manipulations directed at central pacemaker neurons caused arrhythmicity of eclosion and of adult locomotor activity , suggesting a common mechanism . Our results reveal that the calcium and cAMP pathways can alter the functioning of the clock itself . In the PG , these messengers , acting as outputs of the clock or as second messengers for stimuli external to the PG , could also contribute to the circadian gating of adult emergence .
Circadian clocks impose a daily rhythmicity to the behavior and physiology of multicellular organisms . In animals , the circadian system consists of a principal circadian pacemaker located in the central nervous system ( CNS ) as well as of autonomous circadian pacemakers located in most peripheral tissues . At their core , circadian rhythms are produced by intracellular transcriptional/translational feedback loops ( TTFL ) [1 , 2] , and coordination at different levels ensures that the organism express a unified circadian time . At the level of the central pacemaker , a number of transmitters [3–10] and neuropeptides [11–16] mediate the production of coherent network-wide circadian oscillations . In addition , central and peripheral clocks are coordinated , maintaining a stable phase relationship . In mammals this is accomplished through a variety of channels that are still poorly understood , and which include electrical , endocrine , metabolic , and even thermal signaling [17] . In insects , most peripheral clocks are synchronized with the central pacemaker by entrainment to a common signal ( e . g . , light , which can penetrate the translucent exoskeleton , or temperature; [18] ) , with the notable exception of the clock in the non-neuronal prothoracic gland ( PG ) clock , which is coupled to the brain clock by neuropeptide action [19] . In addition to intra- and inter- pacemaker coordination , clocks can also be entrained by external stimuli such as light , which is a powerful entraining signal , as well as by an organism’s physiological and behavioral state [1 , 20] . For example , in mammals the liver clock can be entrained by daily feeding , which retains a phase difference with the central clock once restricted feeding ends [21] . Coordination of circadian activity within the mammalian central pacemaker and the phase-shifting effects of light are mediated intracellularly by calcium and cAMP , which ultimately funnel their actions through the calcium/cAMP-dependent transcription factor , CREB , which acts on CRE ( calcium/cAMP-dependent response ) elements to change clock gene expression [22–26] . In insects , a variety of circumstantial evidence suggests that calcium and cAMP can alter the functioning of the intracellular transcription/translation feedback loop ( TTFL ) of central pacemaker neurons [26] . For example , chronic buffering of calcium within pacemaker cells causes a progressive lengthening of the circadian periodicity of adult locomotor activity [27] and mutations that alter cAMP levels [28] or CREB function [29] alter the fly’s free-running period; likewise , acute changes in pacemaker excitability cause phase shifts in locomotor activity rhythms , which may be mediated by CREB [30] . Nevertheless , evidence for a direct role for calcium , cAMP , and CREB in pacemaker TTFL function is lacking . Indeed , calcium is involved in synaptic transmission and is the second messenger for some circadian neuropeptides ( e . g . , sNPF [19] ) and cAMP is the second messenger for the principal circadian transmitter PDF ( Pigment-Dispersing Factor ) in Drosophila [31–33] . Thus , calcium and cAMP could alter TTFLs circadian clock output indirectly , by changing the complex actions and interactions between different classes of pacemaker neurons , which , for instance , differ in their phasing of calcium oscillations yet share the same timecourse of clock gene expression [34] . For this reason we investigated the function of calcium and cAMP on the functioning of the non-neuronal peripheral PG clock . The PG is a peripheral endocrine gland that produces the molting hormone ecdysone . It contains a clock that is coupled to the central pacemaker and restricts the time of emergence ( eclosion ) of the adult fly to the early part of the day [19 , 35 , 36] . We investigated the role of calcium and cAMP on circadian clock function by determining the consequences on the rhythmicity of emergence of genetically manipulating the levels of these second messengers . We found that increasing or decreasing the levels of calcium and cAMP in the PG caused , respectively , a shortening and a lengthening of the periodicity of emergence . Importantly , knockdown of CREB in the PG caused an arrhythmic pattern of eclosion . Interestingly , the corresponding manipulations directed at central pacemaker neurons had similar effects on the rhythmicity of eclosion and of adult locomotor activity , suggesting a common mechanism . Direct measurements of calcium and cAMP levels in the PG revealed that these vary during the course of the day . Since calcium and cAMP are second messengers of many transduction pathways , our findings suggest that calcium and cAMP could provide a pathway through which different stimuli , both internal and external to the clock , could alter the period of the circadian clock . Furthermore , since calcium and cAMP play an important role in ecdysone synthesis and secretion , our findings raise the possibility that calcium and cAMP , produced as outputs of the clock or as second messengers for stimuli external to the PG , could contribute to the gating of adult emergence .
In order to investigate the role of calcium in regulating circadian clock function , we determined the consequences on the circadian rhythm of emergence of knocking down , specifically in the PG , genes involved in calcium homeostasis . As shown in Fig 1A , reducing the expression of two different calcium channels ( cacophony and Ca-alpha1D; Fig 1Aa and 1Ab ) and of the IP3 ( Inositol 1 , 4 , 5 , -tris-phosphate ) receptor ( IP3R; Fig 1Ac ) significantly lengthened the periodicity of adult emergence ( Fig 1D ) , without affecting the strength of the rhythmicity ( Fig 1E ) . By contrast , knockdown of SERCA ( sarco/endoplasmic reticulum calcium ATPase ) in the PG resulted in a significant shortening of the periodicity of eclosion ( Fig 1Ba and 1D ) , also without significantly altering its associated rhythmicity index ( Fig 1E ) . ( In this case , SERCA was knocked down acutely at the end of metamorphosis using the GAL80ts temperature-sensitive genetic system [37] because SERCA RNAi expression in the PG throughout life was lethal . ) Such manipulations are expected to cause decreases and increases in calcium levels for cacophony , Ca-alpha1D , and IP3R , and SERCA , respectively , and direct measurement of calcium levels in the PG showed that this was indeed the case ( cf . Fig 2C , below ) . We also probed the possible role of CaMKII ( calcium/calmodulin-dependent protein kinase I ) and CASK ( calmodulin-dependent protein kinase activity ) in the PG to determine if target proteins of calcium signaling might have an effect on the eclosion rhythm . As shown Fig 1C and 1D knockdown of CaMKII and of CASK in the PG ( Fig 1Ca and Fig 1Cb , respectively ) caused a significant lengthening of the periodicity of eclosion compared to that of the control ( Fig 1D ) , without affecting strength of rhythmicity ( Fig 1E ) . These results show that changing the levels of intracellular calcium affects the periodicity of the circadian clock . These changes can be effected by reducing calcium entry ( e . g . , through CACOPHONY or CA-ALPHA1D ) or release from intracellular stores ( e . g . , via IP3 ) , and could be mediated by CaMKII or CASK . Flies bearing single UAS-RNAi insertions for genes in calcium homeostasis pathway expressed normal circadian rhythmicity of eclosion ( S1 Fig ) . We measured intracellular calcium levels in the PG in vivo using the genetically-encoded sensor , GCaMP 3 . 2 ( GCaMP ) [38] . This sensor has been extensively used and causes no apparent cytotoxicity or behavioral defects [39] . In particular , it caused no defects in the circadian rhythmicity of eclosion ( S2A–S2C Fig ) or locomotor activity ( S2D–S2F Fig ) when expressed in the PG using the phm-GAL4 driver [19 , 40] . We determined the dynamic range and sensitivity of this sensor by incubating PGs with different concentrations of calcium in the presence of 50 μM calcimycin , a calcium ionophore ( Fig 2A ) . As shown in Fig 2B , we found that the readout from the GCaMP sensor increased monotonically between 0 and 300nM calcium . We then measured changes in calcium levels in the PG during the course of the day ( and subjective day ) at the beginning of metamorphosis ( white pre-pupal stage , WPP ) . We chose this stage because the PG is intact this time and the circadian clock is fully functional [41] , unlike in animals prior to emergence , when PG cells are undergoing apoptosis [42] , which , in our hands , rendered such measurements variable and inconsistent . Overall , GCaMP readings were between 0 . 6 and 0 . 8 , which corresponds to 50-300nM ( see Fig 2B ) , and is consistent with published values . For instance , work done using Manduca sexta PG using Fura-2-loaded PG cells , reported a basal calcium concentration in the range of 50-200nM and maximal levels of 300-500nM following stimulation with the PTTH neuropeptide [43] . Importantly , genetic manipulations expected to increase and decrease the levels of calcium ( expression in the PG of RNAi to cacophony , Ca-alpha1D , and IP3R , and RNAi to SERCA , respectively ) did indeed cause the corresponding changes in GCaMP fluorescence ( Fig 2C ) . This indicates that the GCaMP sensor can detect physiologically-relevant changes in intracellular calcium levels in the PG . We then measured the GCaMP signal at different times of day in the PG of wildtype animals of the same developmental age ( WPP stage ) under a 12h light: 12h dark regime ( LD ) ( Fig 2D ) and under conditions of constant darkness and temperature ( DD ) ( Fig 2E ) . Frequency analyses of these data using JTK_cycle [44] and Lomb-Scargle [45] failed to reveal any significant circadian rhythmicity . Nevertheless , a more restricted ANOVA analysis revealed that calcium levels reached a minimum at 12h after lights on ( beginning of the night; typically referred to as Zeitgeber ( ZT ) time 12 , where ZT0 is lights-on ) under LD conditions ( Fig 2D ) , which was delayed about 3h under DD conditions ( circadian time ( CT ) 15 , early subjective night; where CT0 is the start of the subjective day ) ( Fig 2E ) . The absence of circadian rhythmicity in these data is surprising . However , calcium oscillations in the PG are known to be influenced by the brain [41] . Thus , it is possible that the timecourse we recorded in the PG reflects the sum of two circadian inputs , one driven by the brain , and the other produced by the PG itself . Our results contrast with those obtained previously , by Morioka and colleagues [41] , which showed that baseline intracellular calcium concentrations in the PG reached a single minimum at the beginning of the day under LD condition . However , their readings were done every 6 hours and skipped the timepoint at which we observed an even lower ( and minimal ) level . In arrhythmic per0 mutants , we observed a single minimum under LD conditions at ZT9 ( Fig 2F ) ; we observed no significant changes under DD conditions ( Fig 2G ) , as would be expected for an arrhythmic genotype . In parallel experiments we evaluated the role of cAMP in the control of the circadian clock that controls the timing of adult fly emergence . We found that knockdown of the cAMP phosphodiesterase ( encoded by the dunce gene ) in the PG shortened the periodicity of eclosion ( Fig 3Aa and 3B ) , whereas knockdown in the PG of the calcium-dependent adenylate cyclase ( encoded by the rutabaga gene ) , lengthened the periodicity of eclosion ( Fig 3Ab and 3B ) , without affecting the strength of the rhythms ( Fig 3C ) . Such manipulations are expected to cause increases and decreases in cAMP levels , respectively , and direct measurement of cAMP levels in the PG showed that this was indeed the case ( cf . Fig 4C ) . Furthermore , RNAi inhibition of others elements of the cAMP pathway in the PG also affected the periodicity of eclosion . Indeed , knockdown of Epac ( Exchange protein directly activated by cAMP ) and Rap1 ( Ras-related protein 1 GTPase ) in the PG caused a significant lengthening of the periodicity of eclosion ( Fig 3Ac , 3Ad and 3B ) . Flies bearing single UAS-RNAi insertions for genes in cAMP pathway expressed normal circadian rhythmicity of eclosion ( S3 Fig ) We next monitored cytoplasmic cAMP levels in the PG , using the genetically-encoded Epac1 camps cAMP sensor at the beginning of metamorphosis . This sensor has previously been used to measure cAMP levels in Drosophila neurons [31 , 33] , and driving its expression in the PG did not affect the rhythmicity of eclosion ( S4A–S4C Fig ) or of locomotor activity ( S4D–S4F Fig ) . Stimulating the PG with 100 μM forskolin ( FSK ) , an activator of most forms of adenylyl cyclase [46] , caused the Epac1 signal to fall ( as expected for a FRET-based sensor , for which an increase in cAMP would cause an decrease in signal ) to a steady state level within 10 min ( Fig 4A ) . In order to determine the sensitivity of this sensor , we measured the signal produced by increasing concentrations of 8-Br-cAMP , a membrane permeable analog of cAMP ( Fig 4B ) . The FRET signal in the PG was highest for basal conditions , and decreased monotonically until 8-Br-cAMP concentrations of around 700nM . Importantly , genetic manipulations expected to increase and decrease the levels of cAMP ( expression in the PG of RNAi of dunce and rutabaga , respectively ) did indeed cause the corresponding changes in Epac1 FRET signal , indicating that the Epac1 sensor can detect physiologically-relevant changes in intracellular cAMP levels in the PG ( Fig 4C ) . We then measured the FRET signal in the PG at different times of day in wildtype animals of the same developmental age ( WPP stage ) . As was the case for the calcium measurements , we did not detect any significant rhythmicity in the circadian range using the JTK_cycle [44] and Lomb-Scargle [45] spectral analyses . However , ANOVA analysis did reveal that under LD conditions cAMP levels were highest ( lowest FRET signal ) at ZT18 , and lowest ( highest FRET signal ) around lights-off ( ZT12 ) ( Fig 4D ) . Under DD conditions highest cAMP levels were delayed by 3h ( to CT0 ) , whereas the lowest cAMP levels occurred at the middle of the subjective day ( CT6 ) ( Fig 4E ) . Finally , in arrhythmic per0 mutants , under LD conditions cAMP levels were highest at ZT18 and lowest around ZT21 ( Fig 4F; similarly to rhythmic controls under LD conditions; Fig 4D ) ; we observed no significant changes under DD conditions ( Fig 4G ) , as would be expected for an arrhythmic genotype . The effects of manipulating calcium and cAMP levels in the PG on the periodicity of emergence suggest that these messengers could directly alter the clock TTFLs , and a variety of indirect evidence [30 , 47] suggests that they may act on the TTFL via CREB . As shown in Fig 5Aa and 5B , knockdown of CREB in the PG caused the expression of an arrhythmic pattern of emergence . Furthermore , using cre-luciferase as a reporter for clock output [29] showed that reducing CREB expression in the PG rendered this clock arrhythmic ( Fig 5D ) . An arrhythmic pattern of emergence was also obtained when CREB was knocked down in all clock cells ( Fig 5Ab and 5B ) , and when it was knocked down in all clock cells except for the PG clock ( Fig 5Ac and 5B ) , compared to controls ( Fig 5B and 5C ) indicating that CREB plays a key role in all clock cells . Flies bearing transgenes for CREB RNAi for alone , and for the phm-GAL4 and tim-GAL4 drivers alone expressed normal circadian rhythmicity of eclosion ( S3 Fig and S1 Fig , respectively ) . Our results show that manipulations of calcium and cAMP levels in the PG affect the rhythmicity of eclosion by directly affecting PG clock function , and that these actions may be mediated by CREB . In order to determine if a similar situation obtains for the central clock , we investigated the consequences on the circadian rhythmicity of eclosion of manipulating calcium and cAMP levels in central brain pacemaker neurons . Consistent with the results obtained for the PG , manipulations expected to decrease calcium ( knockdown of calcium channels , cacophony and Ca-alpha1D ) and cAMP levels ( knockdown of rutabaga ) in critical Small Ventral Lateral pacemaker neurons ( sLNv ) using the pdf-GAL4 driver , lengthened of the periodicity of eclosion ( Fig 6Aa–6Ac and 6C ) . By contrast , knockdown of dunce , which is expected to increase the levels of cAMP in these pacemaker neurons , caused a shortening of the periodicity of eclosion ( Fig 6Ad and 6C ) . Finally , similar manipulations using the timeless-GAL4 ( tim-GAL4 ) driver , which is expressed in both central and peripheral pacemaker cells , produced similar results ( Fig 6B and 6D ) . Flies bearing the pdf-gGAL4 driver alone expressed normal circadian rhythmicity of eclosion ( S1 Fig ) . These results are comparable to the ones obtained following similar manipulations in the PG , and suggests that calcium and cAMP signaling plays a similar role in the central and the peripheral clock with respect to the circadian control of adult emergence . Finally , we investigated the effects of manipulating calcium and cAMP levels in clock neurons on the circadian rhythm of locomotor activity of adult flies . As shown in Fig 7A and 7C and also summarized in Table 1 , knockdown in sLNv neurons ( using the pdf-GAL4 driver ) of dunce shortened ( Fig 7Ab , Fig 7C ) , whereas knockdown of rutabaga and IP3R lengthened ( Fig 7Aa and 7Ac , Fig 7C ) , the periodicity of the locomotor activity rhythm . Previous work that manipulated calcium levels in sLNv by expressing buffer protein parvalbumin ( PV ) showed that PV overexpression eventually caused a lengthening of the free-running period [27] . Yet , this result was only visible in flies bearing several ( >2 ) copies of the UAS-PV transgene and also , somewhat paradoxically , only after several weeks in DD . The relatively modest lengthening obtained when expressing IP3R RNAi in sLNv neurons , and the lack of detectable change following knockdown of cacophony and Ca-alpha1D ( Fig 7Ca ) may be due to low effectiveness of these RNAi lines in causing significant changes in calcium levels , and/or may require a longer monitoring period under DD conditions . Finally , and in line with previous work [27] knockdown of these genes in all clock cells ( Fig 7B and 7D; Table 1 ) severely disrupted the free-running rhythms of locomotor activity . Similarly , knockdown of CREB in sLNv neurons ( Fig 7Ad , Fig 7C ) or in all clock neurons ( Fig 7Bd , Fig 7D ) caused the expression of an arrhythmic pattern of locomotor activity . Overall , these results suggest that calcium and cAMP levels affect clock function itself .
Mammalian and insect circadian clocks are produced by intracellular transcriptional/translational feedback loops ( TTFL ) . In the case of neuronal circadian pacemakers , the activity of the cells that make up the pacemaker is then coordinated through transmitter and neuropeptide action . In the mammalian SCN , these messengers cause changes in the intracellular levels of calcium or cAMP and are known to directly affect the TTFL via CREB-mediated changes of transcription [24 , 48] . In Drosophila , by contrast , the relationship between changes in calcium and cAMP and the functioning of TTFLs is less clear . For example , different neurons within the central brain pacemaker exhibit distinctly different phases of calcium oscillation while expressing essentially identically-timed rhythms of clock gene expression [49] . This suggests that the pattern of neuronal activity of different fly pacemaker neurons may be an emergent property that depends on their interconnection . Likewise , use of a CRE-luc transgene to probe CREB expression in the Drosophila brain [50] showed that most neuronal clusters , including non-clock neurons , expressed a circadian rhythmicity of bioluminescence , suggesting that CREB could be part of the clock’s output mechanism . Here we explored directly the relationship between calcium and cAMP levels and clock function using the simpler , non-neuronal , peripheral clock housed within the PG endocrine gland . We found small but significant changes in calcium and cAMP levels during the course of the day , which persisted under constant conditions , suggesting that these second messengers could be part of the output of the clock . These changes did not show a circadian rhythmicity . This contrasts with previously reported measurements of cAMP levels in the adult fly brain , which showed a single maximum [28] and may reflect the fact that the PG clock is a slave of the brain clock [19 , 35] . Importantly , however , we found that manipulations that increased or decreased the levels of calcium and cAMP in the PG caused , respectively , a shortening and lengthening of the periodicity of eclosion , suggesting that they may also affect clock TTFL function . In the case of calcium , these manipulations were effective when they targeted calcium channels as well as elements involved in calcium storage , revealing that both mechanisms could mediate changes in clock periodicity . Furthermore , PG-specific knockdown of CaMKII and CASK also affected clock period , suggesting that the effects of changes in calcium levels on the clock could be effected by phosphorylation . These results suggest that the effects on locomotor activity rhythmicity caused by reducing calcium levels in central neurons [27] could , at least in part , also be due to actions on the intracellular TTFLs . For the case of cAMP , our results similarly suggest that the shortened circadian periodicity of adult locomotor activity rhythmicity reported for dunce mutants [28] , may be due to direct actions on the brain clock TTFL . How might calcium and cAMP ultimately affect the Drosophila clock TTFL ? Circumstantial evidence suggests that these second messengers may funnel their actions through the calcium/cAMP-dependent transcription factor , CREB , as occurs in the mammalian SCN [22–24 , 26] . Indeed , the consequences of acute [30] and sustained [47] changes in pacemaker excitability affect TTFLs and are associated with changes in CREB expression . Here , we found that targeted knockdown of CREB in the PG caused a loss of circadian rhythmicity of eclosion , revealing that CREB plays a key role in the PG clock TTFL function; a similar result was obtained following CREB knockdown in the central clock , indicating that CREB may also be critical for neuronal clock TTFLs . Interestingly , overexpression of CREB-binding protein ( CBP ) in timeless-expressing cells causes an arrhythmic pattern of eclosion and locomotor activity as well as abnormal expression of CLOCK/CYCLE-induced clock genes [51] , which suggest that CREB and CBP proteins may play a critical role in the control of the period transcriptional activity , consistent with the presence of CREB binding sites [29] in this gene . Overall , the picture that is emerging from a variety of studies is that calcium and cAMP are actors in the output of the clock , and also play an important role in translating , via CREB , the actions of stimuli external to the clock into changes in TTFL speed and/or phase . Nevertheless , we also found that knockdown of Epac in the PG also caused a significant lengthening of periodicity , which suggests a role for the Epac pathway in regulating circadian clock periodicity , as occurs in the SCN [23] . Although Epac can lead to the activation of CREB , it can also regulate MAP kinase signaling [52] , and MAP kinase p38 knockdown in Drosophila clock neurons induces a lengthening of the periodicity of locomotor activity by altering the degree of phosphorylation of PER protein , which delays its translocation to the nucleus [53] . This suggests an additional mechanism through which cAMP could alter TTFL cycling . The circadian rhythm of Drosophila adult emergence depends on functional clocks in both the brain and the PG [19 , 35] and recent evidence shows that the brain transmits time information to the PG via the PTTH neuropeptide acting on its receptor , TORSO [19] . Yet , it is unclear how circadian rhythmicity is produced by this system , since PTTH expression does not show circadian rhythmicity in Drosophila [19 , 54 , 55] nor is there any evidence for daily oscillations in molting hormone titers during fly metamorphosis [56] . One possibility is that circadian rhythmicity of emergence is accomplished by fine-tuning the levels of ecdysone ( the precursor of the active steroid , 20-hydroxyecdysone ) production and/or secretion via calcium and cAMP . Although PTTH acts via the tyrosine kinase coupled receptor TORSO , calcium and cAMP have long been associated with PTTH transduction [57] , and more recently , calcium has also been shown to play a critical role in ecdysone secretion from the PG [58] . Thus , changes in calcium and cAMP levels due to clock activity could impinge upon the PTTH transduction pathway , ultimately causing changes in the rate of fall of ecdysone titers , thereby delaying or accelerating the rate of metamorphosis and causing emergence to be confined to a specific window of time . Such a mechanism of gating would not require circadian oscillations in 20-hydroxyecdysone titers , but only subtle changes to the rate of fall of 20-hydroxyecdysone titers , and result in the relatively wide eclosion gate observed in this insect species . This mechanism implies that pathways external to the clock and PTTH transduction could also affect the pace of metamorphosis through cross-talk with the PTTH transduction pathway . The detection of complex patterns of calcium oscillations in the PG [58] suggests that much remains to be understood about the functioning of this critical endocrine organ .
This research was approved by the Bioethics and Biosecurity committees of the Universidad de Valparaíso , Chile . Drosophila strains were raised on standard cornmeal media and , unless noted , were maintained at room temperature ( 20–22°C ) on a 12/12 hours light/dark cycle . The following GAL4 drivers were used: phm-GAL4 [40] , tim-GAL4 and pdf-GAL4 ( generously provided by Paul Taghert , Washington University , USA ) . The phm-GAL80 strain has previously been described [19] . The UAS-GCaMP 3 . 2 calcium sensor was kindly provided by Julie Simpson ( HHMI , Janelia Park , USA ) and the UAS-Epac1 sensor was obtained from Paul Taghert [33] . UAS-RNAi lines were obtained from the National Institute of Genetics , Japan ( NIG ) , the Vienna Drosophila RNAi Center , Vienna , Austria ( VDRC ) and the Bloomington Drosophila Stock Center at Indiana University , Bloomington , USA ( BL ) . Typically , a number of RNAi lines were tested and the most effective one was then used for the majority of the experiments . The lines used and their source is indicated below; the line chosen for the results reported here are indicated in bold: for IP3R ( Inositol 1 , 4 , 5 , -tris-phosphate receptor; CG1063 ) : BL#25937 , VDRC#6486; Dmca1A ( cacophony or Calcium-channel protein α1 subunit A; CG1522 ) : BL#27244 , VDRC#104168; Dmca1D ( Calcium-channel protein α1 subunit D; CG4894 ) : BL#25830; SERCA ( sarco-endoplasmic reticulum calcium ATPase or Calcium ATPase 60A; CG3725 ) : VDRC#4474 , VDRC#107446 , BL#25928; dCREB-A ( cyclic-AMP response element binding protein A; CG7450 ) : BL#31900 , VDRC#110650 , BL#27648; CASK ( calmodulin-dependent protein kinase activity; CG6703 ) : BL#27556 , BL#35309 , BL#32857; dCaMKII ( Calcium/calmodulin-dependent protein kinase II; CG18069 ) : BL#29401; dunce ( cAMP phosphodiesterase or PDE4; CG32498 ) : BL#27250 , rutabaga ( adenylyl cyclase or AC: CG9533 ) : BL#27035 , NIG#9533R-1 , VDRC#5569 , VDRC#101759; Rap1 ( Ras related protein1 GTPase; CG1956 ) : BL#29434 , VDRC#110757; D-EPAC ( Exchange protein directly activated by cAMP; CG34392 ) : BL#29317 , VDRC#50372 , VDRC#50373 . Crosses ( 30–60 females + 15 males ) were reared at 20°C under LD 12:12 ( lights-on at noon ) . Resulting pupae were placed in Trikinetics eclosion monitors ( Trikinetics , Inc . , Waltham , MA , USA ) . The following day the lights were permanently turned off before lights-on and eclosion monitored for 7–8 days . For experiments using tub-GAL80ts , flies were raised at 20°C and entrained under LD 12:12 cycles . The resulting pupae were placed in the eclosion monitors and the temperature raised to 28 . 5°C and kept at this temperature for the remainder of the experiment . Crosses ( 10 females + 5 males ) were reared at 20°C under LD 12:12 light-dark cycle ( lights-on at noon ) . Male progeny were collected under CO2 anesthesia on the day of eclosion , aged and entrained for 5–6 days , then placed in Trikinetics activity monitors . Their activity was then monitored under LD 12:12 conditions for 7–10 days followed by 7–10 days under DD conditions . Periodicity of eclosion and locomotor activity records was analyzed using the Maximum Entropy Spectral Analysis ( MESA ) and Autocorrelation functions using the Matlab-based software from the “Fly Toolbox” software package [59] . The power of the eclosion and locomotor activity rhythms was determined using MESA analysis and the strength of rhythmicity was quantitated using the rhythmicity index ( RI ) derived from the Autocorrelation analysis , and categorized as rhythmic ( RI≥0 . 3 ) , weakly rhythmic ( RI between 0 . 1–0 . 3 ) or arrhythmic ( RI≤0 . 1 and obvious aperiodic records ) [60] . Imaging was carried out as described in Selcho et al . ( 2017 ) [19] . Briefly , brains were dissected in ice-cold Schneider’s medium ( Sigma-Aldrich ) containing 1% antibiotic solution ( 10 , 000Uml-1 penicillin and 10 mg ml-1 streptomycin; Sigma-Aldrich ) , placed on poly-lysine coated FluoroDish plates ( WPI , FL , USA ) . They were then covered with Schneider’s medium containing 1% antibiotic solution , supplemented with 10% fetal bovine serum and 10 mgml-1 insulin ( Sigma-Aldrich ) , and containing 1mM luciferin ( Potassium Salt , Gold Biotechnology Inc . MO , USA ) . Preparations were viewed using an LV200 microscope ( Olympus , Japan ) under 20X magnification and imaged for 72–96 h with an Evolve 512 camera ( Photometrics , Tucson , AZ , USA ) using 20min exposures and 200 X gain . Records were detrended using FIJI [63] . Statistical analyses were carried out using Prism 6 . 0 ( Graphpad Software Inc , CA ) . t-tests and one-way ANOVA , followed by Tukey’s post hoc multiple comparison analyses were used for normally distributed data; Wilcoxon rank sum tests were used for non-normally distributed data . | Circadian clocks impose daily periodicities to animal behavior and physiology . At their core , circadian rhythms are produced by intracellular transcriptional/translational feedback loops ( TTFL ) . TTFLs may be altered by extracellular signals whose actions are mediated intracellularly by calcium and cAMP . In Drosophila , calcium and cAMP levels affect the periodicity of Drosophila circadian rhythms , but whether this is due to direct actions on the TTFLs themselves or is a consequence of changes induced to the complex interrelationship between different classes of central pacemaker neurons is unclear . Here we used the non-neuronal circadian clock located in the prothoracic gland ( PG ) to show that these messengers affect the speed of the circadian clock that controls the timing of adult emergence and suggest that these actions are mediated by CREB . Importantly , since calcium and cAMP are also output signals of the clock , they may contribute to the mechanism that imposes a circadian gating to the timing of adult emergence . | [
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| 2018 | Calcium and cAMP directly modulate the speed of the Drosophila circadian clock |
Urease as a potential target of antimicrobial drugs has received considerable attention given its versatile roles in microbial infection . Development of effective urease inhibitors , however , is a significant challenge due to the deeply buried active site and highly specific substrate of a bacterial urease . Conventionally , urease inhibitors are designed by either targeting the active site or mimicking substrate of urease , which is not efficient . Up to now , only one effective inhibitor—acetohydroxamic acid ( AHA ) —is clinically available , but it has adverse side effects . Herein , we demonstrate that a clinically used drug , colloidal bismuth subcitrate , utilizes an unusual way to inhibit urease activity , i . e . , disruption of urease maturation process via functional perturbation of a metallochaperone , UreG . Similar phenomena were also observed in various pathogenic bacteria , suggesting that UreG may serve as a general target for design of new types of urease inhibitors . Using Helicobacter pylori UreG as a showcase , by virtual screening combined with experimental validation , we show that two compounds targeting UreG also efficiently inhibited urease activity with inhibitory concentration ( IC ) 50 values of micromolar level , resulting in attenuated virulence of the pathogen . We further demonstrate the efficacy of the compounds in a mammalian cell infection model . This study opens up a new opportunity for the design of more effective urease inhibitors and clearly indicates that metallochaperones involved in the maturation of important microbial metalloenzymes serve as new targets for devising a new type of antimicrobial drugs .
Enzymes have been proven to serve as important drug targets , and enzyme inhibitors are among the most successful drugs [1] . One dominant strategy for enzyme-targeted drug design lies in the discovery or synthesis of structure analogues of substrates that resemble the enzyme’s reactivity [2] . However , such a strategy might be restrained if the active sites of enzymes are not solvent exposed or if the substrates of enzymes are highly specific . This is the case for urease , an enzyme that catalyzes the hydrolysis of urea in plants , fungi , and many pathogenic bacteria into ammonia and carbonic acid [3] . X-ray structures of ureases reveal that the conserved active sites consist of a bis-μ-hydroxo dimeric nickel center deeply buried in the supramolecular assembly [4 , 5] . Moreover , urease has a highly specific substrate , i . e . , urea [5] . This makes it very challenging for the development of urease inhibitors by conventional methods . Alternative strategies are urgently needed to design urease inhibitors . Urease has been recognized as a general microbial virulence factor [3] . H . pylori relies on urease to invade the host epithelial cells and ensure successful colonization [6] . The increased pH of the urinary system led by urease from Proteus mirabilis may result in the formation of urinary and kidney stones , catheter occlusion , and kidney infection [7] . Moreover , survival of Mycobacterium species in the phagolysosomes of infected cells depends on its urease activity to neutralize the local acid to avoid the destructive effect caused by cathepsins [8] . Recently , the roles of urease during fungal infections in humans are beginning to be recognized . For example , urease from Cryptococcus neoformans has been observed to facilitate the crossing of the blood–brain barrier in a mice model [9] . Given the importance of urease in the virulence of microbial pathogens , urease is thus considered one of the most important targets in the development of drugs , especially for the treatment of gastric and urinary infections [10 , 11] . Up to now , urease inhibitors were designed exclusively by either attacking the metallocenter or mimicking the substrate of ureases [10 , 12 , 13] . In spite of enormous efforts being made , only one compound—acetohydroxamic acid ( AHA ) —with anti-urease activity has been used clinically for the treatment of urinary tract infections together with antibiotics [13 , 14] . Unfortunately , adverse side effects , including inhibition of the biosynthesis of bone marrow , depressed DNA synthesis , and malformation of embryos at high doses , have been reported [15] . The potential of urease as a target for antimicrobial agents , therefore , has not yet been fully explored . As apo-urease synthesized in microbes is inactive , urease exerts its function only if nickel is inserted into its active site [16] . The assembly of the active site of bacterial ureases , a process also called urease maturation , is a complex and guanosine-5'-triphosphate ( GTP ) -dependent process , involving the cooperative actions of several accessory proteins [16] . For example , the canonical urease system in H . pylori requires UreE , UreF , UreG , and UreH to facilitate activation of the UreAB apo-enzyme . It has been proposed that nickel translocation from UreE to UreG is triggered by the formation of UreE-UreG ( 2E-2G ) complex . Subsequently , Ni-UreG binds to UreF/H to form a supercomplex as an apo-urease/UreF-H-G , in which the final step of nickel insertion into the apo-urease is completed upon the GTP hydrolysis by UreG [17] . Homologs of UreF , UreG , and UreH/D can also be found in urease-producing eukaryotes [16] , indicating the maturation of urease is relatively conserved among different species . We therefore envision that disruption of urease maturation process to inactivate its function might serve as a superior strategy for the design of new types of inhibitors . Urease is known to be a major contributor to the pathogenesis of H . pylori , a transmissible human pathogen strongly related to gastrointestinal diseases including gastritis , peptic ulcers , and even stomach cancer [18] . The bismuth-based quadruple therapy has been used clinically as first-line therapy and surprisingly shown excellent success in eradication of H . pylori , even for antibiotic resistance strains [18–21] . However , the mechanism of action of bismuth drugs is not fully understood [22] . Although it has been hypothesized that bismuth drugs may exert antimicrobial action via inhibition of urease [23] , our previous study on jack bean and Klebsiella aerogenes ureases showed that bismuth compounds exert very low in vitro activity towards inhibition of urease and could not displace nickel from urease [24] . Given the critical role of urease in the survival and pathogenesis of the pathogen [18] , it is highly possible that bismuth inhibits urease activity via an alternative approach rather than direct perturbation on the active site . The knowledge of how bismuth exerts its inhibition on H . pylori urease may promote discovery of new targets for the development of antimicrobial agents . In this report , we first elucidated the mechanism of action of a clinically used bismuth drug , colloidal bismuth subcitrate ( CBS ) , on inhibition of urease of H . pylori with the aim of searching for new targets for design of urease inhibitors . We demonstrate that the bismuth drug inhibited urease activity in H . pylori through binding to the urease accessary protein UreG , thus perturbing its GTPase activity and leading to subsequent disruption of urease maturation . Such a phenomenon was also found in other pathogenic bacteria . Using H . pylori UreG as a target , two compounds were identified by virtual screening and subsequently verified to attenuate urease activity attributable to the disturbance of essential chaperones and exhibited comparable or even better antimicrobial activity towards H . pylori compared to bismuth drug CBS or AHA with IC50 ( urease ) values at micromolar levels . The efficacy of these compounds was further confirmed in a mammalian cell infection model . Overall , our studies offer a new strategy for design of urease inhibitors , as well as a novel paradigm for rational design of a new type of antimicrobial .
Given the critical role of urease in the pathogenesis of H . pylori and the success of bismuth-based drugs in the treatment of H . pylori infection , urease has been proposed to be a key target of bismuth drugs [23] . However , previous studies showed that bismuth exhibited trivial effects on urease activity even at high concentration ( up to several mM level ) [24–26] , which is unlikely to be accountable for its in vivo activity . We hypothesized that Bi ( III ) might inhibit urease activity indirectly , such as through disruption of urease maturation . To search which urease accessory proteins bound to Bi ( III ) , we employed a homemade fluorescent probe Bi-NTA-AC [27] , similar to Ni-NTA-AC [28] , that can rapidly enter live cells to fluorescently label metal binding proteins in our recent report; the labelled proteins can be identified subsequently by proteomics upon photoactivation of arylazide of the probe [29] . Urease-related proteins lightened up by the probe are shown in Fig 1A . UreA and UreB , the two subunits of urease , were fluorescently labeled by Bi-NTA-AC , indicating both UreA and UreB bound to Bi ( III ) in H . pylori cells , consistent with our previous reports [25 , 26] . Among the urease accessory proteins , UreG exhibited blue fluorescence on the gel , indicating that UreG interacted with Bi ( III ) in H . pylori cells , while UreF and UreH were not fluorescently labeled and UreE was not identified both in silver-stained gel and fluorescence image , possibly due to low expression level in the bacterium . We further monitored the interactions of Bi ( III ) with these chaperones in vitro by ultraviolet-visible ( UV-vis ) spectroscopy . Titration of up to four molar equivalents of Bi ( III ) ( as Bi nitrilotriacetic acid [NTA] ) into solutions of apo-UreE or -UreFH complex led to negligible changes in the UV spectra ( S1 Fig ) , in agreement with in vivo data that UreE and UreF/H do not interact with Bi ( III ) . In contrast , upon addition of Bi ( III ) to apo-UreG solution , a peak centered at about 350 nm , assignable to the π ( S ) ( Cys ) →Bi ( III ) ligand-to-metal charge transfer ( LMCT ) [30] , appeared , increased its intensities , and levelled off at a molar ratio of [BiNTA]/[UreG] of ca . 2 ( Fig 1B ) , indicating that two Bi ( III ) ions bound to each UreG monomer . The titration data were nonlinearly fitted via Ryan–Weber equation , and by taking account of Bi-NTA binding affinity [30] , an apparent dissociation constant ( Kd ) of Bi ( III ) to UreG was determined to be 3 . 1 × 10−24 M . As UreG contains only two cysteine residues on the protein surface , Cys48 and Cys66 [31] , we prepared either single ( UreG-C48A or UreG-C66A ) or double mutants ( UreG-C48C66A ) of UreG and performed similar experiments . As expected , titration of Bi ( III ) into solutions of either UreG-C48A or UreG-C66A resulted in increases in intensities of the peak centered at approximately 350 nm with plateaus reached at [BiNTA]/[Protein] of 1:1 . In contrast , titration of Bi ( III ) into UreG-C48C66A led to no absorbance at 350 nm ( S1 Fig ) , indicating that both Cys48 and Cys66 of UreG participated in the binding of Bi ( III ) . As Cys66 is involved in the binding of Ni ( II ) [31] , it is highly possible that Bi ( III ) perturbs the Ni ( II ) coordination to the protein . Indeed , titration of Ni ( II ) ( as NiSO4 ) into the Bi-UreG solution resulted in no changes in the UV spectra in the presence of GTP and Mg ( II ) ( S2 Fig ) , suggesting that the metal binding site became inaccessible to Ni ( II ) once it was occupied by Bi ( III ) . Unexpectedly , stepwise addition of up to three molar equivalents of Bi ( III ) to Ni-UreG did not suppress the characteristic peak at approximately 337 nm ( π ( S ) ( Cys ) →Ni ( II ) LMCT ) , while the peak at approximately 350 nm ( π ( S ) ( Cys ) →Bi ( III ) LMCT ) remained undetectable , indicating the lack of Bi ( III ) coordination to UreG protein when the metal binding site was preloaded with Ni ( II ) ( S2 Fig ) . However , in the presence of GTPase-activating element KHCO3 [17] , gradual addition of Bi ( III ) to UreG solution led to a decrease in the intensity and complete disappearance of the peak at approximately 337 nm accompanied by the emergence of a peak at approximately 350 nm ( S2 Fig ) , indicative of the simultaneous replacement of Ni ( II ) ions by Bi ( III ) from UreG protein . In contrast , titration of Bi ( III ) into Ni-UreG solution in the presence of KHCO3 and GTPγs ( a nonhydrolyzable analogue of GTP ) did not disturbed characteristic Ni-binding peak , while the typical Bi coordination peak at 350 nm was unnoticeable ( S2 Fig ) . Taken together , we demonstrate that Bi ( III ) only disturbed UreG dimer at its GTPase transition state ( i . e . , in the presence of GTPase-activating elements ) , but not at its stable Ni , GTP-bound state . Binding of Bi ( III ) to UreG also induced the tertiary structural changes of Ni-bound UreG in the presence of KHCO3 as revealed by gel filtration chromatography . Incubation of Bi ( III ) with Ni-UreG ( in the presence of KHCO3 ) led to a decrease in the intensity of UreG dimer peak , which was eluted at approximately 13 ml , and appearance of new peaks at elution volumes of approximately 18 ml and approximately 6 ml , with the former assignable to GTP molecule and the latter to high molecular weight oligomer of the protein ( S3 Fig ) . We next examined the effects of Bi-UreG interaction on the formation of UreE-UreG and UreG-UreFH complexes , which are crucial for the Ni ( II ) delivery into the Ni-containing active site of urease [17 , 31] . A previous study revealed that the active complex UreE-UreG was assembled by an UreE dimer and an UreG dimer ( i . e . , 2E-2G ) , while UreG-FH complex was assembled as a dimer of UreG/F/H ( i . e . , 2G-2F-2H ) [17 , 31] . Incubation of Bi ( III ) with UreE-UreG led to the complete disappearance of 2E-2G peak , which was eluted at approximately 11 . 8 ml in gel filtration chromatography , accompanied by the appearance of a new broad peak at elution volumes of approximately 13 ml ( S3 Fig ) . Notably , supplementation of Bi ( III ) had no effects on the elution volumes of UreE dimer ( S4 Fig ) , implying that Bi ( III ) disrupted the formation of 2E-2G complex through targeting UreG . Similarly , UreG was dissociated from 2G-2F-2H complex without disrupting the 2F-2H complex upon Bi ( III ) incubation ( Fig 1C ) . These data confirm that binding of Bi ( III ) to UreG disturbed the protein–protein complexation , which is essential for urease maturation . We then investigated the effects of Bi-UreG interaction on the enzymatic activity of UreG by carrying out GTPase assay in the absence and presence of Bi ( III ) . As shown in Fig 1D , a very low GTPase activity was observed for apo-UreG; upon incubation of apo-UreG with Ni ( II ) , maximum GTPase activity was achieved , confirming the necessity of Ni ( II ) ions for the function of UreG as a GTPase . Addition of equimolar amounts of Bi ( III ) to Ni-UreG led to a drop in GTPase activity by 60% , and further addition of Bi ( III ) resulted in complete inhibition of the enzyme activity , signifying that binding of Bi ( III ) completely abolished GTPase activity of UreG ( Fig 1D ) . We subsequently evaluated the effects of binding of Bi ( III ) to UreG on the activity of urease using cell lysate of E . coli harboring plasmid pHP8080ΔG , which consists of the urease gene cluster except ureG ( as ureABIEFHΔG ) . Purified UreG protein with or without treatment of Ni ( II ) or Bi ( III ) ions was supplemented to the system sequentially . As shown in Fig 1E , the maximum urease activity ( normalized to 100% ) was achieved only when Ni-UreG was supplemented into the cell lysate . In contrast , no observable urease activity was found when apo-UreG was added . When stoichiometric amounts of Bi ( III ) ions were added to Ni-UreG , the urease activity was decreased by 50% and further addition of two or three molar equivalents of Bi ( III ) ions to Ni-UreG led to complete abolishment of urease activity ( Fig 1E ) , demonstrating that the inactivation of urease is attributable to Bi ( III ) binding to UreG . We also investigated the influence of CBS on the GTPase and urease activity in bacterial cells . The ureG gene was complemented to E . coli cells harboring plasmid pHP8080ΔG , after which the GTPase and ureolytic activities of E . coli cells were monitored simultaneously . As shown in S5 Fig , treatment of E . coli with increasing amounts of CBS during bacterial growth resulted in the dramatic concerted declines in both the ureolytic and GTPase activities to approximately 20% compared with those without CBS treatment , implying that binding of Bi ( III ) to UreG abolished its enzymatic activity , leading to inactivation of urease through disruption of urease maturation in engineered bacterium . We have shown that a Bi ( III ) drug ( CBS ) can inhibit urease activation at relatively low concentration ( μM level ) through functional disruption of chaperone UreG in vitro and in an engineered bacterium . We further investigated whether this is also the case in H . pylori . We supplemented various concentrations ( below minimum inhibitory concentration [MIC] ) [25] of CBS to the culture medium during H . pylori growth , then measured urease activity in live bacterial cells . Notably , a dramatic decline in the ureolytic activity of H . pylori urease in live bacterial cells was coupled with the increasing concentration of CBS , and a drop in urease activity by ca . 90% was observed at CBS concentration as low as 20 μM ( Fig 2A ) . In contrast , incubation of CBS ( up to 400 μM ) with extracted H . pylori urease resulted in little inhibitory effect ( Fig 2A ) . We then measured the nickel content in H . pylori cells with or without supplementation of Bi ( III ) to culture medium . As shown in S6 Fig , only a slight decline in nickel content was noticed upon supplementation of CBS , indicating that Bi ( III ) did not influence the influx of Ni ( II ) into bacterial cells . We have shown previously that the expression levels of the urease system were not significantly altered upon treatment with Bi ( III ) -based drugs [25] . We reason that the urease extracted from bacterial lysate might have already been maturated , i . e . , urease exists in the Ni-bound form , and in this case , Bi ( III ) cannot replace Ni ( II ) from the active site . On the other hand , a newly synthesized urease exists in an apo-form and is inactive , which requires urease accessary proteins for maturation . Binding of bismuth drugs to UreG may thus disrupt urease maturation in live bacterial cells . Indeed , when gradient amounts of Ni-bound urease accessory protein UreG were incubated with CBS-treated H . pylori cell lysate , the urease activity was recovered by about 60% ( Fig 2B ) , corroborating the theory that the reduced ureolytic activity of urease is likely due to the Ni ( II ) -deficiency in the active site of enzyme . Notably , supplementation of Ni ( II ) ions alone or CBS-treated Ni-UreG to the bacterial cell lysate failed to restore the urease activity completely ( Fig 2B ) , pinpointing that bismuth drugs inhibited urease activity via targeting accessory protein UreG in vivo . Strikingly , similar phenomena were also observed when CBS was supplied to the culture of various bacterial species ( either gram-negative or gram-positive , even some clinically isolated strains ) , including P . mirabilis , K . pneumoniae , L . hongkongensis , P . aeruginosa , S . aureus , and S . saprophyticus ( Fig 2C ) . Importantly , CBS showed more potent efficiency than AHA towards inhibition of urease activity in all tested bacterial species ( S7 Fig ) . Maturation of ureases from these bacteria also require similar accessary proteins , especially the Ni-dependent GTPase UreG is much conserved among all these bacterial species ( S8 Fig ) . Inhibition of urease activity in vivo by bismuth drugs via disruption of urease maturation appears to be a general feature irrespective of the bacterial species . UreG might , therefore , serve as an alternative target for the design of potent urease inhibitors . As bismuth inhibits urease via disruption of its maturation through perturbation of UreG , we envision that small molecules that functionally disrupt the GTPase activity of UreG could potentially be effective inhibitors of urease in microbes . To validate this hypothesis , we performed a virtual screening using AutoDock Vina [32] . As bismuth inhibits UreG activity by targeting the nickel binding site , which is located on the surface of UreG , we first attempted to find small molecules that could bind to this metal binding site; unfortunately , our initial screening resulted in no hits . We then turned our attention to search for small molecules that may block the guanine nucleotide binding pocket , which is located next to the nickel binding site , given that the contact between nucleotide binding pocket and metal binding site is essential for the nickel insertion into apo-urease [17 , 31] . UreG structure from H . pylori ( PDB 4HI0 ) was used as the docking receptor . A set of 1 , 700 compounds collected from the databases PubChem [33] and BindingDB [34] were used for the screening . Three hundred of these compounds are active compounds as GTPase inhibitors based on the PubChem database [33] , and 1 , 400 of them showed a potential interaction with a G-protein in different species based on BindingDB . We ranked the 1 , 700 compounds based on their docking scores . Out of this screening and based on structural features , physical chemistry properties , and drug-like characteristics , the top 20 compounds were selected for further analysis . Eleven out of these 20 compounds were purchased for further bioactivity testing ( Fig 3A , S9 Fig ) . Five compounds ( cmpd2 , cmpd4 , cmpd8 , cmpd10 , cmpd11 ) could inhibit the GTPase activity of UreG by 37% to 72%; subsequent urease assays showed that two compounds ( cmpd4 , cmpd8 ) exhibited very good activity ( by ca . 60% and 90% , respectively ) towards inhibition of urease activity in H . pylori cells ( S10 Fig ) . We then carried out further validation with cmpd4 and cmpd8 by using both GTPase and urease assay . The inhibitory concentration ( IC ) 50 values towards inhibition of GTPase activity of HpUreG were determined to be 16 . 8 ± 6 . 5 and 13 . 2 ± 7 . 6 μM for cmpd4 and cmpd8 , respectively ( Fig 3B ) , while urease activity in a clinically isolated H . pylori strain was markedly reduced upon treatment with cmpd4 and cmpd8 ( Fig 3C ) with IC50 ( urease ) of 16 . 7 ± 6 . 5 and 9 . 5 ± 7 . 6 μM , respectively . For comparison , the IC50 ( urease ) of a clinically used urease inhibitor , AHA , was also determined to be 1321 ± 291 μM . Clearly , both cmpd4 and cmpd8 exhibited more potent inhibition towards H . pylori urease than AHA . To further evaluate the inhibitory effects of these two compounds on bacterial growth , the compounds were supplemented into the culture medium of H . pylori , and the pH of brucella broth medium was then adjusted to 5 . 0 with sterile dilute hydrochloric acid ( HCl ) ; inoculum was added to an initial optical density at 600 nm ( OD600 ) of ca . 0 . 1 . After 24 h of culture , OD600 values were recorded . As shown in Fig 3D , in the absence of inhibitors , H . pylori exhibited excellent growth in the acidic brucella ( OD600 = 0 . 93 ) compared with that in neutral medium ( OD600 = 0 . 18 ) , consistent with a previous report that H . pylori exhibits facultative acidophilism [35] . In the neutral medium , almost no perturbation on the growth of H . pylori was found for AHA . However , in the acidic medium , the OD600 value of H . pylori culture was reduced moderately by AHA from 0 . 93 to ca . 0 . 71 . Evident decreases in the OD600 values of H . pylori culture to 0 . 23 , 0 . 46 , and 0 . 47 were noted upon the supplementation of CBS , cmpd4 , and cmpd8 into the medium , respectively , indicating UreG inhibitors ( CBS , cmpd4 , and cmpd8 ) hindered the growth of H . pylori via urease inhibition . To investigate whether both cmpd4 and cmpd8 bind UreG , we applied a thermal shift assay . As shown in S11 Fig , in comparison to cmpd1 , which is inactive to inhibit UreG activity , cmpd4 and cmpd8 induced thermal shifts ( approximately 2 . 5°C ) on the melting temperature of UreG , indicative of binding between the compounds and UreG . Given that UreG itself displays a fluorescent signal at around 310 nm and 335 nm upon excitation at 280 nm , which corresponds to emission of intrinsic Tyr and Trp of UreG , respectively , we further characterized the binding of the compounds to UreG by measurement of the quenching of the intrinsic fluorescence of UreG upon addition of the compounds . The addition of cmpd4 and cmpd8 showed negligible effect on the absorption of incident radiation of UreG at 280 nm ( S12 Fig ) , whereas with the addition of cmpd4 and cmpd8 , the peaks at 310 nm and 335 nm ( corresponding to emission of UreG ) were reduced gradually ( Fig 4A and 4B ) ; binding affinities ( Kd ) of UreG to cmpd4 and cmpd8 were determined to be 10 . 6 ± 2 . 3 and 7 . 5 ± 3 . 6 μM , respectively , by nonlinear fitting the curve of fluorescent changes at 310 nm ( Fig 4C and 4D ) . cmpd4 is a self-fluorescent molecule with its excitations at 250 nm , 280 nm , and 310 nm , and emission at 410 nm ( S13 Fig ) ; upon addition of cmpd4 , the emission peak of 410 nm was elevated ( Fig 4A ) . Interestingly , as one of the excitations of cmpd4 ( 310 nm ) overlapped with the emission peak of UreG , the presence of a fluorescence resonance energy transfer ( FRET ) between UreG and cmpd4 was observed ( S14 Fig ) . For comparison , we also determined the binding affinity of substrate GTP to UreG to be around 65 μM ( S15 Fig ) , indicating that cmpd4 and cmpd8 exhibited higher affinity towards UreG than GTP . Computational models were built to reveal the potential binding interfaces between cmpd4/cmpd8 and UreG ( Fig 4E and 4F ) . The binding sites of small molecules were located at the guanine nucleotide binding pocket ( S16 Fig ) , involving G1 motif ( P-loop , magenta color ) and G4 motif ( NKXD motif ) . The main chain amide groups of G1 motif wrapped around both cmpd4 and cmpd8 ( Fig 4E and 4F ) . For cmpd4 , the side chain of T15 in G1 motif formed a hydrogen bond with an O atom of cmpd4 , whereas cmpd8 was stabilized by the side chains of K146 and D148 in G4 motif . Aliphatic regions of R179 and K146 ( red ) in G4 motif had extensive hydrophobic interactions with small molecules ( Fig 4E and 4F ) . To further confirm the interface between UreG and small molecules generated from computational models , we then constructed an UreG mutant ( UreGΔNKXD ) by replacing selected residues ( N145 , K146 , D148 ) with Ala to disrupt G4 motif , which abolished GTP binding of UreG ( S17 Fig ) . As shown in S18 Fig , mutation of these amino acids weakened the binding of UreG to cmpd4 and cmpd8 , with binding affinities of 34 . 7 and 33 . 4 μM . respectively . We further investigated whether binding of cmpd4 or cmpd8 to the guanine nucleotide binding pocket perturbed the Ni ( II ) coordination to the protein . Considering that coordination of Ni ( II ) to UreG results in a ( Cys ) →Ni ( II ) LMCT band centered at about 337 nm [17] , we monitored the UV absorption of UreG samples ( 5 μM ) at 337 nm upon titration of nickel with or without the supplement of cmpd4 or cmpd8 . As shown in S19 Fig , cmpd4 at a ratio of [cmpd4]/[UreG] of 5 and 10 reduced the percentages of Ni ( II ) -bound UreG to ca . 75% and 30% , respectively , and a similar phenomenon was also observed for cmpd8 ( S19 Fig ) , indicative of the disruption of nickel binding property of UreG through targeting nucleotide binding site . We next investigated whether such small molecule inhibitors of urease could be used to treat bacterial infection in a mammalian cell culture infection model . It has been demonstrated that H . pylori exerts toxic effect on host cells via producing ammonia [36] . AGS , a human gastric adenocarcinoma cell line , was used to evaluate the cytotoxicity of H . pylori urease to host cells . H . pylori was cultured microaerophilically in brucella broth medium supplemented with AHA , CBS , cmpd4 , or cmpd8 ( 10 μM ) , respectively , for 24 h . Harvested H . pylori cells were normalized according to optical density reading and then cocultured with AGS cells at a multiplicity of infection ( MOI ) of 20 . After 24 h of bacterial infection , the viability of AGS cells exposed to H . pylori was determined , and the ammonia concentration in medium was measured . As shown in Fig 5 , compared to the control group , the survival rate of cultured AGS cells exposed to H . pylori without small molecule urease inhibitors or with only DMSO in the medium dropped to only 28 . 8% ± 4 . 4% and 27 . 1% ± 5 . 4% respectively . With the addition of AHA to H . pylori , viability of AGS cells was determined to be 40 . 5% ± 8 . 2% . In contrast , survival rates of AGS cells exposed to H . pylori with the addition of CBS , cmpd4 , and cmpd8 were 65 . 8% ± 9 . 9% , 70 . 3% ± 11 . 3% , and 81 . 1% ± 11 . 3% , respectively ( Fig 5A and 5B ) . Accordingly , the ammonia concentrations in culture medium of the bacteria ( HP ) , bacteria with DMSO treatment ( HP + DMSO ) , and with AHA ( HP + AHA ) groups were ca . 2 . 5 mM , which are significantly higher than those groups treated with CBS ( HP + CBS ) or cmpd4/cmpd8 ( HP + cmpd ) , in which the ammonia concentrations were around 1 . 1 mM ( Fig 5C ) . All these data imply that these compounds attenuated virulence of H . pylori during host–pathogen interaction via inhibition of urease activity .
Metalloenzymes serve as potentially druggable targets . Many existing drugs , including acetazolamide and romidepsin , have been designed by targeting zinc metalloenzymes , thus neutralizing their catalytic activity [37] . A common strategy for development of metalloenzyme inhibitors lies in either synthesis of small molecules directly targeting the active sites or identification of substrate mimics that bind to the enzyme with high affinity . However , many metalloenzymes possess complex structures comprising multiple subunits [4 , 38] with the active sites buried deep inside , which makes it difficult to design enzyme inhibitors for therapeutic purposes . Moreover , the high substrate specificity of many enzymes also hinders the drug development . This is exemplified by bacterial ureases , which have a supramolecular assembly and high substrate specificity [5] . Although urease has long been recognized as a critical virulence factor for microbial pathogenesis [3] , there appears to be only one urease inhibitor ( AHA ) clinically available , and even that has detrimental side effects [39] . Alternative strategies are therefore needed to fully explore the potential of urease as an antimicrobial target . Given that insertion of metal cofactors to the active sites of metalloenzymes , namely enzyme maturation , is a prerequisite for full enzymatic activity , and often involving a series of metallochaperones [40] , whereas metallochaperones of bacterial ureases are generally small proteins but essential for assembling the active sites of the enzyme [17 , 41–43] , these metallochaperones might serve as better targets than metalloenzyme itself for designing urease inhibitors . Bismuth drugs have been used clinically to treat H . pylori infection since its first discovery; however , the mechanism of action remains obscure [22] . In this report , we intend to understand how bismuth inhibits urease activity in H . pylori and aim to discover a new target for designing urease inhibitors . By utilizing a homemade fluorescent probe , Bi-NTA-AC [27] , we show that UreG , which plays a crucial role in urease maturation in microbial pathogens [3] , appeared to be the only accessary protein that was found to bind Bi ( III ) in bacterial cells ( Fig 1A ) . Binding of Bi ( III ) to UreG led to dissociation of the protein–protein complexes , e . g . , UreE-UreG and UreG-UreF-UreH ( S3 Fig and Fig 1C ) , that are important for urease maturation [16 , 17 , 31 , 44] . Consequently , bismuth drugs completely abolished the GTPase activity of UreG ( Fig 1D ) . We further demonstrate that bismuth drugs inactivated urease indirectly by disruption of urease maturation via inhibition of UreG activity ( Fig 1E and S5 Fig ) . This is further confirmed by the observation that CBS could efficiently inhibit urease activity of H . pylori only in live pathogens , but not for matured urease ( Fig 2A ) . The newly expressed urease in live bacterium is in an apo-form , and binding of CBS to UreG disrupted the delivery of nickel cofactor to the active site of urease . In contrast , inhibition of urease activity in vitro was very inefficient , even at millimolar concentration [24]; this is attributable to the deeply buried active sites of urease , which is less prone to be attacked by inhibitors [4 , 5] . Our combined data provide solid evidence on the inefficiency of urease inhibitors designed based on directly targeting the active site of urease . Besides its well-known role in acid acclimation for H . pylori , urease is also associated with the development of infection stones caused by bacterial urinary tract infections , including those caused by Klebsiella and Proteus species . Moreover , emerging roles of urease in different processes of microbial infection have also been established , such as participating in nitrogen metabolism [45 , 46] , resisting peroxynitrite pressure [47] , avoiding destruction by phagolysosomes [8] , and facilitating pathogen crossing of the blood–brain barrier [9] . Apparently , there is a great medical urgency for the development of urease inhibitors to treat urease-related microbial infections , which has not been fully explored . In this study , we further show that inactivation of urease activity via disruption of urease maturation process by bismuth was not specific for H . pylori . Instead , similar phenomena were also found in several other bacterial pathogens , such as P . mirabilis , P . aeruginosa , K . pneumoniae , L . hongkongensis , S . aureus , and S . saprophyticus ( Fig 2C ) . Thus , it is highly possible that CBS , which is already a clinically used anti-ulcer drug , could be repurposed for the clinical use of treating urease-related microbial infection . Moreover , considering that UreG is highly conserved among urease-producing microbial species , UreG might serve as a good target for the design of urease inhibitors . Using H . pylori UreG as a showcase , we performed a virtual screening from 1 , 700 compounds and tested 11 hits . Our detailed validation shows that two compounds exhibited good inhibition towards GTPase activity of UreG with IC50 at μM level ( Fig 3B ) . Moreover , these two compounds exhibited more potent inhibitory effects on urease activity in H . pylori cells than the clinically used urease inhibitor AHA ( Fig 3C ) . Distinct from CBS , these two small molecules targeted nucleotide binding pockets instead of nickel binding sites of UreG ( Fig 4 , S16 Fig and S18 Fig ) . The efficacy of the two lead compounds was further validated both in culture medium of H . pylori and in a mammalian cell infection model . ( Fig 3D , Fig 5 ) . Taken together , we demonstrate that the compounds targeting UreG could indeed inhibit urease activity and therefore possess potent antimicrobial activity . Hydroxamic acids are the most widely studied group of urease inhibitors [13] . They inhibit urease through attacking the nickel ions in the active site of urease owing to their well-known metal-complexing properties [48] . However , this kind of compounds exerts only moderate inhibitory activity ( as shown in S7 Fig , the IC50 values of AHA against maturated ureases reach as high as mM level ) . Although AHA has been introduced into clinical use for urinary tract infections , high doses ( approximately 1 , 000 mg/d for adults ) are required due to its relatively low anti-urease activity; thus , severe side effects are noted [13] . Another group of urease inhibitors , which were considered as substrate analogues , shares similar structural characteristics with urea and competes with the substrate binding . Due to high specificity of urea to urease , such inhibitors also only exhibit moderate activity; for example , boric acid acts as a competitive inhibitor of urease from jack bean , P . mirabilis , and K . aerogenes , with Ki values at mM levels [12] . In contrast , CBS , cmpd4 , and cmpd8 inactivated urease in bacteria through targeting accessory protein UreG , either at nickel binding site or GTP binding site , exhibiting more potent activity than clinically used AHA ( Fig 3C and S7 Fig ) . Current compound libraries used in this study were designed for mammalian protein targets such as Homo sapiens and Rattus norvegicus [33] . cmpd4 and cmpd8 have also been found to be active in several high throughput screenings ( HTSs ) against targets in human cells , such as G protein-coupled receptor 55 ( GPR55 ) , Interleukin 1 beta ( IL1β ) , Focal Adhesion Kinase ( FAK ) , and Regulator of G-protein Signaling 7 ( RGS7 ) [49–52] . As expected , these compounds might exhibit toxicity to mammalian cells . Indeed , administration of small molecule inhibitors to AGS cells infected by H . pylori resulted in low viability of AGS ( S20 Fig ) , due to the toxicity of small compounds to mammalian cells . Therefore , it may be necessary to fine-tune structures of cmpd4 and cmpd8 to increase their selectivity and potency based on the rationale that the structure and regulation of bacterial GTPase UreG is distinct from mammalian proteins . Despite the fact that UreG was selected as a showcase study , such a strategy should have a broad application in the development of metalloenzyme inhibitors . Indeed , proteins that are involved in metalation pathways have also been considered as good targets for the development of new classes of pharmaceutical agents [53] . A recent study also demonstrated that small molecules that inhibit the human copper trafficking proteins Atox1 and CCS significantly reduce proliferation of cancer cells , suggesting copper chaperones as new targets for the development of anticancer therapies [54] . In summary , we have discovered a metallochaperone , UreG , as a new target for the design of urease inhibitors based on extensive mechanistic study of bismuth inhibition of urease . Using virtual screening in combination with experimental validation , we demonstrate that compounds that bind and functionally perturb GTPase activity of UreG could indeed inhibit urease activity both in culture medium of H . pylori and in a mammalian cell infection model . Our study clearly indicates that metallochaperones participating in maturation of important microbial metalloenzymes serve as new targets for devising antimicrobial drugs . In comparison with conventional antibiotics , such antimicrobials might have less likelihood of encountering antimicrobial resistance in pathogens , considering the crucial roles of these metalloenzymes in microbial pathogenesis that are less susceptible to mutation .
ureG gene was amplified by PCR from the genomic DNA of H . pylori strain 26 , 695 using the primer pair UreG-For/UreG-Rev ( S1 Table ) , after which sites of restriction endonucleases NdeI and EcoRI would be encoded at the 5′- and 3′- end of the PCR product , respectively . The PCR product and the cloning vector pET28a were digested with the restriction endonucleases ( New England Biolabs ) , followed by T4 ligation using T4 DNA ligase ( New England Biolabs ) to produce the expression plasmid pET28a-UreG . For generating the UreG variants UreG-C48A , UreG-C66A , UreG-C48C66A , and UreGΔNKXD , pET28a-UreG served as the template for site-directed mutagenesis using the Phusion High-Fidelity PCR Kit ( New England Biolabs ) . For analysis of the effect of Bi-binding to UreG on the GTPase activity of UreG and the urease maturation , plasmid pHP8080 was used as the template for the generation of a plasmid with the urease gene cluster excluding the ureG gene ( i . e . , ureG gene-deleted plasmid named pHP8080ΔG ) using primer pair UreA2HΔG-For/UreA2HΔG-Rev . To identify the Bi ( III ) -binding proteins in live H . pylori cells , Bi-NTA-AC was applied to the bacterium according to previous reports [27 , 28] . Prior to treatment with Bi-NTA-AC , the bacterial cells were collected by centrifugation at 4 , 000 g at 4°C for 5 min and were washed in PBS ( pH 7 . 4 ) three times . Cell pellets were resuspended in PBS ( pH 7 . 4 ) , and cells were then incubated with Bi-NTA-AC ( 50 μM ) at 37°C for 30 min with agitation . After that , cells were washed with PBS ( pH 7 . 4 ) three times prior to exposure to UVP UVGL-25 Mineralight UV lamp for 10 min to allow the formation of covalent linkage between the probe and the labeled proteins . Cells ( with or without Bi-NTA-AC treatment ) were lysed by sonication in PBS buffer supplemented with 1 mM PMSF , after which the supernatant was obtained by centrifugation at 10 , 000 g for 30 min at 4°C . The protein concentration in the cell lysates was estimated using BCA protein assay ( Novagen ) . The cell lysates were subjected to analysis by 2-dimensional gel electrophoresis ( 2-DE ) . The samples were desalted by 2-D Clean-Up Kit ( GE Healthcare ) , while the proteins precipitated were rehydrated by rehydration buffer ( 8 M Urea , 2% [w/v] CHAPS , 0 . 28% [w/v] DTT , 2% [v/v] IPG buffer 3–10 NL , and 0 . 002% [v/v] bromophenol blue ) . Isoelectric focusing was performed by Immobiline DryStrip pH 3–10 , 13 cm ( GE Healthcare ) at room temperature . The strips were actively rehydrated for 12 h at 30 V , followed by steps of salt removal: 500 V for four hours and 1 , 000 V for 1 h . The voltage was then gradually increased to 8 , 000 V in 2 h and was maintained constant at 8 , 000 V for an additional 7 h . The proteins were further analyzed by 13% SDS-PAGE . The IEF strips were first treated with DTT ( 10 mg/mL in equilibrating buffer [6 M Urea , 75 mM Tris , pH 8 . 8 , 2% SDS , 30% glycerol , and 0 . 002% bromophenol blue] ) for 15 min at room temperature and were subsequently treated with iodoacetamide ( 25 mg/mL in the same equilibrating buffer ) . The IEF strips were sealed onto SDS-PAGE with agarose sealing solution ( 0 . 5% agarose in SDS-PAGE running buffer with 0 . 002% bromophenol blue ) , after which the SDS-PAGE was resolved at 30 mA/gel for 5 h . The gel ( with Bi-NTA-AC treatment ) was washed in distilled water for 1 h prior to fluorescent imaging on ImageQuant 350 ( GE Healthcare ) at an excitation wavelength ( λex = 365 nm ) . The gels were subsequently subjected to silver staining . The protein spots after fluorescence staining and silver staining were compared , while the identities of the proteins were confirmed by MALDI-TOF MS to identify the potential Bi-binding proteins in H . pylori . P . mirabilis , P . aeruginosa , K . pneumoniae , L . hongkongensis , S . aureus , and S . saprophyticus were cultured in LB broth overnight at 37°C . The overnight culture was inoculated into fresh LB medium at OD600 of approximately 0 . 4; CBS ( 0 , 2 , 5 , 10 , 20 , 50 , 100 , 200 , 400 μM ) was supplemented into the culture medium . H . pylori strain 26 , 695 or a clinical-isolated strain were cultured on Columbia agar base in the presence of 7% laked horse blood ( Oxoid ) and H . pylori selective supplement Dent ( Oxoid ) at 37°C under microaerobic conditions using Campygen 2 . 5L ( Oxoid ) for 3 d . The bacteria were then inoculated at OD600 of approximately 0 . 4 to Brucella broth in the presence of 1 . 4% β-cyclodextrin overnight at 37°C with agitation with the supplement of CBS ( 0 , 2 , 5 , 10 , 20 μM ) into the culture medium . Expression plasmids ( pET28a-UreG , pET28a-UreG-C48A , pET28a-UreG-C66A , pET28a-UreG-C48C66A , and pET28a-UreGΔNKXD ) were transformed into E . coli BL21 ( DE3 ) . An overnight culture was then inoculated into fresh LB broth containing 50 μg/mL kanamycin and was incubated at 37°C . Protein expression was induced at the OD600 of 0 . 6 using 0 . 2 mM IPTG and was carried out at 25°C overnight . Centrifugation was done to collect cells . Cells were lyzed by sonication in Tris buffer ( 20 mM Tris-HCl , pH 7 . 4 , 500 mM NaCl , and 20 mM imidazole ) in the presence of 1 mM PMSF , after which centrifugation of cell lysate at 10 , 000 g at 4°C for 30 min was done to remove the inclusion body . Supernatant was filtered through 0 . 45-μm filter unit ( Sartorius ) and was then applied to HisTrap Ni-NTA column ( GE Healthcare ) pre-equilibrated with the same Tris buffer . Proteins were washed and were eluted with the same Tris buffer containing 50 mM and 300 mM imidazole , respectively . The fraction containing His-tagged UreG ( or its variants ) was buffer-exchanged into a Tris buffer of lower salt concentration ( 20 mM Tris-HCl , pH 7 . 4 , 120 mM NaCl] for cleavage of the His-tag by Thrombin ( Sigma ) at 20°C overnight , followed by the removal of uncleaved proteins using HisTrap Ni-NTA column . The cleavage product , UreG ( or its variants ) , was treated with 5 mM EDTA overnight at 4°C to obtain the apo-form of the proteins . UreG ( or its variants ) was further subjected to purification using HiLoad 16/10 Superdex 75 size exclusion column ( GE Healthcare ) in HEPES buffer ( 20 mM HEPES , 300 mM NaCl , pH 7 . 4 ) with the supplement of 500 μM TCEP for reducing the disulphide bonds in the protein . The peak fractions were concentrated , whereas the protein concentration was estimated by BCA Protein Assay Kit ( Novagen ) . The metal contents in the protein samples were analyzed by ICP-MS to ensure the proteins were not bound with any metal ions prior to other experiments . To monitor binding of Bi ( III ) ions to proteins , UV-vis spectroscopy was employed . The spectra were recorded at room temperature from 500 to 280 nm at a scan speed of 240 nm/min using a 1-cm quartz cuvette . Protein samples ( 25 μM ) were prepared in HEPES buffer ( 20 mM HEPES , 100 mM NaCl , pH 7 . 4 ) with 500 μM TCEP , while various concentrations of Bi ( III ) ions ( using BiNTA as the Bi ( III ) source ) were titrated into apo-UreG or its variants ( UreG-C48A , UreG-C66A , and UreG-C48C66A ) . The solution was allowed to incubate for 10 min prior to recording of the UV spectra . The dissociation constant ( Kd ) of BiNTA to UreG was estimated by nonlinear fitting of the titration curve using the Ryan–Weber equation below . ΔF=C ( [P]+[L]+Kd ) − ( [P]+[L]+Kd ) 2−4[P][L]2 where ΔF refers to the change in signals ( absorbance ) , C is the parameter for the change in signals per unit complex , [P] refers to the protein concentration , and [L] is the concentration of metal ions . The dissociation constant ( Kd ) of BiNTA binding to UreG was estimated to be 1 . 1 μM using the Ryan–Weber equation . Given that log Ka of Bi-NTA is 17 . 55 [55] , the apparent binding constant of Bi ( III ) ions to UreG was calculated to be Kd/Ka = 3 . 1 × 10−24 M . To study the interplay between Bi ( III ) and Ni ( II ) towards binding to UreG , similar UV-vis spectroscopic analysis was performed under different conditions . To investigate whether Bi ( III ) ions affect the Ni ( II ) -binding ability of UreG or vice versa , Ni ( II ) - and Bi ( III ) -bound UreG was first prepared by incubating UreG protein with two molar equivalents of either Ni ( II ) ions ( as NiSO4 ) in HEPES buffer supplemented with 500 μM TCEP , 100 μM GTP , and 1 mM MgSO4 at 4°C for 1 h or Bi ( III ) as ( BiNTA ) . Bi ( III ) or Ni ( II ) ions ( 0 to 3 molar equivalents ) were subsequently titrated into Ni- or Bi-bound UreG , respectively , prior to recording UV spectra . In order to further examine the mode of interaction of Bi ( III ) ions with UreG , Bi ( III ) ions were titrated into Ni-bound UreG in the presence of 1 mM KHCO3 and two molar equivalents of GTP or GTPγS ( a nonhydrolyzable analogue of GTP , guanosine 5'-O- ( 3-thiotriphosphate ) . Oligomerization states of UreG , UreE-UreG ( 2E-2G complex ) , and UreG-UreFH ( 2G-2F-2H complex ) upon different treatment were studied by chromatography using Superdex 200 10/300 GL column ( GE Healthcare ) , which was calibrated with the Gel Filtration Calibration Kit ( low molecular weight ) ( GE Healthcare ) . Ni-loaded UreG was prepared by mixing UreG with NiSO4 ( 1x ) , GTP ( 10x ) , and MgSO4 ( 1 mM ) . The UreG-UreFH ( 2G-2F-2H complex ) was prepared by mixing the apo-UreFH complex with excess apo-UreG . UreE-UreG ( 2E-2G complex ) was prepared by mixing apo-UreE and apo-UreG in the presence of GTP and MgSO4 . Ni-loaded UreG , 2E-2G , and 2G-2F-2H were incubated with two molar equivalents of Bi ( III ) ions ( as BiNTA ) prior to analysis with analytical size exclusion chromatography . A virtual screening method was used to find the potential inhibitors of UreG . A set of 1 , 700 compounds was collected from the PubChem [33] and BindingDB [34] databases for screening using AutoDock Vina [32] . A total of 300 compounds are active from PubChem Bioassay ( AID No . : 588479 , 588622 , 759 ) that already detected as GTPase inhibitors [56–58] . Also , 1 , 400 compounds have been extracted from BindingDB by searching “G-protein” term as target name in advanced search mode . Chemoinformatics python library RDKit [59] was used in python 2 . 7 to generate 3D conformation of these compounds using universal force field ( UFF ) . In the next step , python script “prepare_ligand4 . py , ” which is developed by AutoDockTools [60] , was used to convert the small molecules structures to PDBQT format , which is a required format in AutoDock Vina’s virtual screening procedure . H . pylori urease accessory protein UreG complex ( PDB 4HI0 ) was used as the docking receptor . Using MGLTool , the PDB structure of the receptor was converted to PDBQT format . A binding box with 22 × 16 × 15 Å3 dimensions ( 1 Å step size ) with center point ( −48 . 231 , −0 . 779 , 50 . 178 ) defined for 4HI0 . Finally , a python script was developed to run AutoDock Vina on HKU high-performance computing ( HPC ) center , the compounds have been ranked based on their docking scores with 4HI0 , and the best 20 compounds have been selected for further analysis . Eleven out of these 20 compounds were commercially available in MolPort and were purchased for further bioactivity testing . The chemical structure information of the tested compounds is available in the PubChem Substance through the substance identifier numbers ( SIDs ) as follows: cmpd1:SID = 88333436 [61] , cmpd2:SID = 97342323 [62] , cmpd3:SID = 97368460 [63] , cmpd4:SID = 91955059 [64] , cmpd5:SID = 93231017 [65] , cmpd6:SID = 88183171 [66] , cmpd7:SID = 88329940 [67] , cmpd8:SID = 89225247 [68] , cmpd9:SID = 123792418 [69] , cmpd10:SID = 88426485 [70] , cmpd11:SID = 88662278 [71] . To calculate the Kd values , the fluorescence-quenching titrations were carried out; gradient amounts of cmpd4 , cmpd8 , and GTP ( 0–100 μM , less than 20 μl DMSO in 1 ml buffer ) were titrated into 1 μM UreG in assay buffer . Using the relation between the emission intensity at 310 nm and molecule-UreG–bound species , as well as one site-binding model , the Kd values were calculated by a logistic regression model referring to a previous report [54] . The experiments were carried out in 20 mM HEPES , 100 mM NaCl ( pH 7 . 4 ) . All fluorescence readings were corrected for the dilution effect . A Malachite Green Phosphate Assay Kit ( Abcam ) was utilized to quantify the hydrolysis of GTP by UreG under various conditions . Ni-bound UreG was incubated with different amounts of CBS or small compounds in GTPase assay buffer ( 20 mM HEPES , 100 mM NaCl , pH 7 . 4 , 1 mM MgCl2 , 1% glycerol , 50 μM GTP ) at 4°C for 30 min . GTPase activity of UreG was triggered through the addition of 10 mM KHCO3 , after which the reaction was incubated at 37°C for 30 min; then , the free phosphate from hydrolysis of GTP was determined . Control experiments were conducted to ensure that Bi ( III ) does not interfere with the malachite green assay . To eliminate the potential effect of small compounds on malachite green assay , the assay included a subtraction of the absorbance of reaction with the same amounts of compounds but without UreG enzyme for each reading . The IC50 values for CBS or small compounds against GTPase activity were calculated using a logistic regression model . For comparison , the GTPase activity of negative control ( reaction without UreG enzyme , in the absence of GTPase inhibitors ) was set as 0 , and the activity of positive control ( reaction with Ni-bound UreG , in the absence of GTPase inhibitors ) was set as 100% ( as shown in Fig 1D , Fig 3B , S5 Fig , and S10 Fig ) . Phenol–hypochlorite urease assay was performed to examine the activity of urease in live bacteria or extracted urease and to investigate the role of UreG on the urease activity . Control experiments were conducted to ensure no interference from Bi ( III ) and small compounds on phenol–hypochlorite urease assay . To understand the effect of Bi ( III ) binding to UreG on urease maturation , plasmid pHP8080ΔG was transformed into E . coli BL21 ( DE3 ) , and the overnight bacterial culture was inoculated into fresh LB broth for growth at 37°C until OD600 was about 0 . 6 . Cells were washed three times with M9 minimal medium . Protein expression was carried out in M9 minimal medium in the presence of 0 . 2 mM IPTG . Cells were harvested by centrifugation and subsequently washed with HEPES buffer ( 50 mM HEPES , pH 7 . 5 ) . The cell pellet was resuspended in the same HEPES buffer , which was then lyzed by sonication . Supernatant was obtained by centrifugation at 15 , 000 g at 4°C for 5 min , while BCA Protein Assay Kit was employed to estimate the protein concentration in the supernatant . Sequentially , purified UreG protein ( with or without the treatment with metal ions ) was supplemented to the cell lysate . Ni-bound UreG was prepared by incubating UreG protein with two molar equivalents of Ni ( II ) ions ( as NiSO4 ) , 10 molar equivalents of GTP , and 1 mM MgSO4 at 4°C for 1 h in HEPES buffer supplemented with 500 μM TCEP . To study the effect of CBS on the activity of UreG , Ni-bound UreG was incubated with CBS ( 0–3 molar equivalents ) at 4°C for 30 min and then added to the cell lysate . Cell lysate with the addition of apo-UreG and excess Ni ( II ) ions were also prepared to serve as control . Treatment of cell lysate ( 40 μL ) under various conditions was done prior to mixing with 250 μL of urease buffer ( 50 mM HEPES , 25 mM urea , pH 7 . 5 ) , followed by incubation at 37°C for 30 min . Urease reaction was stopped by the addition of 375 μL of reagent A ( 10 g/L phenol , 50 mg/L sodium nitroprusside ) , followed by the addition of 375 μL of reagent B ( 5 mg/mL sodium hydroxide , 0 . 044% [v/v] sodium hypochlorite ) . The samples were further incubated at 37°C for 30 min prior to measurement of the absorbance at 620 nm using a microplate reader . A series of NH4+ standard ( from NH4Cl ) was also prepared for calibration . Results were normalized against the urease activity measured in the presence of Ni-UreG for comparison . For investigating the direct effect of CBS and AHA on urease enzymes of different bacterial species , ureases of different bacterial species were extracted . Various concentrations of CBS or AHA were then added to the extracted ureases , after which the urease activity was determined . The IC50 ( urease ) values for AHA and CBS were calculated using a logistic regression model . Alternatively , for the analysis of urease activity in live bacteria ( with and without supplementation of CBS or small compounds in cultured medium ) , bacteria were collected by centrifugation at 4 , 000 g for 5 min at 4°C and then lyzed by sonication . Cell lysate were briefly centrifuged at 16 , 000 g for 5 min at 4°C to remove the inclusion body . The supernatant of different bacterial species was subjected to urease assay analysis . Typically , the urease activity of negative control ( reaction without urease enzyme ) was set as 0 , and the activity of positive control ( urease without treatment of potential urease inhibitor ) was set as 100% , unless defined otherwise ( Fig 1E , Fig 2 , Fig 3C , S5 Fig and S7 Fig ) . AGS cells were grown in 24-well Falcon plastic tissue culture dishes to approximately 80% confluency . H . pylori was cultured in brucella broth medium in the presence of AHA , CBS , or small molecule urease inhibitors ( cmpd4 , cmpd8 ) at concentrations below IC50 values for 24 h microaerophilically and was added to the AGS cells on 24-well plastic tissue culture dishes at a final concentration of 5 × 106 CFU/ml . Urea was added to allow a final concentration of 10 mM . After further 24 h of culture , cell viability of AGS cells exposed to H . pylori was determined by cell counting , and the ammonia concentration in medium was measured . The binding of cmpd4 and cmpd8 to UreG was confirmed by a thermal shift assay . The melting points of UreG in the absence of or in the presence of small compounds ( 50 μM ) were measured using Protein Thermal Shift Dye Kit ( Thermo Fisher SCIENTIFIC ) according to the manufacturer’s protocol . | Urease , a metalloenzyme that catalyzes the hydrolysis of urea , plays important roles in the survival and virulence of many microbial pathogens , and has long been considered an important drug target for the development of novel antimicrobials . However , its deeply buried active site and highly specific substrate of bacterial urease make it very challenging to design effective urease inhibitors by conventional approaches . In this study , we reveal that a bismuth-based drug ( colloidal bismuth subcitrate ) inhibits urease activity in an unusual way . This drug binds the urease accessary protein UreG and inhibits its GTPase activity , thus perturbing nickel insertion into the apo-urease , a process called urease maturation . UreG is therefore proposed as an alternative target for the development of urease inhibitors . Using H . pylori UreG as an example , combined with virtual screening and experimental validation , we further show that several small molecules that bind and functionally disrupt UreG could indeed inhibit urease activity in bacteria and in a cell infection model and possess potent antimicrobial activity . In summary , we discovered metallochaperone UreG as a new target for the design of urease inhibitors . Such a strategy should have a broad application in the development of metalloenzyme inhibitors . | [
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| 2018 | Metallochaperone UreG serves as a new target for design of urease inhibitor: A novel strategy for development of antimicrobials |
To slow the inexorable rise of antibiotic resistance we must understand how drugs impact on pathogenesis and influence the selection of resistant clones . Staphylococcus aureus is an important human pathogen with populations of antibiotic-resistant bacteria in hospitals and the community . Host phagocytes play a crucial role in controlling S . aureus infection , which can lead to a population “bottleneck” whereby clonal expansion of a small fraction of the initial inoculum founds a systemic infection . Such population dynamics may have important consequences on the effect of antibiotic intervention . Low doses of antibiotics have been shown to affect in vitro growth and the generation of resistant mutants over the long term , however whether this has any in vivo relevance is unknown . In this work , the population dynamics of S . aureus pathogenesis were studied in vivo using antibiotic-resistant strains constructed in an isogenic background , coupled with systemic models of infection in both the mouse and zebrafish embryo . Murine experiments revealed unexpected and complex bacterial population kinetics arising from clonal expansion during infection in particular organs . We subsequently elucidated the effect of antibiotic intervention within the host using mixed inocula of resistant and sensitive bacteria . Sub-curative tetracycline doses support the preferential expansion of resistant microorganisms , importantly unrelated to effects on growth rate or de novo resistance acquisition . This novel phenomenon is generic , occurring with methicillin-resistant S . aureus ( MRSA ) in the presence of β-lactams and with the unrelated human pathogen Pseudomonas aeruginosa . The selection of resistant clones at low antibiotic levels can result in a rapid increase in their prevalence under conditions that would previously not be thought to favor them . Our results have key implications for the design of effective treatment regimes to limit the spread of antimicrobial resistance , where inappropriate usage leading to resistance may reduce the efficacy of life-saving drugs .
Staphylococcus aureus is an opportunistic human pathogen that causes skin and tissue abscesses , occasionally leading to severe systemic illness and death [1] . Whilst the process of lesion formation itself is becoming better defined [2] , the dynamics of a bacterial population within the host during infection are far less well understood . Whilst it is likely that infection progression and tissue tropism vary between different strains of S . aureus ( such as those that cause osteomyelitis or keratitis [3] , [4] ) , research on zebrafish embryos and mice has indicated an important role of host phagocytes in the infection process [5]–[8] . This leads to an infection “bottleneck” in which a small fraction of the initial inoculum goes on to found a systemic infection . Previous studies have identified bottleneck phenomena for a range of bacterial species [9] , [10] . S . aureus is infamous for its rapid development of antibiotic resistance , which has grown increasingly more relevant with the widespread use of antimicrobials in agriculture and medicine . Alarmingly , where once it was restricted to health-care settings , drug-resistant S . aureus is now also found in the wider community [11] , [12] . Bacteria resistant to antibiotics such as methicillin and tetracycline have been shown to colonize humans in contact with antibiotic-treated livestock [13] , [14] . The emergence of antibiotic resistance in staphylococcal species has been the subject of much study; classically , studies and analyses have focused on the generation of resistance , persistence or tolerance due to advantageous mutations in a sensitive bacterial population challenged by high levels of antibiotics [15]–[17] . Development of resistance may come at a fitness cost [18] , although S . aureus may be able to circumvent this cost via methods such as phenotypic switching [19] . Likewise , resistance to some drugs ( including tetracycline and oxacillin ) is known to be inducible [20] , [21] . Of current interest is the effect of sub-curative concentrations of antibiotics; that is , treatment that confers no significant improvement in the wellbeing of the host . Such low levels of antibiotics might be generated during failure to complete a treatment regime , or encountered via the hospital environment or in agriculture . The effects of a reduced antibiotic dose are the focus of several recent studies and reviews , again largely concerning the generation of mutants [22]–[24] . This area of research spans agriculture [13] , [14] , [25] , [26] , medicine [27] , [28] and food safety [29] and examines multiple bacterial genera . For example , Pseudomonas aeruginosa has been shown to acquire antibiotic resistance more rapidly in the presence of low levels of antibiotic [30] , and is known to be associated with staphylococcal infection , particularly in the lungs of cystic fibrosis patients [31] . Selection of antibiotic-resistant clones during low-level treatment in vivo , however , has not been directly examined . In this study we use a set of isogenic , antibiotic-resistant strains to investigate the bacterial population dynamics within both zebrafish embryo and murine systemic infection models . Using these strains , we elucidate the effect of low levels of antibiotics on bacteria in vivo , showing that it is possible to confer an advantage to an antibiotic-resistant strain , even at levels of antibiotics that do not affect the growth rate of an antibiotic-sensitive strain or its ability to cause a lethal infection .
In order to enhance the ability of infection dynamics studies to examine disease progression in both zebrafish embryos and mice , a set of three antibiotic-resistant S . aureus strains were constructed in the SH1000 background: erythromycin/lincomycin-resistant , EryR ( GMSA015 ) ; kanamycin-resistant , KanR ( GMSA016 ) ; and tetracycline-resistant , TetR ( GMSA017 ) . Growth of the three strains in vitro under favorable conditions exactly matched the wild type parent SH1000 ( Figure S1A ) . Furthermore , virulence assays in the zebrafish embryo model ( 1500 CFU administered into the circulation at 30 hours post fertilization ) resulted in the pattern of mortality one would expect from wild type bacteria ( Figure S1B ) . Resistance cassettes were then transduced into a second strain background , NewHG , creating strains GMSA021 , GMSA022 and GMSA023 ( EryR , KanR and TetR respectively ) . NewHG is a Newman derivative in which the gene encoding regulator SaeS has been repaired , resulting in a level of expression of saeS and downstream virulence factors that matches many other S . aureus strains , including the community-acquired methicillin-resistant S . aureus ( MRSA ) lineage USA300 , as opposed to the much higher levels of expression seen in the presence of the Newman allele [32] . Previous work in the zebrafish embryo model suggested that growth of bacteria in vivo , leading to lethality , occurs after a biological “bottleneck” that often results in clonal expansion of a single strain [5] . This phenomenon was demonstrated by infecting zebrafish embryos 30 hours post fertilization with 1500 CFU of a 1∶1∶1 mixture of the three SH1000 strains ( GMSA015-017 ) and enumerating the strains present in each embryo post mortem ( Figure 1A ) . Infection with NewHG strains ( GMSA021-023 ) gave similar results ( Figure 1B ) . In both backgrounds , differential clonal expansion produced cases wherein either one or two strains out of three predominated ( alongside cases wherein all three strains grew equally ) . There was no preference for the growth of any particular strain over its competitors , with all three antibiotic resistance cassettes proving equally capable of being selected ( p>0 . 1 ) , in agreement with previously published results [5] . Furthermore , we isolated clones that had been selected in vivo and , after mixing with “naïve” bacteria , reintroduced them into the zebrafish model . We found that there was no increase in the selection of these clones relative to their naïve counterparts over three successive passages , suggesting a lack of adaptive mutations , as expected [5] . Therefore in vivo clonal population expansion of the three strains is likely to be entirely stochastic and , hence , endpoint enumeration of these strains is a suitable and reliable method to investigate in vivo dynamics . It is known that intravenous S . aureus infection results in kidney abscesses [2] , [33] and that these likely originate from a small number of founding bacteria [5] . Modeling of infection dynamics has been performed in the murine model using other organisms , including Salmonella enterica [9] , Bacillus anthracis [10] and Yersinia pseudotuberculosis [34] . Herein , a three-strain inoculum was used to investigate the dynamics of systemic staphylococcal disease . Female BALB/c mice were infected intravenously at 7–8 weeks of age with a mixed inoculum of NewHG strains ( GMSA021-023 ) at a 1∶1∶1 ratio totaling 1×107 CFU . At 30 minutes and then at 2 , 6 , 12 , 24 , 48 and 72 hours post infection , mice were sacrificed and their visceral organs ( kidneys , liver , spleen , heart and lungs ) were harvested for bacterial enumeration by homogenization , serial dilution and plating on selective media ( n = 8–10 mice per time-point ) . Bacterial numbers in the blood were found to be consistently low , approaching the limit of detection at all time-points , and are therefore not included in the analysis . Pilot data indicated that the bacteria were unlikely to be found at other sites ( including brain , thigh muscle/bones and the site of infection , i . e . tail ) . A large fraction of the bacterial inoculum was restricted to the liver and spleen for several hours post infection . Neither extreme clonality nor an overall increase in bacterial numbers were observed at this stage , indicating that little bacterial growth was occurring , or that there was an equilibrium between bacterial growth and killing by the host ( Figure 2 ) . As the infection progressed , bacteria were found transiently in the heart ( peaking at 12 hours post infection ) and then in increasingly large numbers in the kidneys ( peaking at 2–3 days post infection ) ( Figure 2A ) . Bacteria in the kidneys showed a high degree of clonality , in most cases representing only one or two strains out of three . Occasionally , highly-clonal bacterial populations were found in the liver at later time-points . This dramatic increase in clonality over time is shown in Figure 2B . Data for individual organs at each time-point are given in supplementary Figure S2 . One mouse ( labelled “*” in Figure S2 ) was sacrificed for humane reasons due to ill health at 42 hours post infection and was excluded from the general analysis . Clonal expansion of bacteria has important implications during mixed-strain infections , especially when a drug-resistant mutant is present among a population of sensitive organisms , and the zebrafish infection model allowed us to investigate this process . The antibiotic dose required to produce a significant reduction in mortality in the zebrafish embryo model can , in some cases , approach fifty times the in vitro MIC . It was hypothesized that a low antibiotic dose , i . e . one that produces no response in fish infected with a drug-sensitive strain , might still offer a preferential advantage to drug-resistant bacteria , as has been observed in vitro [23] . In order to define an appropriate drug concentration , tetracycline response experiments were conducted , whereupon embryos infected with 1500 CFU SH1000 EryR ( GMSA015 ) were immersed in sterile E3 medium containing a range of antibiotic doses for the duration of the experiment ( Figure S3A ) . While 50 µg/ml tetracycline produced a highly significant , curative response ( p = 0 . 0003 ) , 10 µg/ml tetracycline showed a trend towards curing ( p = 0 . 0753 ) and 5 µg/ml was entirely sub-curative ( p = 0 . 2181 ) . We therefore chose 2 . 5 µg/ml tetracycline for subsequent experiments . Embryos treated with sub-curative 2 . 5 µg/ml tetracycline contained approximately 106 CFU of EryR bacteria upon death ( Figure S3B ) , demonstrating no reduction in terminal bacterial load at this dose . This control indicates that any observed skewing of the population in favor of a resistant strain at this antibiotic concentration would not result from failure of the sensitive strain to grow to normal levels in vivo . Lastly , bacterial growth kinetics were assayed in vivo using either the EryR or TetR strain injected alone into zebrafish embryos and either left untreated or treated with 2 . 5 µg/ml tetracycline . Neither strain showed significantly different growth kinetics in vivo in the presence of tetracycline ( p>0 . 9 ) ( Figure S4 ) , indicating that the skewing effect is not simply due to a change in growth rate within the host organism . Growth rate controls were not performed in vitro because the antibiotic concentrations involved would not correspond well with in vivo work . Experiments performed by others show that low antibiotic doses can indeed select for resistant mutants in vitro over the long term [23] , but that the effect requires a far greater number of generations than exhibited by bacterial growth in our in vivo experiments . To ascertain whether a sub-curative dose of antibiotic was indeed able to influence bacterial population dynamics , embryos were infected with a total of 1500 CFU SH1000 EryR and TetR ( GMSA015 and GMSA017 mixed 1∶1 ) , and treated with sub-curative 2 . 5 µg/ml tetracycline . Treated and untreated embryos showed no difference in mortality ( Figure 3A , p = 0 . 7412 ) or in total bacterial numbers per embryo upon death ( Figure 3B , p = 0 . 1452 ) . There were , however , significant differences in the ratios between the strains isolated from treated and untreated embryos ( Figure 3C , p = 0 . 0143 ) . Lowering the tetracycline dose to 1 µg/ml abolished this effect ( Figure S3C , p = 0 . 1008 ) , implying a narrow window ( approximately four-fold ) in which the antibiotic dose is low enough to be statistically sub-curing but high enough to influence population dynamics . Bacteria recovered from these experiments were invariably found to be resistant to a single antibiotic alone , therefore the change in output ratio is not due to the spontaneous generation of resistance among the sensitive population ( as bacteria that had acquired resistance to tetracycline in this way would remain erythromycin-resistant ) . Phagocyte depletion of infected zebrafish , which has been previously shown to prevent stochastic population variation [5] , resulted in untreated and treated groups that were statistically similar even after treatment with a higher dose of 10 µg/ml tetracycline ( Figure 3D; p = 0 . 4855 ) , indicating that phagocytes play a role in the antibiotic skewing phenomenon , and again supporting the hypothesis that phagocytes are an important host niche during S . aureus infection . To discover whether the phenomenon is consistent among other S . aureus strain backgrounds , the same experiments were conducted using NewHG EryR and TetR ( GMSA021 and GMSA023 ) . 2 . 5 µg/ml tetracycline was again shown in pilot experiments to be non-curative in fish infected with the sensitive strain alone ( Figure S5A , p = 0 . 5443 ) . Results of the NewHG mixed population experiment ( 1500 CFU , 1∶1 ) were consistent with those obtained for SH1000 , showing similar embryo mortality between the treated and untreated groups ( Figure S5B , p = 0 . 4575 ) , similar total CFU recovered from both groups ( Figure S5C , p = 0 . 4068 ) and a statistically significant difference between the strain ratios ( Figure 4A , p = 0 . 0296 ) . As expected , this was abolished in phagocyte-depleted embryos at 10 µg/ml tetracycline ( Figure S5D , p = 0 . 2956 ) . Therefore the preferential selection of antibiotic-resistant bacteria at low levels of antibiotic is not limited to a single S . aureus strain . The Gram-negative pathogen Pseudomonas aeruginosa is commonly isolated alongside S . aureus in the lungs of patients with cystic fibrosis [31] and is able to modulate S . aureus growth and virulence factor expression during polymicrobial wound infection [35] . To explore the possibility that extreme skewing of strain ratios extends to genera of bacteria other than Staphylococcus , two drug-resistant P . aeruginosa PAO1-L derivatives , gentamicin-resistant GmR ( GMPA001 ) and tetracycline-resistant TetR ( GMPA002 ) , were constructed . As expected from previous work by others [36] , [37] , live PAO1 ( but not heat-killed PAO1 ) is lethal to zebrafish embryos when injected into the circulation , even at infectious doses as low as 100 CFU ( Figure S6A ) . P . aeruginosa has a naturally high intrinsic resistance to tetracycline and was therefore unaffected by all attempts to cure the infection ( Figure S6B ) using drug doses that were not harmful to the embryo ( ≤100 µg/ml ) . In mixed population experiments with 50 µg/ml tetracycline ( 200 CFU , 1∶1 ) , it was again observed that a lack of significant effect on embryo mortality ( Figure S6C , p = 0 . 9449 ) or terminal CFU load ( Figure S6D , p = 0 . 4474 ) did not prevent the selection phenomenon from occurring ( Figure 4B , p = 0 . 0169 ) . It is noteworthy , however , that P . aeruginosa does not reach a specific bacterial load before embryo mortality ( unlike S . aureus , which consistently reaches approximately 106 CFU ) . Furthermore , in contrast to the S . aureus strains tested above , phagocyte depletion of the host does not abolish the selection phenomenon in P . aeruginosa ( Figure S6E , p<0 . 0001 ) . Thus , despite the difference between S . aureus and P . aeruginosa population kinetics ( and therefore infection dynamics ) in vivo , the two species exhibit the same phenomenon with regards to selection at sub-curative antibiotic doses . It was hypothesized that the effect of sub-curative antibiotic doses on strain ratios might not be restricted to tetracycline . To test this , clinical MRSA isolate BH1CC and its methicillin-sensitive , tetracycline-resistant isogenic partner [38] were used . Experiments were carried out in an identical manner to tetracycline ( i . e . immersion of embryos in antibiotic ) . It was found that 32 µg/ml oxacillin had no effect on the mortality of zebrafish infected with 1500 CFU of either the sensitive strain alone ( Figure S7A , p = 0 . 5390 ) or a 1∶1 mixture of sensitive and resistant strains ( Figure S7B , p = 0 . 2087 ) . Although the total bacterial load upon death was marginally but significantly decreased using this dose of oxacillin ( Figure S7C , p<0 . 0001 ) , this may be explained by the bactericidal action of oxacillin versus the bacteriostatic nature of tetracycline used in previous experiments . Nonetheless , such a decrease had no effect on the disease state or mortality of the infected host . This sub-curative dose of oxacillin produced a highly significant skew towards the resistant strain ( Figure 4C , p = 0 . 0003 ) , indicating that the phenomenon is not restricted to tetracycline and is likely to be an important factor in the clinical treatment of mixed MRSA-MSSA infections . Curiously , the skewing effect was also evident to a minor but significant degree in phagocyte-depleted embryos ( Figure S7D , p = 0 . 0018 ) treated with the same oxacillin dose , suggesting both phagocyte dependent and independent effects . Selection was not significant at 16 µg/ml oxacillin , suggesting a 2-fold concentration range for the effect ( Figure S7E , p = 0 . 0964 ) . Interestingly , antibiotics have been proposed to share a common killing mechanism involving accumulation of reactive oxygen species ( ROS ) within bacterial cells , and subsequent death from oxidative stress , irrespective of the antibiotic target or direct mechanism of action [39] . However , recent reports have questioned the role of ROS in the activity of bactericidal antibiotics [40] , [41] . Therefore , the important components of staphylococcal oxidative stress resistance AhpC and KatA [42] were tested for their role in resistant clone selection in vivo ( Figure S7F ) . Since the virulence of the katA ahpC double mutant ( strain KC043 ) was not affected by oxacillin relative to its SH1000 parent at all concentrations tested ( p>0 . 74 ) , it is unlikely that ROS play a role in antibiotic skewing in the in vivo infection model . Erythromycin is another medically relevant drug which our strain constructs enabled us to test . In the zebrafish embryo model , however , erythromycin proved unable to affect the outcome of infection by immersion alone . Instead , the antibiotic dose was introduced by microinjection not more than two hours after bacterial infection ( untreated controls were instead injected with PBS ) . Results showed no significant difference between treated and untreated groups when comparing mortality during a single-strain infection ( Figure S8A , EryR p = 0 . 0804 , TetR p = 0 . 9964 ) or during a mixed infection ( Figure S8B , p = 0 . 2695 ) , nor was there a significant difference in terminal bacterial load ( Figure S8C , p = 0 . 4438 ) . Yet , again , there was a significant difference in terminal strain ratios ( Figure 4D , p = 0 . 0022 ) . Similarly to tetracycline , no cross-resistance was observed in the strains recovered from embryos post mortem . Therefore , preferential selection of resistant strains ( not associated with the spontaneous generation of resistant mutants ) results from treatment with sub-curative doses of multiple antibiotic classes . In order to extend our findings to mammalian infection , antibiotic skewing experiments were performed using tetracycline in the murine model . Briefly , mixed-strain infections were carried out as for the population dynamics experiments above , and mice were treated with low doses of tetracycline in their drinking water . Mice were sacrificed two days post infection , when the majority of bacteria were expected to be found in clonal abscesses within organs ( i . e . kidneys , liver and spleen ) , which were assayed for bacterial load . In preliminary experiments , the sub-curative tetracycline dose varied between animal groups . Two doses ( 0 . 1 mg/ml and 0 . 2 mg/ml ) were therefore used in the final experiment alongside an untreated control ( Figure 5 ) . There was no significant difference in total CFU per mouse for either dose compared to the control ( p = 0 . 1965 and p = 0 . 4194 respectively ) . Pooled data ( i . e . the sum total of bacteria of each strain per mouse ) showed a trend towards the resistant subpopulation . The variance in the untreated group compared to treated groups makes comparison by Mann-Whitney test highly misleading in this model without using hundreds of animals per group ( an amount that would be impossible for both practical and ethical reasons ) ; therefore , a binomial distribution was used to analyze these data . The underlying assumption of this conservative test is that in an untreated mouse , there is a 50% chance of skewing towards either strain ( which is indeed what we observed; 5/10 towards TetR; p = 0 . 246 ) . The comparison showed that whilst 0 . 1 mg/ml did not give a significantly non-stochastic result ( 6/10 TetR , p = 0 . 205 ) , 0 . 2 mg/ml did ( 8/9 TetR and one mouse not skewed , p = 0 . 0176 ) . Therefore a sub-curative antibiotic dose is able to cause preferential selection of resistant microorganisms during mammalian infection . Furthermore , as expected from our murine infection kinetics experiment , selection at this low antibiotic concentration was a result of growth in the kidneys ( p = 0 . 0349 ) rather than liver ( p = 0 . 2188 ) or spleen ( p = 0 . 2344 ) ( Figure S9 ) .
Although Staphylococcus aureus is an important and increasingly antibiotic-resistant human pathogen , little is known about its disease progression or how antibiotic levels affect population dynamics . This is particularly pertinent for the understanding of S . aureus behavior in a hospital environment , in agriculture or even in the community at large , given the failure of some patients to complete prescribed antibiotic courses . Construction of a set of isogenic strains that produce clonal lesions in the host demonstrated stochastic strain variance during zebrafish embryo infection . In the mouse it was shown that kidney abscesses form in the later stages of infection and each likely originates from a single bacterial founder . Interestingly , most of the initial inoculum is localized to the liver and spleen during the early stages of infection ( up to approximately one day post intravenous administration ) . It is unknown whether bacterial cells founding renal abscesses were physically associated with the kidneys during this critical period , or whether individual bacteria “seed” the kidneys after travelling from another organ such as the liver , spleen , lungs , heart or blood at a later stage . Observation of a mouse that showed a dramatic , systemic infection of just two strains strongly implies that spread to secondary organs can occur after an initial period of clonal expansion . The variable speed at which growth occurs in the kidneys may be indicative of lesion founders “seeding” the kidneys at different times , especially in the case where two or more strains reach substantial but unequal numbers , given that no strain has a competitive advantage in terms of growth rate . In a peritoneal infection model , Rauch and colleagues reported parenchymal kidney abscesses that were identical to those observed post-intravenous infection , and that peritoneum-associated kidney surface abscesses were not associated with parenchymal abscesses [43] . Despite their role in controlling infection [44] , host phagocytes may form the reservoir or delivery vehicle for S . aureus infection [5]–[8] . Although S . aureus colonization is not directly correlated with mortality [45] , the presence of a mixed population of resistant and sensitive bacteria in the host provides a convenient “head start” for resistant bacteria to outgrow the sensitive strain and reach transmissible levels , should the patient become exposed to antibiotics . In the absence of treatment , however , resistance to antimicrobials has typically been thought to impose a fitness cost that enables the parent strain to outcompete antibiotic-resistant strains [18] . We have shown that this is not always the case , as even in the absence of apparent selective pressure , clonal expansion ensures that a pool of resistant bacteria can grow to dominant levels . The use of antibiotics is the primary driving force behind the development of resistance [24] . It has been shown in S . aureus that vancomycin treatment is able to greatly increase a pool of resistant organisms inside a host [46] and that de novo resistance to tetracycline can arise when the drug is given orally at high levels [47] . Here , a combination of zebrafish embryo and murine systemic infection models were used to examine the role of sub-curative concentrations of antibiotics against multiple strains of S . aureus and a Gram-negative pathogen , P . aeruginosa , in vivo . A level of drug so low that it does not alter host mortality or bacterial load ( i . e . a dose that is sub-curative ) is able to produce a statistically significant skew in strain ratios towards a pre-existing resistant subpopulation . In Escherichia coli , low levels of antibiotics such as β-lactams promote stepwise mutations leading to resistance [22] and in Pseudomonas aeruginosa , sub-lethal concentrations of several antibiotics increase mutational frequency [30] . Sub-MIC doses of antibiotics have been demonstrated not only to contribute to the generation of resistant mutants , but also to allow a resistant organism to outcompete its sensitive competitors in long-term in vitro experiments [23] . Critically , our result is not dependent upon development of resistance in the sensitive subpopulation . Furthermore , selection of resistant clones is not simply due to retardation of growth of the sensitive bacteria caused by the antibiotic , as phagocyte-depleted zebrafish embryos ( wherein bacteria grow exponentially from the outset of infection [48] ) do not show a significant skew in favor of the resistant strain , nor did in vivo growth kinetics experiments reveal a significant difference . Since phagocyte-depleted embryos succumb to even a small bacterial dose within 18 hours [48] , the restricted growth of the pathogen within the first 24 hours during our experiments suggests that all bacterial cells are captured by phagocytes immediately upon infection , therefore preferential phagocytosis is unlikely to be a factor . Instead , it is likely that the antibiotic confers an advantage to the resistant strain over the sensitive strain that allows it to better colonize the pre-expansion “niche” or somehow exploit that “niche” more effectively . This phenomenon may be limited to organisms whose mode of pathogenicity relies upon the stochastic selection of clones during infection , but is not restricted to a single species or class of antibiotic . It may be that such organisms are sensitive to low-dose treatment at a particular stage of their infectious cycle , in a niche-dependent fashion . Since the role of reactive oxygen species ( ROS ) in the action of antibiotics is currently under debate [39]–[41] and could provide an explanation for the skewing phenomenon , we examined the effect of ROS during treatment using an S . aureus strain ( ahpC katA ) defective in oxidative stress resistance [42] . Despite extreme sensitivity to oxidative stress the ahpC katA strain was no more susceptible to tetracycline in vivo than its isogenic parent , suggesting that ROS may not play a major role in a generalized antibiotic effect . It is well known that the bacterial transcriptome/proteome can fluctuate when exposed to sub-curative antibiotic doses [49]–[52] , and this is an area of study that can be pursued in future work . Environmental concentrations of antibiotics are commonly found in the ng/L to µg/L range except in extreme cases [53] , [54] , several orders of magnitude lower than the concentrations tested in our in vivo work . We have focused , therefore , on medical treatment of infection . The global spread of antibiotic resistance is a serious threat to human health that must be acted on , as it is inevitable that high-dose antimicrobial chemotherapy increases the selective pressure on the target organism . This has led to an argument that aggressive treatment regimes , such as those currently prescribed , should be reconsidered and that alternative patterns of treatment might be indicated [55] . As discussed above , however , it is becoming accepted that lower drug doses promote stepwise development of resistance and that aggressive drug use dramatically reduces the potential pool of clones from which resistance can develop . Given this information and our data showing that sub-curative antibiotic doses are still able to select for pre-existing resistant organisms , we suggest that removal of the sensitive bacteria before they can develop resistance is still the best strategy for control of microbial disease . Although we have not determined the concentrations of antibiotics active in host tissues , we note that the elimination half-lives of the antibiotics tested in this study vary considerably , from 30 minutes ( oxacillin ) to 6–12 hours ( tetracycline ) [56] . Coupled with our evidence that the sub-curative but selective dose range is two- to four-fold , we hypothesize that bacteria are likely to be in contact with a relevant drug concentration for ample time for selection to occur during treatment . Furthermore , many antibiotics including oxacillin and tetracycline share a renal route of elimination [56] , which concurs with our murine experiments suggesting that the kidneys are important for the antibiotic effects discussed . The exposure time would be greatly amplified by “pulsed” antibiotic therapy . The effect of sub-curative antibiotics may seem modest , however , we note that the evolution of any organism does indeed occur as a result of modest competitive advantages . We believe that our results are indicative of a growing trend in the response of bacterial pathogens to antibiotics , and conclude that carefully prescribed , high-dose antimicrobial chemotherapy remains preferable over the alternatives at this time .
Animal work ( both mice and zebrafish ) was carried out according to guidelines and legislation set out in UK law in the Animals ( Scientific Procedures ) Act 1986 , under Project Licenses PPL 40/3123 , PPL 40/3699 and PPL 40/3574 . Ethical approval was granted by the University of Sheffield Local Ethical Review Panel . Staphylococcus aureus strains ( Table S1 ) were grown using brain heart infusion ( BHI ) liquid or solid medium ( Oxoid ) at 37°C , supplemented with the following antibiotics where appropriate: kanamycin 50 µg/ml , tetracycline 5 µg/ml or erythromycin 5 µg/ml plus lincomycin 25 µg/ml ( Sigma-Aldrich ) . Pseudomonas aeruginosa strains ( Table S1 ) were grown using Luria-Bertani ( LB ) liquid or solid medium ( Oxoid ) at 37°C , supplemented with the following antibiotics where appropriate: tetracycline 125 µg/ml or gentamicin 20 µg/ml ( Sigma-Aldrich ) . The suicide vector pMUTIN4 [57] was used to integrate various antibiotic resistance cassettes downstream of the lysA gene ( which encodes the terminal enzyme in the lysine biosynthetic pathway ) in S . aureus . This provided a convenient method of screening to ensure that clones retained a wild type phenotype; undesired disruptions of lysA would result in lysine auxotrophy . Growth of clones in chemically defined minimal medium lacking lysine showed that the wild type lysA gene remained intact . After construction by standard PCR and restriction/ligation methods , plasmids were introduced into S . aureus RN4220 by electroporation , whereupon they integrated into the chromosome via homologous recombination . The resulting resistance markers were then transferred into other S . aureus strains as required by Φ11 transduction . pMUTIN4 provided erythromycin/lincomycin resistance ( EryR ) , whereas pAISH1 [58] was used to integrate tetracycline resistance ( TetR ) and pGM072 ( pMUTIN4 in which erythromycin resistance was replaced by the resistance cassette from pGL433 [59] ) was used to integrate kanamycin resistance ( KanR ) . BH1CC and its derivative [38] were kindly provided by James O'Gara ( University College Dublin ) . P . aeruginosa PAO1-L derivatives were constructed by integration of mini-Tn7 to a neutral locus according to published protocols [60] . Plasmids containing mini-Tn7 marked with either a gentamicin or tetracycline resistance cassette ( GmR or TetR respectively ) were kindly provided , along with invaluable assistance , from Stephan Heeb ( University of Nottingham ) . London wild-type ( LWT ) zebrafish embryos ( bred in the MRC CDBG aquarium facilities at the University of Sheffield; see Ethics Statement ) were used for all experiments and were incubated in E3 medium at 28°C according to standard protocols [61] . In order to obtain phagocyte-depleted embryos , morpholino-modified antisense oligomers against pu . 1 [62] were injected using a method described previously [48] . Anaesthetized embryos at 30 hours post fertilization were embedded in 3% w/v methylcellulose and injected individually using microcapillary pipettes filled with bacterial suspension of known concentration into the circulation , as previously described [48] . Following infection , embryos were kept individually in 100 µl E3 medium ( with or without experimental antibiotics ) , observed frequently up to 92 hours post infection , dead embryos removed and numbers recorded at each time point . Female BALB/c mice were purchased from Harlan ( UK ) and maintained at the University of Sheffield using standard husbandry procedures . The 7–8 week old mice were inoculated in the tail vein with 100 µl of S . aureus suspension in endotoxin-free PBS ( Sigma ) corresponding to 1×107 CFU per mouse . Viable bacteria in the inoculum were plated on BHI ( plus appropriate antibiotics ) after serial decimal dilution to confirm the accuracy of the bacterial dose . Mice were monitored and sacrificed at various time-points according to experimental design . In order to recover bacteria from host tissues , whole zebrafish embryos or mouse organs were individually homogenized in a suitable volume of PBS using the PreCellys 24-Dual ( Peqlab ) . Homogenates were serially diluted in PBS and plated on BHI ( S . aureus ) or LB ( P . aeruginosa ) agar supplemented with appropriate antibiotics to determine bacterial numbers . Survival experiments were evaluated using the Kaplan-Meier method . Comparisons between curves were performed using the log rank test . For comparisons between two CFU groups , a two-tailed , unpaired Student's t-test was used . For comparisons of strain ratios between two groups ( e . g . treated and non-treated ) in zebrafish embryos , a ( non-parametric ) Mann-Whitney U test was used . Analysis was performed using Prism version 6 . 0 ( GraphPad ) and statistical significance was assumed at p<0 . 05 . In addition , analyses via generalized linear models , linear models on transformed data and Kruskall Wallace tests were performed and provided the same insights . Strain ratios in mice were analyzed by binomial distribution , as explained in Results . Figures show significance to 1 s . f . ( p<0 . 05 ) or NS ( p≥0 . 05 ) , and indicate either mean ( CFU comparison ) or median ( ratio comparison ) values as appropriate . | Staphylococcus aureus is a major cause of human disease , made even more notable due to the spread of antibiotic resistance . We used a combination of animal models to study the spread of bacteria between organs during an infection and the resulting effect of antibiotic intervention . We found that S . aureus infection is highly clonal , following a “bottleneck” in which very few bacterial cells found each abscess . Despite previous in vitro research , the effect of antibiotics on S . aureus infection was poorly understood . We utilized our systemic infection models to study intervention with sub-curative antibiotic doses , such as one might encounter upon failing to complete an antibiotic course . We have shown that such doses are able to support the preferential expansion of antibiotic-resistant organisms during a mixed infection . This selection is due to the clonal pattern of infection , occurring despite a lack of effect on growth rate or on the spontaneous generation of resistance . Furthermore , it is generic to multiple pathogen species , including Pseudomonas aeruginosa , and antibiotic classes , such as with methicillin-resistant S . aureus ( MRSA ) in the presence of oxacillin . Given the current debate in the field , our results have important implications for the design of properly-controlled treatment regimes . | [
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| 2014 | Clonal Expansion during Staphylococcus aureus Infection Dynamics Reveals the Effect of Antibiotic Intervention |
Dengue fever is a major public health problem worldwide , caused by any of four virus ( DENV-1 , DENV-2 , DENV-3 and DENV-4; Flaviviridae: Flavivirus ) , transmitted by Aedes aegypti mosquito . Reducing the levels of infestation by A . aegypti is one of the few current strategies to control dengue fever . Entomological indicators are used by dengue national control program to measure the infestation of A . aegypti , but little is known about predictive power of these indicators to measure dengue risk . In this spatial case-control study , we analyzed the spatial distribution of the risk of dengue and the influence of entomological indicators of A . aegypti in its egg , larva-pupa and adult stages occurring in a mid-size city in the state of São Paulo . The dengue cases were those confirmed by the city's epidemiological surveillance system and the controls were obtained through random selection of points within the perimeter of the inhabited area . The values of the entomological indicators were extrapolated for the entire study area through the geostatistical ordinary kriging technique . For each case and control , the respective indicator values were obtained , according with its geographical coordinates and analyzed by using a generalized additive model . Dengue incidence demonstrated a seasonal behavior , as well as the entomological indicators of all mosquito's evolutionary stages . The infestation did not present a significant variation in intensity and was not a limiting or determining factor of the occurrence of cases in the municipality . The risk maps of the disease from crude and adjusted generalized additive models did not present differences , suggesting that areas with the highest values of entomological indicators were not associated with the incidence of dengue . The inclusion of other variables in the generalized additive models may reveal the modulatory effect for the risk of the disease , which is not found in this study .
Dengue is a disease caused by any of four virus ( DENV-1 , DENV-2 , DENV-3 and DENV-4; Flaviviridae: Flavivirus ) and has become a major global health issue . An epidemic similar to dengue was registered in 1699 in Central America . In Philadelphia , USA , a major epidemic occurred in 1780 and epidemics became common in the early 20th century [1] . There have been references of dengue epidemics , in Brazil , since 1923 but with no laboratorial confirmation until 1986 . Dengue fever outbreaks have occurred in several states since 1986 , with the identification of the DENV-1 virus in 1986 [2] , DENV-2 in 1990 [3] , DENV-3 in 2001 [4] and DENV-4 , isolated in Manaus from 2005 to 2007 [5] and in São Paulo in 2011 [6] . All four serotypes are currently circulating in Brazil [6] . In 2010 , with over one million cases of the disease , the highest reported incidence occurred in the Northern and Central-Western regions , with 621 . 7 and 1 , 536 . 8 cases per 100 , 000 inhabitants , respectively . In these regions , the majority of municipalities presented rates higher than 300 per 100 , 000 inhabitants [7] , [8] . In the state of São Paulo , the incidence in 2010 was of 503 cases per 100 , 000 inhabitants [8] . A . aegypti is still the only vector of epidemiological importance as the transmitter of the dengue virus ( DENV ) in the Americas [9] , [10] . Reducing levels of A . aegypti infestation is one of just a few strategies for disease control , since there is no vaccine available yet . Other undirected strategies refer to basic sanitation , garbage collection and proper water supply , which would eliminate the need for water storage . Besides , we have education activities to improve population commitment in order to eliminate breeding sites . The dengue national control program uses sampling methods to collect data in the field and to build indicators of A . aegypti presence , in the various stages of the vector life cycle , mainly traditional Breteau index and House index [11] . Although adult forms have a direct impact on virus transmission , the most used indicators to measure vector infestation are based on larvae , pupae and eggs [12] . Some studies demonstrate the direct relationship among infestation levels with the risk of epidemics in various regions of the world [13]–[16] , though epidemic transmission is also reported in the presence of very low infestation levels [17]–[20] . In addition , factors associated to human population organization have a decisive role in the circulation of the virus and in the establishment of breeding sites of A . aegypti [21] , which has demonstrated a great ability to adapt to different environmental contexts [9] , [22] , [23] . The multiple factors involved in the transmission of the disease therefore , require different approaches for understanding the forms of transmission . Over the last few decades , geoprocessing and digital mapping techniques were incorporated to the analysis of public health issues , as well as the use of spatial analysis programs to visualize the spatial distribution and spatio-temporal patterns of epidemiological data [24] , [25] . These techniques allow the development of models to predict the risk of disease and territorial infestation , mapping environmental and social conditions associated to such patterns [26] . A number of studies using these techniques have analyzed the spatial and temporal distribution of A . aegypti [27] , [28] and dengue transmission [29]–[32] , as well as their relationships between each other [33] , [34] . One of the main instruments for the operationalization of the vector control program is by monitoring the dispersion and abundance of mosquitoes through entomological indicators . It would be useful to understand the spatial and temporal patterns in small geographical scales [35] . These indicators are currently part of service routines in Brazil , but suffer from a number of limitations and particularities identified from control program perspective , which have not been studied yet . This study has the objective of evaluating the association between the spatial distribution of incidence of dengue and the entomological indicators in a middle-size city in the state of São Paulo , Brazil .
The fieldwork was carried out with the consent of the residents , and no references were made to the names or addresses of the residents , dengue patients , and control individuals . This project was approved by the Research Ethics Committee - CEP/UNITAU , declaration No . 302/12 , protocol 459/09 .
From February to December of 2011 , 4 , 688 buildings were inspected and A . aegypti larvae and/or pupae were found in 186 ( 4% ) buildings . We surveyed 1 , 711 households for the presence of A . aegypti adult forms . We captured 582 specimens of A . aegypti in 17 . 7% of the domiciles . The percentage of adult forms was greater than that of immature forms in the buildings . We collected 11 , 395 eggs; 65 . 4% were in the peridomicile area and 34 . 6% in the intra-domicile . Whilst a larger number of eggs were found outdoors than indoors during the entire period , the number of eggs was larger indoors than outdoors during the driest months ( Table 1 ) . A similar seasonal pattern was observed for the three indicators during the hottest and wettest period of the year ( February to April ) ( Figure 2 ) . During this period , 195 dengue cases were reported , all of which practically occurred during the first semester , the hottest and most humid period of the year . The dengue epidemic curve follows the larva-pupa indicator curve , where there is an increase up to April and a decrease up to the last case reported in September . We also observed a similarity between the curves for the adult forms and egg indicators , i . e . , a decreasing curve up to the end of the dengue epidemic . Figure 3 presents the monthly dengue cases ( points ) and the quarterly entomological indicators estimated by the ordinary kriging , showing areas of different infestation intensity , with a gradient of colors varying from green ( lower values ) to red ( higher values ) . We noticed a similarity among indices for all quarters , although some differences among the three indicators in the quarters can be pointed out . The Q2 quarter adult index did not agree with the larva-pupa and egg indices; in Q3 quarter , the larva and pupae indices did not agree with the adult and egg indices; in the last quarter , Q4 , all the three indices were discordant ( Figure 3 ) . We observed that during Q1 , the cases in March were highly concentrated in the southern region of the map , where the three indicators presented low values . During Q2 , the cases in April were highly concentrated in the eastern region , with low values for eggs and adult indicators . The intensity of the epidemic was reduced during Q3 and Q4 , when dengue cases were reported all over the city , albeit with different patterns of infestation . We observed the incidence of dengue as well as the presence of the vector in different stages of its biological cycle throughout the area during the entire period of the study . However , there was little spatial coincidence amongst the incidence of dengue and the intensity of the entomological indicators . The exceptions were the presence of case clusters and high adult infestation in Q2 and Q3 , and also a degree of agreement between high levels of larva-pupa infestation and incidence of dengue in Q3 ( Figure 3 ) . Table 2 shows the estimated risk of dengue and its respective confidence interval obtained from the adjusted model ( GAM ) . We noted that in most of the analyzed period the highest values of the entomological indicators did not match with the dengue cases areas . An exception was viewed from February to April with the presence of larvae-pupae related with an elevated risk of disease . In the period from May to July the indicators related to eggs and larvae-pupae were associated with a mild risk of dengue . Egg indicator did not present statistical association with cases in the multiple model from February to April , so like the adult form indicator from April to June . All the entomological indicators presented negative association with the occurrence of dengue cases from March to May , showing that areas with elevated indicators presented minor occurrence of dengue cases in the period . Model results corroborating with those observed in Figure 3 in which higher occurrence of dengue cases did not match with higher entomological indicators . The crude relative spatial risk model for dengue ( space only ) and adjusted model ( space + entomological indicators ) for the four quarters , obtained by GAM model are presented in Figure 4 . Maps show a major variation in the transmission risk at different locations throughout the quarters , high in the south during Q1 , in northeast during Q2 , expanding further east during Q3 and to the north and south-central regions during Q4 . Both crude and adjusted models present similar spatial feature , indicating lack of effects of the entomological indicators in the risk of disease incidence . In Figure 3 we observed dengue cases in different areas during the study period , like “moving” through the region . Also the higher or lower intensity of the vector are identified in several areas of the study region , however it did not follow the disease displacement , suggesting that there is not a direct spatial relationship .
In this study , we examined the spatial and temporal distribution of three entomological indicators A . aegypti mosquitos – eggs , larvae-pupae , and adults – in Sumaré . Simultaneously , we analyzed the spatial distribution of dengue incidence and its association with the indicators . The indicators presented similar seasonal behavior throughout the period and vectors were present in the entire area of the study during the dengue transmission period . Nevertheless we observed that the clusters of dengue incidence did not correspond with the distribution of entomological indicators Incidence maps of the disease obtained from crude and adjusted models did not show significant differences in dengue incidence among the areas , suggesting that the vector indicators did not influence dengue incidence in a given space in the municipality . Although we observed a few coincidences between indicators and dengue cases in some areas , in general , the case clusters did not correspond to the highest values of vectors indices . However , caution is required when analyzing these results , especially considering the environmental interventions carried out by the health services team during epidemic periods , such as the chemical treatment of the locations with confirmed cases , the intensification of measures to control breeding areas , and health education . In addition it should be noted that the response of the population to campaign pleas and dengue control measures , especially during an epidemic , may led to the elimination of breeding grounds of immature forms . It is important to consider that the study area experienced infestation for over 15 years and deficient public services , besides the intense traffic between neighborhoods . The circulation of serotype DENV-1 , DENV-2 and DENV-3 in the previous years may have interfered in the transmission of the virus , once a population portion may have acquired immunity to one or more serotypes . These factors may be relevant in explaining the circulation of the virus during the entire period of the study . The longstanding A . aegypti infestation rates in the city may have had a limited role in the circulation of the virus in Sumaré in 2011 . Thus it is plausible that the spatial distribution of the disease depends on the vector distribution , but not necessarily on its highest concentration . Despite the evident seasonality of vector infestation and viral transmission ( temporal association ) , the spatial distribution of disease risk and high rates of entomological indicators did not coincide in multiple models . The spatial analysis could detect risk ( incidence ) on a scale of blocks and neighborhoods . In this way we obtained local estimators of risk instead of mean values of indicators of large areas . The apparent temporal correlation observed in the indicators curve over the period of transmission , was not spatially evidenced , as seen in the results of GAM models . They showed in some periods , negative association , in others , lack of statistical significance , and others risk of dengue . Furthermore , the vector adaptability to environment inhabited by human [47] makes it difficult to control the dengue transmission , because the vectors are abundant enough for triggering and maintaining the circulation of the virus . Other variables like population size , epidemic duration and climate variables seem to determine the spread of the epidemic in longstanding infestation areas , as observed by Siqueira Junior et al . [48] and Chowell et al . [49] . The spatial association among entomological indicators has been the object of a few studies conducted at various locations . In Rio de Janeiro , Honório et al . ( 2009 ) recently reported dengue infection among residents living in areas with a low mosquito density , suggesting that the infection did not occur inside the residence [31] . In Tupã , São Paulo , Barbosa and Lourenço ( 2010 ) did not find a spatial relationship between dengue incidence and larvae infestation [28] . In Bangladesh , Ali et al . ( 2003 ) found a positive association between the dengue incidence and vector infestation in areas close to hospitals [50] . In Campinas , São Paulo , Cordeiro et al . ( 2011 ) showed a positive association between the increase in the larvae density and the incidence of dengue [34] . Chowell et al . ( 2008 ) also recorded a major variation in the weekly dengue incidence among the provinces of Peru , probably because of the level of infection spread by the mosquitoes , climate variation , circulation of different serotypes , and the population's immunological history [49] . Studies on the association between dengue transmission and entomological , environmental , socio-economical , and other indicators have presented conflicting results but reach the consensus that the dynamics of dengue transmission are complex and difficult to understand . Also authors agree that transmission depends on the environmental context and on variables that were not taken into consideration in this study , e . g . , populational immunity , circulating serotypes , and control measures [14]–[16] , [27] , [47] , [51] . Furthermore , underreporting of cases is also assumed because some patients were either asymptomatic or had mild symptoms , which were not reported to the health services , as mentioned in various studies [20] , [31] , [52] , [53] . According to Focks et al . , in order to prevent the transmission of the dengue , it is necessary to maintain vector infestation at critically low levels [54] . The Pan American Health Organization describes that a building index up to 0 . 1% implies low risk of dengue transmission , between 0 . 1 and 5% , medium risk , and above 5% , high risk [15] . However , some authors have reported dengue transmission even when the indices of entomological indicators were relatively low [14] , [18]–[20] . Currently , there is no threshold for the suspicion of the risk of dengue . This study showed that the household infestation was above 5% until May , period that occurred more than 90% of the cases . Honório et al . suggested that information on the patterns of populational movement help improve the understanding about the transmission dynamics of the disease and possible locations of its incidence [31] . Getis states that only one or few infected A . aegypti mosquitoes transmit the virus to several susceptible humans within a period of a few days [55] . According to Kan , the traffic of people and mosquitoes from neighborhoods where dengue incidences have been reported can explain the shift in the pattern of the epidemics [30] . This study was the first to evaluate the spatial relationship between entomological indicators of all stages of the A . aegypti mosquito: egg , larvae and pupae , and adult , and monthly and concurrent measurements as well as dengue incidence . One of the limitations of the study is the lack of data about populational immunity and the interventions implemented in response to the autochthonous transmission that could have influenced the results . Besides , the population's movement patterns and the elements indicating the main transmission locations are unknown [31] . By only analyzing the reported cases during the period without considering the asymptomatic patients and those who did not seek health services , the findings were certainly underestimated . The other limitation was the use of data from a surface smoothed by ordinary kriging , which despite being a linear unbiased estimator , promotes the smoothing of results , thereby overestimating the lowest and underestimating the highest values [44] . Many factors are involved in the spatial spread of an epidemic . Certain factors , e . g . , vector control programs and populational immunity to the circulating virus , may have a modulating effect on the dengue incidence . Besides , the introduction of a virus , its establishment and propagation , and the concomitant movement of various serotypes , owing to the population traffic , may also be part of the spatial dimension of the epidemic spread . In this case , infestation was not a limiting variable for transmission [32] . In this study , we were able to simultaneously analyze the incidence of dengue and conduct a survey on the entomological indicators of A . aegypti; however , we did not find a spatial correlation between the indicators and disease incidence . The infestation did not present a major variation in intensity and was not a limiting or determining factor of dengue incidences in a given space in the municipality . None of the different stages entomological indicators in the vector's lifecycle was a predictor of disease occurrence in areas at risk of dengue transmission . The inclusion of other variables in generalized additive models could eventually reveal other modulating factors that have an influence on spatial pattern of the disease . | Dengue is a disease caused by a virus which has four serotypes DENV-1 , DENV-2 , DENV-3 and DENV-4 . In Americas , A . aegypti is acknowledged as the only dengue vector in America . Currently the only strategy to prevent dengue is controlling A . aegypti mosquitoes . The generalized additive model was used to understand the relationship of the indicators of the presence of eggs , larvae-pupae and adult stages of A . aegypti with the occurrence of dengue cases in a medium sized city of São Paulo state , Brazil . Dengue incidence as well as the entomological indicators in all stages of the mosquito showed a seasonal behavior . The infestation level was not a limiting or a determinant factor of the occurrence of cases in the municipality . Risk maps of the disease , from the crude and adjusted by generalized additive models , showed no differences , suggesting that the entomological indicators did not influence the incidence of dengue in the city . The inclusion of other variables in the generalized additive model could reveal the modulating effect on the disease risk , not found in this study . | [
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| 2014 | Spatial Distribution of the Risk of Dengue and the Entomological Indicators in Sumaré, State of São Paulo, Brazil |
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